Sequencing

11th Annual Sequencing, Finishing, and Analysis in the Future Meeting
SYNERCLUST, A TRULY SCALABLE ORTHOLOG
CLUSTERING TOOL
Wednesday, 1st June 20:00 La Fonda NM Room (1st floor) Poster (PS‐1b.21)
Christophe Georgescu 1 , Alison D Griggs 1 , Aviv Regev 1 , Ilan Wapinski 2 ,
Brian J Haas 1 , Ashlee Earl 1
1 Broad Institute, 2 enEvolv
Accurate ortholog identification is a vital component of comparative genomic studies. Popular
sequence similarity based approaches, such as OrthoMCL, struggle to cluster or‐ thologs when there
are high rates of paralogs, and although phylogeneticbased methods handle paralogs, they are not
sufficiently fast or scalable to work on large sets of whole genomes. Fur‐ thermore, most approaches
do not take synteny into account, which means information useful for distinguishing paralogs is
unused. Synergy, originally developed to work on eukaryotic species, uses a hybrid approach to
resolve ortholog clusters, relying upon sequence similarity, synteny and phy‐ logeny. Here, we present
Synerclust, a tool that takes the fundamentals of Synergy and adds a number of improvements
that retain Synergy’s high accuracy, but makes it amenable to ortholog clustering of hundreds to
thousands of whole genome data sets, representing either eukaryotic or prokaryotic species.
SynerClust bypasses the all vs all Blast requirement inherent to other cluster‐ ing tools by selecting
and comparing cluster representatives at each node in an input species tree. Working from tip to
root, SynerClust solves and keeps track of orthology relationships, ultimately providing the most
parsimonious solution that takes into account gene gains and losses, common‐ place in prokaryotes.
We have also optimized SynerClust for memory usage and made it amenable for running on many
different compute infrastructures.
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