Mar 25, 2016

Clusterflock: A Flocking Algorithm for Isolating Congruent Phylogenomic Datasets

BioRxiv : the Preprint Server for Biology
Apurva NarechaniaPaul J Planet

Abstract

Background Collective animal behavior such as the flocking of birds or the shoaling of fish has inspired a class of algorithms designed to optimize distance-based clusters in various applications including document analysis and DNA microarrays. In the flocking model, individual agents respond only to their immediate environment and move according to a few simple rules. After several iterations the agents self-organize and clusters emerge without the need for partitional seeds. In addition to their unsupervised nature, flocking offers several computational advantages including the potential to decrease the number of required comparisons. Findings In Clusterflock, we implement a flocking algorithm designed to find groups (flocks) of orthologous gene families (OGFs) that share a common evolutionary history. Pairwise distances that measure the phylogenetic incongruence between OGFs guide flock formation. We test this approach on several simulated datasets varying the number of underlying topologies, the proportion of missing data, and evolutionary rates, and show that in datasets containing high levels of missing data and rate heterogeneity, clusterflock outperforms other well-established clustering techniques. We also demonstrate ...Continue Reading

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Mentioned in this Paper

Genome
Genes
Environment
Recombination, Genetic
Pharmacologic Substance
Zebrafish
Aves
Gene Function
Viral Nucleocapsid Location
Chromosomes

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