Literature DB >> 14764563

Comparison of various algorithms for recognizing short coding sequences of human genes.

Feng Gao1, Chun-Ting Zhang.   

Abstract

MOTIVATION: Since the early 1980s of the twentieth century, there has been great progress in the development of computational gene-finding algorithms. Some problems, however, have not yet been solved currently. Recognizing short genes in prokaryotes and short exons in eukaryotes is one of such problems. The paper is devoted to assessing various algorithms, including those currently available and the new ones proposed here, in order to find the best algorithm to solve the issue.
RESULTS: The databases consisting of phase-specific coding and non-coding sequences of human genes with length of 192, 162, 129, 108, 87, 63 and 42 bp, respectively, have been established. Based on the databases and a standard benchmark, 19 algorithms were evaluated, which include the methods of Markov models with orders of 1 through 5, codon usage, hexamer usage, codon preference, amino acid usage, codon prototype, Fourier transform and 8 Z curve methods with various numbers of parameters. Consequently, the Z curve methods with 69 and 189 parameters are the best ones among them, based on the databases constructed here. In addition to the highest recognition accuracy confirmed by 10-fold cross-validation tests, the Z curve methods are much simpler computationally than the second best one, the fifth-order Markov chain model, in which 12 288 parameters are used. We hope that the Z curve methods presented in this paper would be beneficial to the further development of gene-finding algorithms. AVAILABILITY: The programs of various Z curve methods are available on request.

Entities:  

Mesh:

Year:  2004        PMID: 14764563     DOI: 10.1093/bioinformatics/btg467

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


  29 in total

1.  Classifier assessment and feature selection for recognizing short coding sequences of human genes.

Authors:  Kai Song; Ze Zhang; Tuo-Peng Tong; Fang Wu
Journal:  J Comput Biol       Date:  2012-03       Impact factor: 1.479

2.  Evidence of abundant stop codon readthrough in Drosophila and other metazoa.

Authors:  Irwin Jungreis; Michael F Lin; Rebecca Spokony; Clara S Chan; Nicolas Negre; Alec Victorsen; Kevin P White; Manolis Kellis
Journal:  Genome Res       Date:  2011-10-12       Impact factor: 9.043

Review 3.  Identification of replication origins in archaeal genomes based on the Z-curve method.

Authors:  Ren Zhang; Chun-Ting Zhang
Journal:  Archaea       Date:  2005-05       Impact factor: 3.273

4.  ZCURVE 3.0: identify prokaryotic genes with higher accuracy as well as automatically and accurately select essential genes.

Authors:  Zhi-Gang Hua; Yan Lin; Ya-Zhou Yuan; De-Chang Yang; Wen Wei; Feng-Biao Guo
Journal:  Nucleic Acids Res       Date:  2015-05-14       Impact factor: 16.971

5.  Coding sequence density estimation via topological pressure.

Authors:  David Koslicki; Daniel J Thompson
Journal:  J Math Biol       Date:  2014-01-22       Impact factor: 2.259

6.  iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization.

Authors:  Zhen Chen; Pei Zhao; Chen Li; Fuyi Li; Dongxu Xiang; Yong-Zi Chen; Tatsuya Akutsu; Roger J Daly; Geoffrey I Webb; Quanzhi Zhao; Lukasz Kurgan; Jiangning Song
Journal:  Nucleic Acids Res       Date:  2021-06-04       Impact factor: 16.971

7.  Classifying coding DNA with nucleotide statistics.

Authors:  Nicolas Carels; Diego Frías
Journal:  Bioinform Biol Insights       Date:  2009-10-28

8.  BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches.

Authors:  Bin Liu; Xin Gao; Hanyu Zhang
Journal:  Nucleic Acids Res       Date:  2019-11-18       Impact factor: 16.971

9.  Statistical assessment of discriminative features for protein-coding and non coding cross-species conserved sequence elements.

Authors:  Teresa M Creanza; David S Horner; Annarita D'Addabbo; Rosalia Maglietta; Flavio Mignone; Nicola Ancona; Graziano Pesole
Journal:  BMC Bioinformatics       Date:  2009-06-16       Impact factor: 3.169

10.  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

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.