machine learning methods for rna-seq-based transcriptome...
TRANSCRIPT
Machine Learning Methods for RNA-seq-based
Transcriptome Reconstruction
Gunnar Ratsch
Friedrich Miescher Laboratory
Max Planck Society, Tubingen, Germany
NGS Bioinformatics Meeting, Paris (March 24, 2010)
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 1 / 36
Motivation
Discovery of the Nuclein(Friedrich Miescher, 1869) fml
Discovery of Nuclein:from lymphocyte & salmon
“multi-basic acid” (≥ 4)
Tubingen, around 1869
“If one . . . wants to assume that a single substance . . . is the specificcause of fertilization, then one should undoubtedly first and foremostconsider nuclein” (Miescher, 1874)
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 2 / 36
Motivation
Discovery of the Nuclein(Friedrich Miescher, 1869) fml
Discovery of Nuclein:from lymphocyte & salmon
“multi-basic acid” (≥ 4)
Tubingen, around 1869
“If one . . . wants to assume that a single substance . . . is the specificcause of fertilization, then one should undoubtedly first and foremostconsider nuclein” (Miescher, 1874)
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 2 / 36
Motivation
Learning about the Transcriptome What is encoded on the genome and how is it processed? fml
Computational Point of View
How to learn to predict what these processes accomplish?
How well can we predict it from the available information?
Biological View
What can we not predict yet? What is missing?
Can we derive a deeper understanding of these processes?c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 3 / 36
Motivation
Learning about the Transcriptome What is encoded on the genome and how is it processed? fml
Computational Point of View
How to learn to predict what these processes accomplish?
How well can we predict it from the available information?
Biological View
What can we not predict yet? What is missing?
Can we derive a deeper understanding of these processes?c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 3 / 36
Motivation
Learning about the Transcriptome What is encoded on the genome and how is it processed? fml
Computational Point of View
How to learn to predict what these processes accomplish?
How well can we predict it from the available information?
Biological View
What can we not predict yet? What is missing?
Can we derive a deeper understanding of these processes?c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 3 / 36
Motivation
Learning about the Transcriptome What is encoded on the genome and how is it processed? fml
Computational Point of View
How to learn to predict what these processes accomplish?
How well can we predict it from the available information?
Biological View
What can we not predict yet? What is missing?
Can we derive a deeper understanding of these processes?c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 3 / 36
Motivation
Machine LearningLearning from empirical observations fmlGiven: Observations of some complex phenomenonGoal: Learn from data & build predictive models
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 4 / 36
Motivation
Machine LearningLearning from empirical observations fmlGiven: Observations of some complex phenomenonGoal: Learn from data & build predictive models
Example:
Two different classes of observations
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 4 / 36
Motivation
Machine LearningLearning from empirical observations fmlGiven: Observations of some complex phenomenonGoal: Learn from data & build predictive models
Example:
Inferred classification rule
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 4 / 36
Motivation
Machine LearningLearning from empirical observations fmlGiven: Observations of some complex phenomenonGoal: Learn from data & build predictive models
1 Large scale sequence classification
2 Analysis and explanation of learning results
3 Sequence segmentation & structure prediction
Position
k−m
er L
engt
h
−30 −20 −10 0 10 20 30
87654321
0
5
10
Log-
inte
nsity
transcript
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 4 / 36
RNA-Seq Pipeline
Deep RNA Sequencing (RNA-Seq)fml
RNA-Seq allows . . .
High-throughputtranscriptomemeasurements
Qualitative studies
New transcriptsImproved gene models
Quantitative studies athigh resolution
Differential expressionin tissues, conditions,genotypes, etc.
pre-mRNAintron
exon
mRNAshort reads
junction reads
reference genomeFigure adapted from Wikipedia
Goal: Obtain complete transcriptome for further analyses
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 5 / 36
RNA-Seq Pipeline
Deep RNA Sequencing (RNA-Seq)fml
RNA-Seq allows . . .
High-throughputtranscriptomemeasurements
Qualitative studies
New transcriptsImproved gene models
Quantitative studies athigh resolution
Differential expressionin tissues, conditions,genotypes, etc.
pre-mRNAintron
exon
mRNAshort reads
junction reads
reference genomeFigure adapted from Wikipedia
Goal: Obtain complete transcriptome for further analyses
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 5 / 36
RNA-Seq Pipeline
Deep RNA Sequencing (RNA-Seq)fml
RNA-Seq allows . . .
High-throughputtranscriptomemeasurements
Qualitative studies
New transcriptsImproved gene models
Quantitative studies athigh resolution
Differential expressionin tissues, conditions,genotypes, etc.
pre-mRNAintron
exon
mRNAshort reads
junction reads
reference genomeFigure adapted from Wikipedia
Goal: Obtain complete transcriptome for further analyses
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 5 / 36
RNA-Seq Pipeline
Deep RNA Sequencing (RNA-Seq)fml
RNA-Seq allows . . .
High-throughputtranscriptomemeasurements
Qualitative studies
New transcriptsImproved gene models
Quantitative studies athigh resolution
Differential expressionin tissues, conditions,genotypes, etc.
