Abstract
Horizontal gene transfer (HGT) plays an important role for evolutionary innovations within prokaryotic communities and is a crucial event for their survival. Several computational approaches have arisen to identify HGT 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 ones, but they are not extensible to all genes/species and are computationally demanding when dealing with large datasets. In contrast, 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 the genomic scale, but they fail in identifying common HGT events. Here, we present ShadowCaster, a hybrid approach that sequentially combines nucleotide composition-based predictions by support vector machines (SVMs) under the shadow of phylogenetic models independent of tree reconstruction, to improve the detection of HGT events in prokaryotes. ShadowCaster successfully predicted close and distant HGT events in both artificial and bacterial genomes. ShadowCaster 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, encompassing most of the predicted cases made by other methods. ShadowCaster is released at the GitHub platform as an open-source software under the GPLv3 license.
Highlights
Lateral or horizontal gene transfer (HGT) plays an important role in the genome evolution and ecological innovation of prokaryotic communities
Detects atypical genes based on k-mer (k = 8) frequencies using a one-class support vector machine (SVM)
We presenteda anew newsoftware softwarecalled calledShadowCaster, ShadowCaster, aimed aimed to improve events in prokaryotes by reducing the number of falseand positives and the frequently events in prokaryotes by reducing the number of false positives the frequently disagreements between the predictions made by parametric methods and by those with implicit phylogenetic models
Summary
Lateral or horizontal gene transfer (HGT) plays an important role in the genome evolution and ecological innovation of prokaryotic communities. Microbial communities can be considered as complex biological systems where its individuals exchange genes by HGT events. HGTs in bacteria and archaea communities occur more frequently between closely related species than in distant lineages [1]. Some lineage-specific genes, i.e., genes found in one particular taxonomic group and that arise from close HGT events, are usually lost quickly if they result in reduced fitness. Rates of HGT events involving genes critical for survival, growth, and reproduction are high among members of microbial communities that need a quick adaptation to complex environments such as contaminated soil or water [3]. Detecting HGT has been a major focus of attention to better understand microbial evolution. It has proven to be a complex and challenging task
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