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FASTQC Analysis of FASTQ Files Before and After Trimming

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Ravi Gandham

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Computational Biology and Genomics Facility, Indian Veterinary Research Institute Data (Fastq) FASTQC Prinseqlite Trimming/filtering Data Quality Check and Interpretation
Computational Biology and Genomics Facility, Indian Veterinary Research Institute perl prinseq-lite.pl -fastq control.fastq -out_format 5 -min_len 50 -min_qual_mean 25 Input fastq file 1 (FASTA only), 2 (FASTA and QUAL), 3 (FASTQ), 4 (FASTQ and FASTA), or 5 (FASTQ, FASTA and QUAL) Output format Filter sequences shorter than minimum length (here it is 50 nucleotides) Filter sequence with quality score mean below minimum quality mean (here it is 25) PRINSEQ - • generates summary statistics of sequence and quality data • used to filter, reformat and trim next-generation sequence data. • PRINSEQ is available through a user-friendly web interface or as standalone version. Command for quality filtering : perl prinseq-lite.pl -fastq control.fastq -out_format 5 -min_len 50 - min_qual_mean 25 For any further help type :- perl prinseq-lite.pl -h
Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Computational Biology and Genomics Facility, Indian Veterinary Research Institute The output generated from analyzing three files viz. original fastq i.e control_R1.fastq, and the good and bad fastq files generated from prinseq-lite
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Basic Statistics: Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpre simple Control_R1.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Basic Statistics: Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence Control_R1_good.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Basic Statistics: Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpret simple c gives th length an Here all good.fas raw data generate reads. Th Note :- B raises an Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Basic Statistics: Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error.
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module genera simple composition statistics for the file analyzed gives the filename, file type, Sequences, sequen length and GC % . Here all the three files controlR1.fastq, control_ good.fastq and control_R1bad.fastq were analyzed. T raw data file showed > 17 million reads and the good generated after running prinseq-lite showed > 16 mill reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and ne raises an error. Control_R1.fastq Control_R1_good.fa Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Base Sequence Quality
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Control_R1.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Base Sequence Quality
Computational Biology and Genomics Facility, Indian Veterinary Research InstituteInterpretation: This view shows an overview of the range of quality Control_R1_good.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Base Sequence Quality
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interp values For ea in colo The bo range 90% p represe It can means low me mark ( window Note : than 1 raised for any Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Base Sequence Quality
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20.
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module genera simple composition statistics for the file analyzed gives the filename, file type, Sequences, sequen length and GC % . Here all the three files controlR1.fastq, control_ good.fastq and control_R1bad.fastq were analyzed. T raw data file showed > 17 million reads and the good generated after running prinseq-lite showed > 16 mill reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and ne raises an error. Control_R1.fastq Control_R1_good.fa Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Sequence Quality Scores Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Control_R1.fastq C
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Sequence Quality Scores Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Control_R1_good.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Sequence Quality Scores Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretat subset of y under the b file indicatin file in com better in the very poor q (red in colo that data se Note : A wa below 27 - most freque error rate. Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Sequence Quality Scores Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate.
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module genera simple composition statistics for the file analyzed gives the filename, file type, Sequences, sequen length and GC % . Here all the three files controlR1.fastq, control_ good.fastq and control_R1bad.fastq were analyzed. T raw data file showed > 17 million reads and the good generated after running prinseq-lite showed > 16 mill reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and ne raises an error. Control_R1.fastq Control_R1_good.fa Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: Per Base Sequence Content plots the proportion of each base position in a file for which each of the four normal DNA bases has been called. In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in the genome, but in any case they should not be hugely imbalanced from each other. However in all our cases here there is a failure issued due to variation in A to T and G to C percentages at the start of the reads till the 14th base. This also indicates that the reads can be trimmed at the 5’end till the 14th base. Note : A warning is issued if the difference between A and T, or G and C is greater than 10% in any position and a failure is issued if the difference between A and T, or G and C is greater than 20% in any position. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: Per Base Sequence Content plots the proportion of each base position in a file for which each of the four normal DNA bases has been called. In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in the genome, but in any case they should not be hugely imbalanced from each other. However in all our cases here there is a failure issued due to variation in A to T and G to C percentages at the start of the reads till the 14th base. This also indicates that the reads can be trimmed at the 5’end till the 14th base. Note : A warning is issued if the difference between A and T, or G and C is greater than 10% in any position and a failure is issued if the difference between A and T, or G and C is greater than 20% in any position. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute In Control_R1.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: Per Base Sequence Content plots the proportion of each base position in a file for which each of the four normal DNA bases has been called. In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in the genome, but in any case they should not be hugely imbalanced from each other. However in all our cases here there is a failure issued due to variation in A to T and G to C percentages at the start of the reads till the 14th base. This also indicates that the reads can be trimmed at the 5’end till the 14th base. Note : A warning is issued if the difference between A and T, or G and C is greater than 10% in any position and a failure is issued if the difference between A and T, or G and C is greater than 20% in any position. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Control_R1_good.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: Per Base Sequence Content plots the proportion of each base position in a file for which each of the four normal DNA bases has been called. In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in the genome, but in any case they should not be hugely imbalanced from each other. However in all our cases here there is a failure issued due to variation in A to T and G to C percentages at the start of the reads till the 14th base. This also indicates that the reads can be trimmed at the 5’end till the 14th base. Note : A warning is issued if the difference between A and T, or G and C is greater than 10% in any position and a failure is issued if the difference between A and T, or G and C is greater than 20% in any position. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute I p f w t s b g f i s c N a t a Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: Per Base Sequence Content plots the proportion of each base position in a file for which each of the four normal DNA bases has been called. In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in the genome, but in any case they should not be hugely imbalanced from each other. However in all our cases here there is a failure issued due to variation in A to T and G to C percentages at the start of the reads till the 14th base. This also indicates that the reads can be trimmed at the 5’end till the 14th base. Note : A warning is issued if the difference between A and T, or G and C is greater than 10% in any position and a failure is issued if the difference between A and T, or G and C is greater than 20% in any position. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: Per Base Sequence Content plots the proportion of each base position in a file for which each of the four normal DNA bases has been called. In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in the genome, but in any case they should not be hugely imbalanced from each other. However in all our cases here there is a failure issued due to variation in A to T and G to C percentages at the start of the reads till the 14th base. This also indicates that the reads can be trimmed at the 5’end till the 14th base. Note : A warning is issued if the difference between A and T, or G and C is greater than 10% in any position and a failure is issued if the difference between A and T, or G and C is greater than 20% in any position.
