About
Coexpression networks help overcome the limitations of sparsity in gene functional annotations, by providing a means to transfer knowledge from annotated genes to genes yet unannotated, based on the 'guilt-by association' assumptions. RECoN is designed to identify clusters of functionally genes that tightly coexpress in a compendium of abiotic-stress gene expression experiments in rice.
Tutorial
Use RECoN to
1) Upload the differential expression profile of genes in response to an abiotic-stress to find clusters that are most highly expressed/repressed in the experiment. Prepare the transcriptome file in a format like this. This file contains locus ids of all the genes in the first column and their differential expression values in the second column, with a tab space in between, and a header
2) Probe a list of interesting genes and find over-represented clusters within the list. Can be useful to predict pathways related to the query list of genes
3) Search clusters linked to a specific biological process from the drop down menu
4) Use a single gene as a guide to find other genes highly coexpressed with it
Use RECoN to
1) Upload the differential expression profile of genes in response to an abiotic-stress to find clusters that are most highly expressed/repressed in the experiment. Prepare the transcriptome file in a format like this. This file contains locus ids of all the genes in the first column and their differential expression values in the second column, with a tab space in between, and a header
2) Probe a list of interesting genes and find over-represented clusters within the list. Can be useful to predict pathways related to the query list of genes
3) Search clusters linked to a specific biological process from the drop down menu
4) Use a single gene as a guide to find other genes highly coexpressed with it
Citation: Krishnan Arjun, Gupta Chirag, Ambavaram Madana M. R., Pereira Andy (2017). RECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress Response. Frontiers in Plant Science
Upload differential expression profiles
[ upload a .txt file with ALL genes and their fold-change values ]
Enter gene list
[Enter atleast 10 genes, one per line, MSU format only ]
Search pre-listed gene sets
Query a single gene
[start typing a Locus ID in MSU format(LOC_Os...) ]
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