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Combined analysis of gene regulatory network and SNV information enhances identification of potential gene markers in mouse knockout studies with small number of samples

Cited 3 time in Web of Science Cited 3 time in Scopus
Authors
Hur, Benjamin; Chae, Heejoon; Kim, Sun
Issue Date
2015-05-29
Publisher
BioMed Central
Citation
BMC Medical Genomics, 8(Suppl 2):S10
Description
This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
Abstract
Abstract
RNA-sequencing is widely used to measure gene expression level at the whole genome level. Comparing expression data from control and case studies provides good insight on potential gene markers for phenotypes. However, discovering gene markers that represent phenotypic differences in a small number of samples remains a challenging task, since finding gene markers using standard differential expressed gene methods produces too many candidate genes and the number of candidates varies at different threshold values. In addition, in a small number of samples, the statistical power is too low to discriminate whether gene expressions were altered by genetic differences or not. In this study, to address this challenge, we purpose a four-step filtering method that predicts gene markers from RNA-sequencing data of mouse knockout studies by utilizing a gene regulatory network constructed from omics data in the public domain, biological knowledge from curated pathways, and information of single-nucleotide variants. Our prediction method was not only able to reduce the number of candidate genes than the differentialy expressed gene-only filtered method, but also successfully predicted significant genes that were reported in research findings of the data contributors.
Language
English
URI
https://hdl.handle.net/10371/100679
DOI
https://doi.org/10.1186/1755-8794-8-S2-S10
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Computer Science and Engineering (컴퓨터공학부)Journal Papers (저널논문_컴퓨터공학부)
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