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Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning

Rätsch, Gunnar and Sonnenburg, Sören and Srinivasan, Jagan and Witte, Hanh and Müller, Klaus-R. and Sommer, Ralf-J. and Schölkopf, Bernhard (2007) Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning. PLoS Computational Biology, 3 (2). pp. 313-322. ISSN 1553-734X. PMCID PMC1808025.

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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 [1] 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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Srinivasan, Jagan0000-0001-5449-7938
Additional Information:© 2007 Rätsch et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received: February 2, 2006; Accepted: December 20, 2006; Published: February 23, 2007. Funding. This work was supported in part by the IST Programme of the European Community, under the PASCAL Network of Excellence, IST-2002–506778. Partial funding from the German Research Foundation (MU 987/2–1) is appreciated. Competing interests. GR, SS, KRM, and BS are authors of a patent application (PCT WO05116246) related to the technical innovations of the proposed method. Editor: Uwe Ohler, Duke University, United States of America. We gratefully acknowledge inspiring discussions with Anja Neuber, Alexander Zien, Andrei Lupas, Detlef Weigel, Alan Zahler, Koji Tsuda, Christina Leslie, Eleazar Eskin, and Ivo Grosse. Alexander Zien additionally helped with the implementation of the POIMs. We thank Christoph Dieterich for providing access to a draft assembly of the P. pacificus genome. Additionally, we thank Broňa Brejová, Tomáš Vinař, and Ian Korf for their collaboration to conduct the comparisons with ExonHunter and SNAP. Furthermore, we would like to thank Anthony Rogers and Todd Harris for their help to get the new annotation onto the Wormbase Web site. Author contributions. GR, SS, JS, KRM, RJS, and BS conceived and designed the experiments. GR, JS, and HW performed the experiments. GR and SS analyzed the data. SS, RJS, and BS contributed reagents/materials/analysis tools. GR, JS, KRM, RJS, and BS wrote the paper. SS and JS contributed equally to the paper. Supporting Information: Protocol S1. Data Preparation Protocols, Additional Results, and Primer Lists
Funding AgencyGrant Number
European CommunityIST-2002–506778
Deutsche Forschungsgemeinschaft (DFG)MU 987/2–1
Issue or Number:2
PubMed Central ID:PMC1808025
Record Number:CaltechAUTHORS:RATploscb07
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Official Citation:Rätsch G, Sonnenburg S, Srinivasan J, Witte H, Müller KR, et al. (2007) Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning. PLoS Comput Biol 3(2): e20 doi:10.1371/journal.pcbi.0030020
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:7557
Deposited By: Archive Administrator
Deposited On:03 Mar 2007
Last Modified:02 Oct 2019 23:43

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