RNA-Seq

Xiaole Shirley Liu

STAT115, STAT215, BIO298, BIST520

RNA-seq Protocol

2Martin and Wang Nat. Rev. Genet. (2011)

RNA-seq Applications

• Expression levels, differential expression• Alternative splicing, novel isoforms• Novel genes or transcripts, lncRNA• Detect gene fusions• Many different protocols• Can use on any sequenced genome• Better dynamic range, cleaner data

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Experimental Design

• Assessing biological variation requires biological replicates (no need for technical replicates)

• 3 preferred, 2 OK, 1 only for exploratory assays (not good for publications)

• For differential expression, don’t pool RNA from multiple biological replicates

• Batch effects still exist, try to be consistent or process all samples at the same time

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Experimental Design

• Ribo-minus (remove too abundant genes)

• PolyA (mRNA, enrich for exons)

• Strand specific (anti-sense lncRNA)

• Sequencing: – PE (resolve redundancy) or SE: expression– PE for splicing, novel transcripts– Depth: 30-50M differential expression, deeper

transcript assembly– Read length: longer for transcript assembly

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RNA-seq Analysis

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Alignment

• Prefer splice-aware aligners

• TopHat, BWA, STAR (not DNASTAR)

• Sometimes need to trim the beginning bases

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Transcript Assembly

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Reference-based assemblyCufflinks

De novo assemblyTrinity

Quality Control: RSeQC

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Expression Index

• RPKM (Reads per kilobase of transcript per million reads of library)

– Corrects for coverage, gene length

– 1 RPKM ~ 0.3 -1 transcript / cell

– Comparable between different genes within the same dataset

– TopHat / Cufflinks

• FPKM (Fragments), PE libraries, RPKM/2• TPM (transcripts per million)

– Normalizes to transcript copies instead of reads

– Longer transcripts have more reads

– RSEM, HTSeq 10

Differential Expression

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Sequencing Read Distribution

• Poisson distribution: – # events within an interval

• Sequencing data is overdispersed Poisson

• Negative binomial– Def: # of successes

before r failures occur, if

Pb(each success) is p

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Differential Expression

• Negative binomial

for RNA-seq• Variance estimated by

borrowing information from all the genes – hierarchical models

• Test whether μi is the same for gene i between samples j

• FDR?

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Differential Expression

• Should we do differential expression on RPKM/FPKM or TPM?

• Cufflinks: RPKM/FPKM• LIMMA-VOOM and DESeq: TPM• Power to detect DE is proportional to length• Continued development and updates

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Gene A (1kb)

Gene B (8kb)

Alternative Splicing

• Assign reads to splice isoforms

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Isoform Inference

• If given known set of isoforms

• Estimate x to maximize the likelihood of observing n

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Known Isoform Abundance Inference

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Isoform Inference

• With known isoform set, sometimes the gene-level expression level inference is great, although isoform abundances have big uncertainty (e.g. known set incomplete)

• De novo isoform inference is a non-identifiable problem if RNA-seq reads are short and gene is long with too many exons

• Algorithm: MATS

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Gene Fusion

• More seen in cancer samples

• Still a bit hard to call

• TopHatFusion in TopHat2

Maher et al, Nat 200919

Other Applications

• RNA editing– Change on RNA sequence after transcription– Most frequent: A to I (behaves like G), C to U– Evolves from mononucleotide deaminases,

might be involved in RNA degradation

• Circular RNA– Mostly arise from splicing– Varying length, abundance, and stability– Possible function: sponge for RBP or miRNA

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Summary• RNA-seq design considerations• Read mapping

– TopHat, BWA, STAR

• De novo transcriptome assembly: TRINITY• Expression index: FPKM and TPM• Differential expression

– Cufflinks: versatile

– LIMMA-VOOM and DESeq: better variance estimates

• Alternative splicing: MATS• Gene fusion, genome editing, circular RNA

Acknowledgement

• Alisha Holloway

• Simon Andrews

• Radhika Khetani

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