Detecting Structural Variation in Evolved Bacterial Genomes Using Paired-End DNA Sequencing Data

Structural variants are large-scale genome rearrangement events, such as chromosomal inversions, duplications, and deletions, that can lead to innovative evolution that is not possible with point mutations. They can be especially important for microbial speciation and pathogenesis. We developed and tested an algorithm to detect newly evolved structural variants in microbial genomes from paired-end sequencing data. The method looks for read pairs with anomalous distances or orientations between where the two reads map to a reference genome. Then, it scores putative predictions using a statistical model trained on read pairs spanning normal positions in the chromosome. A computational pipeline for carrying out this analysis was implemented in R. The code was tested on genome sequencing data from a population of bacteria from the Lenski Long-Term Evolution Experiment with Escherichia coli that evolved to colonize a new nutrient niche through tandemly duplicating a region of the chromosome. This code could be integrated into the open-source breseq mutation prediction pipeline in the future to improve its ability to detect structural variants.Molecular Bioscience