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RNASeq - Analysis Pipeline for Differential Expression
- 1. RNA-Seq
- 2. RNA-Seq • Application of Next Generation Sequencing technology (NGS) for RNA sequencing for transcript identification and quantification of RNA. • Can be used for: – Estimating the number of transcripts in the sample (transcriptomics or expression profiling) – Reveal sequence variation – Detection of alternate splicing – Gene expression profiles of healthy versus diseased tissue
- 3. RNA-Seq vs Microarray BMC Bioinformatics201415(Suppl 11):S3, DOI: 10.1186/1471-2105-15-S11-S3
- 4. Data Generation Steps REVI EWS Nature Reviews Genetics 12, 671-682 (October 2011) , Doi:10.1038/nrg3068
- 5. RNA-Seq analysis Pipeline for Detecting Differential Expression Genome Biology 2010 11:220, DOI: 10.1186/gb-2010-11-12-220
- 6. Read-Mapping Challenges • NGS Computational challenges • Memory footprint • Millions of short reads • RNA-Seq Special Mapping Concerns • New technology old problems • Exact vs inexact matches From wikipedia
- 7. Algorithms For Read Mapping Build an Index Set of position where reads are most likely to align Refined alignment at the target locations - Hash table - Burrow-Wheeler transform (BWT); FM Index Seed and Extend
- 8. Hash Tables • Use hash tables to store position of all k-mers in a genome 1 2 012345678901234567890 AATCGCATAG ATCGCATAGT TCGCATAGTT CGCATAGTTA GCATAGTTA T - Chr 9, location 0 - Chr 9, location 1 - Chr 9, location 2 - Chr 9, location 3 - Chr 9, location 4 - Chr 9, location 5 AATCGCATAGTTATTAATGCTA
- 9. Output String: TTGGAACC Input String: GCTAGCTA GCTAGCTA CTAGCTAG TAGCTAGC AGCTAGCT GCTAGCTA CTAGCTAG TAGCTAGC AGCTAGCT AGCTAGCT AGCTAGCT CTAGCTAG CTAGCTAG GCTAGCTA GCTAGCTA TAGCTAGC TAGCTAGC Sorting Burrows-Wheeler Transformation BWT • Reversible transformation • Repetitive nature of the outcome makes it easier to compress
- 10. Seed and Extend Read Target ATGCTAGT ATGCTGTT ATGCTAGT Mis-match Match
- 11. RNA-Seq: Special Mapping Concerns www.ensembl.org
- 12. RNA-Seq: Special Mapping Concerns genome.gov Alternate Splicing
- 13. RNA-Seq: Special Mapping Concerns • For RNA sequencing data, many reads will map to the reference genome, but many reads will not because (coming from RNA) they span exon–exon junctions. • Methods to deal with junction reads • Align to the reference transcriptome (well annotated). • Align to the reference genome and build a junction library from known adjacent exons and then align unmapped reads to junction library • Map reads to the genome and identify putative exon (indel finding algorithm); using these candidate exon build all possible exon-exon junctions • De novo assembly of RNA-Seq reads
- 14. RNA-Seq: Special Mapping Concerns Genome Biology 2013 14:R36, DOI: 10.1186/gb-2013-14-4-r36
- 15. Reference Based Mapping Methods BMC Genomics. 2014; 15(1): 570, Doi: 10.1186/1471-2164-15-570
- 16. Tophat2 Genome Biology 2013 14:R36, DOI: 10.1186/gb-2013-14-4-r36
- 17. Transcript Assembly IEEE/ACM Trans Comput Biol Bioinform. 2013 Sep-Oct; 10(5): 1234–1240.
