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



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.



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.
KTH Royal Institute of Technology, Science for Life Laboratory, School of Computer Science and Communication, Solna, SE-171 77, Sweden.
Raja H
RH
Ali
KTH Royal Institute of Technology, Science for Life Laboratory, School of Computer Science and Communication, Solna, SE-171 77, Sweden.
Sayyed A
SA
Muhammad
Department of Numerical Analysis and Computer Science, Stockholm University, Stockholm, SE-100 44, Sweden. arve@nada.su.se.|Swedish e-Science Research Centre, Stockholm, Sweden. arve@nada.su.se.|Science for Life Laboratory, Box 1031, Solna, SE-171 77, Sweden. arve@nada.su.se.
Lars
L
Arvestad
D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D030541:Databases, Genetic; D005658:Fungi / Q000235:genetics; D006801:Humans; D051379:Mice; D010802:Phylogeny; D012689:Sequence Homology, Nucleic Acid; D013045:Species Specificity; D013223:Statistics as Topic; D026801:Synteny
Clustering; Gene family; Gene order conservation; Gene similarity; Gene synteny; Homology inference