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module genera simple composition statistics for the file analyzed gives the filename, file type, Sequences, sequen length and GC % . Here all the three files controlR1.fastq, control_ good.fastq and control_R1bad.fastq were analyzed. T raw data file showed > 17 million reads and the good generated after running prinseq-lite showed > 16 mill reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and ne raises an error. Control_R1.fastq Control_R1_good.fa Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Sequence GC Content
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpret length of Control_R1.fastq Cont
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Sequence GC Content
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation :This module measures the GC content across the whole Control_R1_good.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Sequence GC Content
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretat length of e distribution a roughly correspond shaped dis of biased s files and a the normal good and ra Note : A wa distribution sum of the of the reads Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Sequence GC Content
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure is issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads.
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generat simple composition statistics for the file analyzed. gives the filename, file type, Sequences, sequen length and GC % . Here all the three files controlR1.fastq, control_R good.fastq and control_R1bad.fastq were analyzed. T raw data file showed > 17 million reads and the good f generated after running prinseq-lite showed > 16 milli reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and nev raises an error. Control_R1.fastq Control_R1_good.fas Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation:This module plots out the percentage of base calls at each position for which an N was called. It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. Here no N’s were found in all the datasets. Note : A warning is raised if any position shows an N content of >5% and a failure is issued if any position shows an N content of >20% Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Base N Content Interpretation:This module plots out the percentage of base calls at each position for which an N was called. It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. Here no N’s were found in all the datasets. Note : A warning is raised if any position shows an N content of >5% and a failure is issued if any position shows an N content of >20% Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpreta position f Control_R1.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation:This module plots out the percentage of base calls at each position for which an N was called. It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. Here no N’s were found in all the datasets. Note : A warning is raised if any position shows an N content of >5% and a failure is issued if any position shows an N content of >20% Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Base N Content Interpretation:This module plots out the percentage of base calls at each position for which an N was called. It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. Here no N’s were found in all the datasets. Note : A warning is raised if any position shows an N content of >5% and a failure is issued if any position shows an N content of >20% Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research InstituteInterpretation:This module plots out the percentage of base calls at each Control_R1_good.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation:This module plots out the percentage of base calls at each position for which an N was called. It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. Here no N’s were found in all the datasets. Note : A warning is raised if any position shows an N content of >5% and a failure is issued if any position shows an N content of >20% Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation:This module plots out the percentage of base calls at each position for which an N was called. It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. Here no N’s were found in all the datasets. Note : A warning is raised if any position shows an N content of >5% and a failure is issued if any position shows an N content of >20% Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpre position proportio sequenc Note : A failure is Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation:This module plots out the percentage of base calls at each position for which an N was called. It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. Here no N’s were found in all the datasets. Note : A warning is raised if any position shows an N content of >5% and a failure is issued if any position shows an N content of >20% Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation:This module plots out the percentage of base calls at each position for which an N was called. It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. Here no N’s were found in all the datasets. Note : A warning is raised if any position shows an N content of >5% and a failure is issued if any position shows an N content of >20%
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generat simple composition statistics for the file analyzed. gives the filename, file type, Sequences, sequen length and GC % . Here all the three files controlR1.fastq, control_R good.fastq and control_R1bad.fastq were analyzed. T raw data file showed > 17 million reads and the good f generated after running prinseq-lite showed > 16 milli reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and nev raises an error. Control_R1.fastq Control_R1_good.fas Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Sequence length Distribution Interpretation :This module generates a graph showing the distribution of fragment sizes in the file which was analysed.In many cases this will produce a simple graph showing a peak only at one size, but for variable length FastQ files this will show the relative amounts of each different size of sequence fragment. Here in all the files we have reads with a length of 101 bp. Note : A warning is raised if all sequences are not the same length. A failure is issued if any of the sequences have zero length. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation :This module generates a graph showing the distribution of fragment sizes in the file which was analysed.In many cases this will produce a simple graph showing a peak only at one size, but for variable length FastQ files this will show the relative amounts of each different size of sequence fragment. Here in all the files we have reads with a length of 101 bp. Note : A warning is raised if all sequences are not the same length. A failure is issued if any of the sequences have zero length. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpre sizes in Control_R1.fastq Co
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Sequence length Distribution Interpretation :This module generates a graph showing the distribution of fragment sizes in the file which was analysed.In many cases this will produce a simple graph showing a peak only at one size, but for variable length FastQ files this will show the relative amounts of each different size of sequence fragment. Here in all the files we have reads with a length of 101 bp. Note : A warning is raised if all sequences are not the same length. A failure is issued if any of the sequences have zero length. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research InstituteInterpretation :This module generates a graph showing the distribution of fragment Control_R1_good.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation :This module generates a graph showing the distribution of fragment sizes in the file which was analysed.In many cases this will produce a simple graph showing a peak only at one size, but for variable length FastQ files this will show the relative amounts of each different size of sequence fragment. Here in all the files we have reads with a length of 101 bp. Note : A warning is raised if all sequences are not the same length. A failure is issued if any of the sequences have zero length. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Sequence length Distribution
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretati sizes in the showing a the relative files we ha Note : A w issued if an Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This module generates a graph showing the distribution of fragment sizes in the file which was analysed.In many cases this will produce a simple graph showing a peak only at one size, but for variable length FastQ files this will show the relative amounts of each different size of sequence fragment. Here in all the files we have reads with a length of 101 bp. Note : A warning is raised if all sequences are not the same length. A failure is issued if any of the sequences have zero length. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Sequence length Distribution
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This module generates a graph showing the distribution of fragment sizes in the file which was analysed.In many cases this will produce a simple graph showing a peak only at one size, but for variable length FastQ files this will show the relative amounts of each different size of sequence fragment. Here in all the files we have reads with a length of 101 bp. Note : A warning is raised if all sequences are not the same length. A failure is issued if any of the sequences have zero length.
