New RNA-seq workflows. Charlotte Soneson University of Zurich Brixen 2016

Transcription

1 New RNA-seq workflows Charlotte Soneson University of Zurich Brixen 2016

2 Wikipedia

3 The traditional workflow ALIGNMENT COUNTING ANALYSIS Gene A Gene B... Gene X

4 The traditional workflow slow unnecessary? ALIGNMENT COUNTING inaccurate? doesn t account for all uncertainty in abundance estimates ANALYSIS Gene A Gene B... Gene X

5 The traditional workflow slow unnecessary? ALIGNMENT COUNTING inaccurate? doesn t account for all uncertainty in abundance estimates ANALYSIS Gene A Gene B... Gene X

6 Abundance quantification T1 length = L T2 length = 2L sample 1 sample 2

7 Abundance quantification T1 length = L T2 length = 2L sample 1 sample 2

8 Gene-level read counts T1 length = L T2 length = 2L Gene length = 2.6L sample 1 sample 2 Gene S1 S2 Count reads 150 reads

9 What can we do? Consider another abundance unit that better reflects the underlying abundances ( number of transcript molecules ) Include adjustment of gene counts to reflect underlying isoform composition

10 What can we do? How can we get such values? Are they any good? Consider another abundance unit that better reflects the underlying abundances ( number of transcript molecules ) Include adjustment of gene counts to reflect underlying isoform composition How could such adjustment be done?

11 What can we do? Consider another abundance unit that better reflects the underlying abundances ( number of transcript molecules ) Include adjustment of gene counts to reflect underlying isoform composition How can we get such values? How could such adjustment be done? Are they any good? We need transcript-level information!

12 Abundance units read count for transcript i c i t i = c ir `i TPM i = 10 6 RP KM i = 10 9 fragment length t i P k t k c i `i P k c k library size library size = 10 9 P t i k (t k`k) `i length of transcript i TPM X i / RP KM i TPM i = 10 6 i

13 Abundance units read count for transcript i c i t i = c ir `i TPM i = 10 6 RP KM i = 10 9 fragment length t i P k t k c i `i P k c k library size library size = 10 9 P t i k (t k`k) `i length of transcript i TPM X i / RP KM i TPM i = 10 6 i

14 Abundance units read count for transcript i c i t i = c ir `i TPM i = 10 6 fragment length RP KM i = 10 9 t i P k t k c i `i P k c k library size = 10 9 P t i `i length of transcript i TPM X i / RP KM i TPM i = 10 6 i k (t k`k)

15 Abundance units read count for transcript i c i t i = c ir `i TPM i = 10 6 fragment length RP KM i = 10 9 t i P k t k c i `i P k c k library size = 10 9 P t i `i length of transcript i TPM X i / RP KM i TPM i = 10 6 i k (t k`k)

16 Abundance units read count for transcript i c i t i = c ir `i TPM i = 10 6 fragment length RP KM i = 10 9 t i P k t k c i `i P k c k library size = 10 9 P t i `i length of transcript i TPM X i / RP KM i TPM i = 10 6 i k (t k`k)

17 Offsets ( average transcript lengths ) Similar to correction factors for library size, but sampleand gene-specific Weighted average of transcript lengths, weighted by estimated abundances (TPMs) Average transcript length for gene g in sample s: AT L gs = X i2g is `is, X i2g is =1 `is =e ective length of isoform i (in sample s) is = relative abundance of isoform i in sample s

18 Average transcript lengths T1 length = L T2 length = 2L AT L g1 =1 L +0 2L = L AT L g2 =0 L +1 2L =2L

19 Average transcript lengths T1 length = L T2 length = 2L AT L g1 =0.75 L L =1.25L AT L g2 =0.5 L L =1.5L

20 The traditional workflow ALIGNMENT COUNTING ANALYSIS Gene A Gene B... Gene X

21 The modern workflow MAPPING ESTIMATION ANALYSIS Gene A Gene B... Gene X

22 The mapping step Does not provide full alignment information (i.e., no exact base-by-base alignment). Rather, finds all transcripts (and positions) that a read is compatible with. Comes in various flavors: pseudoalignment (kallisto) lightweight alignment (Salmon) quasimapping (Sailfish, RapMap) Bray et al. 2016; Patro et al. 2014; Patro et al. 2015; Srivastava et al. 2016

23 The estimation step Input: for each read, the equivalence class of compatible transcripts Probabilistic modeling of read generation process, with transcript abundance as parameter EM algorithm Output: estimated abundance of each transcript

24 Step 1: build transcriptome index kallisto name of index transcriptome fasta file Salmon transcriptome fasta file name of index number of cores

25 Where to find transcript fasta?

26 Where to find transcript fasta? reference files for alignment-based workflow

27 Step 2: quantify kallisto name of index output folder # bootstraps number of cores input fastq files Salmon name of index libtype number of cores input fastq files output folder # bootstraps

28 Salmon LIBTYPE argument

29 output kallisto Salmon

30 output kallisto [abundance.tsv] Salmon [quant.sf]

31 Comparison to traditional workflow Salmon/kallisto are considerably faster than traditional alignment +counting -> allow bootstrapping provide more highly resolved estimates (transcripts rather than gene) - can be aggregated to gene level can use a larger fraction of the reads don t give precise alignments (for e.g. visualization in genome browser) - but avoid large alignment files

32 kallisto and Salmon gene counts overall similar SRR Salmon (log(counts + 1)) kallisto (log(counts + 1))

33 Gene-level counts mostly similar to traditional approach SRR Salmon (log(counts + 1)) featurecounts (log(counts + 1))

34 kallisto and Salmon can use slightly more reads Number of mapped reads 3e+07 2e+07 1e+07 0e+00 SRR SRR SRR SRR SRR SRR SRR SRR featurecounts kallisto Salmon

35 How to get the estimated values into R?

36 How to get the estimated values into R?

37 How to get the estimated values into R? TPMs counts ATL offsets

38 A word of warning Abundance estimates for lowly expressed transcripts are highly variable and should be interpreted with caution Estimated TPM True TPM Soneson, Love & Robinson, F1000 Research 2016

39 A word of warning Problematic when coverage of region defining an isoform is low Soneson, Love & Robinson, F1000 Research 2016

40 A word of warning When aggregated to the gene level, abundance estimates are less variable Estimated TPM True TPM Soneson, Love & Robinson, F1000 Research 2016

41 Differential analysis types for RNA-seq Has the total output of a gene changed? DGE Has the expression of individual transcripts changed? DTE Has any isoform of a given gene changed? DTE+G Has the isoform composition for a given gene changed? DTU/DEU - need different abundance quantification of transcriptomic features (genes, transcripts, exons)

42 References Srivastava et al.: RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq read to transcriptomes. Bioinformatics 32:i192-i200 (2016) - RapMap Patro et al.: Accurate, fast, and model-aware transcript expression quantification with Salmon. biorxiv dx.doi.org/ / (2015) - Salmon Bray et al.: Near-optimal probabilistic RNA-seq quantification. Nature Biotechnology 34(5): (2016) - kallisto Patro et al.: Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Nature Biotechnology 32: (2014) - Sailfish Pimentel et al.: Differential analysis of RNA-Seq incorporating quantification uncertainty. biorxiv / (2016) - sleuth Wagner et al.: Measurement of mrna abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory in Biosciences 131: (2012) - TPM vs FPKM Soneson et al.: Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research 4:1521 (2016) - ATL offsets (tximport package) Li et al.: RNA-seq gene expression estimation with read mapping uncertainty. Bioinformatics 26(4): (2010) - TPM, RSEM