Inferring gene correlation networks from transcription factor binding sites.
Gene expression is a highly regulated biological process that is fundamental to the existence of phenotypes of any living organism. The regulatory relations are usually modeled as a network; simply, every gene is modeled as a node and relations are shown as edges between two related genes. This paper presents a novel method for inferring correlation networks, networks constructed by connecting co-expressed genes, through predicting co-expression level from genes promoter's sequences. According to the results, this method works well on biological data and its outcome is comparable to the methods that use microarray as input. The method is written in C++ language and is available upon request from the corresponding author.
Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran.
Ghasem
G
Mahdevar
Abbas
A
Nowzari-Dalini
Mehdi
M
Sadeghi
D000465:Algorithms; D001665:Binding Sites; D015870:Gene Expression; D053263:Gene Regulatory Networks; D016571:Neural Networks, Computer; D011401:Promoter Regions, Genetic; D011485:Protein Binding; D012441:Saccharomyces cerevisiae / Q000235:genetics* / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics* / Q000378:metabolism; D012984:Software; D014157:Transcription Factors / Q000235:genetics* / Q000378:metabolism
D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors