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Background & Motivation Problem & Feature Construction Experiments Design & Results Conclusions and Future Work Exploring Alternative Splicing Features.

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Presentation on theme: "Background & Motivation Problem & Feature Construction Experiments Design & Results Conclusions and Future Work Exploring Alternative Splicing Features."— Presentation transcript:

1 Background & Motivation Problem & Feature Construction Experiments Design & Results Conclusions and Future Work Exploring Alternative Splicing Features using Support Vector Machines Jing Xia 1, Doina Caragea 1, Susan J. Brown 2 1 Computing and Information Sciences Kansas State University, USA 2 Bioinformatics Center Kansas State University, USA Jan 16 2008

2 Background & Motivation Problem & Feature Construction Experiments Design & Results Conclusions and Future Work Outline 1 Background & Motivation 2 Problem & Feature Construction Problem Definition Data Set Feature Construction 3 Experiments Design & Results Experimental Design Experimental Results 4 Conclusions and Future Work Conclusion

3 Background & Motivation Problem & Feature Construction Experiments Design & Results Conclusions and Future Work Alternative Splicing exonintronexonintronexon DNA 5’UTRGTAGGTAG3’UTR Splicing: important step Trasncription TSS ATG during gene expression exonintron exon intron exon pre−mNRA cap Variable splicing process 5’UTRGU AGGT AG 3’UTR (Alternative splicing) one Splicing AUG gene -> many proteins mRNA Translation protein Genes expression: genes to pro- teins

4 Background & Motivation Problem & Feature Construction Experiments Design & Results Conclusions and Future Work Alternative Splicing pre−mRNA Gene Splicing: important step during gene expression Alternative Splicing Variable splicing process (Alternative splicing) one transcript isoforms gene -> many proteins Proteins One genes to many proteins

5 Background & Motivation Problem & Feature Construction Experiments Design & Results Conclusions and Future Work Patterns of Alternative Splicing Constitutively Spliced Exon (CSE) Alternatively Spliced Exon (ASE) Exon skipping (most CSE ASE frequent) exon1exon2exon3exon4 Alternative 5’ splice sites Alternative 3’ splice sites Intron retention Mutually exclusive Here, focus on predicting alternatively spliced exons (ASE) and constitutively spliced exons (CSE) based on SVM

6 Alternative splicing Wet lab experiments finding AS is time Traditionally, align EST to genome alignments (limited to amount of EST available to the genome) Problem & Feature Construction Experiments Design & Results Conclusions and Future Work Background & Motivation

7 Problem & Feature Construction Experiments Design & Results Conclusions and Future Work Identifying Alternative Splicing in genome Transcripts Alternative splicing Wet lab experiments finding AS is time genomic DNA consuming Traditionally, align EST to genome alignments (limited to amount of EST available to the genome) Alternative 3’ Exon Exon Skipping

8 Background & Motivation Problem & Feature Construction Experiments Design & Results Conclusions and Future Work Identifying Alternative Splicing in genome Alternative splicing Wet lab experiments finding AS is time consuming Traditionally, align EST to genome alignments (limited to amount of EST available to the genome) Use machine learning algorithms that to predict AS at the genome level

9 Background & Motivation Problem Definition Problem & Feature Construction Data Set Experiments Design & Results Feature Construction Conclusions and Future Work Problem Definition Problem Definition: given an exon, can we predict it as alternatively spliced exons (ASE) or constitutively spliced exons (CSE)? Constitutively Spliced Exon (CSE) Alternatively Spliced Exon (ASE) CSE ASE CSE exon1exon2exon3exon4

10 Background & Motivation Problem Definition Problem & Feature Construction Data Set Experiments Design & Results Feature Construction Conclusions and Future Work Problem Addressed and Our Approach Problem Definition predict alternatively spliced exons (ASE) vs constitutively spliced exons (CSE) Use Support Vector Machine (SVM) Task:Two-class (ASE and CSE) classification problem Need:Training data set containing labeled examples (ASE & CSE) Learning: Train classifier with training data Application: Predict unknown ASE Need features to represent ASEs & CSEs

11 Background & Motivation Problem Definition Problem & Feature Construction Data Set Experiments Design & Results Feature Construction Conclusions and Future Work Problem Addressed and Our Approach Problem Definition predict alternatively spliced exons (ASE) vs constitutively spliced exons (CSE) Use Support Vector Machine (SVM) Task:Two-class (ASE and CSE) classification problem Need:Training data set containing labeled examples (ASE & CSE) Learning: Train classifier with training data Application: Predict unknown ASE Need features to represent ASEs & CSEs

