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This repository contains the Supplementary Tables for Suriyalaksh et al. Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes. The list of table files can be found in Supplementary table guide.pdf Tables S1, S2 and S3 corresponding to physical gene-gene interaction data are in a separate repository doi:10.5281/zenodo.4382337 Details about some of the Supplementary tables: TableS4_inferred_networks.csv - list of inferred GRNs for specified input combinations (set of input regulators, length of the time sequence, NI tool and prior used). TableS5_consensus_network_member.xlsx - list of groups of topologically similar GRNs (from Table S4) Table S6: edge lists (source,target) for each one of the three consensus networks selected according to the GS validation metrics: middle PFE/AUFE, max AUFE, max PFE. TableS6a_max_AUFE_GRN.txt - max AUFE; largest network - this is the one we used in the main analysis and discussion TableS6b_max_PFE_GRN.xt - max PFE TableS6c_middle_AUFE_PFE_GRN.txt - middle PFE/AUFE TableS7_qRTPCR_ddCt_network_accuracy.csv - gene expression count differences for RNAi knockdown GRN validation experiments. Table S8: Group membership for each one of the nodes in each one of the selected networks according to the SBM that best describes the observed network topology. Each column shows the group membership for each level in a SBM block hierarchy. Our analysis is in the second most coarse-grained level (level 1). TableS8a_max_AUFE_SBM.csv TableS8b_max_PFE_SBM.csv TableS8c_middle_AUFE_PFE_SBM.csv TableS9_glp_gs_datasets.pdf - list of datasets used for defining functional clusters. TableS17_input_regulators_annotated.csv - list input regulators used as input for Network Inference Tools annotated by source type (2nd column): GenAge, known transcription factors (TF) and gene with high variability in the gene expression time series (HV). The third column lists whether that regulator has an orthologue in human (y) according to WormBase (v 278).
ageing, GRN inference, glp-1 C. elegans, lifespan screens, stochastic block models
ageing, GRN inference, glp-1 C. elegans, lifespan screens, stochastic block models
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