Cufflinks Matt Paisner, Hua He, Steve Smith and Brian Lovett

CufflinksMatt Paisner, Hua He, Steve Smith and

Brian Lovett

The Vision• RNAseq can be used for transcript discovery and

abundance estimation• What’s missing: algorithms which aren’t

restricted by prior gene annotations (which are often incomplete) and account for alternative transcription and splicing.

• Hence, Cufflinks.

The Need• Evidence of ambiguous assignment of isoforms.

TSS site/promoter changes and splice site changes were found previously by the authors

• Longer reads and pair end reads do not do enough

The Biology• General assumption of randomization of reads• Central Dogma• Transcription Start Site (TSS)• Splice site• Isoform

Central Dogma and Regulation

Splicing•

Thisblahblahblahblahblahblahisblahblahimportant•

Thisblahblahblahblahblahblahisblahblahimportant• “This” “is” “important” - Exons• “blah” - Introns (Intrusions)

Major Change 1

Major Change 2

Why it matters: Isoforms

• Not only different sizes, but different shapes• Shape determines function• Isoforms would map to the same section of the

genome: undetected without Cufflinks• Separating transcripts into isoforms elucidates a

more realistic representation of what is happening

TopHatMapping short reads

Trapnell et. al, Bioinformatics, 2009

TopHat• No genome reference annotations are needed

• The output of TopHat is the input of Cufflinks.

• Input: Reads and genome

• Output: Read mappings

• Short reads present computational challengeso BOWTIE

How does TopHat Work?!

Big Idea: “Exon Inference”!!

Step 1: Initial Mapping via Bowtie

• Group 1: Mapped Reads (Segments)• Group 2: Initially Unmapped (IUM) Reads

o possibly intron-spanning read

• Based on Group 1, we want to get intron-spanning reads from Group 2

Reference

Mapped Reads

Step 2: Generate Putative Exons

Step 3: Look for Potential Splice Signals

Putative Exons

Step 4: Seed-and-Extend

CufflinksIsoform/Transcript Detection and

Quantification

Trapnell et al, Nature Biotech, 2010

Step 5: Identify Compatible Reads

Two reads are compatible if their overlap contains the exact same implied introns (or none). If two reads are not compatible they are incompatible.

Step 6:

Less BIOLOGY, and NOW it is the time for some GRAPH THEORIES…….

“We emphasize that the definition of a transcription locus is not biological……” - Authors

Step 6: Create Overlap Graph

•

Connect compatible reads in order

Create a DAG

A path in this graph correspondsto a transcript isoform

Theory1. Solving minimum path cover (isoforms) in the

overlap graph implies the fewest transcripts necessary to explain the reads.

2. Solve minimum path cover by finding largest set of individual reads such that no two are compatible.

3. According to Dilworth Thereom, find a maximum matching in a bipartite graph

Step 8: Convert a DAG into a Bipartite Graph

Step 9: Looking for Maximum Matching

inside a bipartite graph via Bipartite

Matching Algorithm

BIPARTITE-MATCHING Algorithm: Add augmenting path via BFS, repeatedly adding the paths into the matching until none can be added.

A path in this graph correspondsto a transcript isoform

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Projective normalization underestimates expression

isoform aisoform b project all isoforms

into genome coordinates

R reads total, r reads for the gene:- ra for isoform a- rb for isoform b

but so

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How should expression levels be estimated?

• A-B are distinguished by the presence of splice junction (a) or (b).

• A-C are distinguished by the presence of splice junction (a) and change in UTR

• B-C are distinguished by the presence of splice junction (b) and change in UTR

(a)(b)

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How should expression levels be estimated?

• Longer transcripts contain more reads.• Reads that could have originated from multiple

transcripts are informative.• Relative abundance estimation requires

“discriminatory reads”.