pre-mRNAintron
exon
mRNAshort reads
junction reads
reference genomeFigure adapted from Wikipedia
Goal: Obtain complete transcriptome for further analyses
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 5 / 36
RNA-Seq Pipeline
Common RNA-Seq Analysis Stepsfml
RNA-SeqReads
Read Alignment
Expression level~ #reads
Significance Testing
Differentially Processed Transcripts/
Segments
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 6 / 36
RNA-Seq Pipeline
Common RNA-Seq Analysis Stepsfml
RNA-SeqReads
Read Alignment
Expression level~ #reads
Significance Testing
Differentially Processed Transcripts/
Segments
De novo Assembly
Transcript Quantitation
TARs and Transcripts
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 6 / 36
RNA-Seq Pipeline
Common RNA-Seq Analysis Stepsfml
RNA-SeqReads
Read Alignment
Expression level~ #reads
Significance Testing
Differentially Processed Transcripts/
Segments
De novo Assembly
Transcript Quantitation
TARs and Transcripts
Transcript Reconstruction
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 6 / 36
RNA-Seq Pipeline
RNA-Seq Pipeline Overviewfml
PALMapper
mGene.ngs
mTim Segmentation
rQuant
Short Reads Transcripts w/ QuantitationAlignm
ents
Transcripts
Read alignment Transcript finding Quantitation
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 7 / 36
RNA-Seq Pipeline
RNA-Seq Pipeline Overviewfml
PALMapper
mGene.ngs
mTim Segmentation
rQuant
Short Reads Transcripts w/ QuantitationAlignm
ents
Transcripts
Read alignment Transcript finding Quantitation
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 7 / 36
PALMapper Read Alignment Overview
Step 1: PALMapper Read Alignment(PALMapper = QPALMA + GenomeMapper) fmlGenomeMapper for (unspliced) read mapping:
Alignments based on GenomeMapper developed in Tubingen forthe 1001 plant genome project [Schneeberger et al., 2009]
k-mer based index, well suited for smaller genomes
More info: http://fml.mpg.de/raetsch/suppl/palmapper
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 8 / 36
PALMapper Read Alignment Overview
Step 1: PALMapper Read Alignment(PALMapper = QPALMA + GenomeMapper) fmlGenomeMapper for (unspliced) read mapping:
Alignments based on GenomeMapper developed in Tubingen forthe 1001 plant genome project [Schneeberger et al., 2009]
k-mer based index, well suited for smaller genomes
ACCGTCGCGCGCGT...TCGGCG...AGAACGCT
TCGCGCGCAACGTCGC CGCG GCGC CGCG GCGC CGCA GCAA CAAC AACG
...matchingk-mers
DNA
Read
k-mers
...
More info: http://fml.mpg.de/raetsch/suppl/palmapper
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 8 / 36
PALMapper Read Alignment Overview
Step 1: PALMapper Read Alignment(PALMapper = QPALMA + GenomeMapper) fmlGenomeMapper for (unspliced) read mapping:
Alignments based on GenomeMapper developed in Tubingen forthe 1001 plant genome project [Schneeberger et al., 2009]
k-mer based index, well suited for smaller genomes
ACCGTCGCGCGCGT...TCGGCG...AGAACGCT
TCGCGCGCAACGTCGC CGCG GCGC CGCG GCGC CGCA GCAA CAAC AACG
...matchingk-mers
DNA
Read
k-mers
...
More info: http://fml.mpg.de/raetsch/suppl/palmapper
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 8 / 36
PALMapper Read Alignment Overview
Step 1: PALMapper Read Alignment(PALMapper = QPALMA + GenomeMapper) fmlGenomeMapper for (unspliced) read mapping:
Alignments based on GenomeMapper developed in Tubingen forthe 1001 plant genome project [Schneeberger et al., 2009]
k-mer based index, well suited for smaller genomes
QPALMA for spliced read alignments:
GenomeMapper identifies seed regions
Spliced alignments by QPALMA [De Bona et al., 2008]
ACCGTCGCGCGCGT...TCGGCG...AGAACGCT
TCGCGCGCAACG
DNA
ReadMore info: http://fml.mpg.de/raetsch/suppl/palmapper
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 8 / 36
PALMapper Read Alignment Accuracy
PALMapper Accuracy EvaluationHow accurately can PALMapper identify introns? fml
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1C. elegans
TopHat
TopHat sensitive
Palmapper
recall precision
PALMapper (3.5h) andTopHat (3.5h/10h) align-ing 24M reads
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 9 / 36
PALMapper Read Alignment Accuracy
PALMapper Accuracy EvaluationHow accurately can PALMapper identify introns? fml
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1C. elegans
TopHat
TopHat sensitive
Palmapper
recall precision
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1H. sapiens (chromosome 1)
Palmapper
recall precision
PALMapper (3.5h) andTopHat (3.5h/10h) align-ing 24M reads
Comparison of PALMapper with other align-ment programs within the RGASP project(preliminary)
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 9 / 36
PALMapper Read Alignment Accuracy
QPALMA: Extended Smith-Waterman Scoringfml
Classical scoring f : Σ× Σ→ R
Source of information
Sequence matches
Computational splicesite predictions
Intron length model
Read qualityinformation
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 10 / 36
PALMapper Read Alignment Accuracy
QPALMA: Extended Smith-Waterman Scoringfml
Classical scoring f : Σ× Σ→ R
Source of information
Sequence matches
Computational splicesite predictions
Intron length model
Read qualityinformation
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 10 / 36
PALMapper Read Alignment Accuracy
QPALMA: Extended Smith-Waterman Scoringfml
Classical scoring f : Σ× Σ→ R
Source of information
Sequence matches
Computational splicesite predictions
Intron length model
Read qualityinformation
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 10 / 36
PALMapper Read Alignment Accuracy
QPALMA: Extended Smith-Waterman Scoringfml
Quality scoring f : (Σ× R)× Σ→ R [De Bona et al., 2008]
Source of information
Sequence matches
Computational splicesite predictions
Intron length model
Read qualityinformation
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 10 / 36
PALMapper Read Alignment Learning Algorithm
Scoring Parameter Inferencefml
What are optimal parameters?
How do we jointly optimize the 336 parameters?
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 11 / 36
PALMapper Read Alignment Learning Algorithm
Scoring Parameter Inferencefml
What are optimal parameters?
How do we jointly optimize the 336 parameters?
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 11 / 36
PALMapper Read Alignment Learning Algorithm
Cartoon: Maximize the Marginfml
truefalse false
Correct alignment is not highest scoring one
Correct alignment is highest scoring one
Can we do better?
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 12 / 36
PALMapper Read Alignment Learning Algorithm
Cartoon: Maximize the Marginfml
truefalse
Correct alignment is not highest scoring one
Correct alignment is highest scoring one
Can we do better?
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 12 / 36
PALMapper Read Alignment Learning Algorithm
Cartoon: Maximize the Marginfml
false true
Technique motivated by SVMs (“large-margin”)
Enforce a margin between correct and incorrect examples
One has to solve a big quadratic problem
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 12 / 36
PALMapper Read Alignment Learning Algorithm
How Can We Generate Data for Training?fml
How do we obtain true alignments for training QPalma?
