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Open Access Highly Accessed Research article

Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources

Mario Stanke1*, Oliver Schöffmann2, Burkhard Morgenstern1 and Stephan Waack2

Author Affiliations

1 lnstitut für Mikrobiologie und Genetik, Universität Göttingen, Göttingen, Germany

2 lnstitut für Numerische und Angewandte Mathematik, Universität Göttingen, Göttingen, Germany

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BMC Bioinformatics 2006, 7:62  doi:10.1186/1471-2105-7-62

Published: 9 February 2006

Abstract

Background

In order to improve gene prediction, extrinsic evidence on the gene structure can be collected from various sources of information such as genome-genome comparisons and EST and protein alignments. However, such evidence is often incomplete and usually uncertain. The extrinsic evidence is usually not sufficient to recover the complete gene structure of all genes completely and the available evidence is often unreliable. Therefore extrinsic evidence is most valuable when it is balanced with sequence-intrinsic evidence.

Results

We present a fairly general method for integration of external information. Our method is based on the evaluation of hints to potentially protein-coding regions by means of a Generalized Hidden Markov Model (GHMM) that takes both intrinsic and extrinsic information into account. We used this method to extend the ab initio gene prediction program AUGUSTUS to a versatile tool that we call AUGUSTUS+. In this study, we focus on hints derived from matches to an EST or protein database, but our approach can be used to include arbitrary user-defined hints. Our method is only moderately effected by the length of a database match. Further, it exploits the information that can be derived from the absence of such matches. As a special case, AUGUSTUS+ can predict genes under user-defined constraints, e.g. if the positions of certain exons are known. With hints from EST and protein databases, our new approach was able to predict 89% of the exons in human chromosome 22 correctly.

Conclusion

Sensitive probabilistic modeling of extrinsic evidence such as sequence database matches can increase gene prediction accuracy. When a match of a sequence interval to an EST or protein sequence is used it should be treated as compound information rather than as information about individual positions.