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Spliced alignment: A new approach to gene recognition

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Combinatorial Pattern Matching (CPM 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1075))

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Abstract

Gene structure prediction is one of the most important problems in computational molecular biology. Previous attempts to solve this problem were based on statistics and artificial intelligence and, surprisingly enough, applications of theoretical computer science methods for gene recognition were almost unexplored. Recent advances in large-scale cDNA sequencing open a way towards a new combinatorial approach to gene recognition. This paper describes a spliced alignment algorithm and a software tool which explores all possible exon assemblies in polynomial time and finds the multiexon structure with the best fit to a related protein. Unlike other existing methods, the algorithm successfully recognizes genes even in the case of short exons or exons with unusual codon usage; we also report correct assemblies for genes with more than 10 exons. On a test sample of human genes with known mammalian relatives the average correlation between the predicted and the actual genes was 99%, which is a very high accuracy as compared with other existing methods. The algorithm correctly reconstructed 87% of genes and the rare discrepancies between the predicted and real exonintron structures were caused by either (i) extremely short (less than 5 amino acids) initial or terminal exons, or (ii) alternative splicing, or (iii) errors in database feature tables. Moreover, the algorithm predicts human genes reasonably well when the homologous protein is non-vertebrate or even prokaryotic. The surprizingly good performance of the method was confirmed by extensive simulations: in particular, with target proteins showing just 25% similarity, the correlation between the predicted and actual genes still was as high as 95%.

The research was supported by DOE grant DE-FG02-95ER61919, Russian Fund of Fundamental Research grant 94-04-12330, grant MTW300 from ISF, and the Russian State Program ”Human Genome”.

The research was supported by DOE grant DE-FG02-95ER61919 and the Russian State Program ”Human Genome”.

The research was supported by DOE grant DE-FG02-95ER61919 by NSF Young Investigator award CCR-9457784.

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Authors and Affiliations

  1. Institute of Protein Research, Russian Academy of Sciences, 142292, Puschino, Moscow region, Russia

    Mikhail S. Gelfand

  2. Laboratory of Mathematical Methods, National Center for Biotechnology NIIGENETIKA, 113545, Moscow, Russia

    Andrey A. Mironov

  3. Departments of Mathematics and Computer Science, University of Southern California, 90089-1113, Los Angeles, CA

    Pavel A. Pevzner

Authors
  1. Mikhail S. Gelfand
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  2. Andrey A. Mironov
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  3. Pavel A. Pevzner
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Dan Hirschberg Gene Myers

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© 1996 Springer-Verlag Berlin Heidelberg

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Gelfand, M.S., Mironov, A.A., Pevzner, P.A. (1996). Spliced alignment: A new approach to gene recognition. In: Hirschberg, D., Myers, G. (eds) Combinatorial Pattern Matching. CPM 1996. Lecture Notes in Computer Science, vol 1075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61258-0_12

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  • DOI: https://doi.org/10.1007/3-540-61258-0_12

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61258-2

  • Online ISBN: 978-3-540-68390-2

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