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RNA-seq: analysis of raw data and preprocessing - part 2
The document presents a detailed guide on the investigation and preprocessing of raw RNA-seq data, emphasizing the importance of experimental design, sequencing options, and quality control methods. It discusses the differences between single-end and paired-end sequencing, the relevance of strandedness, and the consequences of technical and biological contaminations in the data. Preprocessing steps such as removing low-quality and contaminant sequences are critical for accurate differential expression analysis and successful mapping to reference genomes.
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Presentation on the raw data investigation process in sequencing, by Joachim Jacob.
How to decide on the number of samples and depth for reliable statistics; emphasizes pilot data and sequencing options.
Differences between single-end (SE) and paired-end (PE) sequencing; PE aids in splice junction detection and transcriptome assembly.
Stranded protocols generate reads corresponding to sense or antisense strands, influencing read counts and accuracy, especially for overlapping genes.
Read length (e.g., 50 bp) affects alignment precision; it is less crucial for quantification but important for transcriptome assembly.
Illustrates the format of raw Illumina data, including barcodes, quality scores, and sequence representation.
Initial steps for raw data exploration and quality control using FastQC to identify potential data issues.
Details on preprocessing by removing unwanted sequences (technical contamination) to ensure accurate gene expression assignment.
Explains biological contaminants like rRNA and mtDNA; emphasizes removal for clean data before analysis.
Workflow for preprocessing RNA-seq data in Galaxy, covering technical and biological contamination removal.
Keywords related to RNA-seq data, and an exercise on investigating and preprocessing raw data.
Short intermission in the presentation.
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RNA-seq: analysis of raw data and preprocessing - part 2
- 1. Raw data investigation JoachimJacob 20 and 27 January 2014 This presentation is available under the Creative Commons Attribution-ShareAlike 3.0 Unported License. Please refer to http://www.bits.vib.be/ if you use this presentation or parts hereof.
- 2. Experimental setup We havedecided on: ● how many samples per condition ● how deep This determines how reliable the statistics will be, using experience, and tools like Scotty. A wrong experimental design cannot be fixed. Best approach: pilot data (3 samples per condition, 10M) But we have other sequencing options to choose!
- 3. PE versus SEIllumina ● Single end (SE): from each cDNA fragment only one end is read. Paired end (PE): the cDNA fragment is read from both ends. Purify and fragment SE ● PE
- 4. PE versus SEIllumina Single end (SE): ● Gene level differential expression Paired end (PE): ● Novel splice junction detection ● De novo assembly of transcriptome ● Helps with correctly positioning reads on the reference genome sequence. Note: PE not the same as mate pairs.
- 5. Strandedness ● ● Naive protocols obtainreads from cDNA fragments. BUT the link with the sense or antisense strand is broken. Stranded protocols generate reads from one strand, corresponding to the sense or antisense strand (depending on the protocol).
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- 7. Example of astranded protocol ● dUTP protocol to generate stranded reads.
- 8. Importance of strandedness ● ● Strandednesscan bias the read counts compared to non-stranded protocols. Depends on the genome whether you should apply it, e.g. in case genes overlap, the improved benefit of assigning reads to correct genes can outweigh technical variation.
- 9. Length of thereads ● ● ● Does not matter so much (when we want to quantify aligning to a reference sequence): 50 bp will do. The most important point is to be able to accurately position the read on the reference genome sequence, to assign it to the correct gene. Length can become important, if you want to assemble the transcriptome.
- 10. For DE onthe gene level The 'cheapest' protocol for high-throughput sequencing suffices to achieve DE detection: ● SE ● 50bp ● Option: strandedness. Use the money you have left over for increasing the number of replicates.
