Each time series dataset file should contain as many rows as genes are in the genes file and each row should contain the same number of elements. These files are expected to be in CSV format and you can selec the separator at the top of the coniguration dialog. Then, you can select your time series datasets. Once the file is parsed, you can see how many genes where identified in the file at the right of the upload button. Select your genes files which should contain one gene per line. This define the maximum time-lag that the generated rules might have. Open GRNCOP2 from the Apps menu and you'll be presented the configuration dialog. If everything goes well, you will get a new item in the Apps menu called GRNCOP2. GRNCOP2 is capable of processing large scale datasets in order to perform genome-wide studies.įirst you need to install the app throug the app manager.The algorithm can infer the relationships between genes automatically from multiple microarray time series data.The results can be easily interpreted since the rules are derived from schemes that classify the different regulation states.It can infer rules with multiple time-delays.The gene expression value discretization criterion performed is neither arbitrary nor uniform.The approach offers several relevant and distinguishing features in relation to most of the existing methods. The discovered relationships, that represent potential interactions between genes, may be used to predict the gene expression states of a gene in terms of the gene expression values of other genes and, in this way, a putative GRN may then be reconstructed by applying and combining these rules. GRNCOP2 is a Cytoscape app that uses a model-free combinatorial optimization algorithm to infer time-delayed gene regulatory networks from genome-wide time series datasets. GRNCOP2: Gene Regulatory Network inference by Combinatorial OPtimization 2
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