Abstract
The ready availability of vast amounts of genomic sequence data has created the need to rethink comparative genomics algorithms using “big data” approaches. Neptune is an efficient system for rapidly locating differentially abundant genomic content in bacterial populations using an exact k-mer matching strategy, while accommodating k-mer mismatches. Neptune’s loci discovery process identifies sequences that are sufficiently common to a group of target sequences and sufficiently absent from non-targets using probabilistic models. Neptune uses parallel computing to efficiently identify and extract these loci from draft genome assemblies without requiring multiple sequence alignments or other computationally expensive comparative sequence analyses. Tests on simulated and real data sets showed that Neptune rapidly identifies regions that are both sensitive and specific. We demonstrate that this system can identify trait-specific loci from different bacterial lineages. Neptune is broadly applicable for comparative bacterial analyses, yet will particularly benefit pathogenomic applications, owing to efficient and sensitive discovery of differentially abundant genomic loci.
