KisSplice. Identifying and Quantifying SNPs, indels and Alternative Splicing Events from RNA-seq data. 29th may 2013

Transcription

1 Identifying and Quantifying SNPs, indels and Alternative Splicing Events from RNA-seq data 29th may 2013

2 Next Generation Sequencing A sequencing experiment now produces millions of short reads ( 100 nt) in a single run for a reasonable cost ( 10 3 euros) For model species, the first step is usually to map the reads to the reference genome/transcriptome For non model species, the first step is usually to assemble the reads and reconstruct the genome/transcriptome Downstream analysis includes the analysis of polymorphism (SNPs, rearrangements, splicing) Our main idea is to extract polymorphism directly from the reads, and not assemble the genome/transcriptome

3 De-Bruijn Graph The software The Model Algorithm outline De Bruijn graphs (DBG) are used as a first step in many short reads assemblers. Node = k-mer Edge = overlap of k-1 bases Example CACTCAA, k = 3

4 De-Bruijn Graph The software The Model Algorithm outline More complicated example Reference : CACTCAACTG (unknown) read1 CACTCA read2 CAACTG

5 De-Bruijn Graph The software The Model Algorithm outline Even more complicated example Reference : CACTCAACTGACT (unknown) read1 CACTCA read2 CAACTG read3 CTGACT

6 Compressed De Bruijn Graph The Model Algorithm outline Even more complicated example Reference : CACTCAACTGACT (unknown) read1 CACTCA read2 CAACTG read3 CTGACT

7 De-Bruijn Graph The software The Model Algorithm outline An assembly is a walk in the de Bruijn graph, which contains all reads as subwalks This problem is known to be NP-complete In practice, heuristics are used which consist in simplifying the graph to make it linear However, the structures that are removed may correspond to relevant biological structures (SNPs, alternative splicing).

8 Specificities of RNA-seq data The Model Algorithm outline Dynamic range of gene expression Few genes are highly expressed Many are poorly expressed Alternative splicing A gene may give rise to several transcripts

9 The Model Algorithm outline Example of DBG built from RNA-seq data

10 Polymorphism in RNA-seq data The Model Algorithm outline

11 Polymorphism in RNA-seq data The Model Algorithm outline If the purpose is to identify polymorphism, then assemblers are not well suited The variable parts are precisely the ones that will be removed 3 types of polymorphisms are expected in RNA-seq : At the genomic level SNP Approximate tandem repeats At the transcriptomic level Alternative splicing

12 SNPs The software The Model Algorithm outline SNPs correspond to recognizable patterns in the de Bruijn graph Issue : how to discriminate SNPs from sequencing errors? Idea : require a minimum coverage for each path

13 Approximate Tandem Repeats The Model Algorithm outline

14 Alternative splicing events The Model Algorithm outline Exon skipping Intron retention Alternative 5 or 3 splice site

15 Alternative splicing events The Model Algorithm outline Not covered by this pattern : Alternative transcription start and end Mutually exclusive exons

16 The software The Model Algorithm outline!"#$%$&'(")*+$(",*&"-*(.)*&-/01)"!"#$%$&'(")*+$(",*&"-*(.)*&-/01)" A general model for 0%"234 polymorphism in DBG!"#$%$&'(")*+$(",*&"-*(.)*&-/01)" 0%"234 0%" "8"9"-':/1"*,"($%#:/"9;<= 567"8"9"-':/1"*,"($%#:/"9;<= SNP : 2 paths of length 2k 1 567"8"9"-':/1"*,"($%#:/"9;<= >6?"8"="-':/"*,"($%#:/"':")*1:"9;<9@" :/$":A*"-':/1"'(0#% >6?"8"="-':/"*,"($%#:/"':")*1:"9;<9@" :/$":A*"-':/1"'(0#% Repeats : 1 path of length at >6?"8"="-':/"*,"($%#:/"':")*1:"9;<9@"!5"$B$%:"8"="-':/"*,"($%#:/"':")*1:"9;<9" :/$":A*"-':/1"'(0#% most 2k 2, the two paths align!!5"$b$%:"8"="-':/"*,"($%#:/"':")*1:"9;<9"!!5"$b$%:"8"="-':/"*,"($%#:/"':")*1:"9;<9"!!! AS : 1 path of length at most 2k 2!

17 Algorithm outline The software The Model Algorithm outline 1 De Bruijn graph construction ; 2 BiConnected Components decomposition (BCC) ; 3 Four nodes compression (SNPs and sequencing errors) ; 4 Enumeration of all bubbles with a shorter path length at most 2k 2 ; 5 Quantification and classification.

