Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/8534
Title: Yeast gene CMR1/YDL156W is consistently co-expressed with genes participating in DNA-metabolic processes in a variety of stringent clustering experiments
Authors: Abu-Jamous, B
Fa, R
Roberts, DJ
Nandi, AK
Keywords: CMR1/YDL156W;G1/S transition;DNA replication;DNA repair;Binarization of consensus partition matrix
Issue Date: 2013
Publisher: Royal Society
Citation: Journal of the Royal Society Interface, 10(81): Article no. 20120990, 2013
Abstract: The binarization of consensus partition matrices (Bi-CoPaM) method has, among its unique features, the ability to perform ensemble clustering over the same set of genes from multiple microarray datasets by using various clustering methods in order to generate tunable tight clusters. Therefore, we have used the Bi-CoPaM method to the most synchronized 500 cell-cycle-regulated yeast genes from different microarray datasets to produce four tight, specific and exclusive clusters of co-expressed genes. We found 19 genes formed the tightest of the four clusters and this included the gene CMR1/YDL156W, which was an uncharacterized gene at the time of our investigations. Two very recent proteomic and biochemical studies have independently revealed many facets of CMR1 protein, although the precise functions of the protein remain to be elucidated. Our computational results complement these biological results and add more evidence to their recent findings of CMR1 as potentially participating in many of the DNA-metabolism processes such as replication, repair and transcription. Interestingly, our results demonstrate the close co-expressions of CMR1 and the replication protein A (RPA), the cohesion complex and the DNA polymerases α, δ and ɛ, as well as suggest functional relationships between CMR1 and the respective proteins. In addition, the analysis provides further substantial evidence that the expression of the CMR1 gene could be regulated by the MBF complex. In summary, the application of a novel analytic technique in large biological datasets has provided supporting evidence for a gene of previously unknown function, further hypotheses to test, and a more general demonstration of the value of sophisticated methods to explore new large datasets now so readily generated in biological experiments.
Description: © 2013 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited.
URI: http://rsif.royalsocietypublishing.org/content/10/81/20120990
http://bura.brunel.ac.uk/handle/2438/8534
DOI: http://dx.doi.org/10.1098/rsif.2012.0990
ISSN: 1742-5689
Appears in Collections:Electronic and Computer Engineering
Publications
Dept of Electronic and Computer Engineering Research Papers

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