Literature DB >> 11847092

Translation initiation start prediction in human cDNAs with high accuracy.

Artemis G Hatzigeorgiou1.   

Abstract

MOTIVATION: Correct identification of the Translation Initiation Start (TIS) in cDNA sequences is an important issue for genome annotation. The aim of this work is to improve upon current methods and provide a performance guaranteed prediction.
METHODS: This is achieved by using two modules, one sensitive to the conserved motif and the other sensitive to the coding/non-coding potential around the start codon. Both modules are based on Artificial Neural Networks (ANNs). By applying the simplified method of the ribosome scanning model, the algorithm starts a linear search at the beginning of the coding ORF and stops once the combination of the two modules predicts a positive score.
RESULTS: According to the results of the test group, 94% of the TIS were correctly predicted. A confident decision is obtained through the use of the Las Vegas algorithm idea. The incorporation of this algorithm leads to a highly accurate recognition of the TIS in human cDNAs for 60% of the cases. AVAILABILITY: The program is available upon request from the author.

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Year:  2002        PMID: 11847092     DOI: 10.1093/bioinformatics/18.2.343

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


  11 in total

1.  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

2.  Regulation of translation by upstream translation initiation codons of surfactant protein A1 splice variants.

Authors:  Nikolaos Tsotakos; Patricia Silveyra; Zhenwu Lin; Neal Thomas; Mudit Vaid; Joanna Floros
Journal:  Am J Physiol Lung Cell Mol Physiol       Date:  2014-10-17       Impact factor: 5.464

3.  Representative transcript sets for evaluating a translational initiation sites predictor.

Authors:  Jia Zeng; Reda Alhajj; Douglas J Demetrick
Journal:  BMC Bioinformatics       Date:  2009-07-02       Impact factor: 3.169

4.  Improvement in the prediction of the translation initiation site through balancing methods, inclusion of acquired knowledge and addition of features to sequences of mRNA.

Authors:  Lívia Márcia Silva; Felipe Carvalho de Souza Teixeira; José Miguel Ortega; Luis Enrique Zárate; Cristiane Neri Nobre
Journal:  BMC Genomics       Date:  2011-12-22       Impact factor: 3.969

5.  MetWAMer: eukaryotic translation initiation site prediction.

Authors:  Michael E Sparks; Volker Brendel
Journal:  BMC Bioinformatics       Date:  2008-09-18       Impact factor: 3.169

6.  Feature selection for the prediction of translation initiation sites.

Authors:  Guo Liang Li; Tze Yun Leong
Journal:  Genomics Proteomics Bioinformatics       Date:  2005-05       Impact factor: 7.691

7.  Transductive learning as an alternative to translation initiation site identification.

Authors:  Cristiano Lacerda Nunes Pinto; Cristiane Neri Nobre; Luis Enrique Zárate
Journal:  BMC Bioinformatics       Date:  2017-02-02       Impact factor: 3.169

8.  Comparison of computational methods for identifying translation initiation sites in EST data.

Authors:  Afshin Nadershahi; Scott C Fahrenkrug; Lynda B M Ellis
Journal:  BMC Bioinformatics       Date:  2004-02-16       Impact factor: 3.169

9.  Bioinformatic analyses of mammalian 5'-UTR sequence properties of mRNAs predicts alternative translation initiation sites.

Authors:  Jill L Wegrzyn; Thomas M Drudge; Faramarz Valafar; Vivian Hook
Journal:  BMC Bioinformatics       Date:  2008-05-08       Impact factor: 3.169

10.  TITER: predicting translation initiation sites by deep learning.

Authors:  Sai Zhang; Hailin Hu; Tao Jiang; Lei Zhang; Jianyang Zeng
Journal:  Bioinformatics       Date:  2017-07-15       Impact factor: 6.937

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