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An introduction to RNA-seq data analysis
- 1. An introduction to RNA-seq RNA- data analysis Sonika Tyagi Australian Genome Research Facility 1 August 2012
- 2. Outline • Transcriptomics using RNA-seq: Applications • Gene expression profiling workflows • Design Challenges
- 3. RNA sequencing (mRNA-seq or (mRNA- RNA- RNA-seq) “An experimental protocol that uses next- generation sequencing technologies to sequence the RNA molecules within a biological sample in an effort to determine the primary sequence and relative abundance of each RNA”
- 4. A typical RNA-seq experiment RNA- Library preparation and Sequencing Bioinformatics Analysis Nature Reviews Genetics, November 2008; doi:10.1038/nrg2484
- 5. RNA- RNA-seq Application • Allele specific expression: prevelance of transcribed SNPs • Fusion transcripts: e.g., in cancer • Abundance estimation: alternative splicing, RNA-editing, novel transcripts • Gene expression profiling
- 6. Raw sequences (fastq My Answer: files) Quality control (QC) Spliced Read alignment Transcripts reconstruction Differential expression analysis Biology
- 7. Reference Available ? Annotated de novo transcriptome Annotated Genome Assembled/Predicted assembly transcriptome Reads mapping •De novo assembly Reads mapping •Reference assisted Transcripts Transcripts reconstruction reconstruction Summarization a (by CDS, exon, gene, splice junctions ) Tables of counts (digital expression) Biology DE analysis RNA- RNA-seq workflows (GO/Pathways)
- 8. Raw sequences (fastq files) Quality control (QC) Spliced Read alignment Transcripts reconstruction Differential expression analysis Biology
- 9. QC tools
- 10. Raw sequences (fastq files) Quality control (QC) Spliced Read alignment Transcripts reconstruction Differential expression analysis Biology
- 11. Alignments / mapping splice junctions Unspliced read Examples: • Ideal for mapping reads against cDNA aligners • MAQ, Stampy, databases. ELAND • Splice junction/events • Seed methods • BWA, Bowtie are not picked up • Burrow wheel methods Spliced read Examples: • Novel splice junctions can be detected aligners • Tophat,Mapsplice, SpliceMap • Perform better for • Exon first polymorphic regions • Seed – Extend method • GSNAP, QPALMA, and aligning Elandv2e pseudogenes.
- 12. Raw sequences (fastq files) Quality control (QC) Spliced Read alignment Transcripts reconstruction Differential expression analysis Biology
- 13. Transcripts reconstruction Examples: Genome guided • G.mor.se (short reads), cufflinks and Scripture (for long reads) Examples: Genome • Transabyss, velvet+Oases, independent MIRA, cufflinks*
- 14. Genome guided transcriptome assembly
- 15. Genome guided transcriptome assembly doi:10.1038/nrg3068 doi:10.1038/nrg3068 Published online Martin J and Wang Z, Nat Rev Gen 2011
- 16. Raw sequences (fastq files) Quality control (QC) Spliced Read alignment Transcripts reconstruction Differential expression analysis Biology
- 17. Normalisation and DE Library size Examples: RPKM ERANGE, Cuffdiff FPKM edgeR , Myrna TMM Upper quartile Poisson GLM Examples: Negative DEGseq Myrna binomial edgeR, bayseq, Cuffdiff
- 18. Quantification and normalisation 1. Digital expression or raw count: number of reads mapping to a region (exon/ transcript/novel region) 2. Normalize counts* : number of reads per million reads per kb 3. Splice junction detection 4. Compare to existing gene models Nat Meth 2008 ; DOI:10.1038/NMETH.1226
- 19. Differential expression • Normalised gene expression value as RPKM: – reads per kilobase of exon model per million mapped reads • Or FPKM: – fragments per kilobase of exon model per million mapped reads • Compare RPKM/FPKM across conditions or tissues Nat Meth DOI:10.1038/NMETH.1226
- 20. Raw sequences (fastq files) Quality control (QC) Spliced Read alignment Transcripts reconstruction Differential expression analysis Biology
- 21. System Biology: beyond the list of DE genes • Ontologies: GO enrichment, Goseq (R package) • DAVID (http://david.abcc.ncifcrf.gov) • Pathway analysis
- 22. RNA- RNA-seq experiment design challenges • NGS biases: – Libraryprep (GC content, 5’ or 3’ depletion, random hexamer primers, RNA species, bias towards 3’ end …). – Transcript length • Sequencing depth • Single or paired end • Biological or technical replicates • Validation BRIEFINGS IN BIOINFORMATICS. VOL 12. NO 3. 280^287
- 23. RNA- RNA-seq and other transcriptomics methods Nature Reviews Genetics, November 2008; doi:10.1038/nrg2484
- 24. Summary • RNA-seq: more versatile, comprehensive with superior reproducibility and resolution. • Not dependent on prior sequence information: suitable for non-model organisms. • Potentially provides information for all RNA species in the cell and allows discovery of novel ones. • Still an actively developing fields and there are research areas which still need refinement. • Experimental design and validation gold standards to be set.
- 25. Tophat Cufflinks pipeline reference Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 7(3), 562- 78. [article]
- 26. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 7(3), 562- 78. [article]
- 27. R-bioconductor based RNA-seq RNA- packages • edgeR • Voom • Deseq http://bioconductor.org/packages/rele ase/BiocViews.html#___Software