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Comparing Mycobacterium tuberculosis genomes using genome topology networks

BACKGROUND: Over the last decade, emerging research methods, such as comparative genomic analysis and phylogenetic study, have yielded new insights into genotypes and phenotypes of closely related bacterial strains. Several findings have revealed that genomic structural variations (SVs), including g...

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Autores principales: Jiang, Jianping, Gu, Jianlei, Zhang, Liang, Zhang, Chenyi, Deng, Xiao, Dou, Tonghai, Zhao, Guoping, Zhou, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4342819/
https://www.ncbi.nlm.nih.gov/pubmed/25766780
http://dx.doi.org/10.1186/s12864-015-1259-0
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author Jiang, Jianping
Gu, Jianlei
Zhang, Liang
Zhang, Chenyi
Deng, Xiao
Dou, Tonghai
Zhao, Guoping
Zhou, Yan
author_facet Jiang, Jianping
Gu, Jianlei
Zhang, Liang
Zhang, Chenyi
Deng, Xiao
Dou, Tonghai
Zhao, Guoping
Zhou, Yan
author_sort Jiang, Jianping
collection PubMed
description BACKGROUND: Over the last decade, emerging research methods, such as comparative genomic analysis and phylogenetic study, have yielded new insights into genotypes and phenotypes of closely related bacterial strains. Several findings have revealed that genomic structural variations (SVs), including gene gain/loss, gene duplication and genome rearrangement, can lead to different phenotypes among strains, and an investigation of genes affected by SVs may extend our knowledge of the relationships between SVs and phenotypes in microbes, especially in pathogenic bacteria. RESULTS: In this work, we introduce a ‘Genome Topology Network’ (GTN) method based on gene homology and gene locations to analyze genomic SVs and perform phylogenetic analysis. Furthermore, the concept of ‘unfixed ortholog’ has been proposed, whose members are affected by SVs in genome topology among close species. To improve the precision of 'unfixed ortholog' recognition, a strategy to detect annotation differences and complete gene annotation was applied. To assess the GTN method, a set of thirteen complete M. tuberculosis genomes was analyzed as a case study. GTNs with two different gene homology-assigning methods were built, the Clusters of Orthologous Groups (COG) method and the orthoMCL clustering method, and two phylogenetic trees were constructed accordingly, which may provide additional insights into whole genome-based phylogenetic analysis. We obtained 24 unfixable COG groups, of which most members were related to immunogenicity and drug resistance, such as PPE-repeat proteins (COG5651) and transcriptional regulator TetR gene family members (COG1309). CONCLUSIONS: The GTN method has been implemented in PERL and released on our website. The tool can be downloaded from http://homepage.fudan.edu.cn/zhouyan/gtn/, and allows re-annotating the ‘lost’ genes among closely related genomes, analyzing genes affected by SVs, and performing phylogenetic analysis. With this tool, many immunogenic-related and drug resistance-related genes were found to be affected by SVs in M. tuberculosis genomes. We believe that the GTN method will be suitable for the exploration of genomic SVs in connection with biological features of bacterial strains, and that GTN-based phylogenetic analysis will provide additional insights into whole genome-based phylogenetic analysis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-015-1259-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-43428192015-02-28 Comparing Mycobacterium tuberculosis genomes using genome topology networks Jiang, Jianping Gu, Jianlei Zhang, Liang Zhang, Chenyi Deng, Xiao Dou, Tonghai Zhao, Guoping Zhou, Yan BMC Genomics Methodology Article BACKGROUND: Over the last decade, emerging research methods, such as comparative genomic analysis and phylogenetic study, have yielded new insights into genotypes and phenotypes of closely related bacterial strains. Several findings have revealed that genomic structural variations (SVs), including gene gain/loss, gene duplication and genome rearrangement, can lead to different phenotypes among strains, and an investigation of genes affected by SVs may extend our knowledge of the relationships between SVs and phenotypes in microbes, especially in pathogenic bacteria. RESULTS: In this work, we introduce a ‘Genome Topology Network’ (GTN) method based on gene homology and gene locations to analyze genomic SVs and perform phylogenetic analysis. Furthermore, the concept of ‘unfixed ortholog’ has been proposed, whose members are affected by SVs in genome topology among close species. To improve the precision of 'unfixed ortholog' recognition, a strategy to detect annotation differences and complete gene annotation was applied. To assess the GTN method, a set of thirteen complete M. tuberculosis genomes was analyzed as a case study. GTNs with two different gene homology-assigning methods were built, the Clusters of Orthologous Groups (COG) method and the orthoMCL clustering method, and two phylogenetic trees were constructed accordingly, which may provide additional insights into whole genome-based phylogenetic analysis. We obtained 24 unfixable COG groups, of which most members were related to immunogenicity and drug resistance, such as PPE-repeat proteins (COG5651) and transcriptional regulator TetR gene family members (COG1309). CONCLUSIONS: The GTN method has been implemented in PERL and released on our website. The tool can be downloaded from http://homepage.fudan.edu.cn/zhouyan/gtn/, and allows re-annotating the ‘lost’ genes among closely related genomes, analyzing genes affected by SVs, and performing phylogenetic analysis. With this tool, many immunogenic-related and drug resistance-related genes were found to be affected by SVs in M. tuberculosis genomes. We believe that the GTN method will be suitable for the exploration of genomic SVs in connection with biological features of bacterial strains, and that GTN-based phylogenetic analysis will provide additional insights into whole genome-based phylogenetic analysis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-015-1259-0) contains supplementary material, which is available to authorized users. BioMed Central 2015-02-14 /pmc/articles/PMC4342819/ /pubmed/25766780 http://dx.doi.org/10.1186/s12864-015-1259-0 Text en © Jiang et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Jiang, Jianping
Gu, Jianlei
Zhang, Liang
Zhang, Chenyi
Deng, Xiao
Dou, Tonghai
Zhao, Guoping
Zhou, Yan
Comparing Mycobacterium tuberculosis genomes using genome topology networks
title Comparing Mycobacterium tuberculosis genomes using genome topology networks
title_full Comparing Mycobacterium tuberculosis genomes using genome topology networks
title_fullStr Comparing Mycobacterium tuberculosis genomes using genome topology networks
title_full_unstemmed Comparing Mycobacterium tuberculosis genomes using genome topology networks
title_short Comparing Mycobacterium tuberculosis genomes using genome topology networks
title_sort comparing mycobacterium tuberculosis genomes using genome topology networks
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4342819/
https://www.ncbi.nlm.nih.gov/pubmed/25766780
http://dx.doi.org/10.1186/s12864-015-1259-0
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