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generat simple composition statistics for the file analyzed. gives the filename, file type, Sequences, sequen length and GC % . Here all the three files controlR1.fastq, control_R good.fastq and control_R1bad.fastq were analyzed. T raw data file showed > 17 million reads and the good f generated after running prinseq-lite showed > 16 milli reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and nev raises an error. Control_R1.fastq Control_R1_good.fas Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Control_R1.fastq No Overrepresented Sequences Control_R1_good.fastq No Overrepresented Sequences Sequence Count Percentage Possible Source GATCGGAAGA GCACACGTCTG AACTCCAGTCA CTGACCAACTC TCCGTATGC 3950 0.84781596 16915967 TrueSeq Adapter, index 4 (110% over 50 bp) Interpretation: Overrepresented sequences include the sequences that are highly duplicated in your library, as well as any primer and/or adapter dimers that were present in the original library. Adapter sequences are always present in a sequencing experiment at some level, but aren't problematic in small percentages. These adapters will not align to your genome. They can be ignored, or you may use analysis software to remove them. This module lists all of the sequence, which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 200,000 sequences are tracked to the end of the file. It is therefore possible that a sequence,which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module. Here wee find overrepresented sequences of adapter in the bad file Note : A warning is raised if any sequence is found to represent more than 0.1% of the total and a failure is issued if any sequence is found to represent more than 1% of the total. Control_R1_bad.fastq Overrepresented Sequences
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Control_R1.fastq No Overrepresented Sequences Sequence Count Percentage Possible Source GATCGGAAGA GCACACGTCTG AACTCCAGTCA CTGACCAACTC TCCGTATGC 3950 0.84781596 16915967 TrueSeq Adapter, index 4 (110% over 50 bp) I a d a p g r m a I d b t N t r Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Control_R1.fastq No Overrepresented Sequences Control_R1_good.fastq No Overrepresented Sequences Sequence Count Percentage Possible Source GATCGGAAGA GCACACGTCTG AACTCCAGTCA CTGACCAACTC TCCGTATGC 3950 0.84781596 16915967 TrueSeq Adapter, index 4 (110% over 50 bp) Interpretation: Overrepresented sequences include the sequences that are highly duplicated in your library, as well as any primer and/or adapter dimers that were present in the original library. Adapter sequences are always present in a sequencing experiment at some level, but aren't problematic in small percentages. These adapters will not align to your genome. They can be ignored, or you may use analysis software to remove them. This module lists all of the sequence, which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 200,000 sequences are tracked to the end of the file. It is therefore possible that a sequence,which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module. Here wee find overrepresented sequences of adapter in the bad file Note : A warning is raised if any sequence is found to represent more than 0.1% of the total and a failure is issued if any sequence is found to represent more than 1% of the total. Control_R1_bad.fastq Overrepresented Sequences
Computational Biology and Genomics Facility, Indian Veterinary Research Institute No Overrepresented Sequences Interpretation: Overrepresented sequences include the sequences that are highly duplicated in your library, as well as any primer and/or adapter dimers that were present in the original library. Adapter sequences are always present in a sequencing experiment at some level, but aren't problematic in small percentages. These adapters will not align to your genome. They can be ignored, or you may use analysis software to remove them. This module lists all the sequences, which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 200,000 sequences are tracked to the end of the file. It is therefore possible that a sequence,which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module. Here wee find overrepresented sequences of adapter in the bad file Note : A warning is raised if any sequence is found to represent more than 0.1% of the total and a failure is issued if any sequence is found to represent more than 1% of the total.
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generat simple composition statistics for the file analyzed. gives the filename, file type, Sequences, sequen length and GC % . Here all the three files controlR1.fastq, control_R good.fastq and control_R1bad.fastq were analyzed. T raw data file showed > 17 million reads and the good f generated after running prinseq-lite showed > 16 milli reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and nev raises an error. Control_R1.fastq Control_R1_good.fas Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Duplicate sequences Interpretation: This module counts the degree of duplication for every sequence in the set and creates a plot showing the relative number of sequences with different degrees of duplication. In this module analysis occurs only for the first 200,000 different sequences seen. The number of occurrences of these sequences is then tracked through the rest of the file, but any new sequences after the first 200,000 are then discarded. Also, any sequences with more than 10 duplicates are placed into the 10 duplicates category - so it's not unusual to see a small rise in this final category. If the rate at which the duplicate plot falls from unique sequences is slow – showing appreciable proportions of the library with duplication levels of 3-5, and a small spike in the 10+ bin there may be a biological rather than a technical cause. The most common type of library to produce this type of plot is an RNA-Seq library. In this type of library it is expected that some sequences will occur very frequently, and others will be very rare. If you want to see the very rare sequences (eg low copy number transcripts), then you will have to greatly over-sequence the most frequent sequences (eg housekeeping genes), so a high level of duplication in part of the library is unavoidable. Therefore you see a warning in the case of raw and good files Note: A warning is raised if non-unique sequences make up more than 20% of the total. And failure is issued if non-unique sequences make up more than 50% of the total. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpret sequenc sequenc occurs o Control_R1.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Duplicate sequences Interpretation: This module counts the degree of duplication for every sequence in the set and creates a plot showing the relative number of sequences with different degrees of duplication. In this module analysis occurs only for the first 200,000 different sequences seen. The number of occurrences of these sequences is then tracked through the rest of the file, but any new sequences after the first 200,000 are then discarded. Also, any sequences with more than 10 duplicates are placed into the 10 duplicates category - so it's not unusual to see a small rise in this final category. If the rate at which the duplicate plot falls from unique sequences is slow – showing appreciable proportions of the library with duplication levels of 3-5, and a small spike in the 10+ bin there may be a biological rather than a technical cause. The most common type of library to produce this type of plot is an RNA-Seq library. In this type of library it is expected that some sequences will occur very frequently, and others will be very rare. If you want to see the very rare sequences (eg low copy number transcripts), then you will have to greatly over-sequence the most frequent sequences (eg housekeeping genes), so a high level of duplication in part of the library is unavoidable. Therefore you see a warning in the case of raw and good files Note: A warning is raised if non-unique sequences make up more than 20% of the total. And failure is issued if non-unique sequences make up more than 50% of the total. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This module counts the degree of duplication for every Control_R1_good.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Duplicate sequences Interpretation: This module counts the degree of duplication for every sequence in the set and creates a plot showing the relative number of sequences with different degrees of duplication. In this module analysis occurs only for the first 200,000 different sequences seen. The number of occurrences of these sequences is then tracked through the rest of the file, but any new sequences after the first 200,000 are then discarded. Also, any sequences with more than 10 duplicates are placed into the 10 duplicates category - so it's not unusual to see a small rise in this final category. If the rate at which the duplicate plot falls from unique sequences is slow – showing appreciable proportions of the library with duplication levels of 3-5, and a small spike in the 10+ bin there may be a biological rather than a technical cause. The most common type of library to produce this type of plot is an RNA-Seq library. In this type of library it is expected that some sequences will occur very frequently, and others will be very rare. If you want to see the very rare sequences (eg low copy number transcripts), then you will have to greatly over-sequence the most frequent sequences (eg housekeeping genes), so a high level of duplication in part of the library is unavoidable. Therefore you see a warning in the case of raw and good files Note: A warning is raised if non-unique sequences make up more than 20% of the total. And failure is issued if non-unique sequences make up more than 50% of the total. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpr sequen sequen occurs occurre but any any se duplica catego is slow levels o rather t this typ that so rare. transcr sequen of the l raw and Note: A 20% of more th Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Duplicate sequences Interpretation: This module counts the degree of duplication for every sequence in the set and creates a plot showing the relative number of sequences with different degrees of duplication. In this module analysis occurs only for the first 200,000 different sequences seen. The number of occurrences of these sequences is then tracked through the rest of the file, but any new sequences after the first 200,000 are then discarded. Also, any sequences with more than 10 duplicates are placed into the 10 duplicates category - so it's not unusual to see a small rise in this final category. If the rate at which the duplicate plot falls from unique sequences is slow – showing appreciable proportions of the library with duplication levels of 3-5, and a small spike in the 10+ bin may be biological rather than technical. The most common type of library to produce this type of plot is an RNA-Seq library. In this type of library it is expected that some sequences will occur very frequently, and others will be very rare. If you want to see very rare sequences (eg low copy number transcripts), then you will have to greatly over-sequence the most frequent sequences (eg housekeeping genes), so a high level of duplication in part of the library is unavoidable. Therefore you see a warning in the case of raw and good files Note: A warning is raised if non-unique sequences make up more than 20% of the total. And failure is issued if non-unique sequences make up more than 50% of the total. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This module counts the degree of duplication for every sequence in the set and creates a plot showing the relative number of sequences with different degrees of duplication. In this module analysis occurs only for the first 200,000 different sequences seen. The number of occurrences of these sequences is then tracked through the rest of the file, but any new sequences after the first 200,000 are then discarded. Also, any sequences with more than 10 duplicates are placed into the 10 duplicates category - so it's not unusual to see a small rise in this final category. If the rate at which the duplicate plot falls from unique sequences is slow – showing appreciable proportions of the library with duplication levels of 3-5, and a small spike in the 10+ bin may be biological rather than technical. The most common type of library to produce this type of plot is an RNA-Seq library. In this type of library it is expected that some sequences will occur very frequently, and others will be very rare. If you want to see very rare sequences (eg low copy number transcripts), then you will have to greatly over-sequence the most frequent sequences (eg housekeeping genes), so a high level of duplication in part of the library is unavoidable. Therefore you see a warning in the case of raw and good files Note: A warning is raised if non-unique sequences make up more than 20% of the total. And failure is issued if non-unique sequences make up more than 50% of the total.
