ShadowCaster: compositional methods under the shadow of phylogenetic models for the detection of horizontal gene transfer events in prokaryotes.

Horizontal gene transfer (HGT) plays an important role in the evolution of many organisms, especially in prokaryotes where commonly occurs. Microbial communities can improve survival due to the evolutionary innovations induced by HGT events. Thus, several computational approaches have arisen to identify such events in recipient genomes. However, this has been proven to be a complex task due to the generation of a great number of false positives and the prediction disagreement among the existing methods. Phylogenetic reconstruction methods turned out to be the most reliable but they are not extensible to all genes/species and are computationally demanding when dealing with large datasets. On the other hand, the so-called surrogate methods that use heuristic solutions either based on nucleotide composition patterns or phyletic distribution of BLAST hits can be applied easily to genomic scale, however, they fail in identifying common HGT events. Here, we present ShadowCaster, a hybrid approach that sequentially combines compositional features under the shadow of phylogenetic models independent of tree reconstruction to improve the detection of HTG events in prokaryotes. ShadowCaster predicted successfully close and distant HTG events in both artificial and bacterial genomes. It detected HGT related to heavy metal resistance in the genome of Rhodanobacter denitrificans with higher accuracy than the most popular state-of-the-art computational approaches. ShadowCasters predictions showed the highest agreement among those obtained with other assayed methods. ShadowCaster is released as an open-source software under the GPLv3 license. Source code is hosted at https://github.com/dani2s/ShadowCaster and documentation at https://shadowcaster.readthedocs.io/en/latest/.