{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Sarsani V"],"funding":["National Institute of General Medical Sciences","NIGMS NIH HHS","National Science Foundation"],"pagination":["e1009273"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8929702"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["18(3)"],"pubmed_abstract":["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."],"journal":["PLoS computational biology"],"pubmed_title":["Model-based identification of conditionally-essential genes from transposon-insertion sequencing data."],"pmcid":["PMC8929702"],"funding_grant_id":["HDR TRIPODS 1934846","R01-GM135931","R01 GM135931","R35 GM130320","T32 GM139789","R35GM130320"],"pubmed_authors":["He S","Sarsani V","Zeinert R","Aldikacti B","Flaherty P","Chien P"],"additional_accession":[]},"is_claimable":false,"name":"Model-based identification of conditionally-essential genes from transposon-insertion sequencing data.","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.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Mar","modification":"2025-04-03T22:44:55.562Z","creation":"2025-02-19T05:05:12.284Z"},"accession":"S-EPMC8929702","cross_references":{"pubmed":["35255084"],"doi":["10.1371/journal.pcbi.1009273"]}}