12 Background & Motivation Problem Definition Problem & Feature Construction Data Set Experiments Design & Results Feature Construction Conclusions and Future Work Problem Addressed and Our Approach Problem Definition predict alternatively spliced exons (ASE) vs constitutively spliced exons (CSE) Use Support Vector Machine (SVM) Task:Two-class (ASE and CSE) classification problem Need:Training data set containing labeled examples (ASE & CSE) Learning: Train classifier with training data Application: Predict unknown ASE Need features to represent ASEs & CSEs

13 Background & Motivation Problem Definition Problem & Feature Construction Data Set Experiments Design & Results Feature Construction Conclusions and Future Work Problem Addressed and Our Approach Problem Definition predict alternatively spliced exons (ASE) vs constitutively spliced exons (CSE) Use Support Vector Machine (SVM) Task:Two-class (ASE and CSE) classification problem Need:Training data set containing labeled examples (ASE & CSE) Learning: Train classifier with training data Application: Predict unknown ASE Need features to represent ASEs & CSEs

14 Background & Motivation Problem Definition Problem & Feature Construction Data Set Experiments Design & Results Feature Construction Conclusions and Future Work Problem Addressed and Our Approach Problem Definition predict alternatively spliced exons (ASE) vs constitutively spliced exons (CSE) Use Support Vector Machine (SVM) Task:Two-class (ASE and CSE) classification problem Need:Training data set containing labeled examples (ASE & CSE) Learning: Train classifier with training data Application: Predict unknown ASE Need features to represent ASEs & CSEs

15 Background & Motivation Problem Definition Problem & Feature Construction Data Set Experiments Design & Results Feature Construction Conclusions and Future Work Data Set Published data set from the model organism, C. elegans (worm) Includes alternatively spliced exons (ASE) and constitutively spliced exons (CSE) Contains 487 ASEs and 2531 CSEs 100-base local sequences around splice sites Example of data set ASE GTACTATAGCGTGCTG....ACCGTTCGTACTCGCT ASE ATACTATAGCGTCTTG....ACCGATCGTACACGCT CSE GTACTATAGCGTCTTG....ACCGATCGTACTCGCT AG exon GT AG −100 0 +100 −100 0 +100

16 Background & Motivation Problem Definition Problem & Feature Construction Data Set Experiments Design & Results Feature Construction Conclusions and Future Work Data Set Published data set from the model organism, C. elegans (worm) Includes alternatively spliced exons (ASE) and constitutively spliced exons (CSE) Contains 487 ASEs and 2531 CSEs 100-base local sequences around splice sites Example of data set ASE GTACTATAGCGTGCTG....ACCGTTCGTACTCGCT ASE ATACTATAGCGTCTTG....ACCGATCGTACACGCT CSE GTACTATAGCGTCTTG....ACCGATCGTACTCGCT AG exon GT AG −100 0 +100 −100 0 +100

17 Background & Motivation Problem Definition Problem & Feature Construction Data Set Experiments Design & Results Feature Construction Conclusions and Future Work Data Set Published data set from the model organism, C. elegans (worm) Includes alternatively spliced exons (ASE) and constitutively spliced exons (CSE) Contains 487 ASEs and 2531 CSEs 100-base local sequences around splice sites Example of data set ASE GTACTATAGCGTGCTG....ACCGTTCGTACTCGCT ASE ATACTATAGCGTCTTG....ACCGATCGTACACGCT CSE GTACTATAGCGTCTTG....ACCGATCGTACTCGCT AG exon GT AG −100 0 +100 −100 0 +100

18 Background & Motivation Problem Definition Problem & Feature Construction Data Set Experiments Design & Results Feature Construction Conclusions and Future Work Data Set Published data set from the model organism, C. elegans (worm) Includes alternatively spliced exons (ASE) and constitutively spliced exons (CSE) Contains 487 ASEs and 2531 CSEs 100- base local sequences around splice sites Previous work: Motifs captured and identified by kernel G. Ratch et al., Length of exons and flanking introns Sorek et al. Our work: Exploit more biologically significant features Use several additional approaches to derive features

19 Background & Motivation Problem Definition Problem & Feature Construction Data Set Experiments Design & Results Feature Construction Conclusions and Future Work Data Set Published data set from the model organism, C. elegans (worm) Includes alternatively spliced exons (ASE) and constitutively spliced exons (CSE) Contains 487 ASEs and 2531 CSEs 100- base local sequences around splice sites Previous work: Motifs captured and identified by kernel G. Ratch et al., Length of exons and flanking introns Sorek et al. Our work: Exploit more biologically significant features Use several additional approaches to derive features

20 Background & Motivation Problem Definition Problem & Feature Construction Data Set Experiments Design & Results Feature Construction Conclusions and Future Work Feature List Several features known to be biologically important Strength of splice sites (SSS) Motif features Intronic splicing regulator (ISR) Motifs derived from local sequences (MAST) Exonic splicing enhancer (ESE) Reduced set of motif features based on locations of motifs on secondary structure (MAST-R) Optimal folding energy (OPE) Basic sequence features (BSF)

21 Background & Motivation Problem Definition Problem & Feature Construction Data Set Experiments Design & Results Feature Construction Conclusions and Future Work SSS: Strength of Splice Site CGAG exon AGGTAAGT We consider all splice sites CGAG exon AGGTAAGT ∑ exon GGAG AGGTAGGT score = logF(X i ) F(X), CGAG exon AGGTTAGT i CCAG exon AGGTAAGT where X ∈ {A,U,G,C}. i ∈ {−3,+7} −3 +7 −26 +2 for 3’ splice sites (3’ss) and 3’ ss 5’ ss i ∈ {−26,+2} for 5’ splice sites (5’ss).