Simulate realistic transcriptome reads with known origin
Strategy:
1 Estimate relationship between quality score and error probabilityfrom given reads
2 Use annotation of a few genes to simulate spliced reads
3 Introduce errors according to error model using quality stringsfrom given read set
4 Train QPalma on generated read set with known alignments
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 13 / 36
PALMapper Read Alignment Learning Algorithm
How Can We Generate Data for Training?fml
How do we obtain true alignments for training QPalma?
Simulate realistic transcriptome reads with known origin
Strategy:
1 Estimate relationship between quality score and error probabilityfrom given reads
2 Use annotation of a few genes to simulate spliced reads
3 Introduce errors according to error model using quality stringsfrom given read set
4 Train QPalma on generated read set with known alignments
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 13 / 36
PALMapper Read Alignment Reasons for High Accuracy
QPALMA RNA-Seq Read Alignmentfml
Generate set of artificially spliced readsGenomic reads with quality informationGenome annotation for artificially splicing the readsUse 10, 000 reads for training and 30, 000 for testing
Alig
nmen
t Er
ror
Rat
e
SmithW14.19%
Intron9.96% 1.94% 1.78%
Intron+Splice
Intron+Splice+Quality [De Bona et al., 2008]
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 14 / 36
PALMapper Read Alignment Reasons for High Accuracy
QPALMA RNA-Seq Read Alignmentfml
Generate set of artificially spliced readsGenomic reads with quality informationGenome annotation for artificially splicing the readsUse 10, 000 reads for training and 30, 000 for testing
Alig
nmen
t Er
ror
Rat
e
SmithW14.19%
Intron9.96% 1.94% 1.78%
Intron+Splice
Intron+Splice+Quality
Error vs. intron position
[De Bona et al., 2008]
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 14 / 36
PALMapper Read Alignment Reasons for High Accuracy
QPALMA RNA-Seq Read Alignmentfml
Generate set of artificially spliced readsGenomic reads with quality informationGenome annotation for artificially splicing the readsUse 10, 000 reads for training and 30, 000 for testing
Alig
nmen
t Er
ror
Rat
e
SmithW14.19%
Intron9.96% 1.94% 1.78%
Intron+Splice
Intron+Splice+Quality
8nt12nt12nt8ntError vs. intron position
[De Bona et al., 2008]
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 14 / 36
PALMapper Read Alignment Reasons for High Accuracy
Step 2: Transcript Predictionfml
PALMapper
mGene.ngs
mTim Segmentation
rQuant
Short Reads Transcripts w/ QuantitationAlignm
ents
Transcripts
Read alignment Transcript finding Quantitation
A. Coverage segmentation algorithm mTiM for general transcripts(no coding bias/assumption)
B. Extension of the mGene gene finding system to use NGS datafor protein coding transcript prediction (mGene.ngs)
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 15 / 36
mTIM Coverage Segmentation Idea
mTiM: Read Coverage Segmentationfml
Goal: Characterize each base as intergenic, exonic, or intronic
0
10
100
read
cov
erag
e (lo
g-sc
ale)
5 10 15 20 25Kbp
0
Kbpc© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 16 / 36
mTIM Coverage Segmentation Idea
mTiM: Read Coverage Segmentationfml
Goal: Characterize each base as intergenic, exonic, or intronic
0
10
100
read
cov
erag
e (lo
g-sc
ale)
5 10 15 20 25Kbp
0
Kbpc© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 16 / 36
mTIM Coverage Segmentation Idea
mTiM: Read Coverage Segmentationfml
Goal: Characterize each base as intergenic, exonic, or intronic
0
10
100
annotated gene
read
cov
erag
e (lo
g-sc
ale)
5 10 15 20 25Kbp
0
Kbpc© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 16 / 36
mTIM Coverage Segmentation Approach
The mTiM Segmentation Approachfml
intergenic exonic intronic
E IS
Learn to associate a state with each position given its readcoverage and local contextHM-SVM training: Optimize transformations: signal → scoreExtension: Score spliced reads and splice sites
(G. Zeller et al., 2008; G. Zeller et al., in prep., 2009)c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 17 / 36
mTIM Coverage Segmentation Approach
The mTiM Segmentation Approachfml
0
0.2
0.4
0.6
0.8
1
5 6 7 8 9 10 11 12 13 14
read coverage
0
0.2
0.4
5 6 7 8 9 10 11 12 13 14
read coverage
0
0.5
1
feat
ure
scor
e
5 6 7 8 9 10 11 12 13 14
read coverage
intergenic exonic intronic
E IS
Learn to associate a state with each position given its readcoverage and local contextHM-SVM training: Optimize transformations: signal → scoreExtension: Score spliced reads and splice sites
(G. Zeller et al., 2008; G. Zeller et al., in prep., 2009)c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 17 / 36
mTIM Coverage Segmentation Approach
The mTiM Segmentation Approachfml
intergenic exonic intronic
E ISacc
don
0
0.2
0.4
0.6
0.8
1
5 6 7 8 9 10 11 12 13 14
read coverage
0
0.2
0.4
5 6 7 8 9 10 11 12 13 14
read coverage
0
0.5
1
feat
ure
scor
e
5 6 7 8 9 10 11 12 13 14
read coverage
Learn to associate a state with each position given its readcoverage and local contextHM-SVM training: Optimize transformations: signal → scoreExtension: Score spliced reads and splice sites
(G. Zeller et al., 2008; G. Zeller et al., in prep., 2009)c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 17 / 36
mTIM Coverage Segmentation Approach
The mTiM Segmentation Approachfml
Idea: Assume uniform read coverage within exons of same transcript
0
10
100
annotated genes
read
cov
erag
e (lo
g-sc
ale)
5 10 15 20 25Kbp
0
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 18 / 36
mTIM Coverage Segmentation Approach
The mTiM Segmentation Approachfml
Discreteexpression level
1
2
Q
. . .
intergenic exonic intronic
S
EQ
E2
E1
IQ
I2
I1
acc
don
acc
don
acc
don
. . .
. . .
. . .