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- 12. Raw Illumina data Thedata you get arrives as... barcode experiment Compressed, usually with gzip
- 13. Raw Illumina data (thisone: 87196924 lines) @HWI-ST571:202:D1B86ACXX:2:1102:1146:2155 1:N:0:ACAGTG CCAACATCGAGGTCGCAATCTTTTTNANCGATATGAACTCTCCAAAAAAA + @@@FFFDFHHDG?FFHIIJJJJJIJ#1#1:BFFIGJJJJJIJJGIJJJJA @HWI-ST571:202:D1B86ACXX:2:1102:1073:2240 1:N:0:ACAGTG One read (minimum 4 lines) sequence CGGAGCTGAAGGAGAAACTGAAATCCCTGCAATGTGAATTGTACGTTCTT + CCCFFFFFGGHHHIJJJJJJJIJFHIJIIIJJJJGIIIIIEFGHIFCHJI @HWI-ST571:202:D1B86ACXX:2:1102:1385:2192 1:N:0:ACAGTG certainty reading this base at this position ('quality') GTTGGCAGCCCTGGAGCCCTGCCTCGGTGGTTTAGCCAGTACTAGGGGAT + CCCFFFFFHHHHHJJJIJJJJJJGIJJCGHFHIGIHJJJBDHGHHJJJIE @HWI-ST571:202:D1B86ACXX:2:1102:1352:2244 1:N:0:ACAGTG ATTTCCTCTTATTTACGTTGCTTTAAAGCGAGACTTCAACGCCATTTGAC + @@CFFFFFHHFHDFGHIJIIJGIJGGEHGGJB>??FHHGFFFGHIGIECF @HWI-ST571:202:D1B86ACXX:2:1102:1981:2152 1:N:0:ACAGTG CATCGAAGCAAAGCATATAAAGTTANTNNTNNCTGAGTTGTACATATTGC + ??;;D?DB6CDB+<EFE>:AFA443#2##1##11)0:0?9**0??DAGI4 @HWI-ST571:202:D1B86ACXX:2:1102:1877:2165 1:N:0:ACAGTG GAAGTGCCCCGCTGGCAGCACACAAGGAGCAGCCCGCTGCCGGACCACTC + ?@@DDDADFFAA:CEGHBFGAHGD?F@BE9BFF?D@F;'-8AG<B92=;; http://wiki.bits.vib.be/index.php/.fas
- 14. Exploring the rawdata 1) check whether the Fastq file is consistent - 2) Make graphs of some metrics of the raw data http://wiki.bits.vib.be/index.php/.fastq http://wiki.bits.vib.be/index.php/RNAseq_toolbox#Quality_control_and_visualization_of_raw_reads
- 15. FastQC – graphicalexploration http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
- 16. FastQC – perfectexample Reads have good quality!
- 17. FastQC – perfectexample Anna Karenina principle: “There is only one way to be good, but there are many ways to be wrong.” We will start by showing a good sample. Afterwards we will discuss a less good sample. http://en.wikipedia.org/wiki/Anna_Karenina_principle
- 18. FastQC – perfectexample Smooth histogram/ density line towards the right,
- 19. FastQC – perfectexample steady nucleotide distribution. Bias typical for illumina
- 20. FastQC – perfectexample Not strongly fluctuating GC content Bias typical for illumina
- 21. FastQC – perfectexample GC-content nicely bell shaped
- 22. FastQC – perfectexample No N's! (should ring something)
- 23. FastQC – perfectexample All reads have length 50bp,
- 24. FastQC – perfectexample Reads are nicely duplicated: some amount of duplication is to be expected in RNA-seq data.
- 25. FastQC – perfectexample Reads are nicely duplicated: some amount of duplication is to be expected in RNA-seq data.
- 26. FastQC – perfectexample Kmers are short sequence stretches. Sometimes they are overrepresented. But in RNA-seq this is not so important (duplication).
- 27. FastQC – lessgood RNA-seq sample A relatively large Portion of the reads have mistakes at the 3' end of the read.
- 28. FastQC – lessgood RNA-seq sample There is an overrepresentation of reads with a low mean quality score
- 29. FastQC – lessgood RNA-seq sample Not a steady level of different nucleotide fractions
- 30. FastQC – lessgood RNA-seq sample Fluctuates
- 31. FastQC – lessgood RNA-seq sample Heavily skewed versus AT rich reads
- 32. FastQC – lessgood RNA-seq sample Apparently a mixture of two sets of reads with different lengths
- 33. FastQC – lessgood RNA-seq sample Duplication seems a bit on the low side (reported figures are from 60 -75%)
- 34. FastQC – lessgood RNA-seq sample Very highly skewed read number. Often the sequence of Truseq adaptor, or multiplex identifiers can be found here. BLAST can reveal more information!