22 Background & Motivation Problem Definition Problem & Feature Construction Data Set Experiments Design & Results Feature Construction Conclusions and Future Work Motif: sequence pattern that occurs repeatedly in group of sequences Intronic Splicing Regulator: identified in Kabat et al. MAST: derived by MEME using [-100,+100] sequence Exon Splicing Enhancers: based on two assumption ISR exon Illustration of ISR dispersed among sequences

23 Background & Motivation Problem Definition Problem & Feature Construction Data Set Experiments Design & Results Feature Construction Conclusions and Future Work Motif: sequence pattern that occurs repeatedly in group of sequences Intronic Splicing Regulator: identified in Kabat et al. MAST: derived by MEME using [-100,+100] sequence Exon Splicing Enhancers: based on two assumption Example: a 20-base motif derived from sequences around splice sites

24 Background & Motivation Problem Definition Problem & Feature Construction Data Set Experiments Design & Results Feature Construction Conclusions and Future Work Motif: sequence pattern that occurs repeatedly in group of sequences Intronic Splicing Regulator: identified in Kabat et al. MAST: derived by MEME using [-100,+100] sequence Exon Splicing Enhancers: based on two assumption more frequent in exons than in introns more frequent in exons with weak splice sites than in exons with strong splice sites ISR MAST ESE Motifs - dispersed among exons and introns

25 Background & Motivation Problem Definition Problem & Feature Construction Data Set Experiments Design & Results Feature Construction Conclusions and Future Work Pre-mRNA secondary structures motif influence exon recognition AUCCAUGGGCCGGAUGUGACGGUAGUAGGGUAUACGUCACAUAGGCUUCCUCUCAUGA Located at different structure Secondary structure: derived from Mfold filter motifs using secondary structure Loop Stem Optimal Folding Energy: stability of RNA secondary structure

26 Background & Motivation Problem Definition Problem & Feature Construction Data Set Experiments Design & Results Feature Construction Conclusions and Future Work Pre-mRNA secondary structures motif influence exon recognition AUCCAUGGGCCGGAUGUGACGGUAGUAGGGUAUACGUCACAUAGGCUUCCUCUCAUGA Located at different structure Secondary structure: derived from Mfold filter motifs using secondary structure Loop Stem Optimal Folding Energy: stability of RNA secondary structure

27 Background & Motivation Problem Definition Problem & Feature Construction Data Set Experiments Design & Results Feature Construction Conclusions and Future Work GC content (G & C ratio),= A+U+G+C, characteristics of sequence Sequence length Length of exons and length of exons’ flanking introns frames of stop codons Summary of features Motif features Secondary structure Strength of splice sites Sequence features

28 Background & Motivation Problem & Feature ConstructionExperimental Design Experiments Design & ResultsExperimental Results Conclusions and Future Work Experimental Design List of previous defined features as SVM input Combination of different features to represent ASEs & CSEs split1split2split3split4split5 Tune SVM parameters to train (kernel linear, RBF.., Cost C) 20%80% 5−fold cross validation Choose parameters with best cross-validation (CV) accuracy Test trained SVM on testing ASEs & CSEs

29 Background & Motivation Problem & Feature ConstructionExperimental Design Experiments Design & ResultsExperimental Results Conclusions and Future Work Experimental Design List of previous defined features as SVM input Combination of different features to represent ASEs & CSEs split1split2split3split4split5 Tune SVM parameters to train (kernel linear, RBF.., Cost C) 20%80% 5−fold cross validation Choose parameters with best cross-validation (CV) accuracy Test trained SVM on testing ASEs & CSEs

30 Background & Motivation Problem & Feature ConstructionExperimental Design Experiments Design & ResultsExperimental Results Conclusions and Future Work Experimental Design List of previous defined features as SVM input Combination of different features to represent ASEs & CSEs split1split2split3split4split5 Tune SVM parameters to train (kernel linear, RBF.., Cost C) 20%80% 5−fold cross validation Choose parameters with best cross-validation (CV) accuracy Test trained SVM on testing ASEs & CSEs