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generat simple composition statistics for the file analyzed. gives the filename, file type, Sequences, sequen length and GC % . Here all the three files controlR1.fastq, control_R good.fastq and control_R1bad.fastq were analyzed. T raw data file showed > 17 million reads and the good f generated after running prinseq-lite showed > 16 milli reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and nev raises an error. Control_R1.fastq Control_R1_good.fas Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Overrepresented Kmers Interpretation: This module counts the enrichment of every 5-mer within the sequence library. Any k-mer showing more than a 3 fold overall enrichment or a 5 fold enrichment at any given base position will be reported by this module. 
 Note: A warning is raised if any k-mer is enriched more than 3 fold overall, or more than 5 fold at any individual position. And failure is issued if non-unique if any k-mer is enriched more than 10 fold at any individual base position 
 Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpre sequenc fold enric Control_R1.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This module counts the enrichment of every 5-mer within the sequence library. Any k-mer showing more than a 3 fold overall enrichment or a 5 fold enrichment at any given base position will be reported by this module. 
 Note: A warning is raised if any k-mer is enriched more than 3 fold overall, or more than 5 fold at any individual position. And failure is issued if non-unique if any k-mer is enriched more than 10 fold at any individual base position 
 Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This module counts the enrichment of every 5-mer within the sequence library. Any k-mer showing more than a 3 fold overall enrichment or a 5 fold enrichment at any given base position will be reported by this module. 
 Control_R1_good.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This module counts the enrichment of every 5-mer within the sequence library. Any k-mer showing more than a 3 fold overall enrichment or a 5 fold enrichment at any given base position will be reported by this module. 
 Note: A warning is raised if any k-mer is enriched more than 3 fold overall, or more than 5 fold at any individual position. And failure is issued if non-unique if any k-mer is enriched more than 10 fold at any individual base position 
 Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute In se fol No mo An an Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This module counts the enrichment of every 5-mer within the sequence library. Any k-mer showing more than a 3 fold overall enrichment or a 5 fold enrichment at any given base position will be reported by this module. 
 Note: A warning is raised if any k-mer is enriched more than 3 fold overall, or more than 5 fold at any individual position. And failure is issued if non-unique if any k-mer is enriched more than 10 fold at any individual base position 
 Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This module counts the enrichment of every 5-mer within the sequence library. Any k-mer showing more than a 3 fold overall enrichment or a 5 fold enrichment at any given base position will be reported by this module. 
 Note: A warning is raised if any k-mer is enriched more than 3 fold overall, or more than 5 fold at any individual position. And failure is issued if non-unique if any k-mer is enriched more than 10 fold at any individual base position 

FASTQC Analysis of FASTQ Files Before and After Trimming

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FASTQC Analysis of FASTQ Files Before and After Trimming

  • 1. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Data (Fastq) FASTQC Prinseqlite Trimming/filtering Data Quality Check and Interpretation
  • 2. Computational Biology and Genomics Facility, Indian Veterinary Research Institute perl prinseq-lite.pl -fastq control.fastq -out_format 5 -min_len 50 -min_qual_mean 25 Input fastq file 1 (FASTA only), 2 (FASTA and QUAL), 3 (FASTQ), 4 (FASTQ and FASTA), or 5 (FASTQ, FASTA and QUAL) Output format Filter sequences shorter than minimum length (here it is 50 nucleotides) Filter sequence with quality score mean below minimum quality mean (here it is 25) PRINSEQ - • generates summary statistics of sequence and quality data • used to filter, reformat and trim next-generation sequence data. • PRINSEQ is available through a user-friendly web interface or as standalone version. Command for quality filtering : perl prinseq-lite.pl -fastq control.fastq -out_format 5 -min_len 50 - min_qual_mean 25 For any further help type :- perl prinseq-lite.pl -h
  • 3. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
  • 4. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
  • 5. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
  • 6. Computational Biology and Genomics Facility, Indian Veterinary Research Institute The output generated from analyzing three files viz. original fastq i.e control_R1.fastq, and the good and bad fastq files generated from prinseq-lite
  • 7. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Basic Statistics: Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 8. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpre simple Control_R1.fastq
  • 9. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Basic Statistics: Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 10. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence Control_R1_good.fastq
  • 11. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Basic Statistics: Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 12. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpret simple c gives th length an Here all good.fas raw data generate reads. Th Note :- B raises an Control_R1_bad.fastq
  • 13. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Basic Statistics: Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 14. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error.
