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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Main Authors: Bingxin Lu, Hon Wai Leong
Format: Article
Language:English
Published: Elsevier 2016-01-01
Series:Computational and Structural Biotechnology Journal
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2001037016300046
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spelling doaj-cc9e45afbd074079a3541398076556fc2020-11-24T22:31:51ZengElsevierComputational and Structural Biotechnology Journal2001-03702016-01-0114C20020610.1016/j.csbj.2016.05.001Computational methods for predicting genomic islands in microbial genomesBingxin LuHon Wai LeongClusters 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.http://www.sciencedirect.com/science/article/pii/S2001037016300046Pathogenicity islandsSequence compositionGenome segmentationComparative genomicsOutlier detection
collection DOAJ
language English
format Article
sources DOAJ
author Bingxin Lu
Hon Wai Leong
spellingShingle Bingxin Lu
Hon Wai Leong
Computational methods for predicting genomic islands in microbial genomes
Computational and Structural Biotechnology Journal
Pathogenicity islands
Sequence composition
Genome segmentation
Comparative genomics
Outlier detection
author_facet Bingxin Lu
Hon Wai Leong
author_sort Bingxin Lu
title Computational methods for predicting genomic islands in microbial genomes
title_short 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_sort computational methods for predicting genomic islands in microbial genomes
publisher Elsevier
series Computational and Structural Biotechnology Journal
issn 2001-0370
publishDate 2016-01-01
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.
topic Pathogenicity islands
Sequence composition
Genome segmentation
Comparative genomics
Outlier detection
url http://www.sciencedirect.com/science/article/pii/S2001037016300046
work_keys_str_mv AT bingxinlu computationalmethodsforpredictinggenomicislandsinmicrobialgenomes
AT honwaileong computationalmethodsforpredictinggenomicislandsinmicrobialgenomes
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