introduction to rna-seq -...

Introduction to RNA-Seq

David WoodWinter School in Mathematics and Computational Biology

July 1, 2013

RNA is...

Central

DNA

RNA

Protein

Epigenetics

Diverse

tRNA

mRNA

rRNA

Dynamic

Time

Abundance

RNA is...

QuantitativeQualitative

Understand the molecular basis of gene function. Classify and transform cellular states

Integrative

Central

DNA

RNA

Protein

Epigenetics

Diverse

tRNA

mRNA

rRNA

Dynamic

Time

Abundance

RNA studies involve...

Biological System

TechnologyAvailable Resources

Questions

~/bin

Project

DB

RNA studies involve...

Biological System

TechnologyAvailable Resources

Questions

~/bin

Project

DB

This talk: Focusing on reference based mammalian RNA-seq analysis

pA

pA pApAATG ATG

TSS transcription start site pA polyadenylation signalprotein coding regions

ATG translation start site AAA polyadenylationnon-coding regions

genomic DNA microRNAs spliced intron

TSS TSS TSS

TSS

ATG AAA

ATG AAA

ATG AAA

ATG

ATG

ATG

AAA

AAAATG

Transcriptional Complexity

pA

pA pApAATG ATG




TSS TSS TSS

TSS

ATG AAA

ATG AAA

ATG AAA

ATG

ATG

ATG

AAA

AAAATG


PASR miRNAtiRNA

AAAAAA

Alu

pA

pA pApAATG ATG




TSS TSS TSS

TSS

ATG AAA

ATG AAA

ATG AAA

ATG

ATG

ATG

AAA

AAAATG


PASR miRNAtiRNA

AAAAAA

Alu

pA

pA pApAATG ATG




TSS TSS TSS

TSS

ATG AAA

ATG AAA

ATG AAA

ATG

ATG

ATG

AAA

AAAATG


PASR miRNAtiRNA

Mutations Allelic Expression

RNA Editing

pA

pA pApAATG ATGTSS TSS TSS

TSS

AAA

PASR miRNA

ATG AAA

ATG AAA

ATG AAA

ATG

ATG

ATG

AAA

AAAATG

tiRNA

RNA-seq

non-spliced reads

junction readsstrand specific Cloonan et al. Nat Methods 2008; 5:613-619

AAA

Alu

mutations

Advantages of RNA-seq

!"

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%!!!!!"

%#!!!!"

#&" #'" (!" ($" (%" ()" (*" (#" ((" (+" (&" ('" +!" +$"

,-./

01-23

40"

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<-;462/"=;>2/?" @6?-.>.A;/"

/1BCD" <06E>;?6/6"

Discoverygenes, exons, junctions,

UTRs, fusions(Present and Future)


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$!!!!!"

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%!!!!!"

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,-./

01-23

40"

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<-;462/"=;>2/?" @6?-.>.A;/"

/1BCD" <06E>;?6/6"



Dynamic Range

Mortazavi et al. Nat. Methods 2008; 5:621–628


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#!!!!"

$!!!!!"

$#!!!!"

%!!!!!"

%#!!!!"

#&" #'" (!" ($" (%" ()" (*" (#" ((" (+" (&" ('" +!" +$"

,-./

01-23

40"

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<-;462/"=;>2/?" @6?-.>.A;/"

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Dynamic Range


Nucleotide Specific

Typical experiment workflow

Design Experiment

Sample AcquisitionField / Clinic / Lab

Validation

VerificationSample Acquisition

Run Experiment

Obtain RNA

Make Library

Sequencing

Base Calling Mapping

Library QC

Publish

Analysis

Interpretation

1° 2°

3°

3°

2°

Field / Clinic Wet Lab Dry Lab

Library Construction

AAAAA

AAAAA

AAAAA

AAAAAA

AAAFragment

ds-cDNAsynthesis

Ligate adaptors +

Amplify

TargetRNA

rRNA (80%)

tRNA (15%)

5%

cellular RNA

Deplete rRNA

Enrich polyA RNA

Profile (ribosomes)

Capture(tiling arrays)

Sequencing


Design Experiment


Validation


Run Experiment

Obtain RNA

Make Library

Sequencing


Library QC

Publish

Analysis

Interpretation

1° 2°

3°

3°

2°


RNA-seq Mapping

ATG AAA

Challenge #1: Introns

RNA-seq Mapping

ATG AAA


Align to database of junctions or transcriptome

Wood et al. Bioinformatics 2011; 27:580–581

Split Read Alignments

Trapnell et al. Bioinformatics 2009; 25:1105-11

RNA-seq Mapping

ATG AAA


Challenge #2: Correctness

Sufficient OverlapSufficient Evidence





RNA-seq Mapping

ATG AAA


Challenge #2: Correctness

Sufficient OverlapSufficient Evidence

Align to the transcriptome

Challenge #3: Multi-mappers

Sequence Similarity





RNA-seq Mapping

Data QC (clipping)

Align to Filter Set

Align to ‘genome’

Align to ‘junctions’

Split read Alignment

Choose Alignments, DisambiguateExclude Flag and Exclude

Tophat: Trapnell et al. Bioinformatics 2009; 25:1105-11

RNA-seq Mapping

Data QC (clipping)

Align to Filter Set





BAMBAM BAM Alignment Filtering

AnalysisLibrary QC


RNA-seq Mapping

reference?diploid?

gene model?ESTs?

