gene discovery using combined signals from genome sequence and natural selection michael brent...

Gene discovery using combined Gene discovery using combined signals from genome sequence signals from genome sequence

and natural selectionand natural selection

Michael BrentWashington University

The mouse genome analysis group

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Genes are read out via mRNAGenes are read out via mRNA

& processing

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RNA ProcessingRNA Processing

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A typical human gene structureA typical human gene structure

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In a mammalian genomeIn a mammalian genome

Finding all the genes is hard• Mammalian genomes are large

– 5,051 miles of 10pt type– Raleigh to Tripoli, Libya

• Only about 1.5% protein coding– Raleigh to Winston-Salem

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Genes are fairly unconstrainedGenes are fairly unconstrained

Intron length is highly variable• ~5% are 40-100 nt long• ~3% are longer than 30,000 nt

Distance between genes is highly variable• From 103 to 106 nt or more (probably)

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Exons per gene (RefSeq)Exons per gene (RefSeq)

0%

2%

4%

6%

8%

10%

12%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30+

Number of Exons

Pe

rce

nt

of

Ge

ne

s

Ref Seq

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Background is not randomBackground is not random

Segmental duplications• Entire regions duplicate, then diverge slowly

Processed pseudogenes• Spliced transcripts integrate back into the genome

– Sequence is similar to source genes– Generally not functional

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Gene prediction: two approachesGene prediction: two approaches

1. Transcript-based (E.g., GeneWise)A. Map experimentally determined sequences of

spliced transcripts to their genomic sourceB. Map transcript sequences to genomic regions

that could produce similar transcripts

2. De novo (genome only)• Model DNA patterns characteristic of gene

components– Splice donor and accepter– Protein coding sequence– Translation start and stop

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Advantages and disadvantagesAdvantages and disadvantages

Transcript-based • Advantage: conservative

–Evidence of transcription for every exon• Disadvantage: conservative

–Can’t find “truly novel” genes• Still subject to error

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Advantages and disadvantagesAdvantages and disadvantages

De novo• Advantage 1: Less biased toward

–Known transcripts–Transcripts that can be sequenced easily

• Advantage 2: Genome sequencing is easy• Disadvantages

–No direct evidence of transcription–Presumably, more false positives

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Single-genome Single-genome dede novonovo: : GenscanGenscan

Strengths• For mammalian sequence, one of the best

single-genome, de novo gene predictors• Widely used to great practical advantage• De facto standard for mammalian sequence

Limitations• Predicts >45K genes (best est.: 25-30K)• Predicts >315K exons (best est. 200K-250K)• Gets only 9% of known genes exactly right*

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Dual genome Dual genome de novode novo

We developed algorithms that use two genomes to• Reduce the number of false positives• Refined the details of the structures

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Probability model• Assigns probability to annotated DNA sequences:

5’TAGCCTACTGAAATGGACCGCTTCAGCGTGGTAT3’

Optimization algorithm• Given a DNA sequence, find the most probable

annotation, according to the model

Exon5’ UTR Intron

Single-genome de novo methodSingle-genome de novo method

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CCATGGCGTCTTCAGGCAGTGACTC

Genscan’s generative modelGenscan’s generative model

IntronExonIntron

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Generalized

HMM

• States correspond to gene features• Model generates DNA sequence

by passing through states• The probability of annotated DNA

sequence is the probability of –generating the DNA sequence –by passing through states corre-

sponding to the annotation.

Genscan’s generative modelGenscan’s generative model

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Dual genome predictionDual genome prediction

Input• Target and informant genomes

Idea• Patterns of evolution since the last common

ancestor may reveal gene structure

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Two conservation signalsTwo conservation signals

1. Local alignment signal• Selective pressures differ by feature• This leaves a characteristic signature

2. Structural signal• Locations of introns tend to be conserved

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Characteristic local alignmentsCharacteristic local alignments

TTATCCACCAGACCAGATAGATACTTGTCTGCCACCCTC

|||||||||||||||||||| || ||||| || || |||

TTATCCACCAGACCAGATAGGTATTTGTCAGCTACTCTC

Coding exon

CTAGAGATGCAAAAGAAACAGGTACCGCAGTGC---CCC

|||||| || | ||||||||| || || ||

CTAGAGC----AAGAAGACAGGTACCATAGGGCTCTCCT

Intron (non-coding)

human

human

mouse

mouse

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Conservation of intron locationConservation of intron location

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AlignAlign→→predictpredict→→filterfilter→→testtest

WU-BLAST

Aligned Intron Filter

Validation (RT-PCR)

TTATCCACCAGACCAGATAGATACTTGTCTGCCACCCTC

TTATCCACCAGACCAGATAGGTATTTGTCAGCTACTCTC

TCTGCCACC|| || ||TCAGCTACT

TWINSCAN

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gHMM decodingRepresentation change

TCTGCCACC||:||:||

TCTGCCACC|| || ||TCAGCTACT

Conservation sequenceTWINSCAN

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BLAST AlignmentsBLAST Alignments

TargetInformant

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Projecting BLAST AlignmentsProjecting BLAST Alignments

TargetInformant

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Projecting BLAST AlignmentsProjecting BLAST Alignments

TargetInformant

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Projecting BLAST AlignmentsProjecting BLAST Alignments

TargetInformant

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Projecting BLAST AlignmentsProjecting BLAST Alignments

