Selected article for: "high confidence and total number"

Author: Griffiths, Samantha J.; Koegl, Manfred; Boutell, Chris; Zenner, Helen L.; Crump, Colin M.; Pica, Francesca; Gonzalez, Orland; Friedel, Caroline C.; Barry, Gerald; Martin, Kim; Craigon, Marie H.; Chen, Rui; Kaza, Lakshmi N.; Fossum, Even; Fazakerley, John K.; Efstathiou, Stacey; Volpi, Antonio; Zimmer, Ralf; Ghazal, Peter; Haas, Jürgen
Title: A Systematic Analysis of Host Factors Reveals a Med23-Interferon-? Regulatory Axis against Herpes Simplex Virus Type 1 Replication
  • Document date: 2013_8_8
  • ID: 0lyt8gfq_43
    Snippet: Interactions between HSV-1 and human proteins were connected to a network of human protein-protein interactions (a total of 62,310) taken from the databases HPRD [78] (Release 9), BioGRID [79] , DIP [80] , MINT [30] and IntAct (downloaded May 18 th 2010). A high-confidence interaction set (9,829 interactions) was compiled from interactions identified in at least two studies. Betweenness centrality (g(v) of a protein v was calculated as g(v) = gs .....
    Document: Interactions between HSV-1 and human proteins were connected to a network of human protein-protein interactions (a total of 62,310) taken from the databases HPRD [78] (Release 9), BioGRID [79] , DIP [80] , MINT [30] and IntAct (downloaded May 18 th 2010). A high-confidence interaction set (9,829 interactions) was compiled from interactions identified in at least two studies. Betweenness centrality (g(v) of a protein v was calculated as g(v) = gs {v{t (s st (v)/s st ), where s st is the total number of shortest paths from protein s to protein t, and s st (v) is the number of those shortest paths that contain v. Betweenness centrality was normalized by dividing by the total number of protein pairs in the network. Enrichment for functional annotations from gene ontology (GO) [81] , KEGG [82, 83] , REAC-TOME [84, 85] , and BIOCARTA was performed using DAVID [86] . Data on known human protein complexes was retrieved from the CORUM database, and complexes with subunits showing consistently stronger effects (inhibiting or enhancing) than expected by chance were detected using Wilcoxon's rank-sum test. Genes included in the RNAi screen were ranked by their distance from the median knockdown, with the most inhibiting and enhancing genes being ranked highest. FDR was used for multiple testing correction.

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