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Clusterflock: A Flocking Algorithm for Isolating Congruent Phylogenomic Datasets

Apurva Narechania, Richard Baker, Rob DeSalle, Barun Mathema, Sergios-Orestis Kolokotronis, Barry Kreiswirth, Paul J. Planet
doi: https://doi.org/10.1101/045773
Apurva Narechania
1Sackler Institute for Comparative Genomics, American Museum of Natural History, New York, NY
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Richard Baker
1Sackler Institute for Comparative Genomics, American Museum of Natural History, New York, NY
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Rob DeSalle
1Sackler Institute for Comparative Genomics, American Museum of Natural History, New York, NY
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Barun Mathema
2Public Health Research Institute Center, New Jersey Medical School, Rutgers Newark, New Jersey
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Sergios-Orestis Kolokotronis
1Sackler Institute for Comparative Genomics, American Museum of Natural History, New York, NY
4Department of Biological Sciences, Fordham University, Bronx, NY
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Barry Kreiswirth
2Public Health Research Institute Center, New Jersey Medical School, Rutgers Newark, New Jersey
5Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY
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Paul J. Planet
1Sackler Institute for Comparative Genomics, American Museum of Natural History, New York, NY
3Department of Pediatrics, Division of Pediatric Infectious Diseases, Columbia University, College of Physicians and Surgeons, New York, NY
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  • For correspondence: pjp23@columbia.edu
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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 its utility on a known, large-scale recombination event in Staphylococcus aureus. By isolating sets of OGFs with divergent phylogenetic signal, we can pinpoint the recombined region without forcing a pre-determined number of groupings or defining a pre-determined incongruence threshold.

Conclusions Clusterflock is an open source tool that can be used to discover horizontally transferred genes, recombining areas of chromosomes, and the phylogenetic “core” of a genome. Though we use it in an evolutionary context, it is generalizable to any clustering problem. Users can write extensions to calculate any distance metric on the unit interval and use these distances to flock any type of data.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted March 25, 2016.
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Clusterflock: A Flocking Algorithm for Isolating Congruent Phylogenomic Datasets
Apurva Narechania, Richard Baker, Rob DeSalle, Barun Mathema, Sergios-Orestis Kolokotronis, Barry Kreiswirth, Paul J. Planet
bioRxiv 045773; doi: https://doi.org/10.1101/045773
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Clusterflock: A Flocking Algorithm for Isolating Congruent Phylogenomic Datasets
Apurva Narechania, Richard Baker, Rob DeSalle, Barun Mathema, Sergios-Orestis Kolokotronis, Barry Kreiswirth, Paul J. Planet
bioRxiv 045773; doi: https://doi.org/10.1101/045773

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