|Title||Efficient inference of homologs in large eukaryotic pan-proteomes|
|Author(s)||Sheikhizadeh Anari, Siavash; Ridder, Dick de; Schranz, M.E.; Smit, Sandra|
|Source||BMC Bioinformatics 19 (2018)1. - ISSN 1471-2105 - 11 p.|
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Homologous genes - k-mer - Orthology - Pan-genome - Protein similarity|
BACKGROUND: Identification of homologous genes is fundamental to comparative genomics, functional genomics and phylogenomics. Extensive public homology databases are of great value for investigating homology but need to be continually updated to incorporate new sequences. As new sequences are rapidly being generated, there is a need for efficient standalone tools to detect homologs in novel data.
RESULTS: To address this, we present a fast method for detecting homology groups across a large number of individuals and/or species. We adopted a k-mer based approach which considerably reduces the number of pairwise protein alignments without sacrificing sensitivity. We demonstrate accuracy, scalability, efficiency and applicability of the presented method for detecting homology in large proteomes of bacteria, fungi, plants and Metazoa.
CONCLUSIONS: We clearly observed the trade-off between recall and precision in our homology inference. Favoring recall or precision strongly depends on the application. The clustering behavior of our program can be optimized for particular applications by altering a few key parameters. The program is available for public use at https://github.com/sheikhizadeh/pantools as an extension to our pan-genomic analysis tool, PanTools.