Unsupervised Learning Approach for Comparing Multiple Transposon Insertion Sequencing Studies

ABSTRACT Transposon insertion sequencing (TIS) is a widely used technique for conducting genome-scale forward genetic screens in bacteria. However, few methods enable comparison of TIS data across multiple replicates of a screen or across independent screens, including screens performed in different...

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Autores principales:	Troy P. Hubbard, Jonathan D. D’Gama, Gabriel Billings, Brigid M. Davis, Matthew K. Waldor
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2019
Materias:	PCA host-pathogen interactions in vivo screen principal-component analysis Tn-seq Vibrio cholerae Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/0d1c22ae2db24a2e9bee5b0bd77896b4
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spelling	oai:doaj.org-article:0d1c22ae2db24a2e9bee5b0bd77896b42021-11-15T15:22:05ZUnsupervised Learning Approach for Comparing Multiple Transposon Insertion Sequencing Studies10.1128/mSphere.00031-192379-5042https://doaj.org/article/0d1c22ae2db24a2e9bee5b0bd77896b42019-02-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSphere.00031-19https://doaj.org/toc/2379-5042ABSTRACT Transposon insertion sequencing (TIS) is a widely used technique for conducting genome-scale forward genetic screens in bacteria. However, few methods enable comparison of TIS data across multiple replicates of a screen or across independent screens, including screens performed in different organisms. Here, we introduce a post hoc analytic framework, comparative TIS (CompTIS), which utilizes unsupervised learning to enable meta-analysis of multiple TIS data sets. CompTIS first implements screen-level principal-component analysis (PCA) and clustering to identify variation between the TIS screens. This initial screen-level analysis facilitates the selection of related screens for additional analyses, reveals the relatedness of complex environments based on growth phenotypes measured by TIS, and provides a useful quality control step. Subsequently, PCA is performed on genes to identify loci whose corresponding mutants lead to concordant/discordant phenotypes across all or in a subset of screens. We used CompTIS to analyze published intestinal colonization TIS data sets from two vibrio species. Gene-level analyses identified both pan-vibrio genes required for intestinal colonization and conserved genes that displayed species-specific requirements. CompTIS is applicable to virtually any combination of TIS screens and can be implemented without regard to either the number of screens or the methods used for upstream data analysis. IMPORTANCE Forward genetic screens are powerful tools for functional genomics. The comparison of similar forward genetic screens performed in different organisms enables the identification of genes with similar or different phenotypes across organisms. Transposon insertion sequencing is a widely used method for conducting genome-scale forward genetic screens in bacteria, yet few bioinformatic approaches have been developed to compare the results of screen replicates and different screens conducted across species or strains. Here, we used principal-component analysis (PCA) and hierarchical clustering, two unsupervised learning approaches, to analyze the relatedness of multiple in vivo screens of pathogenic vibrios. This analytic framework reveals both shared pan-vibrio requirements for intestinal colonization and strain-specific dependencies. Our findings suggest that PCA-based analytics will be a straightforward widely applicable approach for comparing diverse transposon insertion sequencing screens.Troy P. HubbardJonathan D. D’GamaGabriel BillingsBrigid M. DavisMatthew K. WaldorAmerican Society for MicrobiologyarticlePCAhost-pathogen interactionsin vivo screenprincipal-component analysisTn-seqVibrio choleraeMicrobiologyQR1-502ENmSphere, Vol 4, Iss 1 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	PCA host-pathogen interactions in vivo screen principal-component analysis Tn-seq Vibrio cholerae Microbiology QR1-502
spellingShingle	PCA host-pathogen interactions in vivo screen principal-component analysis Tn-seq Vibrio cholerae Microbiology QR1-502 Troy P. Hubbard Jonathan D. D’Gama Gabriel Billings Brigid M. Davis Matthew K. Waldor Unsupervised Learning Approach for Comparing Multiple Transposon Insertion Sequencing Studies
description	ABSTRACT Transposon insertion sequencing (TIS) is a widely used technique for conducting genome-scale forward genetic screens in bacteria. However, few methods enable comparison of TIS data across multiple replicates of a screen or across independent screens, including screens performed in different organisms. Here, we introduce a post hoc analytic framework, comparative TIS (CompTIS), which utilizes unsupervised learning to enable meta-analysis of multiple TIS data sets. CompTIS first implements screen-level principal-component analysis (PCA) and clustering to identify variation between the TIS screens. This initial screen-level analysis facilitates the selection of related screens for additional analyses, reveals the relatedness of complex environments based on growth phenotypes measured by TIS, and provides a useful quality control step. Subsequently, PCA is performed on genes to identify loci whose corresponding mutants lead to concordant/discordant phenotypes across all or in a subset of screens. We used CompTIS to analyze published intestinal colonization TIS data sets from two vibrio species. Gene-level analyses identified both pan-vibrio genes required for intestinal colonization and conserved genes that displayed species-specific requirements. CompTIS is applicable to virtually any combination of TIS screens and can be implemented without regard to either the number of screens or the methods used for upstream data analysis. IMPORTANCE Forward genetic screens are powerful tools for functional genomics. The comparison of similar forward genetic screens performed in different organisms enables the identification of genes with similar or different phenotypes across organisms. Transposon insertion sequencing is a widely used method for conducting genome-scale forward genetic screens in bacteria, yet few bioinformatic approaches have been developed to compare the results of screen replicates and different screens conducted across species or strains. Here, we used principal-component analysis (PCA) and hierarchical clustering, two unsupervised learning approaches, to analyze the relatedness of multiple in vivo screens of pathogenic vibrios. This analytic framework reveals both shared pan-vibrio requirements for intestinal colonization and strain-specific dependencies. Our findings suggest that PCA-based analytics will be a straightforward widely applicable approach for comparing diverse transposon insertion sequencing screens.
format	article
author	Troy P. Hubbard Jonathan D. D’Gama Gabriel Billings Brigid M. Davis Matthew K. Waldor
author_facet	Troy P. Hubbard Jonathan D. D’Gama Gabriel Billings Brigid M. Davis Matthew K. Waldor
author_sort	Troy P. Hubbard
title	Unsupervised Learning Approach for Comparing Multiple Transposon Insertion Sequencing Studies
title_short	Unsupervised Learning Approach for Comparing Multiple Transposon Insertion Sequencing Studies
title_full	Unsupervised Learning Approach for Comparing Multiple Transposon Insertion Sequencing Studies
title_fullStr	Unsupervised Learning Approach for Comparing Multiple Transposon Insertion Sequencing Studies
title_full_unstemmed	Unsupervised Learning Approach for Comparing Multiple Transposon Insertion Sequencing Studies
title_sort	unsupervised learning approach for comparing multiple transposon insertion sequencing studies
publisher	American Society for Microbiology
publishDate	2019
url	https://doaj.org/article/0d1c22ae2db24a2e9bee5b0bd77896b4
work_keys_str_mv	AT troyphubbard unsupervisedlearningapproachforcomparingmultipletransposoninsertionsequencingstudies AT jonathanddgama unsupervisedlearningapproachforcomparingmultipletransposoninsertionsequencingstudies AT gabrielbillings unsupervisedlearningapproachforcomparingmultipletransposoninsertionsequencingstudies AT brigidmdavis unsupervisedlearningapproachforcomparingmultipletransposoninsertionsequencingstudies AT matthewkwaldor unsupervisedlearningapproachforcomparingmultipletransposoninsertionsequencingstudies
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Unsupervised Learning Approach for Comparing Multiple Transposon Insertion Sequencing Studies

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