Computational approaches to gene prediction.

Full Abstract

The problems associated with gene identification and the prediction of gene structure in DNA sequences have been the focus of increased attention over the past few years with the recent acquisition by large-scale sequencing projects of an immense amount of genome data. A variety of prediction programs have been developed in order to address these problems. This paper presents a review of the computational approaches and gene-finders used commonly for gene prediction in eukaryotic genomes. Two approaches, in general, have been adopted for this purpose:
similarity-based and ab initio techniques. The information gleaned from these methods is then combined via a variety of algorithms, including Dynamic Programming (DP) or the Hidden Markov Model (HMM), and then used for gene prediction from the genomic sequences.

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Author information

Author/s: Do, Jin Hwan (JH); Choi, Dong-Kug (DK);

Affiliation: Bio-food and Drug Research Center, Konkuk University, Chungju 380-701, Republic of Korea.

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't; Review

Journal: Journal of microbiology (Seoul, Korea) (J Microbiol), published in Korea (South). (Language: eng)

Reference: 2006-Apr; vol 44 (issue 2) : pp 137-44

Dates: Created 2006/05/26; Completed 2006/07/12; Revised 2006/11/15;

PMID: 16728949, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Animals
Computational Biology - methods
Eukaryotic Cells
Genes - genetics
Genome - genetics

Genome - genetics
Genomics - methods
Humans
Plants - genetics
Software

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