ArticlePLoS computational biology2026
Introducing gold-standard essential gene datasets for Pseudomonas aeruginosa to enhance Tn-Seq analyses.
Article in PLoS computational biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Transposon insertion sequencing of Pseudomonas aeruginosa identifies multiple intersecting pathways essential for extreme colistin resistance.PLoS pathogens · 2026Article
- ECLIPSE: exploring the dark proteome of ESKAPE pathogens through the sequence similarity network of the Protein Universe Atlas.Bioinformatics (Oxford, England) · 2026Article
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5 authors.
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Abstract
Transposon Sequencing (Tn-Seq) is a high-throughput technique that utilizes transposon mutant libraries to assess gene fitness or essentiality under specific conditions potentially identifying novel therapeutic targets. However, the diversity of statistical methods, bioinformatics tools, and parameters complicates the selection of the most appropriate and reliable analysis pipeline for a given dataset. A significant limitation of existing studies is the absence of a gold-standard set of essential genes (EGs) for evaluating the analysis process. Relying on the original study as a gold-standard is suboptimal, as these results may have been obtained using non-optimal tools. Here, we introduce reliable EG datasets for Pseudomonas aeruginosa to enhance Tn-Seq analyses. By utilizing literature data and sequencing of six samples from PA14 Wild-Type (WT) and PA14 OprD-deficient (ΔoprD), grown in LB medium, we compared EG lists generated by several statistical methods of TRANSIT2 and by the FiTnEss tools. We established a reference dataset of 84 genes found in P. aeruginosa and another gold-standard set composed of 115 genes specific to PA14 grown in LB. Our findings revealed that depending on the analysis method used, retrieval rates of gold-standard genes ranged from 0% to 100%. The Hidden-Markov Model (HMM) method available in TRANSIT2 identified approximately 90% of gold-standard EGs, while FiTnEss identified up to 100%. This study addressed a critical gap in the field by providing gold-standard sets of EGs, enabling comparative evaluation of Tn-Seq analysis methods to help researcher select the most suitable bioinformatics pipeline for a given Tn-Seq dataset. We anticipate that our results will facilitate Tn-Seq analysis comparisons, harmonize P. aeruginosa-related studies, promote standardization and enhance reproducibility. Ultimately, this will lead to more reliable identification of EGs and potential therapeutic targets in P. aeruginosa, advancing our understanding of this important pathogen.
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