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Statistical analysis of genetic interactions in Tn-Seq data
Tn-Seq is an experimental method for probing the functions of genes through construction of complex random transposon insertion libraries and quantification of each mutant's abundance using next-generation sequencing. An important emerging application of Tn-Seq is for identifying genetic intera...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5499643/ https://www.ncbi.nlm.nih.gov/pubmed/28334803 http://dx.doi.org/10.1093/nar/gkx128 |
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author | DeJesus, Michael A. Nambi, Subhalaxmi Smith, Clare M. Baker, Richard E. Sassetti, Christopher M. Ioerger, Thomas R. |
author_facet | DeJesus, Michael A. Nambi, Subhalaxmi Smith, Clare M. Baker, Richard E. Sassetti, Christopher M. Ioerger, Thomas R. |
author_sort | DeJesus, Michael A. |
collection | PubMed |
description | Tn-Seq is an experimental method for probing the functions of genes through construction of complex random transposon insertion libraries and quantification of each mutant's abundance using next-generation sequencing. An important emerging application of Tn-Seq is for identifying genetic interactions, which involves comparing Tn mutant libraries generated in different genetic backgrounds (e.g. wild-type strain versus knockout strain). Several analytical methods have been proposed for analyzing Tn-Seq data to identify genetic interactions, including estimating relative fitness ratios and fitting a generalized linear model. However, these have limitations which necessitate an improved approach. We present a hierarchical Bayesian method for identifying genetic interactions through quantifying the statistical significance of changes in enrichment. The analysis involves a four-way comparison of insertion counts across datasets to identify transposon mutants that differentially affect bacterial fitness depending on genetic background. Our approach was applied to Tn-Seq libraries made in isogenic strains of Mycobacterium tuberculosis lacking three different genes of unknown function previously shown to be necessary for optimal fitness during infection. By analyzing the libraries subjected to selection in mice, we were able to distinguish several distinct classes of genetic interactions for each target gene that shed light on their functions and roles during infection. |
format | Online Article Text |
id | pubmed-5499643 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-54996432017-07-10 Statistical analysis of genetic interactions in Tn-Seq data DeJesus, Michael A. Nambi, Subhalaxmi Smith, Clare M. Baker, Richard E. Sassetti, Christopher M. Ioerger, Thomas R. Nucleic Acids Res Methods Online Tn-Seq is an experimental method for probing the functions of genes through construction of complex random transposon insertion libraries and quantification of each mutant's abundance using next-generation sequencing. An important emerging application of Tn-Seq is for identifying genetic interactions, which involves comparing Tn mutant libraries generated in different genetic backgrounds (e.g. wild-type strain versus knockout strain). Several analytical methods have been proposed for analyzing Tn-Seq data to identify genetic interactions, including estimating relative fitness ratios and fitting a generalized linear model. However, these have limitations which necessitate an improved approach. We present a hierarchical Bayesian method for identifying genetic interactions through quantifying the statistical significance of changes in enrichment. The analysis involves a four-way comparison of insertion counts across datasets to identify transposon mutants that differentially affect bacterial fitness depending on genetic background. Our approach was applied to Tn-Seq libraries made in isogenic strains of Mycobacterium tuberculosis lacking three different genes of unknown function previously shown to be necessary for optimal fitness during infection. By analyzing the libraries subjected to selection in mice, we were able to distinguish several distinct classes of genetic interactions for each target gene that shed light on their functions and roles during infection. Oxford University Press 2017-06-20 2017-02-22 /pmc/articles/PMC5499643/ /pubmed/28334803 http://dx.doi.org/10.1093/nar/gkx128 Text en © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Methods Online DeJesus, Michael A. Nambi, Subhalaxmi Smith, Clare M. Baker, Richard E. Sassetti, Christopher M. Ioerger, Thomas R. Statistical analysis of genetic interactions in Tn-Seq data |
title | Statistical analysis of genetic interactions in Tn-Seq data |
title_full | Statistical analysis of genetic interactions in Tn-Seq data |
title_fullStr | Statistical analysis of genetic interactions in Tn-Seq data |
title_full_unstemmed | Statistical analysis of genetic interactions in Tn-Seq data |
title_short | Statistical analysis of genetic interactions in Tn-Seq data |
title_sort | statistical analysis of genetic interactions in tn-seq data |
topic | Methods Online |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5499643/ https://www.ncbi.nlm.nih.gov/pubmed/28334803 http://dx.doi.org/10.1093/nar/gkx128 |
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