Introduction to RNA-Seq. David Wood Winter School in Mathematics and Computational Biology July 1, PDF Free Download

Introduction to RNA-Seq David Wood Winter School in Mathematics and Computational Biology July 1, 2013

Abundance RNA is... Diverse Dynamic Central DNA rrna Epigenetics trna RNA mrna Time Protein

Abundance RNA is... Diverse Dynamic Central DNA rrna Epigenetics trna RNA mrna Time Protein Qualitative Quantitative Integrative Understand the molecular basis of gene function. Classify and transform cellular states

RNA studies involve... Biological System Questions Project Available Resources Technology DB ~/bin

RNA studies involve... Biological System Questions Project Available Resources Technology DB ~/bin This talk: Focusing on reference based mammalian RNA-seq analysis

Transcriptional Complexity TSS TSS TSS pa pa pa pa TSS genomic DNA micrornas spliced intron TSS transcription start site protein coding regions pa polyadenylation signal translation start site non-coding regions polyadenylation

Transcriptional Complexity TSS TSS TSS pa pa pa pa TSS tirna PASR mirna genomic DNA micrornas spliced intron TSS transcription start site protein coding regions pa polyadenylation signal translation start site non-coding regions polyadenylation

Transcriptional Complexity TSS TSS TSS pa pa pa pa TSS tirna PASR Alu mirna genomic DNA micrornas spliced intron TSS transcription start site protein coding regions pa polyadenylation signal translation start site non-coding regions polyadenylation

Transcriptional Complexity Mutations Allelic Expression TSS TSS TSS pa pa pa pa TSS tirna PASR Alu mirna RNA Editing genomic DNA micrornas spliced intron TSS transcription start site protein coding regions pa polyadenylation signal translation start site non-coding regions polyadenylation

RNA-seq TSS TSS TSS pa pa pa pa TSS tirna PASR Alu mirna non-spliced reads mutations strand specific junction reads Cloonan et al. Nat Methods 2008; 5:613-619

Advantages of RNA-seq Discovery genes, exons, junctions, UTRs, fusions (Present and Future) %#!!!!" %!!!!!",-./01-2340" $#!!!!" $!!!!!" #!!!!"!" #&" #'" (!" ($" (%" ()" (*" (#" ((" (+" (&" ('" +!" +$" 5/06789":6-02;/" <-;462/"=;>2/?" @6?-.>.A;/" /1BCD" <06E>;?6/6"

Advantages of RNA-seq Discovery genes, exons, junctions, UTRs, fusions (Present and Future) Dynamic Range,-./01-2340" %#!!!!" %!!!!!" $#!!!!" $!!!!!" #!!!!"!" #&" #'" (!" ($" (%" ()" (*" (#" ((" (+" (&" ('" +!" +$" 5/06789":6-02;/" <-;462/"=;>2/?" @6?-.>.A;/" /1BCD" <06E>;?6/6" Mortazavi et al. Nat. Methods 2008; 5:621 628

Typical experiment workflow Field / Clinic Wet Lab Dry Lab Run Experiment Design Experiment Sample Acquisition Field / Clinic / Lab Obtain RNA Make Library Sequencing 1 Base Calling Mapping 2 Library QC 2 Sample Acquisition Verification Validation Analysis Interpretation 3 3 Publish

Library Construction trna (15%) 5% Deplete rrna Enrich polya RNA AA AA AA Target RNA rrna (80%) Profile (ribosomes) AA A Fragment cellular RNA Capture (tiling arrays) ds-cdna synthesis Sequencing Ligate adaptors + Amplify

RNA-seq Mapping Challenge #1: Introns

RNA-seq Mapping Challenge #1: Introns Align to database of junctions or transcriptome Split Read Alignments Wood et al. Bioinformatics 2011; 27:580 581 Trapnell et al. Bioinformatics 2009; 25:1105-11

RNA-seq Mapping Challenge #1: Introns Align to database of junctions or transcriptome Split Read Alignments Wood et al. Bioinformatics 2011; 27:580 581 Trapnell et al. Bioinformatics 2009; 25:1105-11 Challenge #2: Correctness Sufficient Overlap Sufficient Evidence

RNA-seq Mapping Data QC (clipping) Align to Filter Set Align to genome Align to junctions Split read Alignment Exclude Flag and Exclude Choose Alignments, Disambiguate Tophat: Trapnell et al. Bioinformatics 2009; 25:1105-11