- 18. RNA-Seq analysis Pipeline for Detecting Differential Expression Genome Biology 2010 11:220, DOI: 10.1186/gb-2010-11-12-220
- 19. Summarizing Reads • Aggregate reads over biological meaningful units such as transcripts or genes • Count the number of reads overlapping exons in a gene (but significant proportion of the reads will also map outside annotated regions Genome Biology 2010 11:220, DOI: 10.1186/gb-2010-11-12-220
- 20. Count Normalization • Number of reads aligned to a gene gives a measure of its level of expression • Normalization of the count data • Sequencing depth • Length bias o decide rom the require- h assem- ut differ ufflinks b Isoform 1 d a Low Short transcript High Long transcript Readcount 21 43 1 2 3 4 Exon unio104 Nature Methods 8, 469–477 (2011), Doi:10.1038/nmeth.1613
- 21. Count Normalization • RPKM (Reads Per Kilobase of exon model per Million mapped reads) • FPKM (Fragments Per Kilobase of exon model per Million mapped reads • TPM (Transcripts per million) Exon length Raw number of reads Number of mapped reads in the sample 1,000,000 RPKM =
- 22. Count Normalization Gene/Transcript Name R1 counts R2 counts A (50 kb) 37000 70000 B (100 kb) 50000 110000 C (200 kb) 50000 88000 D (-- kb) ---- ---- XDD (-- kb) ---- ----- Total number of reads 2000000 4000000
- 23. RPKM Calculation
- 24. RNA-Seq analysis Pipeline for Detecting Differential Expression Genome Biology 2010 11:220, DOI: 10.1186/gb-2010-11-12-220
- 25. Differential Expression • Goal of the DE analysis is to identify the genes for which abundance across different experimental conditions has changed significantly • Biological replicates (to account for biological variation) • Ranked list of genes with associated p-values and fold changes • DE tools: edgeR, DESeq
- 26. Alignment Independent Quantification • Sailfish • Salmon • Kallisto Main Idea • Quantify the abundance of known transcripts • Read mapping is unnecessary • Replace inexact pattern matching with exact sub-pattern counting
- 27. Sailfish Nature Biotechnology 32, 462–464 (2014), Doi:10.1038/nbt.2862
- 28. Transcript: TACGTACTAGACCTAA…..... Read: TGCGTACTAGCCCT K-mers are Robust to Errors
Editor's Notes
- Accurate maps of transcript start and end site Detect sequence rearrangements and abnormal transcript structures (common in tumours) It reflects the current state of the cell and can reveal pathological mechanism
- In the past techniques such as microarray were used to study gene expression. It consists of array of probes whose sequence represents particular regions of the genes to be monitored. But there were several limitations High background levels due to cross hybridization Reliance on prior knowledge about the genome On the other had signal from RNA-Seq data is digital in nature because you get the counts. It has base-pair level resolution and a much higher dynamic range of expression levels. We can find novel transcripts and fusion products.
- Extraction of the RNA Remove contaminant DNA If the goal of the experiment is expression profiling then polyA selection for enriching mRNA in eukaryotes, will miss non-coding RNAs and RNAs that miss polyA tails. So if Other library preparation is to deplete rRNA Library preparation can introduce biases such as amplification of GC-rich regions and generation of duplicate sequence
- Pattern searching and data compression are old computational problems. Exact matches are very quick but inexact matches(SW algothrim) taking into account the snps/indels are very slow.
- First build an index and find the most probable sites where reads can match. Then at these putative sites (narrowed down) do local alignment.
- Reads are coming from the mRNA and we are trying to match them to the genome.
- Splicing is post-transcriptional modification in which non-coding regions are removed. Many transcripts will share exon
- Transcriptomes are incomplete even for well studied species
- In the first step of the alignment you can start by aligning reads to either to the reference genome or to the transcriptome. Alinging to the transcriptome is a new feature in tophat2. It improves overall accuracy and sensitivity of the mapping. It also speeds up the analysis as due to smaller size of the transcriptome. Some of the reads will not be mapped because they are coming form unknown transcripts not present in the annotation and there will also be poorly aligned reads. So the next step is to take these unmapped reads and to find novel splice sites. The way tophat2 does it is by splitting the unmapped reads into non-overlapping segments 25 bp long by default and then these segments are aligned against the genome. The maximum intron size is 100 kb by default and that is the window in which we are looking for the match of left and right segments. When that pattern is detected then tophat2 tries to find the most likely location of the splice sites. After detecting the splice juction, tophat2 puts together based on known junction signals (GT-AG, GC-AG and AT-AC).
- Overview of RNA-seq analysis. Reads produced by an RNA-seq experiment are aligned to the genome, then clustered into a graph structure that is traversed to recover all possible isoforms at one locus. Lastly, a subset of transcripts is selected and their abundance quantified from the input reads.
- Number of reads aligned gives a measure of the level of expression
- Cell type specific exon
- Let A and B being two RNA-seq experiments under same condtions by that I mean no differentially expressed genes. If experiment A generates twice as many reads as much reads as B, it is likely that counts from the experiment A will be doubled Length bias: expected number of reads mapped on a gene is proportional to both the abundance and length of the isoforms transcribed from the that gene Adjust for the sequencing depth (“Million” part) Adjust for the Gene length (“kilobase” part) Sequencign depth of a sample second experiment generates twice as many reads
- Read with errors still has has many ‘good’ k-mers Only k-mers overlapping errors will be discarded or mis-counted