GISMO - gene identification using a support vector machine for ORF classification
Krause L, McHardy AC, Nattkemper TW, Pühler A, Stoye J, Meyer F (2007)
Nucleic Acids Research 35(2): 540-549.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
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Autor*in
Krause, Lutz;
McHardy, Alice C.;
Nattkemper, Tim WilhelmUniBi ;
Pühler, AlfredUniBi ;
Stoye, JensUniBi ;
Meyer, Folker
Einrichtung
Centrum für Biotechnologie > Technologieplattformen > Bioinformatics Resource Facility
Technische Fakultät > AG Biodata Mining
Technische Fakultät > AG Genominformatik
Centrum für Biotechnologie > Arbeitsgruppe T. Nattkemper
Centrum für Biotechnologie > Arbeitsgruppe A. Pühler
Centrum für Biotechnologie > Institut für Bioinformatik
Centrum für Biotechnologie > Institut für Genomforschung und Systembiologie
Fakultät für Biologie
Centrum für Biotechnologie > Arbeitsgruppe J. Stoye
Technische Fakultät > AG Biodata Mining
Technische Fakultät > AG Genominformatik
Centrum für Biotechnologie > Arbeitsgruppe T. Nattkemper
Centrum für Biotechnologie > Arbeitsgruppe A. Pühler
Centrum für Biotechnologie > Institut für Bioinformatik
Centrum für Biotechnologie > Institut für Genomforschung und Systembiologie
Fakultät für Biologie
Centrum für Biotechnologie > Arbeitsgruppe J. Stoye
Abstract / Bemerkung
We present the novel prokaryotic gene finder GISMO, which combines searches for protein family domains with composition-based classification based on a support vector machine. GISMO is highly accurate; exhibiting high sensitivity and specificity in gene identification. We found that it performs well for complete prokaryotic chromosomes, irrespective of their GC content, and also for plasmids as short as 10 kb, short genes and for genes with atypical sequence composition. Using GISMO, we found several thousand new predictions for the published genomes that are supported by extrinsic evidence, which strongly suggest that these are very likely biologically active genes. The source code for GISMO is freely available under the GPL license.
Erscheinungsjahr
2007
Zeitschriftentitel
Nucleic Acids Research
Band
35
Ausgabe
2
Seite(n)
540-549
ISSN
0305-1048
eISSN
1362-4962
Page URI
https://pub.uni-bielefeld.de/record/1783345
Zitieren
Krause L, McHardy AC, Nattkemper TW, Pühler A, Stoye J, Meyer F. GISMO - gene identification using a support vector machine for ORF classification. Nucleic Acids Research. 2007;35(2):540-549.
Krause, L., McHardy, A. C., Nattkemper, T. W., Pühler, A., Stoye, J., & Meyer, F. (2007). GISMO - gene identification using a support vector machine for ORF classification. Nucleic Acids Research, 35(2), 540-549. https://doi.org/10.1093/nar/gkl1083
Krause, Lutz, McHardy, Alice C., Nattkemper, Tim Wilhelm, Pühler, Alfred, Stoye, Jens, and Meyer, Folker. 2007. “GISMO - gene identification using a support vector machine for ORF classification”. Nucleic Acids Research 35 (2): 540-549.
Krause, L., McHardy, A. C., Nattkemper, T. W., Pühler, A., Stoye, J., and Meyer, F. (2007). GISMO - gene identification using a support vector machine for ORF classification. Nucleic Acids Research 35, 540-549.
Krause, L., et al., 2007. GISMO - gene identification using a support vector machine for ORF classification. Nucleic Acids Research, 35(2), p 540-549.
L. Krause, et al., “GISMO - gene identification using a support vector machine for ORF classification”, Nucleic Acids Research, vol. 35, 2007, pp. 540-549.
Krause, L., McHardy, A.C., Nattkemper, T.W., Pühler, A., Stoye, J., Meyer, F.: GISMO - gene identification using a support vector machine for ORF classification. Nucleic Acids Research. 35, 540-549 (2007).
Krause, Lutz, McHardy, Alice C., Nattkemper, Tim Wilhelm, Pühler, Alfred, Stoye, Jens, and Meyer, Folker. “GISMO - gene identification using a support vector machine for ORF classification”. Nucleic Acids Research 35.2 (2007): 540-549.
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Dieses Objekt ist durch das Urheberrecht und/oder verwandte Schutzrechte geschützt. [...]
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2019-09-06T08:48:51Z
MD5 Prüfsumme
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Daten bereitgestellt von European Bioinformatics Institute (EBI)
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Complete genome sequence of enterohemorrhagic Escherichia coli O157:H7 and genomic comparison with a laboratory strain K-12.
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The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes.
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The Bioperl toolkit: Perl modules for the life sciences.
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Large-scale prokaryotic gene prediction and comparison to genome annotation.
Nielsen P, Krogh A., Bioinformatics 21(24), 2005
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Lateral gene transfer and the nature of bacterial innovation.
Ochman H, Lawrence JG, Groisman EA., Nature 405(6784), 2000
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Begley TP, Downs DM, Ealick SE, McLafferty FW, Van Loon AP, Taylor S, Campobasso N, Chiu HJ, Kinsland C, Reddick JJ, Xi J., Arch. Microbiol. 171(5), 1999
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Comparative genomics of thiamin biosynthesis in procaryotes. New genes and regulatory mechanisms.
Rodionov DA, Vitreschak AG, Mironov AA, Gelfand MS., J. Biol. Chem. 277(50), 2002
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