ArticleScientific reports2017
A Noise Trimming and Positional Significance of Transposon Insertion System to Identify Essential Genes in Yersinia pestis.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- Identification ofJournal of bacteriology · 2023Article
- Repression by the H-NS/YmoA histone-like protein complex enables IscR dependent regulation of the Yersinia T3SS.PLoS genetics · 2022Article
- Probabilistic identification of bacterial essential genes via insertion density using TraDIS data with Tn5 libraries.Bioinformatics (Oxford, England) · 2021Article
- PsaF Is a Membrane-Localized pH Sensor That RegulatesJournal of bacteriology · 2021Article
- Global Analysis of Genes Essential for Francisella tularensis Schu S4 GrowthJournal of bacteriology · 2019Article
- High-throughput analysis of Yersinia pseudotuberculosis gene essentiality in optimised in vitro conditions, and implications for the speciation of Yersinia pestis.BMC microbiology · 2018Article
- An integrated computational-experimental approach reveals Yersinia pestis genes essential across a narrow or a broad range of environmental conditions.BMC microbiology · 2017Article
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Authors and funding
12 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Massively parallel sequencing technology coupled with saturation mutagenesis has provided new and global insights into gene functions and roles. At a simplistic level, the frequency of mutations within genes can indicate the degree of essentiality. However, this approach neglects to take account of the positional significance of mutations - the function of a gene is less likely to be disrupted by a mutation close to the distal ends. Therefore, a systematic bioinformatics approach to improve the reliability of essential gene identification is desirable. We report here a parametric model which introduces a novel mutation feature together with a noise trimming approach to predict the biological significance of Tn5 mutations. We show improved performance of essential gene prediction in the bacterium Yersinia pestis, the causative agent of plague. This method would have broad applicability to other organisms and to the identification of genes which are essential for competitiveness or survival under a broad range of stresses.
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