Algorithm?rRNA, tRNA?

Data QC (clipping)

Align to Filter Set





BAMBAM BAM Alignment Filtering

AnalysisLibrary QC



Design Experiment


Validation


Run Experiment

Obtain RNA

Make Library

Sequencing


Library QC

Publish

Analysis

Interpretation

1° 2°

3°

3°

2°


Library Quality Control (QC)

AAAAA

AAAAA

AAAAA

AAAAAA

AAAFragment

ds-cDNAsynthesis

Ligate adaptors +

Amplify

TargetRNA

rRNA (80%)

tRNA (15%)

5%

cellular RNA

Deplete rRNA

Enrich polyA RNA

Profile (ribosomes)


Sequencing


AAAAA

AAAAA

AAAAA

AAAAAA

AAAFragment

ds-cDNAsynthesis

Ligate adaptors +

Amplify

TargetRNA

rRNA (80%)

tRNA (15%)

5%

cellular RNA

Deplete rRNA

Enrich polyA RNA

Profile (ribosomes)


Sequencing

Affects RNA content (Expression

quantification)


AAAAA

AAAAA

AAAAA

AAAAAA

AAAFragment

ds-cDNAsynthesis

Ligate adaptors +

Amplify

TargetRNA

rRNA (80%)

tRNA (15%)

5%

cellular RNA

Deplete rRNA

Enrich polyA RNA

Profile (ribosomes)


Sequencing


quantification)

Affects Insert Size (transcript

identification)


AAAAA

AAAAA

AAAAA

AAAAAA

AAAFragment

ds-cDNAsynthesis

Ligate adaptors +

Amplify

TargetRNA

rRNA (80%)

tRNA (15%)

5%

cellular RNA

Deplete rRNA

Enrich polyA RNA

Profile (ribosomes)


Sequencing


quantification)


identification)

Affects Strand Specificity


AAAAA

AAAAA

AAAAA

AAAAAA

AAAFragment

ds-cDNAsynthesis

Ligate adaptors +

Amplify

TargetRNA

rRNA (80%)

tRNA (15%)

5%

cellular RNA

Deplete rRNA

Enrich polyA RNA

Profile (ribosomes)


Sequencing


quantification)


identification)


Affects Library Complexity

(Tag uniqueness)


AAAAA

AAAAA

AAAAA

AAAAAA

AAAFragment

ds-cDNAsynthesis

Ligate adaptors +

Amplify

TargetRNA

rRNA (80%)

tRNA (15%)

5%

cellular RNA

Deplete rRNA

Enrich polyA RNA

Profile (ribosomes)


Sequencing


quantification)


identification)


Affects Library Complexity

(Tag uniqueness)

Affects Mapping Rate

Paired-end?


Design Experiment


Validation


Run Experiment

Obtain RNA

Make Library

Sequencing


Library QC

Publish

Analysis

Interpretation

1° 2°

3°

3°

2°


Calculate Gene Expression

ATG AAA ATG

Gene A3500nt

(700 reads)

Gene B400nt

(160 reads)

AAA


Calculate Gene Expression

ATG AAA ATG

Gene A3500nt

(700 reads)

Gene B400nt

(160 reads)

