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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2016
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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. |
format | Online Article Text |
id | pubmed-4887561 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT lubingxin computationalmethodsforpredictinggenomicislandsinmicrobialgenomes AT leonghonwai computationalmethodsforpredictinggenomicislandsinmicrobialgenomes |