gdtools_apply

gdtools_apply applies predicted mutations from breseq Genome Diff files to reference sequences to generate applied genomes and characterize structural variations in haploid microbial genomes.

Key Features:

• Integration with breseq Pipeline: Operates as part of the breseq pipeline for predicting mutations in haploid microbial genomes.
• Split-read junction evaluation and repeat matching: Evaluates new sequence junctions using split-read alignments to a reference genome and incorporates matches to repeat sequences.
• Read coverage evenness statistical model: Employs a statistical model of read coverage evenness to validate or reject structural-variation predictions.
• Combined junction and deletion analysis: Combines predictions of new junctions with deleted chromosomal regions to produce biologically relevant mutation descriptions.
• Detection of mobile genetic elements and repeat-mediated events: Detects structural variations including deletions that generate unique breakpoints, insertions of mobile genetic elements, and deletions mediated by mobile elements or repeats.
• Moderate read-depth performance: Reliably predicts structural variations with modest read-depth coverage (>40-fold).
• Input/output handling: Processes a single Genome Diff plus multiple reference files and outputs applied sequences in a unified file format containing detailed structural-variation information.

Scientific Applications:

• Microbial Epidemiology: Identification of structural variations to track the evolution of pathogenic traits in microbes.
• Experimental Evolution: Analysis of mutations that contribute to new lifestyles or adaptations in microbial populations.
• Synthetic Biology: Assessment of mutational impacts on engineered genomes to inform genome design and modification.
• Genetics Research: Elucidation of the genetic basis of spontaneous mutations, exemplified by E. coli K-12 mutation accumulation experiments.

Methodology:

Processes a single Genome Diff and multiple reference files, applies all identified mutations to reference sequences, evaluates new junctions with split-read alignments and repeat matches, employs a read-coverage evenness statistical model to accept or reject predictions, combines junctions with deleted regions, and outputs results in a unified file format.