breseq

breseq detects structural variations and sequence-level mutations in haploid microbial genomes from short-read DNA resequencing data for identification and annotation of genomic rearrangements relative to a reference genome.

Key Features:

• Split-read alignment analysis: Detects mutations by analyzing split-read alignments against a reference genome to identify breakpoints and novel junctions.
• Novel sequence-junction detection: Evaluates and predicts new sequence junctions, including those involving repetitive elements.
• Statistical coverage model: Uses a statistical model assessing read coverage evenness to refine predictions and reduce false positives.
• Integration of junctions and deletions: Integrates novel junction predictions with deletions in chromosomal regions to produce coherent mutation calls.
• Annotation of gene impacts: Provides biologically meaningful descriptions of mutations and their impacts on genes.
• Detection of mobile element events: Identifies mobile genetic element insertions and deletions mediated by these elements, including transposon insertions and large-scale chromosomal changes.
• Validated on Escherichia coli: Demonstrated on simulated Escherichia coli genomes with unique breakpoint sequences and complex mutational events.
• Reference-genome requirement: Operates relative to a closely related reference genome for accurate mutation calling.
• Read-depth sensitivity: Capable of reliable predictions with modest short-read coverage (greater than ~40-fold).

Scientific Applications:

• Microbial epidemiology: Detects structural variants and insertions relevant to pathogen evolution and outbreak investigation.
• Experimental evolution: Identifies complex mutations arising during evolution experiments, including rearrangements and mobile element activity.
• Synthetic biology: Characterizes engineered genomic changes and unintended rearrangements in microbial strains.
• Genetics and mutation discovery: Reanalyzes mutation-accumulation datasets to quantify contributions of structural variations, such as the ~25% contribution of transposon insertions and large-scale changes reported for E. coli K-12.

Methodology:

Analyzes short-read DNA resequencing data using split-read alignments to detect novel sequence junctions, applies a statistical model assessing read coverage evenness to refine calls, and integrates junction predictions with deletion calls to annotate mutation impacts.