LifeStyle-Specific-Islands (LiSSI): Integrated Bioinformatics Platform for Genomic Island Analysis

Eudes Barbosa, Richard Rottger, Anne-Christin Hauschild, Siomar de Castro Soares, Sebastian Boecker, Vasco Azevedo, Jan Baumbach

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Abstract

Distinct bacteria are able to cope with highly diverse lifestyles; for instance, they can be free living or host-associated. Thus, these organisms must possess a large and varied genomic arsenal to withstand different environmental conditions. To facilitate the identification of genomic features that might influence bacterial adaptation to a specific niche, we introduce LifeStyle-Specific-Islands (LiSSI). LiSSI combines evolutionary sequence analysis with statistical learning (Random Forest with feature selection, model tuning and robustness analysis). In summary, our strategy aims to identify conserved consecutive homology sequences (islands) in genomes and to identify the most discriminant islands for each lifestyle.
Original languageEnglish
JournalJournal of Integrative Bioinformatics
Volume14
Issue number2
Number of pages7
ISSN1613-4516
DOIs
Publication statusPublished - 2017

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genomic islands
bioinformatics
lifestyle
genomics
sequence homology
niches
learning
sequence analysis
environmental factors
genome
bacteria
organisms

Keywords

    Cite this

    Barbosa, Eudes ; Rottger, Richard ; Hauschild, Anne-Christin ; Soares, Siomar de Castro ; Boecker, Sebastian ; Azevedo, Vasco ; Baumbach, Jan. / LifeStyle-Specific-Islands (LiSSI): Integrated Bioinformatics Platform for Genomic Island Analysis. In: Journal of Integrative Bioinformatics. 2017 ; Vol. 14, No. 2.
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    abstract = "Distinct bacteria are able to cope with highly diverse lifestyles; for instance, they can be free living or host-associated. Thus, these organisms must possess a large and varied genomic arsenal to withstand different environmental conditions. To facilitate the identification of genomic features that might influence bacterial adaptation to a specific niche, we introduce LifeStyle-Specific-Islands (LiSSI). LiSSI combines evolutionary sequence analysis with statistical learning (Random Forest with feature selection, model tuning and robustness analysis). In summary, our strategy aims to identify conserved consecutive homology sequences (islands) in genomes and to identify the most discriminant islands for each lifestyle.",
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    author = "Eudes Barbosa and Richard Rottger and Anne-Christin Hauschild and Soares, {Siomar de Castro} and Sebastian Boecker and Vasco Azevedo and Jan Baumbach",
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    LifeStyle-Specific-Islands (LiSSI): Integrated Bioinformatics Platform for Genomic Island Analysis. / Barbosa, Eudes; Rottger, Richard; Hauschild, Anne-Christin; Soares, Siomar de Castro; Boecker, Sebastian; Azevedo, Vasco; Baumbach, Jan.

    In: Journal of Integrative Bioinformatics, Vol. 14, No. 2, 2017.

    Research output: Contribution to journalJournal articleResearchpeer-review

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    T1 - LifeStyle-Specific-Islands (LiSSI): Integrated Bioinformatics Platform for Genomic Island Analysis

    AU - Barbosa, Eudes

    AU - Rottger, Richard

    AU - Hauschild, Anne-Christin

    AU - Soares, Siomar de Castro

    AU - Boecker, Sebastian

    AU - Azevedo, Vasco

    AU - Baumbach, Jan

    PY - 2017

    Y1 - 2017

    N2 - Distinct bacteria are able to cope with highly diverse lifestyles; for instance, they can be free living or host-associated. Thus, these organisms must possess a large and varied genomic arsenal to withstand different environmental conditions. To facilitate the identification of genomic features that might influence bacterial adaptation to a specific niche, we introduce LifeStyle-Specific-Islands (LiSSI). LiSSI combines evolutionary sequence analysis with statistical learning (Random Forest with feature selection, model tuning and robustness analysis). In summary, our strategy aims to identify conserved consecutive homology sequences (islands) in genomes and to identify the most discriminant islands for each lifestyle.

    AB - Distinct bacteria are able to cope with highly diverse lifestyles; for instance, they can be free living or host-associated. Thus, these organisms must possess a large and varied genomic arsenal to withstand different environmental conditions. To facilitate the identification of genomic features that might influence bacterial adaptation to a specific niche, we introduce LifeStyle-Specific-Islands (LiSSI). LiSSI combines evolutionary sequence analysis with statistical learning (Random Forest with feature selection, model tuning and robustness analysis). In summary, our strategy aims to identify conserved consecutive homology sequences (islands) in genomes and to identify the most discriminant islands for each lifestyle.

    KW - Bacteria

    KW - Lifestyle

    KW - Machine Learning

    KW - Island

    KW - Homologous genes

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