gdtools_annotate

gdtools_annotate annotates and characterizes structural variations in haploid microbial genomes from DNA resequencing data by leveraging breseq outputs to identify and describe insertions, deletions, transposon movements and novel sequence junctions.

Key Features:

• Identification of structural variations: Detects large-scale genomic rearrangements including deletions, insertions and transposon movements by identifying novel sequence junctions via split-read alignments to a reference genome, including junctions involving repeat sequences.
• Statistical validation: Employs a statistical model of read coverage evenness to validate structural variation predictions and reduce false positives.
• Comprehensive mutation descriptions: Integrates predictions of new junctions and deleted chromosomal regions to generate biologically relevant descriptions of mutations and their impacts on genes.
• Performance characteristics: Demonstrated reliable detection with modest read-depth coverage (>40-fold) in tests on simulated Escherichia coli genomes and real mutation accumulation experiments.
• Empirical contribution: In evaluated datasets, identified structural variations that account for approximately 25% of spontaneous mutations.

Scientific Applications:

• Microbial epidemiology: Characterizes structural variants relevant to outbreak and transmission studies in microbial populations.
• Experimental evolution: Identifies structural mutations arising in laboratory evolution and mutation accumulation experiments to study evolutionary dynamics.
• Synthetic biology: Detects unintended genomic rearrangements and transposon movements that affect engineered constructs.
• Genetics and functional genomics: Provides mutation-level descriptions to link structural variants to gene disruptions and phenotypic consequences.

Methodology:

Uses breseq outputs from DNA resequencing of haploid microbial genomes, evaluates support for novel sequence junctions from split-read alignments including mappings to repeat sequences, applies a statistical model of read coverage evenness to validate structural variation calls, and integrates junction and deleted-region predictions to produce mutation descriptions.