ArticlePLoS computational biology2007
Improving the Caenorhabditis elegans genome annotation using machine learning.
Article in PLoS computational biology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
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- Multiomics Research: Principles and Challenges in Integrated Analysis.Biodesign research · 2024Review
- HEC-ASD: a hybrid ensemble-based classification model for predicting autism spectrum disorder disease genes.BMC bioinformatics · 2022Article
- SpliceFinder: ab initio prediction of splice sites using convolutional neural network.BMC bioinformatics · 2019Article
- ML2Motif-Reliable extraction of discriminative sequence motifs from learning machines.PloS one · 2017Article
- LEARNING PARSIMONIOUS ENSEMBLES FOR UNBALANCED COMPUTATIONAL GENOMICS PROBLEMS.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2017Article
- Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming.PLoS computational biology · 2016Article
- SeqGL Identifies Context-Dependent Binding Signals in Genome-Wide Regulatory Element Maps.PLoS computational biology · 2015Article
- SVM2Motif--Reconstructing Overlapping DNA Sequence Motifs by Mimicking an SVM Predictor.PloS one · 2015Article
- MITIE: Simultaneous RNA-Seq-based transcript identification and quantification in multiple samples.Bioinformatics (Oxford, England) · 2013Article
- Characterization of the C. elegans erlin homologue.BMC cell biology · 2012Article
- Exploiting physico-chemical properties in string kernels.BMC bioinformatics · 2010Article
- mGene: accurate SVM-based gene finding with an application to nematode genomes.Genome research · 2009Article
- A thermodynamic approach to PCR primer design.Nucleic acids research · 2009Article
- mGene.web: a web service for accurate computational gene finding.Nucleic acids research · 2009Article
- Detecting polymorphic regions in Arabidopsis thaliana with resequencing microarrays.Genome research · 2008Article
- Strategies for identifying RNA splicing regulatory motifs and predicting alternative splicing events.PLoS computational biology · 2008Review
- Global discriminative learning for higher-accuracy computational gene prediction.PLoS computational biology · 2007Article
- Accurate splice site prediction using support vector machines.BMC bioinformatics · 2007Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For modern biology, precise genome annotations are of prime importance, as they allow the accurate definition of genic regions. We employ state-of-the-art machine learning methods to assay and improve the accuracy of the genome annotation of the nematode Caenorhabditis elegans. The proposed machine learning system is trained to recognize exons and introns on the unspliced mRNA, utilizing recent advances in support vector machines and label sequence learning. In 87% (coding and untranslated regions) and 95% (coding regions only) of all genes tested in several out-of-sample evaluations, our method correctly identified all exons and introns. Notably, only 37% and 50%, respectively, of the presently unconfirmed genes in the C. elegans genome annotation agree with our predictions, thus we hypothesize that a sizable fraction of those genes are not correctly annotated. A retrospective evaluation of the Wormbase WS120 annotation [] of C. elegans reveals that splice form predictions on unconfirmed genes in WS120 are inaccurate in about 18% of the considered cases, while our predictions deviate from the truth only in 10%-13%. We experimentally analyzed 20 controversial genes on which our system and the annotation disagree, confirming the superiority of our predictions. While our method correctly predicted 75% of those cases, the standard annotation was never completely correct. The accuracy of our system is further corroborated by a comparison with two other recently proposed systems that can be used for splice form prediction: SNAP and ExonHunter. We conclude that the genome annotation of C. elegans and other organisms can be greatly enhanced using modern machine learning technology.
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