Next Generation Genome Annotation with mgene.ngs
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1 Next Generation Genome Annotation with mgene.ngs Jonas Behr, 1 Regina Bohnert, 1 Georg Zeller, 1,2 Gabriele Schweikert, 1,2,3 Lisa Hartmann, 1 and Gunnar Rätsch 1 1 Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany 2 Max Planck Institute for Developmental Biology, Tübingen, Germany 3 Max Planck Institute for Biological Cybernetics, Tübingen, Germany Friedrich Miescher Laboratory of the Max Planck Society ISCB-SC, July 9, 2010
2 Introduction What is mgene.ngs doing? Task: Identification of protein coding genes Why is this Task important: Large number of newly sequenced genomes Automated annotation highly important Still not solved: Ab initio gene prediction: C. elegans 50% Coghlan et al. [2008] H. sapiens 20% Guigó et al. [2006] What can be done? Exploit Next Generation mrna sequencing (RNA-seq) data c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
3 Introduction What is mgene.ngs doing? Task: Identification of protein coding genes Why is this Task important: Large number of newly sequenced genomes Automated annotation highly important Still not solved: Ab initio gene prediction: C. elegans 50% Coghlan et al. [2008] H. sapiens 20% Guigó et al. [2006] What can be done? Exploit Next Generation mrna sequencing (RNA-seq) data c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
4 Introduction What is mgene.ngs doing? Task: Identification of protein coding genes Why is this Task important: Large number of newly sequenced genomes Automated annotation highly important Still not solved: Ab initio gene prediction: C. elegans 50% Coghlan et al. [2008] H. sapiens 20% Guigó et al. [2006] What can be done? Exploit Next Generation mrna sequencing (RNA-seq) data c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
5 Introduction What is mgene.ngs doing? Task: Identification of protein coding genes Why is this Task important: Large number of newly sequenced genomes Automated annotation highly important Still not solved: Ab initio gene prediction: C. elegans 50% Coghlan et al. [2008] H. sapiens 20% Guigó et al. [2006] What can be done? Exploit Next Generation mrna sequencing (RNA-seq) data c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
6 RNA-seq Deep RNA Sequencing (RNA-seq) RNA-seq allows... pre-mrna exon intron High-throughput transcriptome measurements Qualitative studies Quantitative studies at high resolution mrna short reads junction reads reference genome Figure adapted from Wikipedia c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
7 RNA-seq Deep RNA Sequencing (RNA-seq) RNA-seq allows... pre-mrna exon intron High-throughput transcriptome measurements Qualitative studies Quantitative studies at high resolution mrna short reads junction reads reference genome Figure adapted from Wikipedia c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
8 RNA-seq Deep RNA Sequencing (RNA-seq) RNA-seq allows... pre-mrna exon intron High-throughput transcriptome measurements Qualitative studies Quantitative studies at high resolution mrna short reads junction reads reference genome Figure adapted from Wikipedia c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
9 RNA-seq RNA-seq read coverage Annotated genes sorted by expression level low high 0% 100%!" &! &" %! %" $! $" #! #"! "!! c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
10 RNA-seq RNA-seq read coverage Annotated genes sorted by expression level low high 0% 100%!" &!!" &! &" %! %" $! $" #! #"! &" %! %" $! $" #! #"! "!! "!! c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
11 RNA-seq RNA-seq read coverage Annotated genes sorted by expression level low high 0% 100%!" &!!" &! &" %! %" $! $" #! #"! "!!!" &! &" %! %" $! $" #! #"! "!! &" %! %" $! $" #! #"! "!! c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
12 mgene.ngs strategies Strategy of mgene.ngs Integrate RNA-seq and genomic sequence information Hidden semi Markov Support Vector Machines (HsM-SVMs) Learn to trade off sources of information during the training Address uncertainty in both types of data Adapt to error rates of different RNA-seq protocols Model long range dependencies e.g ORF constraint c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
13 mgene.ngs strategies Strategy of mgene.ngs Integrate RNA-seq and genomic sequence information Hidden semi Markov Support Vector Machines (HsM-SVMs) Learn to trade off sources of information during the training Address uncertainty in both types of data Adapt to error rates of different RNA-seq protocols Model long range dependencies e.g ORF constraint c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
14 mgene.ngs strategies Strategy of mgene.ngs Integrate RNA-seq and genomic sequence information Hidden semi Markov Support Vector Machines (HsM-SVMs) Learn to trade off sources of information during the training Address uncertainty in both types of data Adapt to error rates of different RNA-seq protocols Model long range dependencies e.g ORF constraint c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
15 mgene.ngs strategies Strategy of mgene.ngs Integrate RNA-seq and genomic sequence information Hidden semi Markov Support Vector Machines (HsM-SVMs) Learn to trade off sources of information during the training Address uncertainty in both types of data Adapt to error rates of different RNA-seq protocols Model long range dependencies e.g ORF constraint c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