TargetInformant

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Conservation sequenceConservation sequence

CTAGAGATGCAAAAGAAACAGGTACCGCAGTGC---CCC

Synthetic (projected) local alignmenthuman

mouse

|||||| | ||||||||| || || ||

CTAGAG AGACAGGTACCATAGGGCTCTCCT

Pair each nucleotide of the target with• “|” if it is aligned and identical

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Conservation sequenceConservation sequence

CTAGAGATGCAAAAGAAACAGGTACCGCAGTGC---CCC

Synthetic (projected) local alignmenthuman

mouse

|||||| |:|||||||||::||:|| ||:

CTAGAG AGACAGGTACCATAGGGCTCTCCT

Pair each nucleotide of the target with• “|” if it is aligned and identical• “:” if it is aligned to mismatch or gap

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Conservation sequenceConservation sequence

CTAGAGATGCAAAAGAAACAGGTACCGCAGTGC---CCC

Synthetic (projected) local alignmenthuman

mouse

||||||. . . . . . . . .|:|||||||||::||:|| ||:

CTAGAG AGACAGGTACCATAGGGCTCTCCT

Pair each nucleotide of the target with• “|” if it is aligned and identical• “:” if it is aligned to mismatch or gap• “.” if it is unaligned

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Conservation sequenceConservation sequence

CTAGAGATGCAAAAGAAACAGGTACCGCAGTGC---CCC

Conservation sequencehuman

||||||. . . . . . . . .|:|||||||||::||:|| ||:

Pair each nucleotide of the target with• “|” if it is aligned and identical• “:” if it is aligned to mismatch or gap• “.” if it is unaligned

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Conservation sequenceConservation sequence

CTAGAGATGCAAAAGAAACAGGTACCGCAGTGCCCC

Conservation sequencehuman

||||||. . . . . . . . .|:|||||||||::||:||||:

Pair each nucleotide of the target with• “|” if it is aligned and identical• “:” if it is aligned to mismatch or gap• “.” if it is unaligned

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Probability model• Assigns probability to annotated DNA:

5’TAGCCTACTGAAATGGACCGCTTCAGCGTGGTAT3’ |||........|:||||:|||||||||:||::||

Optimization• Given DNA and conservation sequence, find the most

probable annotation, according to the model

Exon5’ UTR Intron

Twinscan: Extending the modelTwinscan: Extending the model

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• Each state “generates” DNA and conservation sequence independently

• Probability of annotated DNA and conservation sequence is probability of generating the DNA and conservation sequence by passing through corresponding states

TwinscanTwinscan

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Performance EvaluationPerformance Evaluation

RefSeq• A set ~13,000 “Known” mRNAs• Represents ~40-50% of human genes

–Usually, only one of several splices• Mapping to genome is imperfect• Best available gold standard

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0%

2%

4%

6%

8%

10%

12%

14%

16%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30+

Number of Exons

Pe

rce

nt

of

Ge

ne

s

Ref Seq

Twinscan

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Short term goalShort term goal

All multi-exon human genes• Predict accurately

–Integrate information from more genomes• Verify at least one intron experimentally• Follow up with full-length verification

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AcknowledgmentsAcknowledgmentsFunding agencies

• National Institutes of Health (NHGRI)• National Science Foundation (DBI)

Sequencing centers• Sanger, Whitehead, Wash. U.

My group• Ian Korf, Paul Flicek, Evan Keibler, Ping Hu

Collaborators• Roderic Guigo, Josep Abril, Genis Parra

– Pankaj Agarwal• Stylianos Antonarakis, Alexandre Reymond, Manolis

Dermitzakis

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Other cladesOther clades

Plants• Arabidopsis thaliana, cabbage, rice

Nematodes• C. elegans, C. briggsae

Fungi• Cryptococcus neoformans (JEC21, H99)

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Pair HMM algorithms (SLAM,…)Pair HMM algorithms (SLAM,…)

• Input is orthologous sequences.• Aligns and predicts simultaneously, using a

joint probability model• Predicts orthologous genes in 2 sequences• All predicted CDS is aligned• Some aligned regions are not predicted CDS

–Labeled conserved non-coding sequence

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The algorithms (SLAM,…)The algorithms (SLAM,…)

sgp2• Alignment before prediction (tblastx)• Predicts genes in target sequence only• Don’t need orthologous input sequences

–Paralogs & low-coverage shotgun can help• Modifies scores of all potential exons, by

–At each base, add tblastx score of best overlapping local alignment (roughly)

–To gene-id scores of that potential exon

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The algorithmsThe algorithmsTWINSCAN

• Alignment before prediction (blastn)• Predicts in target sequence only• Modifies scores of all potential exons, UTRs,

splice sites, start and stop models, by–At each base, apply a feature-specific

scoring model (estimated for this purpose)–to the best overlapping local alignment,

and adding the result–To Genscan scores for that feature

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% Aligned, CDS vs. other% Aligned, CDS vs. other

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Coding Non-coding

Genscansgp2TwinscanSanger

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QuerySequence

tblastxHSPs

geneidExons

HSPsProjectio

ns

SGPExons

Syntenic Gene Prediction (sgp2)Syntenic Gene Prediction (sgp2)

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Why work on gene finding?Why work on gene finding?

Genes are• Components responsible for biological function• Variations cause human disease / susceptibility• Controls for modifying biological function

–Human gene therapy–Agriculture–Nanotechnology, etc.