Literature DB >> 11894112

A comparative guide to gene prediction tools for the bioinformatics amateur.

J Jones1, J K Field, J M Risk.   

Abstract

Several hundred programs using different algorithms have been designed to predict individual coding features within any genomic sequence, but none of these tools covers all aspects of a gene or is 100% accurate in its prediction. Automated simultaneous processing of the results from a number of these programs minimizes the chance of a false positive prediction and quickly generates integrated data. We report here on the analysis of two known genes in 5 and 25 kb segments of genomic sequence using four genome annotation packages, NIX, RUMMAGE, Genotator and EMBOSS. Gene predictions were confirmed using cDNA sequences and a comparison was made between the packages. This study showed a similarity in the ability of NIX, RUMMAGE and Genotator to predict well-characterised genes and basic structures, but poor exon prediction for a small, 3 exon gene. However, the BLAST subprograms of all three packages correctly identified the 3 exons. In addition, EST BLAST subprograms identified a previously undescribed, possible 5' untranslated exon for the smaller gene and a number of putative alternatively spliced exons in the larger gene. Overall, NIX was found to be the most user-friendly package, in terms of easy access to databases and the interactive graphical display of results.

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Year:  2002        PMID: 11894112     DOI: 10.3892/ijo.20.4.697

Source DB:  PubMed          Journal:  Int J Oncol        ISSN: 1019-6439            Impact factor:   5.650


  2 in total

1.  ERK Activation Globally Downregulates miRNAs through Phosphorylating Exportin-5.

Authors:  Hui-Lung Sun; Ri Cui; JianKang Zhou; Kun-Yu Teng; Yung-Hsuan Hsiao; Kotaro Nakanishi; Matteo Fassan; Zhenghua Luo; Guqin Shi; Esmerina Tili; Huban Kutay; Francesca Lovat; Caterina Vicentini; Han-Li Huang; Shih-Wei Wang; Taewan Kim; Nicola Zanesi; Young-Jun Jeon; Tae Jin Lee; Jih-Hwa Guh; Mien-Chie Hung; Kalpana Ghoshal; Che-Ming Teng; Yong Peng; Carlo M Croce
Journal:  Cancer Cell       Date:  2016-11-14       Impact factor: 31.743

2.  Importing statistical measures into Artemis enhances gene identification in the Leishmania genome project.

Authors:  Gautam Aggarwal; E A Worthey; Paul D McDonagh; Peter J Myler
Journal:  BMC Bioinformatics       Date:  2003-06-07       Impact factor: 3.169

  2 in total

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