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Computational methods for predicting genomic islands in microbial genomes

Clusters of genes acquired by lateral gene transfer in microbial genomes, are broadly referred to as genomic islands (GIs). GIs often carry genes important for genome evolution and adaptation to niches, such as genes involved in pathogenesis and antibiotic resistance. Therefore, GI prediction has gr...

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Detalles Bibliográficos
Autores principales: Lu, Bingxin, Leong, Hon Wai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4887561/
https://www.ncbi.nlm.nih.gov/pubmed/27293536
http://dx.doi.org/10.1016/j.csbj.2016.05.001
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author Lu, Bingxin
Leong, Hon Wai
author_facet Lu, Bingxin
Leong, Hon Wai
author_sort Lu, Bingxin
collection PubMed
description Clusters of genes acquired by lateral gene transfer in microbial genomes, are broadly referred to as genomic islands (GIs). GIs often carry genes important for genome evolution and adaptation to niches, such as genes involved in pathogenesis and antibiotic resistance. Therefore, GI prediction has gradually become an important part of microbial genome analysis. Despite inherent difficulties in identifying GIs, many computational methods have been developed and show good performance. In this mini-review, we first summarize the general challenges in predicting GIs. Then we group existing GI detection methods by their input, briefly describe representative methods in each group, and discuss their advantages as well as limitations. Finally, we look into the potential improvements for better GI prediction.
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spelling pubmed-48875612016-06-10 Computational methods for predicting genomic islands in microbial genomes Lu, Bingxin Leong, Hon Wai Comput Struct Biotechnol J Short Survey Clusters of genes acquired by lateral gene transfer in microbial genomes, are broadly referred to as genomic islands (GIs). GIs often carry genes important for genome evolution and adaptation to niches, such as genes involved in pathogenesis and antibiotic resistance. Therefore, GI prediction has gradually become an important part of microbial genome analysis. Despite inherent difficulties in identifying GIs, many computational methods have been developed and show good performance. In this mini-review, we first summarize the general challenges in predicting GIs. Then we group existing GI detection methods by their input, briefly describe representative methods in each group, and discuss their advantages as well as limitations. Finally, we look into the potential improvements for better GI prediction. Research Network of Computational and Structural Biotechnology 2016-05-07 /pmc/articles/PMC4887561/ /pubmed/27293536 http://dx.doi.org/10.1016/j.csbj.2016.05.001 Text en © 2016 Lu, Leong. Published by Elsevier B.V. on behalf of the Research Network of Computational and Structural Biotechnology. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Short Survey
Lu, Bingxin
Leong, Hon Wai
Computational methods for predicting genomic islands in microbial genomes
title Computational methods for predicting genomic islands in microbial genomes
title_full Computational methods for predicting genomic islands in microbial genomes
title_fullStr Computational methods for predicting genomic islands in microbial genomes
title_full_unstemmed Computational methods for predicting genomic islands in microbial genomes
title_short Computational methods for predicting genomic islands in microbial genomes
title_sort computational methods for predicting genomic islands in microbial genomes
topic Short Survey
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4887561/
https://www.ncbi.nlm.nih.gov/pubmed/27293536
http://dx.doi.org/10.1016/j.csbj.2016.05.001
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