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To Access the Data autoHGPEC: Automated prediction of novel disease-gene and disease- disease associations and evidence collection based on a random walk on heterogeneous network Duc-Hau Le1,*, Trang T.H. Tran1 1School of Computer Science and Engineering, Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam. *To whom correspondence should be addressed. User Manual 1 Table of Contents I. Setup ............................................................................................................................................ 3 II. Overview of autoHGPEC ............................................................................................................ 4 III. Case study: Prediction of novel breast cancer-associated genes and diseases ............................. 5 1. Run autoHGPEC in Cytoscape ................................................................................................ 5 Step 1: Construct a heterogeneous network ................................................................................. 5 Step 2: Select a disease of interest ............................................................................................... 5 Step 3: Select candidate sets ........................................................................................................ 6 Step 4: Prioritize .......................................................................................................................... 6 Step 5: Examine ranked genes and diseases ................................................................................ 7 Visualization ............................................................................................................................ 7 Search Evidences ................................................................................................................... 10 2. Automate autoHGPEC using CyREST Command API......................................................... 11 Step 1: Construct a heterogeneous network ............................................................................... 11 Step 2: Select a disease of interest ............................................................................................. 12 Step 3: Select candidate sets ...................................................................................................... 15 Step 4: Prioritize ........................................................................................................................ 17 Step 5: Examine ranked genes and diseases .............................................................................. 19 Visualize ................................................................................................................................ 19 Search Evidences ................................................................................................................... 20 3. Automate autoHGPEC from R .............................................................................................. 20 Step 1: Construct a heterogeneous network ............................................................................... 21 Step 2: Select a disease of interest ............................................................................................. 21 Step 3: Select candidate sets ...................................................................................................... 21 Step 4: Prioritize ........................................................................................................................ 22 Step 5: Examine ranked genes and diseases .............................................................................. 23 Visualize ................................................................................................................................ 23 Search Evidences ................................................................................................................... 23 IV. Reference ................................................................................................................................... 24 2 I. Setup - autoHGPEC 1.0 can only run on Cytoscape 3.6 (or later) platform, which have Automation features, therefore user should download this version at http://cytoscape.org/ - Cytoscape need JRE to run, therefore download JRE version 7.x or later from http://www.oracle.com/technetwork/java/index.html and install it. - Install Cytoscape to the root folder (e.g., /Applications/Cytoscape_v3.6.0). - Download autoHGPEC_v1.0.jar file from http://hgpec.sourceforge.net/ or https://sites.google.com/site/duchaule2011/bioinformatics-tools/autohgpec. Then, install it by going to Apps à App Manager…. After that, choose Install from file…, then select the downloaded autoHGPEC_v1.0.jar file. - Create folders Data in the root folder of Cytoscape (e.g., /Applications/Cytoscape_v3.6.0). - Download GO annotation data at ftp.ncbi.nlm.nih.gov/gene/DATA/gene2go.gz, then extract and store in the Data folder (e.g., /Applications/Cytoscape_v3.6.0/Data). - Note that: autoHGPEC_v1.0 can work on Windows, Ubuntu and Mac OS. 3 II. Overview of autoHGPEC After installing, autoHGPEC will be automatically loaded in the App menu of Cytoscape The main tasks (Prediction of Genes and Diseases, and Evidence Collection) of autoHGPEC are completed after five steps: - Step 1: Construct a Heterogeneous network - Step 2: Select a disease of interest (including 2 sub steps) o 1. Select a disease o 2. Create training list - Step 3: Provide Candidate Gene Set (including 4 options) o All remaining genes in the Gene network o Neighbors of training genes in Chromosome o Neighbors of training genes in Gene network o Susceptible Chromosome Regions/Bands - Step 4: Prioritize (candidate genes and diseases) - Step 5: Examine Ranked Genes and Diseases o Search Evidences o Visualize These five steps can be performed - In Cytoscape like HGPEC (Le and Pham, 2017) - Using CyREST Command API - From R statistics (https://www.r-project.org) 4 III. Case study: Prediction of novel breast cancer-associated genes and diseases In the following section, we show the ability of autoHPEC in identifying novel breast cancer-associated genes and diseases. 1. Run autoHGPEC in Cytoscape Step 1: Construct a heterogeneous network To this end, we select a phenotypic disease similarity network containing 5,080 diseases and 19,729 interactions (i.e., Disease_Similarity_Network_5) and a human protein interaction network containing 10,486 genes and 50,791 interactions (i.e., Default_Human_PPI_Network). Then, we connect them by known disease-gene associations from either OMIM (Amberger, et al., 2009) or DisGeNET (Piñero, et al., 2017) to construct a heterogeneous network of diseases and genes by clicking Apps à autoHGPEC à Step 1: Construct a Heterogeneous Network To construct a heterogeneous network: 1. Select a disease similarity network. 2. Select known disease-gene associations 3. Select a network of genes/proteins (e.g., the preinstalled one or one imported from Cytoscape). 4. Click OK to connect these two networks by the known disease-gene associations. Note that: - For disease similarity network: We pre-installed 3 networks corresponding to 5, 10 or 15 nearest neighbors, which were extracted from a phenotypic disease similarity matrix data collected from (van Driel, et al., 2006) - For gene/protein interaction network: o We pre-installed a human physical protein interaction network collected from ftp://ftp.ncbi.nlm.nih.gov/gene/GeneRIF/interactions.gz. o However, user can use other protein/gene interaction networks by importing them to Cytoscape (File à Import à Network from table (Text/MS Excel)…). Genes/Proteins in the network must be identified by Gene Entrez ID. - For known disease-gene associations: User can select from either OMIM or DisGeNET Step 2: Select a disease of interest We select breast cancer (OMIM ID: 114480), then create training list by click menu Apps à autoHGPEC à Step 2: Select a disease of interest - Step 2.1. Select a disease: Apps à autoHGPEC à Step 2: Select a disease of interest à 1. Select a disease Enter disease keyword to retrieve a list of disease phenotypes from OMIM. Here are 4 phenotypes from OMIM related to “breast cancer” - Step 2.2. Create Training List: Apps à autoHGPEC à Step 2: Select a disease of interest à 2. Create Training List 5 Here are the training lists include the disease of interest (OMIM ID: 114480) and its 21 known associated genes. The disease of interest (OMIM ID: 114480) A total of 21 known associated genes Step 3: Select candidate sets For candidate diseases, all remaining diseases are specified as candidate diseases by default. Therefore, there are 5,079 diseases in this set. For candidate genes, select menu Apps à autoHGPEC à Step 3: Provide Candidate Gene Set, then we select option All remaining genes in Gene Network. As a result, a total of 10,465 remaining genes were selected as candidate genes. Four ways to construct a candidate gene set: - Neighbors of Training Genes in Gene Network o User must define distance of With option All remaining genes in Gene Network, 10,465 remaining neighbors to training genes genes were selected as candidate genes - Neighbors Of Training Genes in Chromosome (also known as Artificial Linkage Interval) o User must define number of neighbors of each training gene in the same chromosome. - All remaining genes in Gene Network - Susceptible Chromosome Regions/Bands o User selects candidate genes from susceptible chromosome regions/bands. Step 4: Prioritize We set three parameters
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