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On the number of genomic pacemakers: a geometric approach

Overview of attention for article published in Algorithms for Molecular Biology, December 2014
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Title
On the number of genomic pacemakers: a geometric approach
Published in
Algorithms for Molecular Biology, December 2014
DOI 10.1186/s13015-014-0026-0
Pubmed ID
Authors

Sagi Snir

Abstract

The universal pacemaker (UPM) model extends the classical molecular clock (MC) model, by allowing each gene, in addition to its individual intrinsic rate as in the MC, to accelerate or decelerate according to the universal pacemaker. Under UPM, the relative evolutionary rates of all genes remain nearly constant whereas the absolute rates can change arbitrarily. It was shown on several taxa groups spanning the entire tree of life that the UPM model describes the evolutionary process better than the MC model. In this work we provide a natural generalization to the UPM model that we denote multiple pacemakers (MPM). Under the MPM model every gene is still affected by a single pacemaker, however the number of pacemakers is not confined to one. Such a model induces a partition over the gene set where all the genes in one part are affected by the same pacemaker and task is to identify the pacemaker partition, or in other words, finding for each gene its associated pacemaker. We devise a novel heuristic procedure, relying on statistical and geometrical tools, to solve the problem and demonstrate by simulation that this approach can cope satisfactorily with considerable noise and realistic problem sizes. We applied this procedure to a set of over 2000 genes in 100 prokaryotes and demonstrated the significant existence of two pacemakers.

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Mendeley readers

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The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 40%
Student > Ph. D. Student 1 20%
Student > Doctoral Student 1 20%
Unknown 1 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 40%
Computer Science 1 20%
Unknown 2 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 January 2015.
All research outputs
#20,247,117
of 22,775,504 outputs
Outputs from Algorithms for Molecular Biology
#233
of 264 outputs
Outputs of similar age
#295,049
of 352,205 outputs
Outputs of similar age from Algorithms for Molecular Biology
#9
of 12 outputs
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