Literature DB >> 10743554

Finding prokaryotic genes by the 'frame-by-frame' algorithm: targeting gene starts and overlapping genes.

A M Shmatkov1, A A Melikyan, F L Chernousko, M Borodovsky.   

Abstract

MOTIVATION: Tightly packed prokaryotic genes frequently overlap with each other. This feature, rarely seen in eukaryotic DNA, makes detection of translation initiation sites and, therefore, exact predictions of prokaryotic genes notoriously difficult. Improving the accuracy of precise gene prediction in prokaryotic genomic DNA remains an important open problem.
RESULTS: A software program implementing a new algorithm utilizing a uniform Hidden Markov Model for prokaryotic gene prediction was developed. The algorithm analyzes a given DNA sequence in each of six possible global reading frames independently. Twelve complete prokaryotic genomes were analyzed using the new tool. The accuracy of gene finding, predicting locations of protein-coding ORFs, as well as the accuracy of precise gene prediction, and detecting the whole gene including translation initiation codon were assessed by comparison with existing annotation. It was shown that in terms of gene finding, the program performs at least as well as the previously developed tools, such as GeneMark and GLIMMER. In terms of precise gene prediction the new program was shown to be more accurate, by several percentage points, than earlier developed tools, such as GeneMark.hmm, ECOPARSE and ORPHEUS. The results of testing the program indicated the possibility of systematic bias in start codon annotation in several early sequenced prokaryotic genomes. AVAILABILITY: The new gene-finding program can be accessed through the Web site: http:@dixie.biology.gatech.edu/GeneMark/fbf.cgi CONTACT: mark@amber.gatech.edu.

Mesh:

Substances:

Year:  1999        PMID: 10743554     DOI: 10.1093/bioinformatics/15.11.874

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  14 in total

1.  GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions.

Authors:  J Besemer; A Lomsadze; M Borodovsky
Journal:  Nucleic Acids Res       Date:  2001-06-15       Impact factor: 16.971

2.  Dictionary-driven prokaryotic gene finding.

Authors:  Tetsuo Shibuya; Isidore Rigoutsos
Journal:  Nucleic Acids Res       Date:  2002-06-15       Impact factor: 16.971

3.  A comparative genomic method for computational identification of prokaryotic translation initiation sites.

Authors:  Megon Walker; Vladimir Pavlovic; Simon Kasif
Journal:  Nucleic Acids Res       Date:  2002-07-15       Impact factor: 16.971

4.  Evaluating bacterial gene-finding HMM structures as probabilistic logic programs.

Authors:  Søren Mørk; Ian Holmes
Journal:  Bioinformatics       Date:  2012-01-03       Impact factor: 6.937

5.  Complete genome sequence of the giant virus OBP and comparative genome analysis of the diverse ΦKZ-related phages.

Authors:  Anneleen Cornelissen; Stephen C Hardies; Olga V Shaburova; Victor N Krylov; Wesley Mattheus; Andrew M Kropinski; Rob Lavigne
Journal:  J Virol       Date:  2011-11-30       Impact factor: 5.103

6.  Complete genomic sequence and mass spectrometric analysis of highly diverse, atypical Bacillus thuringiensis phage 0305phi8-36.

Authors:  Julie A Thomas; Stephen C Hardies; Mandy Rolando; Shirley J Hayes; Karen Lieman; Christopher A Carroll; Susan T Weintraub; Philip Serwer
Journal:  Virology       Date:  2007-07-30       Impact factor: 3.616

7.  Dual control of quorum sensing by two TraM-type antiactivators in Agrobacterium tumefaciens octopine strain A6.

Authors:  Chao Wang; Hai-Bao Zhang; Guozhou Chen; Lingling Chen; Lian-Hui Zhang
Journal:  J Bacteriol       Date:  2006-04       Impact factor: 3.490

8.  Characterization of a Brucella species 25-kilobase DNA fragment deleted from Brucella abortus reveals a large gene cluster related to the synthesis of a polysaccharide.

Authors:  N Vizcaíno; A Cloeckaert; M S Zygmunt; L Fernández-Lago
Journal:  Infect Immun       Date:  2001-11       Impact factor: 3.441

9.  Prokaryotic gene finding based on physicochemical characteristics of codons calculated from molecular dynamics simulations.

Authors:  Poonam Singhal; B Jayaram; Surjit B Dixit; David L Beveridge
Journal:  Biophys J       Date:  2008-03-07       Impact factor: 4.033

10.  MetaGene: prokaryotic gene finding from environmental genome shotgun sequences.

Authors:  Hideki Noguchi; Jungho Park; Toshihisa Takagi
Journal:  Nucleic Acids Res       Date:  2006-10-05       Impact factor: 16.971

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