Carry “expression level” information between exons of same transcript
(G. Zeller et al., 2008; G. Zeller et al., in prep., 2010)c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 19 / 36
mTIM Coverage Segmentation Approach
Discriminative training of HM-SVMsfml
f : R? → Σ?
given a sequence of hybridization measurements χ ∈ R?
predicts a state sequence (path) σ ∈ Σ?
Discriminant function Fθ : R? × Σ? → R such that fordecoding: f (χ) = argmax
σ∈S?
Fθ(χ, σ).
Training:For each training example (χ(i), σ(i)), enforce a large margin ofseparation
Fθ(χ(i), σ(i))− Fθ(χ
(i), σ) ≥ ρ
between the correct path σ(i) and any other wrong path σ 6= σ(i).
A quadratic programming problem (QP) is solved to optimize θ.[Altun et al., 2003, Ratsch et al., 2007, Zeller et al., 2008b]
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 20 / 36
mTIM Coverage Segmentation Approach
Discriminative training of HM-SVMsfml
f : R? → Σ?
given a sequence of hybridization measurements χ ∈ R?
predicts a state sequence (path) σ ∈ Σ?
Discriminant function Fθ : R? × Σ? → R such that fordecoding: f (χ) = argmax
σ∈S?
Fθ(χ, σ).
Training:For each training example (χ(i), σ(i)), enforce a large margin ofseparation
Fθ(χ(i), σ(i))− Fθ(χ
(i), σ) ≥ ρ
between the correct path σ(i) and any other wrong path σ 6= σ(i).
A quadratic programming problem (QP) is solved to optimize θ.[Altun et al., 2003, Ratsch et al., 2007, Zeller et al., 2008b]
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 20 / 36
mTIM Coverage Segmentation Approach
Discriminative training of HM-SVMsfml
f : R? → Σ?
given a sequence of hybridization measurements χ ∈ R?
predicts a state sequence (path) σ ∈ Σ?
Discriminant function Fθ : R? × Σ? → R such that fordecoding: f (χ) = argmax
σ∈S?
Fθ(χ, σ).
Training:For each training example (χ(i), σ(i)), enforce a large margin ofseparation
Fθ(χ(i), σ(i))− Fθ(χ
(i), σ) ≥ ρ
between the correct path σ(i) and any other wrong path σ 6= σ(i).
A quadratic programming problem (QP) is solved to optimize θ.[Altun et al., 2003, Ratsch et al., 2007, Zeller et al., 2008b]
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 20 / 36
mTIM Coverage Segmentation Results
Preliminary Evaluation (C. elegans)fml
CDS (precision+recall)/2
expression percentiles [%]
mTiM
10 20 30 40 50 60 70 80 90 1000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Sensitivity heavily depends on read densityc© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 21 / 36
mTIM Coverage Segmentation Results
Preliminary Evaluation (C. elegans)fml
CDS (precision+recall)/2
expression percentiles [%]
mTiM
10 20 30 40 50 60 70 80 90 1000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Sensitivity heavily depends on read densityc© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 21 / 36
Next Generation Gene Finding Idea
Computational Gene Finding Labeling the Genome fml
DNA
pre-mRNA
mRNA
Protein
polyAcap
TSS
SpliceDonor
SpliceAcceptor
SpliceDonor
SpliceAcceptor
TIS Stop
polyA/cleavage
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 22 / 36
Next Generation Gene Finding Idea
Computational Gene Finding Labeling the Genome fml
DNA
pre-mRNA
mRNA
Protein
polyAcap
TSS Donor Acceptor Donor Acceptor
TIS Stop
polyA/cleavage
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 22 / 36
Next Generation Gene Finding Idea
Computational Gene Finding Labeling the Genome fml
DNA
pre-mRNA
mRNA
Protein
polyAcap
TSS Donor Acceptor Donor Acceptor
TIS Stop
polyA/cleavage
TSS TIS cleaveStop
Don Acc
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 22 / 36
Next Generation Gene Finding Idea
mGene-based Transcript Predictionfml
acc
don
tss
tis
stop
True gene model 2 3 4 5
STEP 1: SVM Signal Predictions
genomic position
genomic position
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 23 / 36
Next Generation Gene Finding Idea
mGene-based Transcript Predictionfml
acc
don
tss
tis
stop
True gene model 2 3 4 5
F(x,y)
transform features
STEP 1: SVM Signal Predictions
STEP 2: Integration
genomic position
genomic position
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 23 / 36
Next Generation Gene Finding Idea
mGene-based Transcript Predictionfml
acc
don
tss
tis
stop
True gene model 2 3 4 5
Wrong gene model
large margin
F(x,y)
transform features
STEP 1: SVM Signal Predictions
STEP 2: Integration
genomic position
genomic position
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 23 / 36
Next Generation Gene Finding Modeling Uncertainty
Learning to use Expression Measurementsfml
Two approaches:
Heuristic to incorporate ESTs/reads/tiling array measurementsto refine predictions
Directly use evidence during learning to learn to appropriatelyweight its importance
Exon Level Transcript LevelSN SP F SN SP F
ab initio 82.3 82.6 82.5 43.1 49.5 46.1ESTs heuristic 85.3 84.7 85.0 49.5 56.4 52.7ESTs trained 84.8 85.8 85.3 50.5 57.8 53.9
Gene prediction in C. elegans (CDS evaluation)
Behr et al., in pre., 2010
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 24 / 36
Next Generation Gene Finding Modeling Uncertainty
mGene-based Transcript Predictionfml
acc
don
tss
tis
stop
True gene model 2 3 4 5
Wrong gene model
large margin
F(x,y)
transform features
STEP 1: SVM Signal Predictions
STEP 2: Integration
genomic position
genomic position
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 25 / 36
Next Generation Gene Finding Modeling Uncertainty
mGene-based Transcript Predictionfml
acc
don
tss
tis
stop
position
True gene model 2 3 4 5
Wrong gene model
large margin
score
transform SVM outputs with PLiF
STEP 1: SVM Signal Predictions
STEP 2: Integration
Expression Evidence
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 25 / 36
Next Generation Gene Finding Modeling Uncertainty
Learning to use Expression Measurementsfml
Two approaches:
Heuristic to incorporate ESTs/reads/tiling array measurementsto refine predictions
Directly use evidence during learning to learn to appropriatelyweight its importance
Exon Level Transcript LevelSN SP F SN SP F
ab initio 82.3 82.6 82.5 43.1 49.5 46.1ESTs heuristic 85.3 84.7 85.0 49.5 56.4 52.7ESTs trained 84.8 85.8 85.3 50.5 57.8 53.9RNA-Seq trained 84.6 84.9 84.8 49.1 55.2 52.0RNA-Seq/ESTs trained 84.7 86.9 85.8 50.3 60.5 54.9
Gene prediction in C. elegans (CDS evaluation)