(a)(b)

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A model for RNA-Seq

• = r Transcript proportions for assignment of reads to transcripts

• L = Likelihood of this assignment

• R = all reads

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A model for RNA-Seq

• = T All transcripts

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A model for RNA-Seq

Define:

• Expected possible positions for an arbitrary fragment in Transcript t

• F(i) = pr(random fragment has length i)

• l(t) = Full length of transcript t

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A model for RNA-Seq

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A model for RNA-Seq

• It (r) = Implied length of r’s fragment if r is assigned to transcript t

• Recall: F(i) = pr(fragment length = i)

36

Projective normalization underestimates expression

isoform aisoform b project all isoforms

into genome coordinates

R reads total, r reads for the gene:- ra for isoform a- rb for isoform b

but so

37

A model for RNA-Seq

• Now we have a maximum likelihood function in terms of , r the distribution of reads among transcripts.

• Non-negative linear model

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Inference with the sequencing model

• Maximum likelihood function is concave - optimization using the EM algorithm.

• Asymptotic MLE theory leads to a covariance matrix for the estimator in the form of the inverse of the observed Fisher information matrix

• Importance sampling from the posterior distribution used for estimating the abundances from the posterior expectation, and 95% confidence intervals for the estimates.

• This approach extends the log linear model of H. Jiang and W. Wong, Bioinformatics 2009 to a linear model for paired end reads.

• For more background see Li et al., Bioinformatics, 2010 and Bullard et al., BMC Bioinformatics, 2010.

Utility of Cufflinks• mRNA as proxy for gene expression & action• Control points

o transcriptional vs o post transcriptional

• Does isoform-level discovery & quantification matter? o Apparently, yeso Putatively discovered about 12K new isoforms while recovering about

13K knowno Plus other stuff…

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The skeletal myogenesis transcriptomeRNA-Seq (2x75bp GAIIx) along time course of mouse C2C12 differentiation

-24 hours

60 hours

168 hours

differentiation(starting at 0 hours)

fusion

myotubemyoctyte

120 hours

Illustration based on: Ohtake et al, J. Cell Sci., 2006; 119:3822-3832

•84,369,078 reads

•140,384,062reads

• 82,138,212reads

•123,575,666reads

•66,541,668alignments

•103,681,081alignments

•47,431,271alignments

•89,162,512alignments

•10,754,363to junctions

•19,194,697to junctions

•9,015,806to junctions

•17,449,848to junctions

•58,008transfrags

•69,716transfrags

•55,241transfrags

•63,664transfrags

Slide courtesy of Hector Corrada Bravo

Validation

Validation

43

Projective normalization underestimates expression

isoform aisoform b project all isoforms

into genome coordinates

R reads total, r reads for the gene:- ra for isoform a- rb for isoform b

but so

Slide courtesy of Hector Corrada Bravo

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Discovery is necessary for accurate abundance

estimates

Slide courtesy of Hector Corrada Bravo

Some Questions…• Do isoforms of a given gene have interesting

temporal patterns?o Increasing, decreasing, more complex…

• What does this mean biologically?• What about transcriptional versus post

transcriptional regulation?o Differential transcriptiono Differential splicing

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Dynamics of Myc expression

Slide courtesy of Hector Corrada Bravo

Overloading Metric using Jensen-Shannon Divergence

Metric:

One-sided t-test under the null hypothesis that there is no difference in abundance;Type I errors controlled with Benjamini-Hotchberg correction (FDR)

Average EntropyEntropy of Average

Regulatory Overloading

Differential splicing

Differential TSS preference

231

101

17

FibronectinTropomyosin 1Mef2d…

Fhl3Fhl1Myl1…

# Genes (FDR < 0.05)

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Dynamics of Myc expression

d( , )

Slide courtesy of Hector Corrada Bravo

New TSS = New Points of Regulation

TSS=Transcription Start Site

What would a “collapsed” RNA-seq alignment look like? Microarray?

Questions?

I am the DNA, and Iwant a protein!

The DNA wants a protein.

Transcription

Translation

mRNAProtein