NDLI: Phenotype-dependent coexpression gene clusters: application to normal and premature ageing

Content Provider	ACM Digital Library
Author	Hannenhalli, Sridhar Cao, Kan Das, Avinash Xiong, Zheng-Mei Wang, Kun
Copyright Year	2015
Abstract	Hutchinson Gilford progeria syndrome (HGPS) is a rare genetic disease with symptoms of aging at a very early age. Its molecular basis is not entirely clear, although profound gene expression changes have been reported, and there are some known and other presumed overlaps with normal aging process. Identification of genes with aging - or HGPS-associated expression changes is thus an important problem. However, standard regression approaches are currently unsuitable for this task due to limited sample sizes, thus motivating development of alternative approaches. Here, we report a novel iterative multiple regression approach that leverages co-expressed gene clusters to identify gene clusters whose expression co-varies with age and/or HGPS. We have applied our approach to novel RNA-seq profiles in fibroblast cell cultures at three different cellular ages, both from HGPS patients and normal samples. After establishing the robustness of our approach, we perform a comparative investigation of biological processes underlying normal aging and HGPS. Our results recapitulate previously known processes underlying aging as well as suggest numerous unique processes underlying aging and HGPS. The approach could also be useful in detecting phenotype-dependent co-expression gene clusters in other contexts with limited sample sizes.
Starting Page	30
Ending Page	39
Page Count	10
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2014.2359446
Volume Number	12
Issue Number	1
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-01-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Algorithms Biology and genetics Models Parameter learning
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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