  • 15. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module genera simple composition statistics for the file analyzed gives the filename, file type, Sequences, sequen length and GC % . Here all the three files controlR1.fastq, control_ good.fastq and control_R1bad.fastq were analyzed. T raw data file showed > 17 million reads and the good generated after running prinseq-lite showed > 16 mill reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and ne raises an error. Control_R1.fastq Control_R1_good.fa Control_R1_bad.fastq
  • 16. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Base Sequence Quality
  • 17. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Control_R1.fastq
  • 18. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Base Sequence Quality
  • 19. Computational Biology and Genomics Facility, Indian Veterinary Research InstituteInterpretation: This view shows an overview of the range of quality Control_R1_good.fastq
  • 20. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Base Sequence Quality
  • 21. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interp values For ea in colo The bo range 90% p represe It can means low me mark ( window Note : than 1 raised for any Control_R1_bad.fastq
  • 22. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Base Sequence Quality
  • 23. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This view shows an overview of the range of quality values across all bases at each position in the FastQ file. 
 For each position a BoxWhisker type plot is drawn. The central line (red in colour) is the median value.
 The box (yellow in colour) represents the inter-quartile 
 range (25-75%). The upper and lower whiskers represent the 10% and 90% points. The line (blue in colour) that runs across the graphs represents the mean quality.
 It can be appreciated that output of good files looks to have the best means quality in comparison to raw data file. The bad file had a very low mean quality thereby, a failure was issued as indicated by a cross mark (red in colour) against per base sequence quality in the main window of that data set. Note : A warning will be issued if the lower quartile for any base is less than 10, or if the median for any base is less than 25. A failure was raised if the lower quartile for any base is less than 5 or if the median for any base is less than 20.
  • 24. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module genera simple composition statistics for the file analyzed gives the filename, file type, Sequences, sequen length and GC % . Here all the three files controlR1.fastq, control_ good.fastq and control_R1bad.fastq were analyzed. T raw data file showed > 17 million reads and the good generated after running prinseq-lite showed > 16 mill reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and ne raises an error. Control_R1.fastq Control_R1_good.fa Control_R1_bad.fastq
  • 25. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Sequence Quality Scores Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 26. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Control_R1.fastq C
  • 27. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Sequence Quality Scores Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 28. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Control_R1_good.fastq
  • 29. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Sequence Quality Scores Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 30. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretat subset of y under the b file indicatin file in com better in the very poor q (red in colo that data se Note : A wa below 27 - most freque error rate. Control_R1_bad.fastq
  • 31. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Sequence Quality Scores Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 32. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: The per sequence quality score report allows us to see if a subset of your sequences have universally low quality values. The area under the bell shaped curve was greater for the good file than the raw data file indicating that most of the sequences had very good quality in the good file in comparison to the raw data file.Average quality per read is also better in the good file. In the bad file most of the sequences in the bad had very poor quality. A failure was also issued as indicated by a cross mark (red in colour) against per sequence quality score in the main window of that data set. Note : A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate. A failure error is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate.
  • 33. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module genera simple composition statistics for the file analyzed gives the filename, file type, Sequences, sequen length and GC % . Here all the three files controlR1.fastq, control_ good.fastq and control_R1bad.fastq were analyzed. T raw data file showed > 17 million reads and the good generated after running prinseq-lite showed > 16 mill reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and ne raises an error. Control_R1.fastq Control_R1_good.fa Control_R1_bad.fastq
  • 34. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: Per Base Sequence Content plots the proportion of each base position in a file for which each of the four normal DNA bases has been called. In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in the genome, but in any case they should not be hugely imbalanced from each other. However in all our cases here there is a failure issued due to variation in A to T and G to C percentages at the start of the reads till the 14th base. This also indicates that the reads can be trimmed at the 5’end till the 14th base. Note : A warning is issued if the difference between A and T, or G and C is greater than 10% in any position and a failure is issued if the difference between A and T, or G and C is greater than 20% in any position. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation: Per Base Sequence Content plots the proportion of each base position in a file for which each of the four normal DNA bases has been called. In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in the genome, but in any case they should not be hugely imbalanced from each other. However in all our cases here there is a failure issued due to variation in A to T and G to C percentages at the start of the reads till the 14th base. This also indicates that the reads can be trimmed at the 5’end till the 14th base. Note : A warning is issued if the difference between A and T, or G and C is greater than 10% in any position and a failure is issued if the difference between A and T, or G and C is greater than 20% in any position. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 35. Computational Biology and Genomics Facility, Indian Veterinary Research Institute In Control_R1.fastq
  • 36. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: Per Base Sequence Content plots the proportion of each base position in a file for which each of the four normal DNA bases has been called. In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in the genome, but in any case they should not be hugely imbalanced from each other. However in all our cases here there is a failure issued due to variation in A to T and G to C percentages at the start of the reads till the 14th base. This also indicates that the reads can be trimmed at the 5’end till the 14th base. Note : A warning is issued if the difference between A and T, or G and C is greater than 10% in any position and a failure is issued if the difference between A and T, or G and C is greater than 20% in any position. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 37. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Control_R1_good.fastq
  • 38. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: Per Base Sequence Content plots the proportion of each base position in a file for which each of the four normal DNA bases has been called. In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in the genome, but in any case they should not be hugely imbalanced from each other. However in all our cases here there is a failure issued due to variation in A to T and G to C percentages at the start of the reads till the 14th base. This also indicates that the reads can be trimmed at the 5’end till the 14th base. Note : A warning is issued if the difference between A and T, or G and C is greater than 10% in any position and a failure is issued if the difference between A and T, or G and C is greater than 20% in any position. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 39. Computational Biology and Genomics Facility, Indian Veterinary Research Institute I p f w t s b g f i s c N a t a Control_R1_bad.fastq
  • 40. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: Per Base Sequence Content plots the proportion of each base position in a file for which each of the four normal DNA bases has been called. In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in the genome, but in any case they should not be hugely imbalanced from each other. However in all our cases here there is a failure issued due to variation in A to T and G to C percentages at the start of the reads till the 14th base. This also indicates that the reads can be trimmed at the 5’end till the 14th base. Note : A warning is issued if the difference between A and T, or G and C is greater than 10% in any position and a failure is issued if the difference between A and T, or G and C is greater than 20% in any position. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 41. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: Per Base Sequence Content plots the proportion of each base position in a file for which each of the four normal DNA bases has been called. In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in the genome, but in any case they should not be hugely imbalanced from each other. However in all our cases here there is a failure issued due to variation in A to T and G to C percentages at the start of the reads till the 14th base. This also indicates that the reads can be trimmed at the 5’end till the 14th base. Note : A warning is issued if the difference between A and T, or G and C is greater than 10% in any position and a failure is issued if the difference between A and T, or G and C is greater than 20% in any position.