18 on simulated data Simulated Data Real Data Comparison with Trinity Unresolved BCCs Assembly Vs Mapping We simulated the sequencing of one drosophila gene with two alternative transcripts (using FluxSimulator) For different values of the coverage, we test if our method recovers the AS event recovers the AS event when the coverage is above 8X Trinity recovers the AS event when the coverage is above 18X Note : Trinity s purpose is more general as it reconstructs full-length transcripts, but for this task, it is less sensitive

19 Impact of k The software Simulated Data Real Data Comparison with Trinity Unresolved BCCs Assembly Vs Mapping At 8X, kmin=17, kmax=29

20 on real data The software Simulated Data Real Data Comparison with Trinity Unresolved BCCs Assembly Vs Mapping In order to assess if our predicted AS events are true positives, we need to test our method in the case where a reference genome is available. Data : Human Body Map 2.0 data (ERP00546) 2 tissues (out of 16) : brain and liver 75 bp reads, 32M and 39M

21 BCCs repartition The software Simulated Data Real Data Comparison with Trinity Unresolved BCCs Assembly Vs Mapping

22 Confirming AS events Simulated Data Real Data Comparison with Trinity Unresolved BCCs Assembly Vs Mapping We align the two paths of the bubble to the reference genome using blat If the two paths align with the same initial and final coordinates, then it is a true positive Otherwise, it is a false positive Next, we check if the alignment coordinates correspond to an annotated AS event If the coordinates match, then it is a known event Otherwise it is a novel AS event

23 Confirming AS events Simulated Data Real Data Comparison with Trinity Unresolved BCCs Assembly Vs Mapping

24 Annotated exon skipping Simulated Data Real Data Comparison with Trinity Unresolved BCCs Assembly Vs Mapping

25 Annotated intron retention Simulated Data Real Data Comparison with Trinity Unresolved BCCs Assembly Vs Mapping

26 Novel alternative 5 splice site Simulated Data Real Data Comparison with Trinity Unresolved BCCs Assembly Vs Mapping

27 Novel complex event Simulated Data Real Data Comparison with Trinity Unresolved BCCs Assembly Vs Mapping

28 Novel AS events are less expressed Simulated Data Real Data Comparison with Trinity Unresolved BCCs Assembly Vs Mapping

29 Novel AS events are shorter Simulated Data Real Data Comparison with Trinity Unresolved BCCs Assembly Vs Mapping 1 short AS events tend to be under-annotated (Ex : NAGNAG) 2 we also detect genomic indels that are within genes, which we mistake for AS events

30 Simulated Data Real Data Comparison with Trinity Unresolved BCCs Assembly Vs Mapping Comparison with Trinity on real data Memory usage is better (5Gb / 100M reads) is faster, which is expected because it solves a simpler task finds 4099 events, while Trinity finds 1123, out which 570 are common 50% of the events found only by Trinity are false positives The rest is hidden in very large BCCs, and we can recover part of it using larger values of k

31 Unresolved BCC The software Simulated Data Real Data Comparison with Trinity Unresolved BCCs Assembly Vs Mapping

32 Unresolved BCC The software Simulated Data Real Data Comparison with Trinity Unresolved BCCs Assembly Vs Mapping This is not an elephant, this is a gene family :)

33 Assembly Vs Mapping Simulated Data Real Data Comparison with Trinity Unresolved BCCs Assembly Vs Mapping For model species, the pipeline is usually TopHat + Cufflinks Even in this case, (or other assembly-based approaches) may be useful. Example of event missed by Cufflinks, but which is annotated

34 (on going) With Didier Auboeuf (CRCL) Validation by RT-PCR Almost all novel events are validated Novel events found both by and Cufflinks are almost all validated Novel events found by alone are validated only if : The minor isoform has a relative abundance of at least 15 % The splicing event is simple, not complex

35 in practice Input : fasta/q files Output : 5 files (SNPs, AS events, Repeats, Indels <3nt, others) Format :

36 After the counts Testing if a variant is specific to a condition : M Reduced : Y v,c = µ + β variant v M Full : Y (v, c) = µ + β variant v + β cond c + β cond c + β variant cond v,c µ : local mean expression of the gene that contains the variant βv variant βc cond : contribution of variant v : contribution of condition c Counts are modelled using a negative binomial We compute the likelihood of both models and test with a χ 2

37 2IGV Combining output with the context given by a full length transcriptome assembler ( Trinity, Oases, etc.) Visualisation in a genome browser (IGV) The colour of an alignment depends on the log10( RPKM ) ( Read Per Kilobase per Millions mapped reads)

38 2IGV

39 Conclusion The software detects various polymorphisms (SNPs, AS, repeats ) in RNA-seq data It provides quantification for such events. is more sensitive than Trinity for finding AS events is relevant for studies without model species It brings information even when there is a model species and can be used in addition to classical pipeline

40 People and download Download : DBG construction People Rennes : Rayan Chikhi, Pavlos Antoniou, Guillaume Rizk, Raluca Uricaru, Pierre Peterlongo Lyon : Gustavo Sacomoto, Alice Julien-Laferrière, David Parsons, Janice Kielbassa, Lilia Brinza, Marie-France Sagot, Vincent Miele, Vincent Lacroix

41 Thank you! Questions?

42 Further analysis on short events