Introduction to RNA-Seq. David Wood Winter School in Mathematics and Computational Biology July 1, 2013
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1 Introduction to RNA-Seq David Wood Winter School in Mathematics and Computational Biology July 1, 2013
2 Abundance RNA is... Diverse Dynamic Central DNA rrna Epigenetics trna RNA mrna Time Protein
3 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
4 RNA studies involve... Biological System Questions Project Available Resources Technology DB ~/bin
5 RNA studies involve... Biological System Questions Project Available Resources Technology DB ~/bin This talk: Focusing on reference based mammalian RNA-seq analysis
6 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
7 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
8 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
9 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
10 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:
11 Advantages of RNA-seq Discovery genes, exons, junctions, UTRs, fusions (Present and Future) %#!!!!" %!!!!!",-./ " $#!!!!" $!!!!!" #!!!!"!" #&" #'" (!" ($" (%" ()" (*" (#" ((" (+" (&" ('" +!" +$" 5/06789":6-02;/" /1BCD" <06E>;?6/6"
12 Advantages of RNA-seq Discovery genes, exons, junctions, UTRs, fusions (Present and Future) Dynamic Range,-./ " %#!!!!" %!!!!!" $#!!!!" $!!!!!" #!!!!"!" #&" #'" (!" ($" (%" ()" (*" (#" ((" (+" (&" ('" +!" +$" 5/06789":6-02;/" /1BCD" <06E>;?6/6" Mortazavi et al. Nat. Methods 2008; 5:
13 Advantages of RNA-seq Discovery genes, exons, junctions, UTRs, fusions (Present and Future) Dynamic Range,-./ " %#!!!!" %!!!!!" $#!!!!" $!!!!!" #!!!!"!" #&" #'" (!" ($" (%" ()" (*" (#" ((" (+" (&" ('" +!" +$" 5/06789":6-02;/" /1BCD" <06E>;?6/6" Mortazavi et al. Nat. Methods 2008; 5: Nucleotide Specific
14 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
15 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
16 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
17 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
18 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
19 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
20 RNA-seq Mapping Challenge #1: Introns
21 RNA-seq Mapping Challenge #1: Introns Align to database of junctions or transcriptome Split Read Alignments Wood et al. Bioinformatics 2011; 27: Trapnell et al. Bioinformatics 2009; 25:
22 RNA-seq Mapping Challenge #1: Introns Align to database of junctions or transcriptome Split Read Alignments Wood et al. Bioinformatics 2011; 27: Trapnell et al. Bioinformatics 2009; 25: Challenge #2: Correctness Sufficient Overlap Sufficient Evidence
23 RNA-seq Mapping Challenge #1: Introns Align to database of junctions or transcriptome Split Read Alignments Wood et al. Bioinformatics 2011; 27: Trapnell et al. Bioinformatics 2009; 25: Challenge #2: Correctness Challenge #3: Multi-mappers Sufficient Overlap Sufficient Evidence Align to the transcriptome Sequence Similarity
24 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:
25 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: BAM BAM BAM Alignment Filtering Library QC Analysis
26 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: BAM BAM BAM Alignment Filtering Library QC Analysis
27 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
28 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
29 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
30 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
31 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 Affects Strand Specificity Sequencing Ligate adaptors + Amplify
32 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 Affects Strand Specificity Sequencing Ligate adaptors + Amplify Affects Library Complexity (Tag uniqueness)
33 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 Affects Strand Specificity Affects Mapping Rate Paired-end? Sequencing Ligate adaptors + Amplify Affects Library Complexity (Tag uniqueness)
34 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
35 Calculate Gene Expression Gene A 3500nt (700 reads) Gene B 400nt (160 reads)
36 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 L N R = Gene Read Count L = Length of gene N = Library Size Mortazavi et al. Nat. Methods 2008; 5:
37 Further Normalisation Repeat Normalise to mappable gene length Koehler et al. Bioinformatics 2010
38 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
39 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
40 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
41 Calculate Feature Expression
42 Calculate Feature Expression Exonic Region
43 Calculate Feature Expression Exonic Region Exon Junction
44 Calculate Feature Expression Exonic Region Exon Junction Intronic Region
45 Calculate Feature Expression Exonic Region Exon Junction Intronic Region Exon Boundary
46 Calculate Feature Expression Exonic Region Exon Junction Intronic Region Exon Boundary Intergenic Region
47 Calculate Feature Expression Calculate RPKM for any feature Exonic Region Exon Junction Intronic Region Exon Boundary Intergenic Region
48 Calculate Feature Expression Calculate RPKM for any feature Exonic Region Exon Junction Intronic Region Exon Boundary Intergenic Region Extended 3 UTR
49 Calculate Feature Expression Calculate RPKM for any feature Exonic Region Exon Junction Intronic Region Exon Boundary Intergenic Region Extended 3 UTR Retained Intron
50 Calculate Transcript Expression
51 Calculate Transcript Expression diagnostic feature
52 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
53 Calculate Transcript Expression
54 Calculate Transcript Expression Approach #2: Expression estimated Construct bipartite graph, then finds minimum path Cufflinks: Trapnell et al. Nat. Biotech. 2010, 28:
55 Calculate Transcript Expression Approach #2: Expression estimated Construct bipartite graph, then finds minimum path Cufflinks: Trapnell et al. Nat. Biotech. 2010, 28: Estimates expression for all transcripts Incorporates ambiguous reads Model can fail in complex / highly expressed regions More statistically robust Error rate largely unknown
56 Expressed or not? Cond. 1 Cond. 2 Cond. 3 Frequency not expressed expressed Need to determine expression cut-off value log2 (expression)
57 Expressed or not? 1 Expressed if > 1 RPKM Has literature support Lacks sensitivity Arbitrary
58 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
59 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
60 Expressed or not? Expressed if > 1 RPKM Expressed if above intergenic background Incorporate replicate information Has literature support Cut-off based on empirical evidence np IDR 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 th percentile
61 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 Rep 1 vs Rep 2 Rep 2 vs Rep 1 Mean Cut off log2 (expression) bins
62 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 Rep 1 vs Rep 2 Rep 2 vs Rep 1 Mean Cut off log2 (expression) bins Choose what is reasonable for your experiment, be consistent!
63 Nucleotide-Resolution Analysis Imprinting ICR
64 Nucleotide-Resolution Analysis Imprinting eqtl sqtl
65 Nucleotide-Resolution Analysis Imprinting eqtl sqtl Complex Traits
66 Nucleotide-Resolution Analysis Imprinting eqtl sqtl Complex Traits Allelic Fraction A B C SNPs
67 Nucleotide-Resolution Analysis Imprinting eqtl sqtl Complex Traits A B C SNPs Allelic Fraction Density Expected Mean Observed Mean Reference bias Fraction of RNA seq Reads Matching Reference Allele Degner et al. Bioinformatics 2009
68 Nucleotide-Resolution Analysis Imprinting eqtl sqtl Complex Traits A B C SNPs Allelic Fraction Density Expected Mean Observed Mean Reference bias Map to a diploid genome Fraction of RNA seq Reads Matching Reference Allele AlleleSeq: Rozowsky et al. Mol. Sys. Bio 2011 Degner et al. Bioinformatics 2009
69 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
70 The future of RNA-seq (now) Single Cell Shalek, et al. Nature 2013
71 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
72 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)
73 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 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; 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.
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