RNA-seq Mapping rrna, trna? reference? diploid? gene model? ESTs? Algorithm? Data QC (clipping) Align to Filter Set Align to genome Align to junctions Split read Alignment Exclude Flag and Exclude Choose Alignments, Disambiguate Tophat: Trapnell et al. Bioinformatics 2009; 25:1105-11 BAM BAM BAM Alignment Filtering Library QC Analysis

Library Quality Control (QC) trna (15%) 5% Deplete rrna Enrich polya RNA AA AA AA Target RNA rrna (80%) Profile (ribosomes) AA A Fragment cellular RNA Capture (tiling arrays) ds-cdna synthesis Sequencing Ligate adaptors + Amplify

Library Quality Control (QC) trna (15%) 5% Deplete rrna Enrich polya RNA AA AA AA Target RNA Affects RNA content (Expression quantification) rrna (80%) Profile (ribosomes) AA A Fragment cellular RNA Capture (tiling arrays) ds-cdna synthesis Sequencing Ligate adaptors + Amplify

Library Quality Control (QC) trna (15%) 5% Deplete rrna Enrich polya RNA AA AA AA Target RNA Affects RNA content (Expression quantification) rrna (80%) cellular RNA Profile (ribosomes) Capture (tiling arrays) AA A Fragment Affects Insert Size (transcript identification) ds-cdna synthesis Sequencing Ligate adaptors + Amplify

Calculate Gene Expression Gene A 3500nt (700 reads) Gene B 400nt (160 reads)

Calculate Gene Expression Gene A 3500nt (700 reads) Gene B 400nt (160 reads) RPKM = 2.0 RPKM = 4.0 Reads Per Kilobase per Million 10 RPKM = R 3 10 6 L N R = Gene Read Count L = Length of gene N = Library Size Mortazavi et al. Nat. Methods 2008; 5:621 628

Further Normalisation Repeat Normalise to mappable gene length Koehler et al. Bioinformatics 2010

Further Normalisation Repeat Normalise to mappable gene length Scale Expression Values by TMM Koehler et al. Bioinformatics 2010 Cellular RNA Cond. 1 Cond. 2 Robinson et al. Genome Biology 2010; 11:R25

Further Normalisation Repeat Normalise to mappable gene length Scale Expression Values by TMM Koehler et al. Bioinformatics 2010 Cellular RNA RPKM Cond. 1 Cond. 2 Cond. 1 Cond. 2 Robinson et al. Genome Biology 2010; 11:R25

Further Normalisation Repeat Normalise to mappable gene length Scale Expression Values by TMM Koehler et al. Bioinformatics 2010 Robinson et al. Genome Biology 2010; 11:R25 Benjamini et al. NAR; 2012 Normalise to GC content of region

Calculate Feature Expression

Calculate Feature Expression Exonic Region

Calculate Feature Expression Exonic Region Exon Junction

Calculate Feature Expression Exonic Region Exon Junction Intronic Region

Calculate Feature Expression Exonic Region Exon Junction Intronic Region Exon Boundary

Calculate Feature Expression Exonic Region Exon Junction Intronic Region Exon Boundary Intergenic Region

Calculate Feature Expression Calculate RPKM for any feature Exonic Region Exon Junction Intronic Region Exon Boundary Intergenic Region

Calculate Feature Expression Calculate RPKM for any feature Exonic Region Exon Junction Intronic Region Exon Boundary Intergenic Region Extended 3 UTR

Calculate Feature Expression Calculate RPKM for any feature Exonic Region Exon Junction Intronic Region Exon Boundary Intergenic Region Extended 3 UTR Retained Intron

Calculate Transcript Expression

Calculate Transcript Expression diagnostic feature

Calculate Transcript Expression diagnostic feature Approach #1: Expression calculated using diagnostic features Strong Evidence Easy to calculate Sampling Variability Lacks statistical robustness Dependent on gene model Excludes Transcripts ALEXA-seq: Griffith et al. Nat. Methods 2010; 11:R25

Calculate Transcript Expression

Calculate Transcript Expression Approach #2: Expression estimated Construct bipartite graph, then finds minimum path Cufflinks: Trapnell et al. Nat. Biotech. 2010, 28:511-515

Calculate Transcript Expression Approach #2: Expression estimated Construct bipartite graph, then finds minimum path Cufflinks: Trapnell et al. Nat. Biotech. 2010, 28:511-515 Estimates expression for all transcripts Incorporates ambiguous reads Model can fail in complex / highly expressed regions More statistically robust Error rate largely unknown

Expressed or not? Cond. 1 Cond. 2 Cond. 3 Frequency not expressed expressed Need to determine expression cut-off value log2 (expression)

Expressed or not? 1 Expressed if > 1 RPKM Has literature support Lacks sensitivity Arbitrary