- 35. FastQC – lessgood RNA-seq sample Specific patterns of Specific kmers. Note: A and T rich
- 36. Quality control ofraw data Proceed? Or rerun? This QC can guide you to which preprocessing steps you need to apply for sure. The extra time and money needed to correct the biases can sometimes justify a rerun of the experiment. This QC shows which preprocessing steps have already been made by the sequencing provider.
- 37. Preprocessing Removing unwanted partsof the raw data so it helps as much as possible with reaching our goal: defining differentially expressed genes. 1) removing technical contamination ● Low quality read parts ● Technical sequences: adaptors ● PhiX internal control sequences 2) removing biological contamination ● polyA-tails ● rRNA sequences ● mtDNA sequences After this, we run FastQC again.
- 38. Technical contamination Our goalis to define DE expression, for this we need to assign reads with a high confidence to the correct genomic location. Removal of low quality read parts: they have a higher chance to contain errors, and cause noise in our read counts.
- 39. Technical contamination Our goalis to define DE expression, for this we need to assign reads with a high confidence to the correct genomic location. Removal of low quality read parts: they have a higher chance to contain errors, and cause noise in our read counts.
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- 41. Technical contamination Our goalis to define DE expression, for this we need to assign reads with a high confidence to the correct genomic location. Removal of adaptor sequences (and other technical sequences, such as multiplex) as they cannot be mapped to the reference genome.
- 42. Technical contamination List oftechnical sequences Our goal is to define DE expression, for this we need to assign reads with a high confidence to the correct genomic location. Advised to use defaults Removal of adaptor sequences (and other technical sequences, such as multiplex) as they cannot be mapped to the reference genome. http://code.google.com/p/ea-utils/wiki/FastqMcf
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- 44. Technical contamination Never removeduplicate reads! Highly expressed genes can have genuine duplicate reads, which are not due to the PCR amplification step in the protocol. ● PhiX sequences: the DNA of Phi X bacteriophage is spiked in to monitor and optimize sequencing on Illumina machines. Your sequencing provider should filter out those sequences before delivery. You can filter them out by aligning your reads to the PhiX genome. ● http://en.wikipedia.org/wiki/Phi_X_174
- 45. Biological contamination cell Mitochondria contain rRNA,mRNA and mtDNA rRNA and non-coding (95% of RNA) nucleus mRNA (5% of RNA)
- 46. Biological contamination Mitochondrial rRNA andnc mRNAs are captured with oligo-dT coated beads. Occasionally, non-protein coding sequences are also captured (especially since mtRNA and rRNA can be relatively rich in AT). We can remove them via homology searching (BLAST) with known non-protein coding sequences. mRNA (5% of RNA)
- 47. Biological contamination AAAAAAAAAAAAA mRNAs arepost-transcriptionally modified: e.g. the addition of a poly-A tail. If our goal is to map the reads to a reference genome sequence, the polyA tails should be removed. This can be viewed as some source of 'biological contamination' in our sequences (…).
- 48. Biological contamination ● Get thenon-protein coding sequences via Biomart. Mitochondrial genome sequence also.
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- 51. Filter the biologicalcontamination Your reads The biological reads Imported via Biomart We are interested in the reads that don't map!
- 52. Filter the biologicalcontamination Your reads The biological reads Imported via Biomart We are interested in the reads that don't map!
- 53. Doing this inGalaxy Useful: take a sample of your reads: fastq-to-tabular, select random lines, tabular-to-fastq 1. create a new history 2. load the sample data in 3. Run fastqMcf to remove technical sequences 4. Run bowtie to match against biological sequence databases, and keep reads that don't match. 5. Summarize: fastqc → make a workflow of this sample history. → run the workflow on all your samples in parallel → store the cleaned reads in a data library.
- 54. Summary preprocessing Your reads …... Formatconsistent? Errors in quality? Your groomed reads Trends in raw data? QC report ... ….... …... Get technical contaminants - …. Your groomed reads without technical contamination Get biological contaminants - …. - …. Your groomed reads without technical and biological contamination ….... ... …... How does your data look now? QC
- 55. Keywords Paired end Stranded reads gzip fastq Biologicalcontamination Technical contamination Adapter sequence Write in your own words what the terms mean
- 56. Exercise → investigating andpreprocessing raw RNA-seq data
- 57.