Behr et al., in prep., 2010
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 26 / 36
Next Generation Gene Finding Results
Preliminary Evaluation (C. elegans)fml
CDS (precision+recall)/2
expression percentiles [%]
mGene ab initiomGene.ngs
10 20 30 40 50 60 70 80 90 1000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 27 / 36
Next Generation Gene Finding Results
Preliminary Evaluation (C. elegans)fml
CDS (precision+recall)/2
expression percentiles [%]
mTiMmGene ab initiomGene.ngs
10 20 30 40 50 60 70 80 90 1000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 27 / 36
Next Generation Gene Finding Alternative Transcripts
Digestionfml
mTiM and mGene.ngs predict single transcripts
mTiM exploits “uniformity” of read coverage among exons ofsame transcript
mGene.ngs uses more assumptions on structure of transcripts
Alt. Transcripts: Spliced reads for splicing graph completion:
Paths through splicing graph define alternative transcripts
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 28 / 36
Next Generation Gene Finding Alternative Transcripts
Digestionfml
mTiM and mGene.ngs predict single transcripts
mTiM exploits “uniformity” of read coverage among exons ofsame transcript
mGene.ngs uses more assumptions on structure of transcripts
Alt. Transcripts: Spliced reads for splicing graph completion:
Paths through splicing graph define alternative transcripts
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 28 / 36
Next Generation Gene Finding Alternative Transcripts
Digestionfml
mTiM and mGene.ngs predict single transcripts
mTiM exploits “uniformity” of read coverage among exons ofsame transcript
mGene.ngs uses more assumptions on structure of transcripts
Alt. Transcripts: Spliced reads for splicing graph completion:
Transcript prediction(result of mGene/mTIM)
Spliced reads (result of QPALMA)
Paths through splicing graph define alternative transcripts
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 28 / 36
Next Generation Gene Finding Alternative Transcripts
Digestionfml
mTiM and mGene.ngs predict single transcripts
mTiM exploits “uniformity” of read coverage among exons ofsame transcript
mGene.ngs uses more assumptions on structure of transcripts
Alt. Transcripts: Spliced reads for splicing graph completion:
Transcript prediction(result of mGene/mTIM)
Spliced reads (result of QPALMA)
Infered edges
Paths through splicing graph define alternative transcripts
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 28 / 36
Next Generation Gene Finding Alternative Transcripts
Digestionfml
mTiM and mGene.ngs predict single transcripts
mTiM exploits “uniformity” of read coverage among exons ofsame transcript
mGene.ngs uses more assumptions on structure of transcripts
Alt. Transcripts: Spliced reads for splicing graph completion:
Transcript prediction(result of mGene/mTIM)
Spliced reads (result of QPALMA)
Infered edges
Paths through splicing graph define alternative transcripts
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 28 / 36
Transcript Quantitation with rQuant
RNA-Seq Pipeline Overviewfml
PALMapper
mGene.ngs
mTim Segmentation
rQuant
Short Reads Transcripts w/ QuantitationAlignm
ents
Transcripts
Read alignment Transcript finding Quantitation
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 29 / 36
Transcript Quantitation with rQuant Biases
RNA-Seq Biases and Quantitationfml
Sequencer short sequence reads
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
mRNA
fragmentation
sequence library
RT
fragmen-tation
RT
exonic reads
genome
transcripts
junction reads
aligned reads
Biases due to . . .
cDNA library construction
Sequencing
Read mapping
relative transcript position 5’ -> 3’
read
cov
erag
e 0
20
40
60
80
(average over annotated
transcripts of length ≈1kb for the
C. elegans SRX001872 dataset)c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 30 / 36
Transcript Quantitation with rQuant Biases
RNA-Seq Biases and Quantitationfml
Sequencer short sequence reads
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
mRNA
fragmentation
sequence library
RT
fragmen-tation
RT
exonic reads
genome
transcripts
junction reads
aligned reads
Biases due to . . .
cDNA library construction
Sequencing
Read mapping
relative transcript position 5’ -> 3’
read
cov
erag
e 0
20
40
60
80
(average over annotated
transcripts of length ≈1kb for the
C. elegans SRX001872 dataset)c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 30 / 36
Transcript Quantitation with rQuant Approach
rQuant – Basic Ideafml
A
read
cov
erag
e
Short transcript
relative transcript position 5’ -> 3’
Mi = wAAi + wBBi ⇒ minwA,wB
∑i ` (Mi , Ri)
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 31 / 36
Transcript Quantitation with rQuant Approach
rQuant – Basic Ideafml
A
B
read
cov
erag
e
Short transcript
relative transcript position 5’ -> 3’
read
cov
erag
e
Long transcript
relative transcript position 5’ -> 3’
Mi = wAAi + wBBi ⇒ minwA,wB
∑i ` (Mi , Ri)
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 31 / 36
Transcript Quantitation with rQuant Approach
rQuant – Basic Ideafml
read
cov
erag
e
genome position 5’ -> 3’
Short transcript
read
cov
erag
e
genome position 5’ -> 3’
Long transcript
A
B
Mi = wAAi + wBBi ⇒ minwA,wB
∑i ` (Mi , Ri)
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 31 / 36
Transcript Quantitation with rQuant Approach
rQuant – Basic Ideafml
read
cov
erag
e
genome position 5’ -> 3’
Mixture of transcripts
read
cov
erag
e
genome position 5’ -> 3’
Short transcript
read
cov
erag
e
genome position 5’ -> 3’
Long transcript
MA
B
Mi = wAAi + wBBi ⇒ minwA,wB
∑i ` (Mi , Ri)
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 31 / 36
Transcript Quantitation with rQuant Approach
rQuant – Basic Ideafml
read
cov
erag
e
genome position 5’ -> 3’
Mixture of transcripts
expected
observed
read
cov
erag
e
genome position 5’ -> 3’
Short transcript
read
cov
erag
e
genome position 5’ -> 3’
Long transcript
MA
B R
Mi = wAAi + wBBi ⇒ minwA,wB
∑i ` (Mi , Ri)
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 31 / 36
Transcript Quantitation with rQuant Approach
rQuant – Iterative Algorithmfml
1 Optimise transcript weights: minw
∑i `
(∑t w (t)p
(t)i , Ri
)2 Optimise profile weights: minp
∑i `
(∑t w (t)p
(t)i , Ri
)3 Repeat 1. and 2. until convergence.