Expressed or not? 1 Expressed if > 1 RPKM Has literature support Lacks sensitivity Arbitrary 2 Expressed if above intergenic background Frequency log2 Expression 95th percentile

Expressed or not? 1 2 3 Expressed if > 1 RPKM Expressed if above intergenic background Incorporate replicate information Has literature support Cut-off based on empirical evidence np IDR 0 0.1 0.3 0.5 0.7 0.9 1 Based on observed reproducibility Lacks sensitivity Still somewhat arbitrary Requires replicates log2 (expression) bins Arbitrary Frequency Rep 1 vs Rep 2 Rep 2 vs Rep 1 Mean Cut off log2 Expression 11 7 3 1 5 9 13 17 21 25 95th percentile

Expressed or not? 1 Expressed if > 1 RPKM Has literature support Lacks sensitivity Arbitrary 2 Expressed if above intergenic background Cut-off based on empirical evidence Still somewhat arbitrary Frequency log2 Expression 95th percentile 3 Incorporate replicate information Based on observed reproducibility Requires replicates np IDR 0 0.1 0.3 0.5 0.7 0.9 1 Rep 1 vs Rep 2 Rep 2 vs Rep 1 Mean Cut off 11 7 3 1 5 9 13 17 21 25 log2 (expression) bins

Nucleotide-Resolution Analysis Imprinting ICR

Nucleotide-Resolution Analysis Imprinting eqtl sqtl

Nucleotide-Resolution Analysis Imprinting eqtl sqtl Complex Traits

Nucleotide-Resolution Analysis Imprinting eqtl sqtl Complex Traits Allelic Fraction A B C SNPs

Nucleotide-Resolution Analysis Imprinting eqtl sqtl Complex Traits A B C SNPs Allelic Fraction Density 0.0 0.5 1.0 1.5 2.0 Expected Mean Observed Mean Reference bias 0.0 0.2 0.4 0.6 0.8 1.0 Fraction of RNA seq Reads Matching Reference Allele Degner et al. Bioinformatics 2009

Nucleotide-Resolution Analysis Imprinting eqtl sqtl Complex Traits A B C SNPs Allelic Fraction Density 0.0 0.5 1.0 1.5 2.0 Expected Mean Observed Mean Reference bias Map to a diploid genome 0.0 0.2 0.4 0.6 0.8 1.0 Fraction of RNA seq Reads Matching Reference Allele AlleleSeq: Rozowsky et al. Mol. Sys. Bio 2011 Degner et al. Bioinformatics 2009

The future of RNA-seq (now) Single Cell Shalek, et al. Nature 2013

The future of RNA-seq (now) Single Cell Huge Cohort Genotype-Tissue Expression project (GTEx) 900 donors 30,000 RNA-seq data sets! Shalek, et al. Nature 2013 Lonsdale, et al. Nature Genetics 2013

Summary 1 Choose an alignment approach suitable for your experiment, available resources and tools 2 Assess library quality, specifically rrna contamination, insert size, strand specificity and library complexity 3 Gene and Feature Expression can be calculated using count data, and normalised by length, library size and GC content 4 Transcript expression calculation requires alternative approaches and algorithms, which although common, are largely unproven 5 RNA-seq can interrogate nucleotide specific questions, but be careful of alignment biases (diploid mapping can help here)

Questions and References Cloonan et al. Nat Methods 2008; Stem cell transcriptome profiling via massive-scale mrna sequencing Mortazavi et al. Nat. Methods 2008; Mapping and quantifying mammalian transcriptomes by RNA-Seq Wood et al. Bioinformatics 2011; X-MATE: A flexible system for mapping short read data Trapnell et al. Bioinformatics 2009; TopHat: discovering splice junctions with RNA-Seq Koehler et al. Bioinformatics 2010. The Uniqueome: A mappability resource for short-tag sequencing Robinson et al. Genome Biology 2010; A scaling normalization method for differential expression analysis of RNA-seq data. Benjamini et al. NAR; 2012. Summarizing and correcting the GC content bias in high-throughput sequencing Griffith et al. Nat. Methods 2010; Alternative expression analysis by RNA sequencing. Trapnell et al. Nat. Biotech. 2010; Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform Degner et al. Bioinformatics 2009; Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing Rozowsky et al. Mol. Sys. Bio 2011; AlleleSeq: analysis of allele-specific expression and binding in a Shalek, et al. Nature 2013; Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells Lonsdale, et al. Nature Genetics 2013; The Genotype-Tissue Expression (GTEx) project.

Introduction to RNA-Seq. David Wood Winter School in Mathematics and Computational Biology July 1, 2013