  • 42. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module genera simple composition statistics for the file analyzed gives the filename, file type, Sequences, sequen length and GC % . Here all the three files controlR1.fastq, control_ good.fastq and control_R1bad.fastq were analyzed. T raw data file showed > 17 million reads and the good generated after running prinseq-lite showed > 16 mill reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and ne raises an error. Control_R1.fastq Control_R1_good.fa Control_R1_bad.fastq
  • 43. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Sequence GC Content
  • 44. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpret length of Control_R1.fastq Cont
  • 45. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Sequence GC Content
  • 46. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation :This module measures the GC content across the whole Control_R1_good.fastq
  • 47. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Sequence GC Content
  • 48. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretat length of e distribution a roughly correspond shaped dis of biased s files and a the normal good and ra Note : A wa distribution sum of the of the reads Control_R1_bad.fastq
  • 49. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure was issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Sequence GC Content
  • 50. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation :This module measures the GC content across the whole length of each sequence in a file and compares it to a modeled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. In our data sets a warning is raised for the raw and good files and a failure is issued for the bad file. It is clear that the deviation from the normal distribution is greater in the bad file output in comparison to the good and raw file outputs. Note : A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads. And failure is issued if the sum of the deviations from the normal distribution represents more than 30% of the reads.
  • 51. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generat simple composition statistics for the file analyzed. gives the filename, file type, Sequences, sequen length and GC % . Here all the three files controlR1.fastq, control_R good.fastq and control_R1bad.fastq were analyzed. T raw data file showed > 17 million reads and the good f generated after running prinseq-lite showed > 16 milli reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and nev raises an error. Control_R1.fastq Control_R1_good.fas Control_R1_bad.fastq
  • 52. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation:This module plots out the percentage of base calls at each position for which an N was called. It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. Here no N’s were found in all the datasets. Note : A warning is raised if any position shows an N content of >5% and a failure is issued if any position shows an N content of >20% Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Base N Content Interpretation:This module plots out the percentage of base calls at each position for which an N was called. It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. Here no N’s were found in all the datasets. Note : A warning is raised if any position shows an N content of >5% and a failure is issued if any position shows an N content of >20% Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 53. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpreta position f Control_R1.fastq
  • 54. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation:This module plots out the percentage of base calls at each position for which an N was called. It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. Here no N’s were found in all the datasets. Note : A warning is raised if any position shows an N content of >5% and a failure is issued if any position shows an N content of >20% Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Per Base N Content Interpretation:This module plots out the percentage of base calls at each position for which an N was called. It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. Here no N’s were found in all the datasets. Note : A warning is raised if any position shows an N content of >5% and a failure is issued if any position shows an N content of >20% Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 55. Computational Biology and Genomics Facility, Indian Veterinary Research InstituteInterpretation:This module plots out the percentage of base calls at each Control_R1_good.fastq
  • 56. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation:This module plots out the percentage of base calls at each position for which an N was called. It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. Here no N’s were found in all the datasets. Note : A warning is raised if any position shows an N content of >5% and a failure is issued if any position shows an N content of >20% Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation:This module plots out the percentage of base calls at each position for which an N was called. It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. Here no N’s were found in all the datasets. Note : A warning is raised if any position shows an N content of >5% and a failure is issued if any position shows an N content of >20% Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 57. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpre position proportio sequenc Note : A failure is Control_R1_bad.fastq
  • 58. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation:This module plots out the percentage of base calls at each position for which an N was called. It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. Here no N’s were found in all the datasets. Note : A warning is raised if any position shows an N content of >5% and a failure is issued if any position shows an N content of >20% Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 59. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation:This module plots out the percentage of base calls at each position for which an N was called. It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. Here no N’s were found in all the datasets. Note : A warning is raised if any position shows an N content of >5% and a failure is issued if any position shows an N content of >20%
  • 60. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generat simple composition statistics for the file analyzed. gives the filename, file type, Sequences, sequen length and GC % . Here all the three files controlR1.fastq, control_R good.fastq and control_R1bad.fastq were analyzed. T raw data file showed > 17 million reads and the good f generated after running prinseq-lite showed > 16 milli reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and nev raises an error. Control_R1.fastq Control_R1_good.fas Control_R1_bad.fastq
  • 61. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Sequence length Distribution Interpretation :This module generates a graph showing the distribution of fragment sizes in the file which was analysed.In many cases this will produce a simple graph showing a peak only at one size, but for variable length FastQ files this will show the relative amounts of each different size of sequence fragment. Here in all the files we have reads with a length of 101 bp. Note : A warning is raised if all sequences are not the same length. A failure is issued if any of the sequences have zero length. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation :This module generates a graph showing the distribution of fragment sizes in the file which was analysed.In many cases this will produce a simple graph showing a peak only at one size, but for variable length FastQ files this will show the relative amounts of each different size of sequence fragment. Here in all the files we have reads with a length of 101 bp. Note : A warning is raised if all sequences are not the same length. A failure is issued if any of the sequences have zero length. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 62. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpre sizes in Control_R1.fastq Co
  • 63. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Sequence length Distribution Interpretation :This module generates a graph showing the distribution of fragment sizes in the file which was analysed.In many cases this will produce a simple graph showing a peak only at one size, but for variable length FastQ files this will show the relative amounts of each different size of sequence fragment. Here in all the files we have reads with a length of 101 bp. Note : A warning is raised if all sequences are not the same length. A failure is issued if any of the sequences have zero length. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 64. Computational Biology and Genomics Facility, Indian Veterinary Research InstituteInterpretation :This module generates a graph showing the distribution of fragment Control_R1_good.fastq
  • 65. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation :This module generates a graph showing the distribution of fragment sizes in the file which was analysed.In many cases this will produce a simple graph showing a peak only at one size, but for variable length FastQ files this will show the relative amounts of each different size of sequence fragment. Here in all the files we have reads with a length of 101 bp. Note : A warning is raised if all sequences are not the same length. A failure is issued if any of the sequences have zero length. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Sequence length Distribution
  • 66. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretati sizes in the showing a the relative files we ha Note : A w issued if an Control_R1_bad.fastq
  • 67. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This module generates a graph showing the distribution of fragment sizes in the file which was analysed.In many cases this will produce a simple graph showing a peak only at one size, but for variable length FastQ files this will show the relative amounts of each different size of sequence fragment. Here in all the files we have reads with a length of 101 bp. Note : A warning is raised if all sequences are not the same length. A failure is issued if any of the sequences have zero length. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Sequence length Distribution
  • 68. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This module generates a graph showing the distribution of fragment sizes in the file which was analysed.In many cases this will produce a simple graph showing a peak only at one size, but for variable length FastQ files this will show the relative amounts of each different size of sequence fragment. Here in all the files we have reads with a length of 101 bp. Note : A warning is raised if all sequences are not the same length. A failure is issued if any of the sequences have zero length.
