Model-based identification of conditionally-essential genes from transposon-insertion sequencing data.
The understanding of bacterial gene function has been greatly enhanced by recent advancements in the deep sequencing of microbial genomes. Transposon insertion sequencing methods combines next-generation sequencing techniques with transposon mutagenesis for the exploration of the essentiality of gen...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-03-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1009273 |
| _version_ | 1798044711066271744 |
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| author | Vishal Sarsani Berent Aldikacti Shai He Rilee Zeinert Peter Chien Patrick Flaherty |
| author_facet | Vishal Sarsani Berent Aldikacti Shai He Rilee Zeinert Peter Chien Patrick Flaherty |
| author_sort | Vishal Sarsani |
| collection | DOAJ |
| description | The understanding of bacterial gene function has been greatly enhanced by recent advancements in the deep sequencing of microbial genomes. Transposon insertion sequencing methods combines next-generation sequencing techniques with transposon mutagenesis for the exploration of the essentiality of genes under different environmental conditions. We propose a model-based method that uses regularized negative binomial regression to estimate the change in transposon insertions attributable to gene-environment changes in this genetic interaction study without transformations or uniform normalization. An empirical Bayes model for estimating the local false discovery rate combines unique and total count information to test for genes that show a statistically significant change in transposon counts. When applied to RB-TnSeq (randomized barcode transposon sequencing) and Tn-seq (transposon sequencing) libraries made in strains of Caulobacter crescentus using both total and unique count data the model was able to identify a set of conditionally beneficial or conditionally detrimental genes for each target condition that shed light on their functions and roles during various stress conditions. |
| first_indexed | 2024-04-11T23:08:33Z |
| format | Article |
| id | doaj.art-62eb5e3090604adcb422522af5ac6b6e |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-11T23:08:33Z |
| publishDate | 2022-03-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-62eb5e3090604adcb422522af5ac6b6e2022-12-22T03:57:55ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-03-01183e100927310.1371/journal.pcbi.1009273Model-based identification of conditionally-essential genes from transposon-insertion sequencing data.Vishal SarsaniBerent AldikactiShai HeRilee ZeinertPeter ChienPatrick FlahertyThe understanding of bacterial gene function has been greatly enhanced by recent advancements in the deep sequencing of microbial genomes. Transposon insertion sequencing methods combines next-generation sequencing techniques with transposon mutagenesis for the exploration of the essentiality of genes under different environmental conditions. We propose a model-based method that uses regularized negative binomial regression to estimate the change in transposon insertions attributable to gene-environment changes in this genetic interaction study without transformations or uniform normalization. An empirical Bayes model for estimating the local false discovery rate combines unique and total count information to test for genes that show a statistically significant change in transposon counts. When applied to RB-TnSeq (randomized barcode transposon sequencing) and Tn-seq (transposon sequencing) libraries made in strains of Caulobacter crescentus using both total and unique count data the model was able to identify a set of conditionally beneficial or conditionally detrimental genes for each target condition that shed light on their functions and roles during various stress conditions.https://doi.org/10.1371/journal.pcbi.1009273 |
| spellingShingle | Vishal Sarsani Berent Aldikacti Shai He Rilee Zeinert Peter Chien Patrick Flaherty Model-based identification of conditionally-essential genes from transposon-insertion sequencing data. PLoS Computational Biology |
| title | Model-based identification of conditionally-essential genes from transposon-insertion sequencing data. |
| title_full | Model-based identification of conditionally-essential genes from transposon-insertion sequencing data. |
| title_fullStr | Model-based identification of conditionally-essential genes from transposon-insertion sequencing data. |
| title_full_unstemmed | Model-based identification of conditionally-essential genes from transposon-insertion sequencing data. |
| title_short | Model-based identification of conditionally-essential genes from transposon-insertion sequencing data. |
| title_sort | model based identification of conditionally essential genes from transposon insertion sequencing data |
| url | https://doi.org/10.1371/journal.pcbi.1009273 |
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