RNA-seq: analysis of raw data and preprocessing - part 2

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Second presentation slides of the 'RNA-seq for DE analysis' training. See http://www.bits.vib.be for more information.

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RNA-seq: analysis of raw data and preprocessing - part 2

  1. 1. Raw data investigation Joachim Jacob 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. 2. Experimental setup We have decided 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. 3. PE versus SE Illumina ● 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. 4. PE versus SE Illumina 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. 5. Strandedness ● ● Naive protocols obtain reads 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).
  6. 6. Strandedness Not stranded Stranded
  7. 7. Example of a stranded protocol ● dUTP protocol to generate stranded reads.
  8. 8. Importance of strandedness ● ● Strandedness can 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. 9. Length of the reads ● ● ● 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. 10. For DE on the 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.
  11. 11. Illumina Truseq protocol sdf
  12. 12. Raw Illumina data The data you get arrives as... barcode experiment Compressed, usually with gzip
  13. 13. Raw Illumina data (this one: 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. 14. Exploring the raw data 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. 15. FastQC – graphical exploration http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
  16. 16. FastQC – perfect example Reads have good quality!
  17. 17. FastQC – perfect example 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. 18. FastQC – perfect example Smooth histogram/ density line towards the right,
  19. 19. FastQC – perfect example steady nucleotide distribution. Bias typical for illumina
  20. 20. FastQC – perfect example Not strongly fluctuating GC content Bias typical for illumina
  21. 21. FastQC – perfect example GC-content nicely bell shaped
  22. 22. FastQC – perfect example No N's! (should ring something)
  23. 23. FastQC – perfect example All reads have length 50bp,
  24. 24. FastQC – perfect example Reads are nicely duplicated: some amount of duplication is to be expected in RNA-seq data.
  25. 25. FastQC – perfect example Reads are nicely duplicated: some amount of duplication is to be expected in RNA-seq data.
  26. 26. FastQC – perfect example Kmers are short sequence stretches. Sometimes they are overrepresented. But in RNA-seq this is not so important (duplication).
  27. 27. FastQC – less good RNA-seq sample A relatively large Portion of the reads have mistakes at the 3' end of the read.
  28. 28. FastQC – less good RNA-seq sample There is an overrepresentation of reads with a low mean quality score
  29. 29. FastQC – less good RNA-seq sample Not a steady level of different nucleotide fractions
  30. 30. FastQC – less good RNA-seq sample Fluctuates
  31. 31. FastQC – less good RNA-seq sample Heavily skewed versus AT rich reads
  32. 32. FastQC – less good RNA-seq sample Apparently a mixture of two sets of reads with different lengths
  33. 33. FastQC – less good RNA-seq sample Duplication seems a bit on the low side (reported figures are from 60 -75%)
  34. 34. FastQC – less good 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. 35. FastQC – less good RNA-seq sample Specific patterns of Specific kmers. Note: A and T rich
  36. 36. Quality control of raw 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. 37. Preprocessing Removing unwanted parts of 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. 38. Technical contamination Our goal is 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. 39. Technical contamination Our goal is 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.
  40. 40. Technical contamination
  41. 41. Technical contamination Our goal is 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. 42. Technical contamination List of technical 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
  43. 43. Fastq-mcf output http://code.google.com/p/ea-utils/wiki/FastqMcf
  44. 44. Technical contamination Never remove duplicate 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. 45. Biological contamination cell Mitochondria contain rRNA, mRNA and mtDNA rRNA and non-coding (95% of RNA) nucleus mRNA (5% of RNA)
  46. 46. Biological contamination Mitochondrial rRNA and nc 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. 47. Biological contamination AAAAAAAAAAAAA mRNAs are post-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. 48. Biological contamination ● Get the non-protein coding sequences via Biomart. Mitochondrial genome sequence also.
  49. 49. Biological contamination
  50. 50. Biological contamination
  51. 51. Filter the biological contamination Your reads The biological reads Imported via Biomart We are interested in the reads that don't map!
  52. 52. Filter the biological contamination Your reads The biological reads Imported via Biomart We are interested in the reads that don't map!
  53. 53. Doing this in Galaxy 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. 54. Summary preprocessing Your reads …... Format consistent? 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. 55. Keywords Paired end Stranded reads gzip fastq Biological contamination Technical contamination Adapter sequence Write in your own words what the terms mean
  56. 56. Exercise → investigating and preprocessing raw RNA-seq data
  57. 57. Break

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