FASTQINS

FASTQINS maps transposon insertions from Tn-seq data to enable identification and estimation of gene essentiality in microbial genomes.

Key Features:

• Detection of Transposon Insertions: Identifies transposon insertion sites from Tn-seq data with high precision for downstream gene essentiality analysis.
• Artifact Correction: Detects and corrects artifacts in Tn-seq datasets to reduce biases that can skew interpretation.
• Integration with ANUBIS: Integrates the ANUBIS library of functions to evaluate and adjust for known and previously uncharacterized deviating factors in Tn-seq analysis.
• Essentiality Estimation Models: Implements established essentiality estimation models and novel approaches that do not require a training set of genes to predict gene essentiality.
• Standardized Processing and Curation: Provides standardized processing, treatment, curation, and analysis workflows for Tn-seq datasets.
• High Resolution Mapping: Supports essentiality estimation and artifact correction at ~1.5-bp resolution.
• Implementation: Delivered as a Python-based bioinformatics pipeline.

Scientific Applications:

• Evolutionary Biology: Facilitates identification of minimal gene sets and studies of genome essentiality relevant to evolutionary questions.
• Synthetic Biology: Informs design and evaluation of reduced genomes and synthetic constructs by providing gene essentiality estimates.
• Reduced-Genome Studies: Enables high-resolution analysis of reduced genomes, exemplified by application to Mycoplasma pneumoniae.
• Vaccine Design: Supplies genome essentiality estimates that can inform the design of live vaccines.
• Optimization of Microbial Growth Conditions: Provides essentiality-based insights useful for optimizing microbial growth conditions in experimental and applied settings.

Methodology:

Python-based pipeline performs detailed analysis of Tn-seq data to identify transposon insertion sites and correct artifacts, integrating ANUBIS functions to evaluate and mitigate deviating factors and applying essentiality estimation models including approaches that do not require training sets.