Literature DB >> 25016190

iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition.

Wei Chen1, Peng-Mian Feng2, En-Ze Deng3, Hao Lin4, Kuo-Chen Chou5.   

Abstract

Translation is a key process for gene expression. Timely identification of the translation initiation site (TIS) is very important for conducting in-depth genome analysis. With the avalanche of genome sequences generated in the postgenomic age, it is highly desirable to develop automated methods for rapidly and effectively identifying TIS. Although some computational methods were proposed in this regard, none of them considered the global or long-range sequence-order effects of DNA, and hence their prediction quality was limited. To count this kind of effects, a new predictor, called "iTIS-PseTNC," was developed by incorporating the physicochemical properties into the pseudo trinucleotide composition, quite similar to the PseAAC (pseudo amino acid composition) approach widely used in computational proteomics. It was observed by the rigorous cross-validation test on the benchmark dataset that the overall success rate achieved by the new predictor in identifying TIS locations was over 97%. As a web server, iTIS-PseTNC is freely accessible at http://lin.uestc.edu.cn/server/iTIS-PseTNC. To maximize the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web server to obtain the desired results without the need to go through detailed mathematical equations, which are presented in this paper just for the integrity of the new prection method.
Copyright © 2014 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Physicochemical properties; Pseudo trinucleotide composition; Support vector machine; Translation initiation site; Web server; iTIS-PseTNC

Mesh:

Substances:

Year:  2014        PMID: 25016190     DOI: 10.1016/j.ab.2014.06.022

Source DB:  PubMed          Journal:  Anal Biochem        ISSN: 0003-2697            Impact factor:   3.365


  59 in total

1.  iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition.

Authors:  Hao Lin; En-Ze Deng; Hui Ding; Wei Chen; Kuo-Chen Chou
Journal:  Nucleic Acids Res       Date:  2014-10-31       Impact factor: 16.971

2.  Prediction of Protein-Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures.

Authors:  Guang-Hui Liu; Hong-Bin Shen; Dong-Jun Yu
Journal:  J Membr Biol       Date:  2015-11-12       Impact factor: 1.843

3.  iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples.

Authors:  Muhammad Kabir; Maqsood Hayat
Journal:  Mol Genet Genomics       Date:  2015-08-30       Impact factor: 3.291

4.  TargetFreeze: Identifying Antifreeze Proteins via a Combination of Weights using Sequence Evolutionary Information and Pseudo Amino Acid Composition.

Authors:  Xue He; Ke Han; Jun Hu; Hui Yan; Jing-Yu Yang; Hong-Bin Shen; Dong-Jun Yu
Journal:  J Membr Biol       Date:  2015-06-10       Impact factor: 1.843

5.  Identifying N 6-methyladenosine sites in the Arabidopsis thaliana transcriptome.

Authors:  Wei Chen; Pengmian Feng; Hui Ding; Hao Lin
Journal:  Mol Genet Genomics       Date:  2016-09-02       Impact factor: 3.291

6.  PreTIS: A Tool to Predict Non-canonical 5' UTR Translational Initiation Sites in Human and Mouse.

Authors:  Kerstin Reuter; Alexander Biehl; Laurena Koch; Volkhard Helms
Journal:  PLoS Comput Biol       Date:  2016-10-21       Impact factor: 4.475

7.  Decomposition of RNA methylome reveals co-methylation patterns induced by latent enzymatic regulators of the epitranscriptome.

Authors:  Lian Liu; Shao-Wu Zhang; Yu-Chen Zhang; Hui Liu; Lin Zhang; Runsheng Chen; Yufei Huang; Jia Meng
Journal:  Mol Biosyst       Date:  2014-11-05

8.  Comparison of genomic data via statistical distribution.

Authors:  Saeid Amiri; Ivo D Dinov
Journal:  J Theor Biol       Date:  2016-07-25       Impact factor: 2.691

9.  Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou's General Pseudo Amino Acid Composition.

Authors:  Khurshid Ahmad; Muhammad Waris; Maqsood Hayat
Journal:  J Membr Biol       Date:  2016-01-08       Impact factor: 1.843

10.  Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation.

Authors:  Ruifeng Xu; Jiyun Zhou; Hongpeng Wang; Yulan He; Xiaolong Wang; Bin Liu
Journal:  BMC Syst Biol       Date:  2015-02-06
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.