16 mgene.ngs strategies Strategy of mgene.ngs Integrate RNA-seq and genomic sequence information Hidden semi Markov Support Vector Machines (HsM-SVMs) Learn to trade off sources of information during the training Address uncertainty in both types of data Adapt to error rates of different RNA-seq protocols Model long range dependencies e.g ORF constraint c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
17 HsM-SVM mgene.ngs strategies genomic position True gene model STEP 1: SVM Signal Predictions tss tis acc don stop Score genomic position c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
18 HsM-SVM mgene.ngs strategies genomic position True gene model STEP 1: SVM Signal Predictions tss tis acc don stop Score genomic position c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
19 HsM-SVM mgene.ngs strategies genomic position True gene model STEP 1: SVM Signal Predictions tss tis acc don stop RNA-seq coverage Score genomic position c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
20 HsM-SVM mgene.ngs strategies genomic position True gene model STEP 1: SVM Signal Predictions tss tis acc don stop RNA-seq coverage Score genomic position c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
21 mgene.ngs strategies Label generation from RNA-seq data Input Tools Output RNA-seq label generation gene structures (high expressed genes) RNA-seq read alignments c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
22 mgene.ngs strategies Label generation from RNA-seq data Input Tools Output RNA-seq label generation gene structures (high expressed genes) RNA-seq read alignments mgene.ngs training trained gene predictor genomic DNA sequence c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
23 mgene.ngs strategies Label generation from RNA-seq data Input Tools Output RNA-seq label generation gene structures (high expressed genes) RNA-seq read alignments mgene.ngs training trained gene predictor genomic DNA sequence mgene.ngs prediction gene structures c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
24 Results on C. elegans Results 0.7 mgene.ngs only sequence F score expression percentile c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
25 Results on C. elegans Results 0.7 mgene.ngs only sequence mgene.ngs no subsampling F score expression percentile c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
26 Results on C. elegans Results 0.7 mgene.ngs only sequence mgene.ngs no subsampling mgene.ngs subsampling F score expression percentile c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
27 Results on C. elegans Results 0.7 mgene.ngs only sequence mgene.ngs no subsampling mgene.ngs subsampling mgene.ngs using annotation F score expression percentile c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
28 Results on C. elegans Results mgene.ngs only sequence mgene.ngs no subsampling mgene.ngs subsampling mgene.ngs using annotation cufflinks Trapnell et al F score expression percentile c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
29 Results Conclusion Transcript identification only from RNA-seq very difficult mgene.ngs Highly accurate in a large range of expression levels Can infer alternative isoforms Not relying on previous genome annotation Can benefit from genome annotation if available Scales to mammalian sized genomes c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
30 Results Further information and the slides of this talk at: Acknowledgments Quantification: Regina Bohnert Library preparation: Gene finding: Alignments: Programming: Shogun: Discussions: Supervision: Lisa Hartmann, Lisa Smith Georg Zeller, Gabriele Schweikert, Gunnar Rätsch Andre Kahles, Geraldine Jean, Gunnar Rätsch Vipin T Sreedharan Sören Sonnenburg Philipp Drewe, Sebastian Schultheiss, Christian Widmer Gunnar Rätsch c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
31 Results Infer alternative isoforms mgene.ngs prediction Build splicegraph using spliced reads Generate transcripts from graph rquant: explain coverage by weighted sum of transcripts c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
32 Results Infer alternative isoforms mgene.ngs prediction Build splicegraph using spliced reads Generate transcripts from graph rquant: explain coverage by weighted sum of transcripts c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
33 Results Infer alternative isoforms mgene.ngs prediction Build splicegraph using spliced reads Generate transcripts from graph rquant: explain coverage by weighted sum of transcripts c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
34 Results Infer alternative isoforms mgene.ngs prediction Build splicegraph using spliced reads Generate transcripts from graph 30% 25% 45% 0% rquant: explain coverage by weighted sum of transcripts c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
35 References I Results A. Coghlan, T.J. Fiedler, S.J. McKay, P. Flicek, T.W. Harris, D. Blasiar, The ngasp Consortium, and L.D. Stein. ngasp: the nematode genome annotation assessment project. BMC Bioinformatics, R. Guigó, J.F. Flicek, P. Abril, A. Reymond, J. Lagarde, F. Denoeud, S. Antonarakis, M. Ashburner, V.B. Bajic, E. Birney, R. Castelo, E. Eyras, C. Ucla, T.R. Gingeras, J. Harrow, T. Hubbard, S.E. Lewis, and M.G. Reese. EGASP: The human ENCODE genome annotation assessment project. Genome Biology, 7(S2), c Jonas Behr Next Generation Genome Annotation ISCB-SC, July 9, / 11
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