Literature DB >> 17646317

Genomic characterization of perturbation sensitivity.

Jung Hun Ohn1, Jihun Kim, Ju Han Kim.   

Abstract

MOTIVATION: In determining the function of a gene, it provides much information to observe the changes in a biological system after disruption of the gene of interest through its knockout. Thanks to the microarray technology, it is now possible to profile transcriptional changes of the whole genome, thus differentiating genes that are significantly affected by the knockout. Based on microarray experiments of hundreds of different knockouts, we assigned the so called, 'Perturbation Sensitivity', to the Saccharomyces cerevisiae genome by the frequency of significant changes in the transcript level in hundreds of knockout conditions. Biologically, it reflects the degree of a gene's sensitivity to external perturbations.
RESULTS: Through gradually enriching gene sets with more perturbation sensitive genes, we show that perturbation sensitive genes are usually not essential and their coding proteins have fewer physical interaction partners and more transcription factors bind to their upstream sequences. And the two extreme gene groups, perturbation sensitive versus perturbation resistant, have mutually exclusive functional annotations.

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Year:  2007        PMID: 17646317     DOI: 10.1093/bioinformatics/btm172

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  7 in total

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Authors:  Dokyoon Kim; Sungeun Kim; Shannon L Risacher; Li Shen; Marylyn D Ritchie; Michael W Weiner; Andrew J Saykin; Kwangsik Nho
Journal:  Multimodal Brain Image Anal (2013)       Date:  2013

2.  Identifying Stages of Kidney Renal Cell Carcinoma by Combining Gene Expression and DNA Methylation Data.

Authors:  Su-Ping Deng; Shaolong Cao; De-Shuang Huang; Yu-Ping Wang
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2016-09-09       Impact factor: 3.710

3.  Expression sensitivity analysis of human disease related genes.

Authors:  Liang-Xiao Ma; Ya-Jun Wang; Jing-Fang Wang; Xuan Li; Pei Hao
Journal:  Biomed Res Int       Date:  2013-11-24       Impact factor: 3.411

4.  Human gene expression sensitivity according to large scale meta-analysis.

Authors:  Pei Hao; Siyuan Zheng; Jie Ping; Kang Tu; Christian Gieger; Rui Wang-Sattler; Yang Zhong; Yixue Li
Journal:  BMC Bioinformatics       Date:  2009-01-30       Impact factor: 3.169

5.  Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction.

Authors:  Dokyoon Kim; Je-Gun Joung; Kyung-Ah Sohn; Hyunjung Shin; Yu Rang Park; Marylyn D Ritchie; Ju Han Kim
Journal:  J Am Med Inform Assoc       Date:  2014-07-07       Impact factor: 4.497

6.  Intra-relation reconstruction from inter-relation: miRNA to gene expression.

Authors:  Dokyoon Kim; Hyunjung Shin; Je-Gun Joung; Su-Yeon Lee; Ju Han Kim
Journal:  BMC Syst Biol       Date:  2013-10-16

7.  Yin and Yang of disease genes and death genes between reciprocally scale-free biological networks.

Authors:  Hyun Wook Han; Jung Hun Ohn; Jisook Moon; Ju Han Kim
Journal:  Nucleic Acids Res       Date:  2013-08-09       Impact factor: 16.971

  7 in total

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