  • 69. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generat simple composition statistics for the file analyzed. gives the filename, file type, Sequences, sequen length and GC % . Here all the three files controlR1.fastq, control_R good.fastq and control_R1bad.fastq were analyzed. T raw data file showed > 17 million reads and the good f generated after running prinseq-lite showed > 16 milli reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and nev raises an error. Control_R1.fastq Control_R1_good.fas Control_R1_bad.fastq
  • 70. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Control_R1.fastq No Overrepresented Sequences Control_R1_good.fastq No Overrepresented Sequences Sequence Count Percentage Possible Source GATCGGAAGA GCACACGTCTG AACTCCAGTCA CTGACCAACTC TCCGTATGC 3950 0.84781596 16915967 TrueSeq Adapter, index 4 (110% over 50 bp) Interpretation: Overrepresented sequences include the sequences that are highly duplicated in your library, as well as any primer and/or adapter dimers that were present in the original library. Adapter sequences are always present in a sequencing experiment at some level, but aren't problematic in small percentages. These adapters will not align to your genome. They can be ignored, or you may use analysis software to remove them. This module lists all of the sequence, which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 200,000 sequences are tracked to the end of the file. It is therefore possible that a sequence,which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module. Here wee find overrepresented sequences of adapter in the bad file Note : A warning is raised if any sequence is found to represent more than 0.1% of the total and a failure is issued if any sequence is found to represent more than 1% of the total. Control_R1_bad.fastq Overrepresented Sequences
  • 71. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Control_R1.fastq No Overrepresented Sequences Sequence Count Percentage Possible Source GATCGGAAGA GCACACGTCTG AACTCCAGTCA CTGACCAACTC TCCGTATGC 3950 0.84781596 16915967 TrueSeq Adapter, index 4 (110% over 50 bp) I a d a p g r m a I d b t N t r Control_R1_bad.fastq
  • 72. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Control_R1.fastq No Overrepresented Sequences Control_R1_good.fastq No Overrepresented Sequences Sequence Count Percentage Possible Source GATCGGAAGA GCACACGTCTG AACTCCAGTCA CTGACCAACTC TCCGTATGC 3950 0.84781596 16915967 TrueSeq Adapter, index 4 (110% over 50 bp) Interpretation: Overrepresented sequences include the sequences that are highly duplicated in your library, as well as any primer and/or adapter dimers that were present in the original library. Adapter sequences are always present in a sequencing experiment at some level, but aren't problematic in small percentages. These adapters will not align to your genome. They can be ignored, or you may use analysis software to remove them. This module lists all of the sequence, which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 200,000 sequences are tracked to the end of the file. It is therefore possible that a sequence,which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module. Here wee find overrepresented sequences of adapter in the bad file Note : A warning is raised if any sequence is found to represent more than 0.1% of the total and a failure is issued if any sequence is found to represent more than 1% of the total. Control_R1_bad.fastq Overrepresented Sequences
  • 73. Computational Biology and Genomics Facility, Indian Veterinary Research Institute No Overrepresented Sequences Interpretation: Overrepresented sequences include the sequences that are highly duplicated in your library, as well as any primer and/or adapter dimers that were present in the original library. Adapter sequences are always present in a sequencing experiment at some level, but aren't problematic in small percentages. These adapters will not align to your genome. They can be ignored, or you may use analysis software to remove them. This module lists all the sequences, which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 200,000 sequences are tracked to the end of the file. It is therefore possible that a sequence,which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module. Here wee find overrepresented sequences of adapter in the bad file Note : A warning is raised if any sequence is found to represent more than 0.1% of the total and a failure is issued if any sequence is found to represent more than 1% of the total.
  • 74. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generat simple composition statistics for the file analyzed. gives the filename, file type, Sequences, sequen length and GC % . Here all the three files controlR1.fastq, control_R good.fastq and control_R1bad.fastq were analyzed. T raw data file showed > 17 million reads and the good f generated after running prinseq-lite showed > 16 milli reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and nev raises an error. Control_R1.fastq Control_R1_good.fas Control_R1_bad.fastq
  • 75. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Duplicate sequences Interpretation: This module counts the degree of duplication for every sequence in the set and creates a plot showing the relative number of sequences with different degrees of duplication. In this module analysis occurs only for the first 200,000 different sequences seen. The number of occurrences of these sequences is then tracked through the rest of the file, but any new sequences after the first 200,000 are then discarded. Also, any sequences with more than 10 duplicates are placed into the 10 duplicates category - so it's not unusual to see a small rise in this final category. If the rate at which the duplicate plot falls from unique sequences is slow – showing appreciable proportions of the library with duplication levels of 3-5, and a small spike in the 10+ bin there may be a biological rather than a technical cause. The most common type of library to produce this type of plot is an RNA-Seq library. In this type of library it is expected that some sequences will occur very frequently, and others will be very rare. If you want to see the very rare sequences (eg low copy number transcripts), then you will have to greatly over-sequence the most frequent sequences (eg housekeeping genes), so a high level of duplication in part of the library is unavoidable. Therefore you see a warning in the case of raw and good files Note: A warning is raised if non-unique sequences make up more than 20% of the total. And failure is issued if non-unique sequences make up more than 50% of the total. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 76. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpret sequenc sequenc occurs o Control_R1.fastq
  • 77. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Duplicate sequences Interpretation: This module counts the degree of duplication for every sequence in the set and creates a plot showing the relative number of sequences with different degrees of duplication. In this module analysis occurs only for the first 200,000 different sequences seen. The number of occurrences of these sequences is then tracked through the rest of the file, but any new sequences after the first 200,000 are then discarded. Also, any sequences with more than 10 duplicates are placed into the 10 duplicates category - so it's not unusual to see a small rise in this final category. If the rate at which the duplicate plot falls from unique sequences is slow – showing appreciable proportions of the library with duplication levels of 3-5, and a small spike in the 10+ bin there may be a biological rather than a technical cause. The most common type of library to produce this type of plot is an RNA-Seq library. In this type of library it is expected that some sequences will occur very frequently, and others will be very rare. If you want to see the very rare sequences (eg low copy number transcripts), then you will have to greatly over-sequence the most frequent sequences (eg housekeeping genes), so a high level of duplication in part of the library is unavoidable. Therefore you see a warning in the case of raw and good files Note: A warning is raised if non-unique sequences make up more than 20% of the total. And failure is issued if non-unique sequences make up more than 50% of the total. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 78. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This module counts the degree of duplication for every Control_R1_good.fastq
