Literature DB >> 14751985

Using functional and organizational information to improve genome-wide computational prediction of transcription units on pathway-genome databases.

P R Romero1, P D Karp.   

Abstract

MOTIVATION: The prediction of transcription units (TUs, which are similar to operons) is an important problem that has been tackled using many different approaches. The availability of complete microbial genomes has made genome-wide TU predictions possible. Pathway-genome databases (PGDBs) add metabolic and other organizational (i.e. protein complexes) information to the annotated genome, and are able to capture TU organization information. These characteristics of PGDBs make them a suitable framework for the development and implementation of TU predictors.
RESULTS: We implemented a TU predictor that uses only intergenic distance and functional classification of genes to predict TU boundaries, and applied it to EcoCyc, our PGDB of Escherichia coli. To this original predictor, we added information on metabolic pathways, protein complexes and transporters, all readily available in EcoCyc, in order to generate an enhanced predictor. The enhanced predictor correctly predicted 80% of the known E.coli TUs (69% of the known operons), a moderate improvement over the original predictor's performance (75% of TUs and 65% of operons correctly predicted), demonstrating that the extra information available in the PGDB does indeed improve prediction performance. Performance of this E.coli-based predictor on a genome other than that of E.coli was tested on BsubCyc, our computationally generated PGDB for Bacillus subtilis, for which a set of 100 known operons is available. Prediction accuracy decreased substantially (46% of the known operons correctly predicted). This was due in part to missing information in BsubCyc, which prevented full use of the predictor's features. The augmented predictor has been implemented as part of our Pathway Tools software suite, and can be used to populate a PGDB with predicted TUs. AVAILABILITY: The TU predictor is included in version 7.0 of the Pathway Tools software suite. Pathway Tools 7.0 is available free of charge to academic institutions and for a fee to commercial enterprises. It runs on Sun Solaris 8, Linux and Windows. TUs predicted on the Caulobacter crescentus and Mycobacterium tuberculosis (H37Rv) genomes are available in our CauloCyc and MtbrvCyc databases, available at the BioCyc web site (http://biocyc.org). To obtain version 7.0 of Pathway Tools, follow the directions in our web site, http://biocyc.org/download.shtml.

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Year:  2004        PMID: 14751985     DOI: 10.1093/bioinformatics/btg471

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


  35 in total

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2.  Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology.

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3.  The BioCyc collection of microbial genomes and metabolic pathways.

Authors:  Peter D Karp; Richard Billington; Ron Caspi; Carol A Fulcher; Mario Latendresse; Anamika Kothari; Ingrid M Keseler; Markus Krummenacker; Peter E Midford; Quang Ong; Wai Kit Ong; Suzanne M Paley; Pallavi Subhraveti
Journal:  Brief Bioinform       Date:  2019-07-19       Impact factor: 11.622

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Authors:  Julie L Stoudenmire; Alicia L Schmidt; Melissa P Tumen-Velasquez; Kathryn T Elliott; Nicole S Laniohan; S Walker Whitley; Nickolaus R Galloway; Melesse Nune; Michael West; Cory Momany; Ellen L Neidle; Anna C Karls
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5.  High accuracy operon prediction method based on STRING database scores.

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6.  Shewregdb: database and visualization environment for experimental and predicted regulatory information in Shewanella oneidensis mr-1.

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7.  Binary particle swarm optimization for operon prediction.

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8.  REMap: Operon map of M. tuberculosis based on RNA sequence data.

Authors:  Shaaretha Pelly; Kathryn Winglee; Fang Fang Xia; Rick L Stevens; William R Bishai; Gyanu Lamichhane
Journal:  Tuberculosis (Edinb)       Date:  2016-04-29       Impact factor: 3.131

9.  The conservation and evolutionary modularity of metabolism.

Authors:  José M Peregrín-Alvarez; Chris Sanford; John Parkinson
Journal:  Genome Biol       Date:  2009-06-12       Impact factor: 13.583

10.  Annotation-based inference of transporter function.

Authors:  Thomas J Lee; Ian Paulsen; Peter Karp
Journal:  Bioinformatics       Date:  2008-07-01       Impact factor: 6.937

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