AAA

RPKM = 2.0 RPKM = 4.0

RPKM = R 103 106L N

× ×

Reads Per Kilobase per Million

L = Length of geneN = Library Size

R = Gene Read Count

Further Normalisation

ATG AAA

Repeat

Normalise to “mappable” gene length

Koehler et al. Bioinformatics 2010


ATG AAA

Repeat



Robinson et al. Genome Biology 2010; 11:R25

Scale Expression Values by TMM

Cellular RNA

Cond. 1 Cond. 2


ATG AAA

Repeat





Cellular RNA

Cond. 1 Cond. 2

RPKM

Cond. 1 Cond. 2


ATG AAA

Repeat





Benjamini et al. NAR; 2012

Normalise to GC content of

region

Calculate ‘Feature’ Expression

ATG AAA

ATG AAA


ATG AAA

ATG AAA

Exonic Region


ATG AAA

ATG AAA

Exonic Region Exon Junction


ATG AAA

ATG AAA

Exonic Region Exon Junction Intronic Region


ATG AAA

ATG AAA

Exonic Region Exon Junction Intronic Region Exon Boundary


ATG AAA

ATG AAA

Exonic Region Exon Junction Intronic Region Exon Boundary Intergenic Region


ATG AAA

ATG AAA


Calculate RPKM for any feature


ATG AAA

ATG AAA



Extended 3’ UTR

ATG AAA


ATG AAA

ATG AAA



Extended 3’ UTR

ATG AAA

ATG AAA

Retained Intron

Calculate Transcript Expression

ATG AAA

ATG AAA

ATG AAA

ATG


ATG AAA

ATG AAA

ATG AAA

ATG

diagnostic feature


ATG AAA

ATG AAA

ATG AAA

ATG

diagnostic feature

Approach #1: Expression calculated using diagnostic features

Strong Evidence

Excludes Transcripts

Sampling Variability

Lacks statistical robustness

Easy to calculate

Dependent on gene model

ALEXA-seq: Griffith et al. Nat. Methods 2010; 11:R25


ATG AAA

ATG AAA

ATG AAA

ATG


ATG AAA

ATG AAA

ATG AAA

ATG

Approach #2: Expression estimatedConstruct bipartite graph, then finds minimum path

Cufflinks: Trapnell et al. Nat. Biotech. 2010, 28:511-515


ATG AAA

ATG AAA

ATG AAA

ATG

Estimates expression for all transcripts

Model can fail in complex / highly

expressed regions

More statistically robust Error rate largely unknown

Incorporates ambiguous reads

Approach #2: Expression estimatedConstruct bipartite graph, then finds minimum path

Cufflinks: Trapnell et al. Nat. Biotech. 2010, 28:511-515

Expressed or not?

ATG AAA

ATG AAA

ATG AAA

Cond. 1

Cond. 2

Cond. 3

Freq

uenc

y

log2 (expression)

not “expressed” “expressed”

Need to determine ‘expression’ cut-off value

Expressed or not?

Expressed if > 1 RPKM

1

Lacks sensitivity ArbitraryHas literature

support

Expressed or not?


1

Expressed if above intergenic

background

2

log2 Expression

Freq

uenc

y

95th percentile


support

Expressed or not?


1


background

2

log2 Expression

Freq

uenc

y

95th percentile

Cut-off based on empirical

evidence

Still somewhat arbitrary


support

Expressed or not?


1


background

2

log2 Expression

Freq

uenc

y

95th percentile


evidence


Incorporate replicate

information

3Based on observed

reproducibility

Requires replicates


support

−log2 (expression) bins

np−I

DR

Rep 1 vs Rep 2Rep 2 vs Rep 1MeanCut−off

00.

10.

30.

50.

70.

91

−11 −7 −3 1 5 9 13 17 21 25

Expressed or not?


1


background

2

log2 Expression

Freq

uenc

y

95th percentile


evidence


Incorporate replicate

information

3Based on observed

reproducibility

Requires replicates

Choose what is reasonable for your experiment, be consistent!


support

−log2 (expression) bins

np−I

DR

Rep 1 vs Rep 2Rep 2 vs Rep 1MeanCut−off

00.

10.

30.

50.

70.

91

−11 −7 −3 1 5 9 13 17 21 25

Nucleotide-Resolution Analysis

ATG AAA

ATG AAA

ICR

Imprinting


ATG AAA

ATG AAA

Imprinting

sQTLeQTL


ATG AAA

ATG AAA

Imprinting

sQTLeQTLComplex Traits


ATG AAA

ATG AAA

Imprinting

eQTLComplex Traits

A B CSNPs

Allelic Fraction

sQTL


ATG AAA

ATG AAA

Imprinting

eQTLComplex Traits

A B CSNPs

Allelic Fraction

sQTL

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.5

1.0

1.5

2.0

Fraction of RNA−seq Reads Matching Reference Allele

Dens

ity

Expected MeanObserved Mean

Degner et al. Bioinformatics 2009

Reference bias


ATG AAA

ATG AAA

Imprinting

eQTLComplex Traits

A B CSNPs

Allelic Fraction

sQTL

Map to a diploid genome

AlleleSeq: Rozowsky et al. Mol. Sys. Bio 2011

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.5

1.0

1.5

2.0

Fraction of RNA−seq Reads Matching Reference Allele

Dens

ity

Expected MeanObserved Mean

Degner et al. Bioinformatics 2009

Reference bias


Design Experiment


Validation


Run Experiment

Obtain RNA

Make Library

Sequencing


Library QC

Publish

Analysis

Interpretation

1° 2°

3°

3°

2°


The future of RNA-seq (now)Single Cell

Shalek, et al. Nature 2013

The future of RNA-seq (now)Single Cell

Shalek, et al. Nature 2013

Huge Cohort

900 donors 30,000 RNA-seq

data sets!

Genotype-Tissue Expression project (GTEx)

Lonsdale, et al. Nature Genetics 2013

Summary

Choose an alignment approach suitable for your experiment, available resources and tools

Assess library quality, specifically rRNA contamination, insert size, strand specificity and library complexity

Gene and ‘Feature’ Expression can be calculated using count data, and normalised by length, library size and GC content

Transcript expression calculation requires alternative approaches and algorithms, which although common, are largely unproven

RNA-seq can interrogate nucleotide specific questions, but be careful of alignment biases (diploid mapping can help here)

1

2

3

4

5

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 -...

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