Cytoscape is often used for visualization and analysis of metabolic pathways.For example,based on KEGG data,a reader for KEGG Markup Language(KGML)is used to load files into Cytoscape.However,although multiple genes c...Cytoscape is often used for visualization and analysis of metabolic pathways.For example,based on KEGG data,a reader for KEGG Markup Language(KGML)is used to load files into Cytoscape.However,although multiple genes can be responsible for the same reaction,the KGMLreader KEGGScape only presents the first listed gene in a network node for a given reaction.This can lead to incorrect interpretations of the pathways.Our new method,FunHoP,shows all possible genes in each node,making the pathways more complete.FunHoP collapses all genes in a node into one measurement using read counts from RNA-seq.Assuming that activity for an enzymatic reaction mainly depends upon the gene with the highest number of reads,and weighting the reads on gene length and ratio,a new expression value is calculated for the node as a whole.Differential expression at node level is then applied to the networks.Using prostate cancer as model,we integrate RNA-seq data from two patient cohorts with metabolism data from literature.Here we show that FunHoP gives more consistent pathways that are easier to interpret biologically.Code and documentation for running FunHoP can be found at https://github.com/kjerstirise/FunHoP.展开更多
基金supported by a PhD position from Enabling Technologies, Norwegian University of Science and Technology (NTNU)the Department of Clinical and Molecular Medicine (IKOM), NTNU to KR+5 种基金the Liaison Committee between the Central Norway Regional Health Authority (RHA) and the NTNU to MBREuropean Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant No. 758306) gave funding to MBTFunding support for MPC_Transcriptome sequencing to identify non-coding RNAs in prostate cancer was provided through the NIH Prostate SPORE (Grant Nos. P50CA69568 and R01 R01CA132874)the Early Detection Research Network (Grant No. U01 CA111275)the Department of Defense Grant (Grant No. W81XWH-11-1-0331)the National Center for Functional Genomics (Grant No. W81XWH-11-1-0520)
文摘Cytoscape is often used for visualization and analysis of metabolic pathways.For example,based on KEGG data,a reader for KEGG Markup Language(KGML)is used to load files into Cytoscape.However,although multiple genes can be responsible for the same reaction,the KGMLreader KEGGScape only presents the first listed gene in a network node for a given reaction.This can lead to incorrect interpretations of the pathways.Our new method,FunHoP,shows all possible genes in each node,making the pathways more complete.FunHoP collapses all genes in a node into one measurement using read counts from RNA-seq.Assuming that activity for an enzymatic reaction mainly depends upon the gene with the highest number of reads,and weighting the reads on gene length and ratio,a new expression value is calculated for the node as a whole.Differential expression at node level is then applied to the networks.Using prostate cancer as model,we integrate RNA-seq data from two patient cohorts with metabolism data from literature.Here we show that FunHoP gives more consistent pathways that are easier to interpret biologically.Code and documentation for running FunHoP can be found at https://github.com/kjerstirise/FunHoP.
