SynAPhy and SynAVal: Mining a Synteny-Similarity Graph to Resolve Orthology of Proteins in Fungal Genomes

Phylogenomics is the study of evolution of proteins and the genomic events of speciation, duplication, horizontal gene transfer, and gene loss. It is critical to distinguish between orthologs created by speciation, and paralogs created by duplication, in order to accurately predict the function of a protein using annotation transfer by homology. In an age where complete genomes are available, we leverage synteny, the genomic context of a gene, for resolving orthology. We introduce the synteny-similarity graph. We present SynAPhy, a novel graph-based approach for clustering proteins. SynAPhy computes the “syntenic reciprocal best hits” of proteins across genomes. The synteny-similarity graphs are input to the MCL algorithm to determine orthologous clusters across genomes. There is no gold standard genome scale dataset to evaluate the capability of SynAPhy in generating orthologous clusters. We therefore present SynAVal, an evaluation framework that can be applied to an orthology prediction technique. The results of applying SynAVal on eight fungal genomes show that SynAVal with synteny resolution can successfully resolve potential confusions raised by 8.98% of all proteins, and resolve 23.33% of the subset of the proteins likely to cause confusions.