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Nucleic Acids Research, 2002, Vol. 30, No. 19 4103-4117
© 2002 Oxford University Press

Current methods of gene prediction, their strengths and weaknesses

Catherine Mathé*, Marie-France Sagot1, Thomas Schiex2 and Pierre Rouzé3

Institut de Pharmacologie et Biologie Structurale, UMR 5089, 205 route de Narbonne, F-31077 Toulouse Cedex, France, 1 INRIA Rhône-Alpes, UMR 5558 Biométrie et Biologie Évolutive, Université Claude Bernard, Lyon I, 43 Boulevard du 11 Novembre, F-69622 Villeurbanne Cedex, France, 2 INRA Toulouse, Département de Biométrie et Intelligence Artificielle, Chemin de Borde Rouge, BP 27, F-31326 Castanet-Tolosan Cedex, France and 3 Laboratoire Associé de l’INRA (France), Universiteit Gent, Ledeganckstraat 35, B-9000 Gent, Belgium

*To whom correspondence should be addressed. Tel: +33 5 61 17 59 53; Fax: +33 5 61 17 59 94; Email: catherine.mathe{at}ipbs.fr

While the genomes of many organisms have been sequenced over the last few years, transforming such raw sequence data into knowledge remains a hard task. A great number of prediction programs have been developed that try to address one part of this problem, which consists of locating the genes along a genome. This paper reviews the existing approaches to predicting genes in eukaryotic genomes and underlines their intrinsic advantages and limitations. The main mathematical models and computational algorithms adopted are also briefly described and the resulting software classified according to both the method and the type of evidence used. Finally, the several difficulties and pitfalls encountered by the programs are detailed, showing that improvements are needed and that new directions must be considered.


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