Detecting rare structural variation in evolving microbial populations from new sequence junctions using breseq.

Area of Science:

• Genomics
• Microbial Evolution
• Bioinformatics

Background:

• Structural variations (SV), including insertions, deletions, and rearrangements, are key drivers of microbial evolution.
• Detecting SV from short-read sequencing data is challenging, limiting their routine analysis in population genetics.
• Existing methods struggle to identify rare SV, hindering a complete understanding of genome dynamics.

Purpose of the Study:

• To implement and validate a new algorithm within the breseq pipeline for detecting polymorphic structural variations (SV) in microbial populations.
• To assess the contribution of SV to overall genetic diversity in evolution experiments.
• To identify rare SV and transiently competing mutations in key genes.

Main Methods:

• Developed and integrated an SV detection algorithm into the breseq computational pipeline.
• The algorithm analyzes split-read alignments to identify and quantify SV within samples.
• Tested using simulated data and applied to Escherichia coli samples from the Lenski Long-Term Evolution Experiment (LTEE).

Main Results:

• SV constitute approximately 25% of the genetic diversity observed in the LTEE samples.
• The method successfully identified rare SV, revealing instances of transiently competing mutations in important genes.
• Unexpectedly, mutations in two prominent early genes were found to be consistently outcompeted in the long term.

Conclusions:

• The breseq SV detection algorithm provides a more comprehensive view of genome dynamics in microbial evolution experiments.
• This approach offers a lower detection limit for rare SV compared to single-nucleotide polymorphism (SN) detection methods.
• The findings highlight the significant role of SV in microbial adaptation and the complex interplay of competing mutations.