gene AT1G01240chromosome 1, forward strand
99,960 100,368 100,776 101,184 101,592
70
60
50
40
30
20
10
0
read
cou
nts
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 32 / 36
Transcript Quantitation with rQuant Approach
rQuant – Iterative Algorithmfml
1 Optimise transcript weights: minw
∑i `
(∑t w (t)p
(t)i , Ri
)2 Optimise profile weights: minp
∑i `
(∑t w (t)p
(t)i , Ri
)3 Repeat 1. and 2. until convergence.
gene AT1G01240chromosome 1, forward strand
AT1G01240.1 +110 1,178
625
99,960 100,368 100,776 101,184 101,592
27.20
70
60
50
40
30
20
10
0
read
cou
nts
27.20
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 32 / 36
Transcript Quantitation with rQuant Approach
rQuant – Iterative Algorithmfml
1 Optimise transcript weights: minw
∑i `
(∑t w (t)p
(t)i , Ri
)2 Optimise profile weights: minp
∑i `
(∑t w (t)p
(t)i , Ri
)3 Repeat 1. and 2. until convergence.
gene AT1G01240chromosome 1, forward strand
AT1G01240.1 +110 1,178
625
AT1G01240.2 +190 1,178
436
99,960 100,368 100,776 101,184 101,592
4.80
27.20
4.80
70
60
50
40
30
20
10
0
read
cou
nts
27.20
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 32 / 36
Transcript Quantitation with rQuant Approach
rQuant – Iterative Algorithmfml
1 Optimise transcript weights: minw
∑i `
(∑t w (t)p
(t)i , Ri
)2 Optimise profile weights: minp
∑i `
(∑t w (t)p
(t)i , Ri
)3 Repeat 1. and 2. until convergence.
gene AT1G01240chromosome 1, forward strand
AT1G01240.1 +110 1,178
625
AT1G01240.2 +190 1,178
436
AT1G01240.3 +1,941
99,960 100,368 100,776 101,184 101,592
6.00
4.80
27.20
6.004.80
70
60
50
40
30
20
10
0
read
cou
nts
27.20
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 32 / 36
Transcript Quantitation with rQuant Approach
rQuant – Iterative Algorithmfml
1 Optimise transcript weights: minw
∑i `
(∑t w (t)p
(t)i , Ri
)2 Optimise profile weights: minp
∑i `
(∑t w (t)p
(t)i , Ri
)3 Repeat 1. and 2. until convergence.
gene AT1G01240chromosome 1, forward strand
AT1G01240.1 +110 1,178
625
AT1G01240.2 +190 1,178
436
AT1G01240.3 +1,941
99,960 100,368 100,776 101,184 101,592
6.00
4.80
27.20
6.004.80
70
60
50
40
30
20
10
0
read
cou
nts
27.20
38.00
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 32 / 36
Transcript Quantitation with rQuant Approach
rQuant – Iterative Algorithmfml
1 Optimise transcript weights: minw
∑i `
(∑t w (t)p
(t)i , Ri
)2 Optimise profile weights: minp
∑i `
(∑t w (t)p
(t)i , Ri
)3 Repeat 1. and 2. until convergence.
distance to transcript start/end
pro!
le w
eigh
t
0 50 200 450 800 1250 1800 2450 "2450 "1800 "1250 "800 "450 "200 "50 0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
gene AT1G01240chromosome 1, forward strand
AT1G01240.1 +110 1,178
625
AT1G01240.2 +190 1,178
436
AT1G01240.3 +1,941
99,960 100,368 100,776 101,184 101,592
6.00
4.80
27.20
6.004.80
70
60
50
40
30
20
10
0
read
cou
nts
27.20
38.00
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 32 / 36
Transcript Quantitation with rQuant Approach
rQuant – Iterative Algorithmfml
1 Optimise transcript weights: minw
∑i `
(∑t w (t)p
(t)i , Ri
)2 Optimise profile weights: minp
∑i `
(∑t w (t)p
(t)i , Ri
)3 Repeat 1. and 2. until convergence.
distance to transcript start/end
pro!
