OneGenE is a method for causality based, gene-centered network inference which has been applied to two Vitis vinifera and spp. transcriptomic datasets, one released in 2016 based on microarray and RNAseq data and one released in 2022 based on RNAseq data only. From this page you can download:
- The whole set of annotated OneGenE expansion lists:
- computed on Vitis vinifera dataset 2016 (28,013 lists): OneGene_v1 (779 MB)
- computed on Vitis vinifera dataset 2022 (22,383 lists): OneGene_v2 ( MB)
NB: to download a limited set of expansion lists, we suggest to use the interactive query interface provided on this website.
- The transcriptomic dataset used as input to run OneGenE for expansion lists computing.
- expression data matrix obtained after pre-processing of the Vespucci dataset 2016 (29,090 genes and 2,017 contrasts, Moretto et al., 2016 ). The pre-processing procedure, described in (Malacarne et al., 2018), comprised three steps: (1) removal of contrasts with more than 55% of missing values; (2) removal of genes with more than 55% of missing values; (3) for each gene, replacement of the remaining missing values with the median of its contrasts values. Vespucci 2016 dataset: 28,013 genes and 1,131 contrasts (109 MB).
- expression data matrix obtained after pre-processing of the Vespucci TPM dataset 2022 (29,090 genes and 2,017 contrasts, Moretto et al., 2022 ). The pre-processing procedure comprised three steps: (1) removal of contrasts with more than 40 % of missing values; (2) removal of genes with more than 40 % of missing values; (3) for each gene, replacement of the remaining missing values with the median of its contrasts values. Vespucci 2022 dataset: 22,383 genes and 3,588 contrasts ( MB)
The Cytoscape style to correctly and easily represent the network information, such as edge weight, positive or negative correlation and node label.