Literature DB >> 12854962

Gene recognition from questionable ORFs in bacterial and archaeal genomes.

Ling-Ling Chen1, Chun-Ting Zhang.   

Abstract

The ORFs of microbial genomes in annotation files are usually classified into two groups: the first corresponds to known genes; whereas the second includes 'putative', 'probable', 'conserved hypothetical', 'hypothetical', 'unknown' and 'predicted' ORFs etc. Since the annotation is not 100% accurate, it is essential to confirm which ORF of the latter group is coding and which is not. Starting from known genes in the former, this paper describes an improved Z curve method to recognize genes in the latter. Ten-fold cross-validation tests show that the average accuracy of the algorithm is greater than 99% for recognizing the known genes in 57 bacterial and archaeal genomes. The method is then applied to recognize genes of the latter group. The likely non-coding ORFs in each of the 57 bacterial or archaeal genomes studied here are recognized and listed at the website http://tubic.tju.edu.cn/ZCURVE_C_html/noncoding.html. The working mechanism of the algorithm has been discussed in details. A computer program, called ZCURVE_C, was written to calculate a coding score called Z-curve score for ORFs in the above 57 bacterial and archaeal genomes. Coding/non-coding is simply determined by the criterion of Z-curve score > 0/ Z-curve score < 0. A website has been set up to provide the service to calculate the Z-curve score. A user may submit the DNA sequence of an ORF to the server at http://tubic.tju.edu.cn/ZCURVE_C/Default.cgi, and the Z-curve score of the ORF is calculated and returned to the user immediately.

Mesh:

Year:  2003        PMID: 12854962     DOI: 10.1080/07391102.2003.10506908

Source DB:  PubMed          Journal:  J Biomol Struct Dyn        ISSN: 0739-1102


  6 in total

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Authors:  Farris L Poole; Brian A Gerwe; Robert C Hopkins; Gerrit J Schut; Michael V Weinberg; Francis E Jenney; Michael W W Adams
Journal:  J Bacteriol       Date:  2005-11       Impact factor: 3.490

2.  Re-annotation of two hyperthermophilic archaea Pyrococcus abyssi GE5 and Pyrococcus furiosus DSM 3638.

Authors:  Junxiang Gao; Ji Wang
Journal:  Curr Microbiol       Date:  2011-11-06       Impact factor: 2.188

3.  An integrative method for identifying the over-annotated protein-coding genes in microbial genomes.

Authors:  Jia-Feng Yu; Ke Xiao; Dong-Ke Jiang; Jing Guo; Ji-Hua Wang; Xiao Sun
Journal:  DNA Res       Date:  2011-09-08       Impact factor: 4.458

4.  DIGA--a database of improved gene annotation for phytopathogens.

Authors:  Na Gao; Ling-Ling Chen; Hong-Fang Ji; Wei Wang; Ji-Wei Chang; Bei Gao; Lin Zhang; Shi-Cui Zhang; Hong-Yu Zhang
Journal:  BMC Genomics       Date:  2010-01-21       Impact factor: 3.969

5.  Theoretical prediction and experimental verification of protein-coding genes in plant pathogen genome Agrobacterium tumefaciens strain C58.

Authors:  Qian Wang; Yang Lei; Xiwen Xu; Gejiao Wang; Ling-Ling Chen
Journal:  PLoS One       Date:  2012-09-11       Impact factor: 3.240

6.  Recognition of Protein-coding Genes Based on Z-curve Algorithms.

Authors:  Feng -Biao Guo; Yan Lin; Ling -Ling Chen
Journal:  Curr Genomics       Date:  2014-04       Impact factor: 2.236

  6 in total

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