Finding novel genes in bacterial communities isolated from the environment (original) (raw)
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Bielefeld University, Center for Biotechnology (CeBiTec) D-33594 Bielefeld
Germany
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Bielefeld University, Center for Biotechnology (CeBiTec) D-33594 Bielefeld
Germany
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Bielefeld University, Center for Biotechnology (CeBiTec) D-33594 Bielefeld
Germany
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Fellowship for Interpretation of Genomes
Burr Ridge IL
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Department of Biology, San Diego State University
San Diego, CA
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Center for Microbial Sciences
San Diego, CA
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Universität Bielefeld, Lehrstuhl für Genetik, Fakultät für Biologie D-33594 Bielefeld
Germany
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Department of Biology, San Diego State University
San Diego, CA
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Center for Microbial Sciences
San Diego, CA
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Bielefeld University, Center for Biotechnology (CeBiTec) D-33594 Bielefeld
Germany
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Universität Bielefeld, Technische Fakultät D-33594 Bielefeld
Germany
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Lutz Krause, Naryttza N. Diaz, Daniela Bartels, Robert A. Edwards, Alfred Pühler, Forest Rohwer, Folker Meyer, Jens Stoye, Finding novel genes in bacterial communities isolated from the environment, Bioinformatics, Volume 22, Issue 14, July 2006, Pages e281–e289, https://doi.org/10.1093/bioinformatics/btl247
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Abstract
Motivation: Novel sequencing techniques can give access to organisms that are difficult to cultivate using conventional methods. When applied to environmental samples, the data generated has some drawbacks, e.g. short length of assembled contigs, in-frame stop codons and frame shifts. Unfortunately, current gene finders cannot circumvent these difficulties. At the same time, the automated prediction of genes is a prerequisite for the increasing amount of genomic sequences to ensure progress in metagenomics.
Results: We introduce a novel gene finding algorithm that incorporates features overcoming the short length of the assembled contigs from environmental data, in-frame stop codons as well as frame shifts contained in bacterial sequences. The results show that by searching for sequence similarities in an environmental sample our algorithm is capable of detecting a high fraction of its gene content, depending on the species composition and the overall size of the sample. The method is valuable for hunting novel unknown genes that may be specific for the habitat where the sample is taken. Finally, we show that our algorithm can even exploit the limited information contained in the short reads generated by 454 technology for the prediction of protein coding genes.
Availability: The program is freely available upon request.
Contact: Lutz.Krause@CeBiTec.Uni-Bielefeld.DE
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