Literature DB >> 12835277

GeneComber: combining outputs of gene prediction programs for improved results.

Sohrab P Shah1, Graham P McVicker, Alan K Mackworth, Sanja Rogic, B F Francis Ouellette.   

Abstract

UNLABELLED: We recently demonstrated that combining the output from Genscan and HMMgene can provide increased accuracy of gene predictions. We have created a robust software system that runs algorithms previously described on DNA sequences and provides a public web interface to the system for use by the biological community worldwide. The GeneComber system performs ab initio gene prediction by first taking a user inputted DNA sequence and running Genscan and HMMgene. The outputs of Genscan and HMMgene are then integrated using the EUI, GI and EUI_frame algorithms. All results are then stored into a relational database management system (RDBMS) and can then be retrieved through a web interface. The web interface provides a unified view of the GeneComber predictions by graphically overlaying outputs from Genscan, HMMgene, EUI, GI and EUI_frame. Outputs can also be retrieved in general feature format (GFF) or FASTA format. The software is written in the Perl programming language and is both dependent on and interoperable with the Bioperl toolkit. It includes high-level application programming interfaces (APIs) to run Genscan, HMMgene and a database API to insert prediction results into an RDBMS. The APIs are assembled into the genecomber script which is executed by the web interface or can be run directly from the Unix command line. The web interface is written in PHP and is structured so as to be easily modified for viewing data from any database that stores gene structures. AVAILABILITY: The GeneComber public web interface and supplementary information is located at http://bioinformatics.ubc.ca/genecomber The source code is released under the GNU General Public License and is available at ftp://ftp.bioinformatics.ubc.ca/pub/genecomber/software.

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Year:  2003        PMID: 12835277     DOI: 10.1093/bioinformatics/btg139

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


  8 in total

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Authors:  Avril Coghlan; Richard Durbin
Journal:  Bioinformatics       Date:  2007-05-05       Impact factor: 6.937

3.  Combining gene prediction methods to improve metagenomic gene annotation.

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Journal:  BMC Bioinformatics       Date:  2011-01-13       Impact factor: 3.169

4.  Identification and characterization of the Plasmodium vivax thrombospondin-related apical merozoite protein.

Authors:  Alvaro Mongui; Diana I Angel; Darwin A Moreno-Perez; Silvana Villarreal-Gonzalez; Hannia Almonacid; Magnolia Vanegas; Manuel A Patarroyo
Journal:  Malar J       Date:  2010-10-13       Impact factor: 2.979

5.  A Novel Quality Measure and Correction Procedure for the Annotation of Microbial Translation Initiation Sites.

Authors:  Lex Overmars; Roland J Siezen; Christof Francke
Journal:  PLoS One       Date:  2015-07-23       Impact factor: 3.240

6.  eCAMBer: efficient support for large-scale comparative analysis of multiple bacterial strains.

Authors:  Michal Wozniak; Limsoon Wong; Jerzy Tiuryn
Journal:  BMC Bioinformatics       Date:  2014-03-05       Impact factor: 3.169

7.  Reduce manual curation by combining gene predictions from multiple annotation engines, a case study of start codon prediction.

Authors:  Thomas H A Ederveen; Lex Overmars; Sacha A F T van Hijum
Journal:  PLoS One       Date:  2013-05-10       Impact factor: 3.240

8.  IPred - integrating ab initio and evidence based gene predictions to improve prediction accuracy.

Authors:  Franziska Zickmann; Bernhard Y Renard
Journal:  BMC Genomics       Date:  2015-02-26       Impact factor: 3.969

  8 in total

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