ArticleBMC bioinformatics2019
Statistical analysis of variability in TnSeq data across conditions using zero-inflated negative binomial regression.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 20 citations in OpenAlex.
- A scalable gut epithelial organoid model reveals the genome-wide colonization landscape of a human-adapted pathogen.Nature genetics · 2025Article
- Pangenome evaluation of gene essentiality inMicrobiology spectrum · 2024Article
- Cofitness network connectivity determines a fuzzy essential zone in open bacterial pangenome.mLife · 2024Article
- Air pollution and age-dependent changes in emotional behavior across early adolescence in the U.S.Environmental research · 2024Article
- Role of the CrcB transporter of Pseudomonas putida in the multi-level stress response elicited by mineral fluoride.Environmental microbiology · 2022Article
- Model-based identification of conditionally-essential genes from transposon-insertion sequencing data.PLoS computational biology · 2022Article
- Analysis of Gene Essentiality from TnSeq Data Using Transit.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Comprehensive understanding of Tn5 insertion preference improves transcription regulatory element identification.NAR genomics and bioinformatics · 2021Article
- Oxidative damage and delayed replication allow viableScience translational medicine · 2021Article
- Article
- The Increased Accumulation of Staphylococcus aureus Virulence Factors Is Maximized in aInfection and immunity · 2021Article
- Transposon Insertion Sequencing, a Global Measure of Gene Function.Annual review of genetics · 2020Review
- A decade of advances in transposon-insertion sequencing.Nature reviews. Genetics · 2020Review
- Distinct Bacterial Pathways Influence the Efficacy of Antibiotics against Mycobacterium tuberculosis.mSystems · 2020Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundDeep sequencing of transposon mutant libraries (or TnSeq) is a powerful method for probing essentiality of genomic loci under different environmental conditions. Various analytical methods have been described for identifying conditionally essential genes whose tolerance for insertions varies between two conditions. However, for large-scale experiments involving many conditions, a method is needed for identifying genes that exhibit significant variability in insertions across multiple conditions.
resultsIn this paper, we introduce a novel statistical method for identifying genes with significant variability of insertion counts across multiple conditions based on Zero-Inflated Negative Binomial (ZINB) regression. Using likelihood ratio tests, we show that the ZINB distribution fits TnSeq data better than either ANOVA or a Negative Binomial (in a generalized linear model). We use ZINB regression to identify genes required for infection of M. tuberculosis H37Rv in C57BL/6 mice. We also use ZINB to perform a analysis of genes conditionally essential in H37Rv cultures exposed to multiple antibiotics.
conclusionsOur results show that, not only does ZINB generally identify most of the genes found by pairwise resampling (and vastly out-performs ANOVA), but it also identifies additional genes where variability is detectable only when the magnitudes of insertion counts are treated separately from local differences in saturation, as in the ZINB model.
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