le w
eigh
t
0 50 200 450 800 1250 1800 2450 "2450 "1800 "1250 "800 "450 "200 "50 0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
gene AT1G01240chromosome 1, forward strand
AT1G01240.1 +110 1,178
625
AT1G01240.2 +190 1,178
436
AT1G01240.3 +1,941
99,960 100,368 100,776 101,184 101,592
4.95
8.35
23.99
70
60
50
40
30
20
10
0
read
cou
nts
8.354.95
23.99
37.29
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 32 / 36
Transcript Quantitation with rQuant Results
rQuant Evaluation Ifml
rQuant: Position-wise with profiles (estimating library and mapping bias)
compared to
Position-wise, without profiles
Segment-wise, without profiles (e.g., Jiang and Wong [2009] )
Segment-wise, with profiles (e.g. Flux Capacitor [Sammeth, 2009a])
Estimate transcript abundances
Using simulated data for A. thaliana (Flux Simulator [Sammeth, 2009b])
Subset of alternatively spliced genes
Evaluation: Spearman correlation between
Simulated RNA expression level and
Predicted transcript weights
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 33 / 36
Transcript Quantitation with rQuant Results
rQuant Evaluation Ifml
rQuant: Position-wise with profiles (estimating library and mapping bias)
compared to
Position-wise, without profiles
Segment-wise, without profiles (e.g., Jiang and Wong [2009] )
Segment-wise, with profiles (e.g. Flux Capacitor [Sammeth, 2009a])
Estimate transcript abundances
Using simulated data for A. thaliana (Flux Simulator [Sammeth, 2009b])
Subset of alternatively spliced genes
Evaluation: Spearman correlation between
Simulated RNA expression level and
Predicted transcript weights
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 33 / 36
Transcript Quantitation with rQuant Results
rQuant Evaluation Ifml
rQuant: Position-wise with profiles (estimating library and mapping bias)
compared to
Position-wise, without profiles
Segment-wise, without profiles (e.g., Jiang and Wong [2009] )
Segment-wise, with profiles (e.g. Flux Capacitor [Sammeth, 2009a])
Estimate transcript abundances
Using simulated data for A. thaliana (Flux Simulator [Sammeth, 2009b])
Subset of alternatively spliced genes
Evaluation: Spearman correlation between
Simulated RNA expression level and
Predicted transcript weights
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 33 / 36
Transcript Quantitation with rQuant Results
rQuant Evaluation Ifml
rQuant: Position-wise with profiles (estimating library and mapping bias)
compared to
Position-wise, without profiles
Segment-wise, without profiles (e.g., Jiang and Wong [2009] )
Segment-wise, with profiles (e.g. Flux Capacitor [Sammeth, 2009a])
Estimate transcript abundances
Using simulated data for A. thaliana (Flux Simulator [Sammeth, 2009b])
Subset of alternatively spliced genes
Evaluation: Spearman correlation between
Simulated RNA expression level and
Predicted transcript weights
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 33 / 36
Transcript Quantitation with rQuant Results
rQuant Evaluation Ifml
rQuant: Position-wise with profiles (estimating library and mapping bias)
compared to
Position-wise, without profiles
Segment-wise, without profiles (e.g., Jiang and Wong [2009] )
Segment-wise, with profiles (e.g. Flux Capacitor [Sammeth, 2009a])
Estimate transcript abundances
Using simulated data for A. thaliana (Flux Simulator [Sammeth, 2009b])
Subset of alternatively spliced genes
Evaluation: Spearman correlation between
Simulated RNA expression level and
Predicted transcript weights
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 33 / 36
Transcript Quantitation with rQuant Results
rQuant Evaluation Ifml
rQuant: Position-wise with profiles (estimating library and mapping bias)
compared to
Position-wise, without profiles
Segment-wise, without profiles (e.g., Jiang and Wong [2009] )
Segment-wise, with profiles (e.g. Flux Capacitor [Sammeth, 2009a])
Estimate transcript abundances
Using simulated data for A. thaliana (Flux Simulator [Sammeth, 2009b])
Subset of alternatively spliced genes
Evaluation: Spearman correlation between
Simulated RNA expression level and
Predicted transcript weights
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 33 / 36
Transcript Quantitation with rQuant Results
rQuant Evaluation IIfml
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Spea
rman
Cor
rela
tion
Across transcripts
Within genes (mean)
Segment-wise, w/o pro!les
(Bohnert et al., submitted, 2010)
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 34 / 36
Transcript Quantitation with rQuant Results
rQuant Evaluation IIfml
Position-wise, w/o pro!les
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Spea
rman
Cor
rela
tion
Across transcripts
Within genes (mean)
Segment-wise, w/o pro!les
Segment-wise, w/ pro!les
(Bohnert et al., submitted, 2010)
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 34 / 36
Transcript Quantitation with rQuant Results
rQuant Evaluation IIfml
rQuantPosition-wise,
w/ pro!lesPosition-wise, w/o pro!les
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Spea
rman
Cor
rela
tion
Across transcripts
Within genes (mean)
Segment-wise, w/o pro!les
Segment-wise, w/ pro!les
(Bohnert et al., submitted, 2010)
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 34 / 36
Transcript Quantitation with rQuant Results
Galaxy-based Web Services for NGS Analysesfml
Galaxy-based web service http://galaxy.fml.mpg.de
PALMapper http://fml.mpg.de/raetsch/suppl/palmapper
mGene http://mgene.org/web
mTIM http://fml.mpg.de/raetsch/suppl/mtim (in prep.)
rQuant http://fml.mpg.de/raetsch/suppl/rquant/web
(Ratsch et al., in preparation, 2010)
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 35 / 36
Summary
Summaryfml
PALMapperSplice site predictions improve alignment performanceOutperforms many other read mappers in intron accuracy
mTiMHigh specificity, sensitivity depends on read coverageBetter for identifying transcripts specific to experimental data
mGeneHigh sensitivity (also for lowly expressed genes)Identifies also non-expressed genes ⇒ good for annotation
rQuantModels library prep., sequencing, alignment biasesAccurately quantifies transcripts
Galaxy instanceEasy use of these tools
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 36 / 36
Summary
Summaryfml
PALMapperSplice site predictions improve alignment performanceOutperforms many other read mappers in intron accuracy
mTiMHigh specificity, sensitivity depends on read coverageBetter for identifying transcripts specific to experimental data
mGeneHigh sensitivity (also for lowly expressed genes)Identifies also non-expressed genes ⇒ good for annotation
rQuantModels library prep., sequencing, alignment biasesAccurately quantifies transcripts
Galaxy instanceEasy use of these tools
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 36 / 36
Summary
Summaryfml
PALMapperSplice site predictions improve alignment performanceOutperforms many other read mappers in intron accuracy
mTiMHigh specificity, sensitivity depends on read coverageBetter for identifying transcripts specific to experimental data
mGeneHigh sensitivity (also for lowly expressed genes)Identifies also non-expressed genes ⇒ good for annotation
rQuantModels library prep., sequencing, alignment biasesAccurately quantifies transcripts
Galaxy instanceEasy use of these tools
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 36 / 36
Summary
Summaryfml
PALMapperSplice site predictions improve alignment performanceOutperforms many other read mappers in intron accuracy
mTiMHigh specificity, sensitivity depends on read coverageBetter for identifying transcripts specific to experimental data
mGeneHigh sensitivity (also for lowly expressed genes)Identifies also non-expressed genes ⇒ good for annotation
rQuantModels library prep., sequencing, alignment biasesAccurately quantifies transcripts
Galaxy instanceEasy use of these tools
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 36 / 36
Summary
Summaryfml
PALMapperSplice site predictions improve alignment performanceOutperforms many other read mappers in intron accuracy
mTiMHigh specificity, sensitivity depends on read coverageBetter for identifying transcripts specific to experimental data
mGeneHigh sensitivity (also for lowly expressed genes)Identifies also non-expressed genes ⇒ good for annotation
rQuantModels library prep., sequencing, alignment biasesAccurately quantifies transcripts
Galaxy instanceEasy use of these tools
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 36 / 36
Summary
Acknowledgementsfml
Fabio De Bona
Alignments
Jonas Behr
Gene finding
Georg Zeller
Segmentation
Regina Bohnert
Quantitation
RGASP Team
Jonas Behr (FML)
Georg Zeller (FML & MPI)
Regina Bohnert (FML)
Funding by DFG &Max Planck Society.
