ShadowCaster: Compositional Methods under the Shadow of Phylogenetic Models to Detect Horizontal Gene Transfers in Prokaryotes

• Tipo: Artigo em Revista Científica Internacional
• Data: 2020

Autores

Sanchez Soto, D (Autor) Outra; Aguero Chapin, G (Autor) Outra; Armijos Jaramillo, V (Autor) Outra; Perez Castillo, Y (Autor) Outra; Tejera, E (Autor) Outra; Agostinho Antunes (Autor) FCUP; Sanchez Rodriguez, A (Autor) Outra

Revista

Título: Genes

Vol. 11

Páginas: 1-12

ISSN: 2073-4425

Editora: MDPI

Indexação

ISI Web of Knowledge - 8 Citações

Scopus - 7 Citações

Outras Informações

ID Authenticus: P-00S-CP9

DOI: 10.3390/genes11070756

Abstract (EN): 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 ofRhodanobacter denitrificanswith 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12