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      PHAST: A Fast Phage Search Tool

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          PHAge Search Tool (PHAST) is a web server designed to rapidly and accurately identify, annotate and graphically display prophage sequences within bacterial genomes or plasmids. It accepts either raw DNA sequence data or partially annotated GenBank formatted data and rapidly performs a number of database comparisons as well as phage ‘cornerstone’ feature identification steps to locate, annotate and display prophage sequences and prophage features. Relative to other prophage identification tools, PHAST is up to 40 times faster and up to 15% more sensitive. It is also able to process and annotate both raw DNA sequence data and Genbank files, provide richly annotated tables on prophage features and prophage ‘quality’ and distinguish between intact and incomplete prophage. PHAST also generates downloadable, high quality, interactive graphics that display all identified prophage components in both circular and linear genomic views. PHAST is available at ( http://phast.wishartlab.com).

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          Complete genome sequence and comparative analysis of the metabolically versatile Pseudomonas putida KT2440.

          K E Nelson, C Weinel, I. T. Paulsen … (2002)
          Pseudomonas putida is a metabolically versatile saprophytic soil bacterium that has been certified as a biosafety host for the cloning of foreign genes. The bacterium also has considerable potential for biotechnological applications. Sequence analysis of the 6.18 Mb genome of strain KT2440 reveals diverse transport and metabolic systems. Although there is a high level of genome conservation with the pathogenic Pseudomonad Pseudomonas aeruginosa (85% of the predicted coding regions are shared), key virulence factors including exotoxin A and type III secretion systems are absent. Analysis of the genome gives insight into the non-pathogenic nature of P. putida and points to potential new applications in agriculture, biocatalysis, bioremediation and bioplastic production.
          Article 0 comments Cited 315 times – based on 0 reviews     Review now
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            Microbial gene identification using interpolated Markov models.

            Lars White, S Kasif, A Delcher … (1998)
            This paper describes a new system, GLIMMER, for finding genes in microbial genomes. In a series of tests on Haemophilus influenzae , Helicobacter pylori and other complete microbial genomes, this system has proven to be very accurate at locating virtually all the genes in these sequences, outperforming previous methods. A conservative estimate based on experiments on H.pylori and H. influenzae is that the system finds >97% of all genes. GLIMMER uses interpolated Markov models (IMMs) as a framework for capturing dependencies between nearby nucleotides in a DNA sequence. An IMM-based method makes predictions based on a variable context; i.e., a variable-length oligomer in a DNA sequence. The context used by GLIMMER changes depending on the local composition of the sequence. As a result, GLIMMER is more flexible and more powerful than fixed-order Markov methods, which have previously been the primary content-based technique for finding genes in microbial DNA.
            Article 0 comments Cited 190 times – based on 0 reviews     Review now
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              Microbial gene identification using interpolated Markov models

              S. L. Salzberg, A. L. Delcher, S Kasif … (1998)
              This paper describes a new system, GLIMMER, for finding genes in microbial genomes. In a series of tests on Haemophilus influenzae , Helicobacter pylori and other complete microbial genomes, this system has proven to be very accurate at locating virtually all the genes in these sequences, outperforming previous methods. A conservative estimate based on experiments on H.pylori and H. influenzae is that the system finds >97% of all genes. GLIMMER uses interpolated Markov models (IMMs) as a framework for capturing dependencies between nearby nucleotides in a DNA sequence. An IMM-based method makes predictions based on a variable context; i.e., a variable-length oligomer in a DNA sequence. The context used by GLIMMER changes depending on the local composition of the sequence. As a result, GLIMMER is more flexible and more powerful than fixed-order Markov methods, which have previously been the primary content-based technique for finding genes in microbial DNA.
              Article 0 comments Cited 127 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (publisher-id): nar
                Journal ID (hwp): nar
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date Collection: 1 July 2011
                Publication date (Print): 1 July 2011
                Publication date (Electronic): 14 June 2011
                Publication date PMC-release: 14 June 2011
                Volume: 39
                Issue: Web Server issue , Web Server issue
                Pages: W347-W352
                Affiliations
                1Department of Biological Sciences, 2Department of Computing Science, University of Alberta and 3National Research Council, National Institute for Nanotechnology (NINT), Edmonton, AB, Canada T6G 2E8
                Author notes
                *To whom correspondence should be addressed. Tel: +780 492 0383; Fax: +780 492 5305; Email: david.wishart@ 123456ualberta.ca
                Article
                Publisher ID: gkr485
                DOI: 10.1093/nar/gkr485
                PMC ID: 3125810
                PubMed ID: 21672955
                SO-VID: c11082e2-364e-44d2-8d43-84e4f81dcceb
                Copyright © © The Author(s) 2011. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 28 February 2011
                Date revision received : 21 April 2011
                Date accepted : 26 May 2011
                Page count
                Pages: 6
                Categories
                Subject: Articles

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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