  • 79. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Duplicate sequences Interpretation: This module counts the degree of duplication for every sequence in the set and creates a plot showing the relative number of sequences with different degrees of duplication. In this module analysis occurs only for the first 200,000 different sequences seen. The number of occurrences of these sequences is then tracked through the rest of the file, but any new sequences after the first 200,000 are then discarded. Also, any sequences with more than 10 duplicates are placed into the 10 duplicates category - so it's not unusual to see a small rise in this final category. If the rate at which the duplicate plot falls from unique sequences is slow – showing appreciable proportions of the library with duplication levels of 3-5, and a small spike in the 10+ bin there may be a biological rather than a technical cause. The most common type of library to produce this type of plot is an RNA-Seq library. In this type of library it is expected that some sequences will occur very frequently, and others will be very rare. If you want to see the very rare sequences (eg low copy number transcripts), then you will have to greatly over-sequence the most frequent sequences (eg housekeeping genes), so a high level of duplication in part of the library is unavoidable. Therefore you see a warning in the case of raw and good files Note: A warning is raised if non-unique sequences make up more than 20% of the total. And failure is issued if non-unique sequences make up more than 50% of the total. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 80. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpr sequen sequen occurs occurre but any any se duplica catego is slow levels o rather t this typ that so rare. transcr sequen of the l raw and Note: A 20% of more th Control_R1_bad.fastq
  • 81. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Duplicate sequences Interpretation: This module counts the degree of duplication for every sequence in the set and creates a plot showing the relative number of sequences with different degrees of duplication. In this module analysis occurs only for the first 200,000 different sequences seen. The number of occurrences of these sequences is then tracked through the rest of the file, but any new sequences after the first 200,000 are then discarded. Also, any sequences with more than 10 duplicates are placed into the 10 duplicates category - so it's not unusual to see a small rise in this final category. If the rate at which the duplicate plot falls from unique sequences is slow – showing appreciable proportions of the library with duplication levels of 3-5, and a small spike in the 10+ bin may be biological rather than technical. The most common type of library to produce this type of plot is an RNA-Seq library. In this type of library it is expected that some sequences will occur very frequently, and others will be very rare. If you want to see very rare sequences (eg low copy number transcripts), then you will have to greatly over-sequence the most frequent sequences (eg housekeeping genes), so a high level of duplication in part of the library is unavoidable. Therefore you see a warning in the case of raw and good files Note: A warning is raised if non-unique sequences make up more than 20% of the total. And failure is issued if non-unique sequences make up more than 50% of the total. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 82. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This module counts the degree of duplication for every sequence in the set and creates a plot showing the relative number of sequences with different degrees of duplication. In this module analysis occurs only for the first 200,000 different sequences seen. The number of occurrences of these sequences is then tracked through the rest of the file, but any new sequences after the first 200,000 are then discarded. Also, any sequences with more than 10 duplicates are placed into the 10 duplicates category - so it's not unusual to see a small rise in this final category. If the rate at which the duplicate plot falls from unique sequences is slow – showing appreciable proportions of the library with duplication levels of 3-5, and a small spike in the 10+ bin may be biological rather than technical. The most common type of library to produce this type of plot is an RNA-Seq library. In this type of library it is expected that some sequences will occur very frequently, and others will be very rare. If you want to see very rare sequences (eg low copy number transcripts), then you will have to greatly over-sequence the most frequent sequences (eg housekeeping genes), so a high level of duplication in part of the library is unavoidable. Therefore you see a warning in the case of raw and good files Note: A warning is raised if non-unique sequences make up more than 20% of the total. And failure is issued if non-unique sequences make up more than 50% of the total.
  • 83. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation : The Basic Statistics module generates simple composition statistics for the file analyzed. It gives the filename, file type, Sequences, sequence length and GC % . Here all the three files controlR1.fastq, control_R1 good.fastq and control_R1bad.fastq were analyzed. The raw data file showed > 17 million reads and the good file generated after running prinseq-lite showed > 16 million reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and never raises an error. Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq Interpretation : The Basic Statistics module generat simple composition statistics for the file analyzed. gives the filename, file type, Sequences, sequen length and GC % . Here all the three files controlR1.fastq, control_R good.fastq and control_R1bad.fastq were analyzed. T raw data file showed > 17 million reads and the good f generated after running prinseq-lite showed > 16 milli reads. The bad file showed only 0.4 million reads. Note :- Basic Statistics never raises a warning and nev raises an error. Control_R1.fastq Control_R1_good.fas Control_R1_bad.fastq
  • 84. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Overrepresented Kmers Interpretation: This module counts the enrichment of every 5-mer within the sequence library. Any k-mer showing more than a 3 fold overall enrichment or a 5 fold enrichment at any given base position will be reported by this module. 
 Note: A warning is raised if any k-mer is enriched more than 3 fold overall, or more than 5 fold at any individual position. And failure is issued if non-unique if any k-mer is enriched more than 10 fold at any individual base position 
 Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 85. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpre sequenc fold enric Control_R1.fastq
  • 86. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This module counts the enrichment of every 5-mer within the sequence library. Any k-mer showing more than a 3 fold overall enrichment or a 5 fold enrichment at any given base position will be reported by this module. 
 Note: A warning is raised if any k-mer is enriched more than 3 fold overall, or more than 5 fold at any individual position. And failure is issued if non-unique if any k-mer is enriched more than 10 fold at any individual base position 
 Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 87. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This module counts the enrichment of every 5-mer within the sequence library. Any k-mer showing more than a 3 fold overall enrichment or a 5 fold enrichment at any given base position will be reported by this module. 
 Control_R1_good.fastq
  • 88. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This module counts the enrichment of every 5-mer within the sequence library. Any k-mer showing more than a 3 fold overall enrichment or a 5 fold enrichment at any given base position will be reported by this module. 
 Note: A warning is raised if any k-mer is enriched more than 3 fold overall, or more than 5 fold at any individual position. And failure is issued if non-unique if any k-mer is enriched more than 10 fold at any individual base position 
 Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 89. Computational Biology and Genomics Facility, Indian Veterinary Research Institute In se fol No mo An an Control_R1_bad.fastq
  • 90. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This module counts the enrichment of every 5-mer within the sequence library. Any k-mer showing more than a 3 fold overall enrichment or a 5 fold enrichment at any given base position will be reported by this module. 
 Note: A warning is raised if any k-mer is enriched more than 3 fold overall, or more than 5 fold at any individual position. And failure is issued if non-unique if any k-mer is enriched more than 10 fold at any individual base position 
 Control_R1.fastq Control_R1_good.fastq Control_R1_bad.fastq
  • 91. Computational Biology and Genomics Facility, Indian Veterinary Research Institute Interpretation: This module counts the enrichment of every 5-mer within the sequence library. Any k-mer showing more than a 3 fold overall enrichment or a 5 fold enrichment at any given base position will be reported by this module. 
 Note: A warning is raised if any k-mer is enriched more than 3 fold overall, or more than 5 fold at any individual position. And failure is issued if non-unique if any k-mer is enriched more than 10 fold at any individual base position