Thank you for your attention!
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 37 / 36
Summary
Acknowledgementsfml
Fabio De Bona
Alignments
Jonas Behr
Gene finding
Georg Zeller
Segmentation
Regina Bohnert
Quantitation
RGASP Team
Jonas Behr (FML)
Georg Zeller (FML & MPI)
Regina Bohnert (FML)
Funding by DFG &Max Planck Society.
Thank you for your attention!
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 37 / 36
Summary
References I
Y. Altun, I. Tsochantaridis, and T. Hofmann. Hidden Markov Support Vector Machines. InProc. 20th Int. Conf. Mach. Learn., pages 3–10, 2003.
J. Behr, G. Schweikert, J. Cao, F. De Bona, G. Zeller, S. Laubinger, S. Ossowski,K. Schneeberger, D. Weigel, and G. Ratsch. Rna-seq and tiling arrays for improved genefinding. URL http:
//www.fml.tuebingen.mpg.de/raetsch/lectures/RaetschGenomeInformatics08.pdf.Oral presentation at the CSHL Genome Informatics Meeting, September 2008.
RM Clark, G Schweikert, C Toomajian, S Ossowski, G Zeller, P Shinn, N Warthmann, TT Hu,G Fu, DA Hinds, H Chen, KA Frazer, DH Huson, B Scholkopf, M Nordborg, G Ratsch,JR Ecker, and D Weigel. Common sequence polymorphisms shaping genetic diversity inarabidopsis thaliana. Science, 317(5836):338–342, 2007. ISSN 1095-9203 (Electronic). doi:10.1126/science.1138632.
F. De Bona, S. Ossowski, K. Schneeberger, and G. Ratsch. Qpalma: Optimal splicedalignments of short sequence reads. Bioinformatics, 24:i174–i180, 2008.
Hui Jiang and Wing Hung Wong. Statistical inferences for isoform expression in RNA-Seq.Bioinformatics, 25(8):1026–1032, April 2009.
G. Ratsch and S. Sonnenburg. Accurate splice site detection for Caenorhabditis elegans. InK. Tsuda B. Schoelkopf and J.-P. Vert, editors, Kernel Methods in Computational Biology.MIT Press, 2004.
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 38 / 36
Summary
References II
G. Ratsch, S. Sonnenburg, and B. Scholkopf. RASE: recognition of alternatively spliced exonsin C. elegans. Bioinformatics, 21(Suppl. 1):i369–i377, June 2005.
G. Ratsch, S. Sonnenburg, J. Srinivasan, H. Witte, K.-R. Muller, R. Sommer, and B. Schlkopf.Improving the c. elegans genome annotation using machine learning. PLoS ComputationalBiology, 3(2):e20, 2007. URL http://dx.doi.org/10.1371/journal.pcbi.0030020.eor.
M. Sammeth. The Flux Capacitor. Website, 2009a. http://flux.sammeth.net/capacitor.html.
M. Sammeth. The Flux Simulator. Website, 2009b. http://flux.sammeth.net/simulator.html.
Korbinian Schneeberger, Jorg Hagmann, Stephan Ossowski, Norman Warthmann, SandraGesing, Oliver Kohlbacher, and Detlef Weigel. Simultaneous alignment of short readsagainst multiple genomes. Genome Biol, 10(9):R98, Jan 2009. doi:10.1186/gb-2009-10-9-r98. URL http://genomebiology.com/2009/10/9/R98.
Gabriele Schweikert, Alexander Zien, Georg Zeller, Jonas Behr, Christoph Dieterich,Cheng Soon Ong, Petra Philips, Fabio De Bona, Lisa Hartmann, Anja Bohlen, Nina Kruger,Soren Sonnenburg, and Gunnar Ratsch. mgene: Accurate svm-based gene finding with anapplication to nematode genomes. Genome Research, 2009. URLhttp://genome.cshlp.org/content/early/2009/06/29/gr.090597.108.full.pdf+html.Advance access June 29, 2009.
S. Sonnenburg, G. Ratsch, A. Jagota, and K.-R. Muller. New methods for splice-siterecognition. In Proc. International Conference on Artificial Neural Networks, 2002.
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 39 / 36
Summary
References III
Soren Sonnenburg, Alexander Zien, and Gunnar Ratsch. ARTS: Accurate Recognition ofTranscription Starts in Human. Bioinformatics, 22(14):e472–480, 2006.
G Zeller, RM Clark, K Schneeberger, A Bohlen, D Weigel, and G Ratsch. Detectingpolymorphic regions in arabidopsis thaliana with resequencing microarrays. Genome Res, 18(6):918–929, 2008a. ISSN 1088-9051 (Print). doi: 10.1101/gr.070169.107.
G. Zeller, S. Henz, S. Laubinger, D. Weigel, and G. Ratsch. Transcript normalization andsegmentation of tiling array data. In Proc. PSB 2008. World Scientific, 2008b.
A. Zien, G. Ratsch, S. Mika, B. Scholkopf, T. Lengauer, and K.-R. Muller. Engineering SupportVector Machine Kernels That Recognize Translation Initiation Sites. BioInformatics, 16(9):799–807, September 2000.
c© Gunnar Ratsch (FML, Tubingen) Methods for Transcriptome Analysis NGS Bioinformatics, Paris 40 / 36