GenFamClust: an accurate, synteny-aware and reliable homology inference algorithm

BMC Evol Biol. 2016 Jun 4;16(1):120. doi: 10.1186/s12862-016-0684-2.


Background: Homology inference is pivotal to evolutionary biology and is primarily based on significant sequence similarity, which, in general, is a good indicator of homology. Algorithms have also been designed to utilize conservation in gene order as an indication of homologous regions. We have developed GenFamClust, a method based on quantification of both gene order conservation and sequence similarity.

Results: In this study, we validate GenFamClust by comparing it to well known homology inference algorithms on a synthetic dataset. We applied several popular clustering algorithms on homologs inferred by GenFamClust and other algorithms on a metazoan dataset and studied the outcomes. Accuracy, similarity, dependence, and other characteristics were investigated for gene families yielded by the clustering algorithms. GenFamClust was also applied to genes from a set of complete fungal genomes and gene families were inferred using clustering. The resulting gene families were compared with a manually curated gold standard of pillars from the Yeast Gene Order Browser. We found that the gene-order component of GenFamClust is simple, yet biologically realistic, and captures local synteny information for homologs.

Conclusions: The study shows that GenFamClust is a more accurate, informed, and comprehensive pipeline to infer homologs and gene families than other commonly used homology and gene-family inference methods.

Keywords: Clustering; Gene family; Gene order conservation; Gene similarity; Gene synteny; Homology inference.

MeSH terms

  • Algorithms*
  • Animals
  • Cluster Analysis
  • Databases, Genetic
  • Fungi / genetics
  • Humans
  • Mice
  • Phylogeny
  • Sequence Homology, Nucleic Acid*
  • Species Specificity
  • Statistics as Topic
  • Synteny*

Associated data

  • figshare/10.6084/m9.figshare.1536467