![]() Although Cytoscape has newer versions, jActiveModules and ClueGO plugins are verified to work in Cytoscape version 2.6.3.Įxternal tools: PANOGA utilizes four external web-servers to functionalize SNPs, i.e., SPOT, F-SNP, SNPnexus, SNPinfo jActiveModules plugin of Cytoscape to identify sub-networks ClueGO plugin 60 of Cytoscape 68 for functional enrichment of the identified sub-networks. Hence, to follow PANOGA protocol, users need to install Cytoscape version 2.6.3 by on a local computer by following the steps in Box 2 of the Cytoscape paper, published in Nature protocols. Subnetwork identification and functional enrichment steps of PANOGA protocol are realized by Cytoscape plugins. Each of these two Pw values combine functional information of a SNP and the genotypic p-value of a SNP, that is found to be significant in GWAS.Ĭytoscape: Cytoscape is an open source network data integration, analysis, and visualization platform. Sample_spot_fsnp_snpnexus.pvals: contains SPOT and F-SNP weighted p-values (Pw-values) for each SNP associated gene. This file illustrates the SIF, which offers a straightforward means to import networks into Cytoscape as text. HumanPPI.sif: contains a protein–protein interaction (PPI) network in a sif file format, as detailed in Figure 2. In addition to the sample GWAS data, several additional data files are available for readers wishing to follow this protocol as a tutorial: A sample PANOGA input file and SPOT, F-SNP, SNPnexus and SNPinfo input files are made available in PANOGA_protocol/data/ as sample_panoga_input.txt, sample_spot_input.txt, sample_fsnp_input.txt, sample_snpnexus_input.txt, sample_snpinfo_input.txt). As a result of the preprocessing step of PANOGA, four additional files in SPOT, F-SNP, SNPnexus and SNPinfo input file formats are created. genotypic p-values in a tab-delimited text file or excel file, as detailed in Figure 2. ( ).ĭata files: This protocol begins with a GWAS dataset containing SNP rsIDs vs. Java 2 platform: Standard Edition, version 5.0 or higher (Java SE 5 or higher). Hardware requirements: We recommend a 1 GHz CPU or higher, a high-end graphics card, 500MB of available hard disk space, at least 1 GB of free physical RAM and a minimum screen resolution of 1,024×768. EquipmentĪ computer with Windows or Linux OS and internet access. The pathways are ranked according to the significance scores and are referred as the SNP targeted pathways. Next, we find the KEGG pathways in these subnetworks and determine the significance of the modifications on these pathways. We then map these genes to a protein-protein interaction (PPI) network and determine the connected subnetworks targeted by the SNPs. To this end, our methodology starts with functionalization of several significant SNPs to identify effected genes. That’s why pathways have higher explanatory power towards understanding disease development mechanism. In this method, we hypothesize that these factors are crippling similar pathways in individuals. One possible reason of multifactorial diseases is the alterations in the activity of biological pathways. Since each factor would have modest effect on the disease development mechanism, it is challenging to identify significant individual factors. Multiple factors act on complex diseases. In this protocol, starting with a list of SNPs found to be associated with a disease in a GWAS, we propose a novel methodology to determine disease related (SNP targeted) pathways through the identification of SNP targeted genes within these pathways. genotypic p-values and is available at: Introduction The program accepts tab delimited or excel file containing SNP rsIDs vs. Additionally, PANOGA helps to identify other disease related genes, not targeted by the SNPs, which are also located within these affected pathways. We provide a protocol (termed PANOGA, Pathway and Network Oriented GWAS (Genome-wide association study) Analysis) to devise functionally important pathways through the identification of genes within these pathways, where these genes are targeted by single nucleotide polymorphisms (SNPs) obtained from the GWAS analysis. While each of these variations extends slightly the likelihood of having the disease, they work together to give birth to the perturbations in normal biological processes. One possible reason of multifactorial diseases is the alterations in the activity of biological pathways, where a series of mutations occur in distinct genes. The identification of the variants that explain familial risk of a specific disease is important since it enables the development of genetic risk prediction tests, diagnosis tools and therapeutical applications. Authors: Burcu Bakir-Gungor & Osman Ugur Sezerman ![]()
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