Analyse de données de séquençage haut débit
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1 Analyse de données de séquençage haut débit Vincent Lacroix Laboratoire de Biométrie et Biologie Évolutive INRIA ERABLE 9ème journée ITS 21 & 22 novembre 2017 Lyon
2 Sequencing is cheap Applications include De novo genome assembly Resequencing (SNPs, indels, rearrangements) Transcriptome analysis (RNA-seq) Protein-DNA interactions (Chip-seq) Metagenomics The difficulty is not to generate data, but to analyse it Many datasets are superficially analysed, or not analysed at all Bioinformatics bottleneck 9ème journée ITS 21 & 22 novembre 2017 Lyon
3 Bioinformatics analysis of sequencing data starts by either Mapping your reads to a reference genome De novo assembling your reads Downstream analysis depends on the application (RNAseq, ChipSeq, etc) For each application, there is not yet a unique reference pipeline Choosing which pipeline to use requires to understand what it captures/misses 9ème journée ITS 21 & 22 novembre 2017 Lyon
4 Bioinformatics analysis of sequencing data starts by either Mapping your reads to a reference genome De novo assembling your reads Downstream analysis depends on the application (RNAseq, ChipSeq, etc) For each application, there is not yet a unique reference pipeline Choosing which pipeline to use requires to understand what are its limitation 9ème journée ITS 21 & 22 novembre 2017 Lyon
5 Complementarity of assembly-first and mapping-first approaches for alternative splicing annotation and differential analysis from RNAseq data Clara Benoit-Pilven, Camille Marchet, Emilie Chautard, Leandro Lima, Marie-Pierre Lambert, Gustavo Sacomoto, Amandine Rey, Audric Cologne, Sophie Terrone, Louis Dulaurier, Jean-Baptiste Claude, Cyril Bourgeois, Didier Auboeuf, Vincent Lacroix
6 Alternative Splicing exon gene intron transcription pre-mrna splicing mrna translation protein
7 Alternative Splicing exon gene intron transcription pre-mrna alternative splicing mrna translation protein
8 Alternative Splicing exon gene intron transcription More than 90 % of multi-exon genes produce at least 2 isoforms Deregulation of alternative splicing in many diseases (rare diseases & cancer) pre-mrna alternative splicing STOP mrna translation protein no protein
9 RNAseq data mrnas (~1000nt) Reads (100nt)
10 RNAseq data mrnas (~1000nt) Reads (100nt)
11 Introduction Alternative Splicing and RNA-seq data Gencode Annotations : genes, 3 transcripts per gene. Assessing which gene/transcript is expressed in which tissue/condition can in principle be done through RNAseq The main challenges are: Reads are short (100nt) and can be assigned to multiple transcripts (1000nt) Some transcripts are annotated, some are novel Some transcripts are highly expressed, many are poorly expressed 1
12 Introduction Annotation and Differential Analysis Annotation: Identify and quantify all transcripts present in a given condition Differential Analysis : Assess which genes are differentially spliced across conditions (treatment / control, population 1 / population 2, disease / control) 1
13 Introduction 1 Two approaches to assemble transcripts Haas and Zody, Nature Biotech
14 Introduction 3 What is the overlap between the predictions of the two approaches? Identify pros and cons of assembly-first and mapping-first methods Mapping first Assembly first Comparison done on alternative skipped exon (ASE) events only Public dataset (ENCODE) from neuroblastoma SK-N-SH cell line with o without retinoic acid (RA) treatment RA treatment during 2 days Sk-n-sh cell line SK-N-SH Differenciated Sk-n-sh cell line
15 Compared pipelines
16 Results Mapping-first approach finds many unfrequent variants 5
17 Results Mapping-first approach finds many unfrequent variants 5
18 Results The overlap between methods increases when unfrequent variants are filtered out Unfrequent variant = less than 5 reads, or relative abundance < 10 % 6
19 Results Some abundant transcripts are systematically missed by one approach 6
20 Results 6 Experimental Validations
21 Results 6 Annotation summary Mapping-first is stronger for rare variants and exonised Alus Assembly-first is stronger for novel variants and recent paralogs Should I care about these differences? Does it have an impact on my differential analysis?
22 1 Statistical Analysis Count regression with negative binomial distribution Generalised linear model, 2 way design, with interaction Mean gene expression Contribution of isoform i Interaction term Contribution of condition j Target hypothesis: Likelihood ratio test P-values adjusted with benjamini-hochberg procedure
23 Comparison after differential analysis AS events predicted by both pipelines have some quantification differences, especially for complex events (red dots)
24 Comparison to other methods
25 Conclusion & Perspectives 10 Conclusion & Perspectives Annotating alternative splicing with a single approach leads to missing a large number of candidates. These candidates cannot be neglected, since many of them are differentially regulated across conditions. We advocate for the use of a combination of both mappingfirst and assembly-first approaches for annotation and differential analysis of alternative splicing from RNA-seq data. UNION Mapping first Assembly first INTERSECTION Mapping first Assembly first
26 1 Software availability
27 Results 6 Recent paralogs Missed by FaRLine RASA4 and RASA4B are recent paralogs Multi-mapping reads are discarded by mapping-first approaches KisSplice co-assembles the two paralogs, and states that they collectively produce two transcripts Confirmed experimentally by RT-PCR
28 Results 6 Exons overlapping repeats Missed by KisSplice RAB5C contains an exonised Alu Since this exon is annotated, FaRLine finds it KisSplice fails to assemble it, because the bubble has more than 5 branches (i.e. too many Alu copies in the dataset) Confirmed experimentally by RT-PCR
29 Results 6 Complex events Missed by KisSplice The skipping of E6 with E4 and E7 as flanking exons is reported only by FaRLine KisSplice discards E4-E6 junction because it is supported only by 55 reads, which is less then 2 % of the read flow leaving E4
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