31 Background & Motivation Problem & Feature ConstructionExperimental Design Experiments Design & ResultsExperimental Results Conclusions and Future Work Experimental Design List of previous defined features as SVM input Combination of different features to represent ASEs & CSEs split1split2split3split4split5 Tune SVM parameters to train (kernel linear, RBF.., Cost C) 20%80% 5−fold cross validation Choose parameters with best cross-validation (CV) accuracy Test trained SVM on testing ASEs & CSEs

32 Background & Motivation Problem & Feature ConstructionExperimental Design Experiments Design & ResultsExperimental Results Conclusions and Future Work Experimental Design List of previous defined features as SVM input Combination of different features to represent ASEs & CSEs split1split2split3split4split5 Tune SVM parameters to train (kernel linear, RBF.., Cost C) 20%80% 5−fold cross validation Choose parameters with best cross-validation (CV) accuracy Test trained SVM on testing ASEs & CSEs

33 Background & Motivation Problem & Feature ConstructionExperimental Design Experiments Design & ResultsExperimental Results Conclusions and Future Work Experimental results Results of alternatively spliced exon classification. All features, including ISR motifs, are used. CCross Validation ScoreTest score fp 1%AUC %fp 1%AUC% Split10.0532.4586.5556.4890.05 Split20.0539.3388.3252.0489.04 Split30.137.5687.7638.7187.97 Split40.0140.8689.0237.6384.42 Split50.136.4887.5035.7985.69

34 Background & Motivation Problem & Feature ConstructionExperimental Design Experiments Design & ResultsExperimental Results Conclusions and Future Work Experimental results 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 Mixed-Feas (85.55%) Base-Feas(78.78%) 0 00.20.40.60.81 False Positive Rate Comparison of ROC curves obtained using basic features only and basic features plus other mixed features (except conserved ISR motifs). Models trained using 5-fold CV with C = 1.

35 Background & Motivation Problem & Feature ConstructionExperimental Design Experiments Design & ResultsExperimental Results Conclusions and Future Work Experimental results AUC score comparison between data sets with secondary struc- tural features and data sets without secondary structural fea- tures

36 Background & Motivation Problem & Feature ConstructionExperimental Design Experiments Design & ResultsExperimental Results Conclusions and Future Work Motif Evaluation Intersection between motifs derived from sequences & intronic splicing regulators

37 Background & Motivation Problem & Feature ConstructionExperimental Design Experiments Design & ResultsExperimental Results Conclusions and Future Work Motif Evaluation Conserved ESE in metazoans (animals), Human and Mouse

38 Background & Motivation Problem & Feature ConstructionExperimental Design Experiments Design & ResultsExperimental Results Conclusions and Future Work Motif Evaluation Comparison with A. thaliana

39 Background & Motivation Problem & Feature Construction Conclusion Experiments Design & Results Conclusions and Future Work Conclusions Alternative splicing (AS) events can be found using transcripts Machine learning effectively used for prediction of AS events Identified features informative in predicting AS Explored comparatively comprehensive feature sets from biological point of view

40 Background & Motivation Problem & Feature Construction Conclusion Experiments Design & Results Conclusions and Future Work Conclusions Alternative splicing (AS) events can be found using transcripts Machine learning effectively used for prediction of AS events Identified features informative in predicting AS Explored comparatively comprehensive feature sets from biological point of view

41 Background & Motivation Problem & Feature Construction Conclusion Experiments Design & Results Conclusions and Future Work Conclusions Alternative splicing (AS) events can be found using transcripts Machine learning effectively used for prediction of AS events Identified features informative in predicting AS Explored comparatively comprehensive feature sets from biological point of view

42 Background & Motivation Problem & Feature Construction Conclusion Experiments Design & Results Conclusions and Future Work Conclusions Alternative splicing (AS) events can be found using transcripts Machine learning effectively used for prediction of AS events Identified features informative in predicting AS Explored comparatively comprehensive feature sets from biological point of view

43 Background & Motivation Problem & Feature Construction Conclusion Experiments Design & Results Conclusions and Future Work Future Work Apply this approach to specific organism Identify motifs more accurately Refine relationships between features (2nd Structure:w and motifs) Learn other types of AS events (not only skipped exons) adapted from "Detection of Alternative Splicing Events Using Machine Learning"

44 Background & Motivation Problem & Feature Construction Conclusion Experiments Design & Results Conclusions and Future Work Thank you for your attention! Questions? Related work RASE http://www.fml.tuebingen.mpg.de/raetsch/projects/RASE Acknowledgement data set from Dr. Ratsch’s FML group http://www.fml.tuebingen.mpg.de/raetsch/ projects/RASE/altsplicedexonsplits.tar.gz Dr. Caragea’s MLB group http://people.cis.ksu.edu/~dcaragea/mlb Dr. Brown’s Bininformatics Center at KSU http://bioinformatics.ksu.edu

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