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Clusterflock: a flocking algorithm for isolating congruent phylogenomic datasets.

Apurva Narechania¹, Richard Baker¹, Rob DeSalle¹

¹Sackler Institute for Comparative Genomics, American Museum of Natural History, New York, NY, 10024, USA.

|October 26, 2016

Summary

This summary is machine-generated.

Clusterflock uses a novel flocking algorithm to identify groups of orthologous gene families (OGFs) with shared evolutionary histories. This unsupervised method excels at clustering data with high levels of missing information and evolutionary rate variation.

Keywords:

Data mining Flocking algorithm Horizontal gene transfer Recombination Staphylococcus aureus Swarms Unsupervised clustering

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Area of Science:

Bioinformatics
Computational Biology
Evolutionary Biology

Background:

Collective animal behavior algorithms, inspired by flocking and shoaling, are used for distance-based clustering.
Flocking models feature agents responding to local environments with simple rules, leading to self-organized clusters without seeds.
These algorithms offer unsupervised clustering and computational benefits, reducing comparison needs.

Purpose of the Study:

To introduce Clusterflock, a tool implementing a flocking algorithm for grouping orthologous gene families (OGFs) with shared evolutionary histories.
To evaluate Clusterflock's performance against established clustering techniques, particularly on complex simulated datasets.
To demonstrate Clusterflock's capability in identifying evolutionary events like recombination in bacterial genomes.

Main Methods:

Implemented a flocking algorithm where pairwise phylogenetic incongruence distances guide the formation of OGF clusters (flocks).
Tested Clusterflock on simulated datasets with varying topologies, missing data proportions, and evolutionary rates.
Applied Clusterflock to identify a recombination event in Staphylococcus aureus by isolating OGFs with divergent phylogenetic signals.

Main Results:

Clusterflock outperforms other clustering methods on datasets with high missing data and rate heterogeneity.
The tool successfully pinpointed a large-scale recombination event in Staphylococcus aureus.
It identified sets of OGFs with divergent phylogenetic signals without requiring pre-set cluster numbers or incongruence thresholds.

Conclusions:

Clusterflock is an open-source tool for discovering horizontally transferred genes, recombined chromosomal regions, and genomic cores.
While applied in an evolutionary context, the algorithm is generalizable to diverse clustering problems.
Users can extend Clusterflock to compute custom distance metrics for various data types.