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id="content" lang="en"><span class="anchor" id="top"></span> <span class="anchor" id="line-1"></span><span class="anchor" id="line-2"></span><p class="line867"><strong>The Bader Lab is involved in a number of collaborative open-source bioinformatics projects designed to make biological pathway data easy to visualize and analyze.</strong> <span class="anchor" id="line-3"></span><span class="anchor" id="line-4"></span><p class="line867"><div class="table-of-contents"><p class="table-of-contents-heading">Contents<ol><li> <a href="#Biological_Network_Analysis_and_Visualization_Software">Biological Network Analysis and Visualization Software</a><ol><li> <a href="#Cytoscape">Cytoscape</a></li><li> <a href="#NetMatch">NetMatch</a></li><li> <a href="#MCODE">MCODE</a></li><li> <a href="#BRAIN">BRAIN</a></li><li> <a href="#LOLA">LOLA</a></li><li> <a href="#DoMo-Pred_Yeast">DoMo-Pred Yeast</a></li><li> <a href="#DoMo-Pred_Human">DoMo-Pred Human</a></li><li> <a href="#Enrichment_Map">Enrichment Map</a></li><li> <a href="#WordCloud">WordCloud</a></li><li> <a href="#AutoAnnotate">AutoAnnotate</a></li><li> <a href="#SIREN">SIREN</a></li><li> <a href="#SocialNetworkApp">SocialNetworkApp</a></li></ol></li><li> <a href="#Biological_Pathway_and_Network_Database_Software">Biological Pathway and Network Database Software</a><ol><li> <a href="#GeneMANIA">GeneMANIA</a></li><li> <a href="#cPath:_Pathway_Commons_data_integration_and_web_services">cPath: Pathway Commons data integration and web services</a></li><li> <a href="#netDx">netDx</a></li></ol></li><li> <a href="#Pathway_and_Network_Data">Pathway and Network Data</a><ol><li> <a href="#Pathway_Commons">Pathway Commons</a></li><li> <a href="#The_Cancer_Cell_Map">The Cancer Cell Map</a></li></ol></li><li> <a href="#Pathway_Data_Exchange_Standards">Pathway Data Exchange Standards</a><ol><li> <a href="#BioPAX">BioPAX</a></li><li> <a href="#PSI-MI">PSI-MI</a></li></ol></li><li> <a href="#Community_Web_Sites">Community Web Sites</a><ol><li> <a href="#Pathguide">Pathguide</a></li><li> <a href="#DV-IMPACT">DV-IMPACT</a></li><li> <a href="#HuRI_-_Human_Reference_Interactome">HuRI - Human Reference Interactome</a></li><li> <a href="#YeRI_-_Yeast_Reference_Interactome">YeRI - Yeast Reference Interactome</a></li><li> <a href="#MIMP">MIMP</a></li><li> <a href="#POW">POW</a></li><li> <a href="#PRM-DB">PRM-DB</a></li><li> <a href="#RShiny_apps">RShiny apps</a></li></ol></li></ol></div> <span class="anchor" id="line-5"></span><span class="anchor" id="line-6"></span><p class="line867"> <h1 id="Biological_Network_Analysis_and_Visualization_Software">Biological Network Analysis and Visualization Software</h1> <span class="anchor" id="line-7"></span><p class="line867"> <h2 id="Cytoscape">Cytoscape</h2> <span class="anchor" id="line-8"></span><p class="line874">Cytoscape is a bioinformatics software platform for visualizing molecular interaction networks and integrating these interactions with gene expression profiles and other state data. Additional features are available as plugins. Plugins are available for network and molecular profiling analyses, new layouts, additional file format support and connection with databases. Plugins may be developed using the Cytoscape open Java software architecture by anyone and plugin community development is encouraged. <span class="anchor" id="line-9"></span><span class="anchor" id="line-10"></span><p class="line867"><a class="http" href="http://www.cytoscape.org">http://www.cytoscape.org</a> <span class="anchor" id="line-11"></span><span class="anchor" id="line-12"></span><p class="line862">Cytoscape was originally developed at the <a class="http" href="http://systemsbiology.org/">Institute of Systems Biology</a> and is now a collaborative effort involving many different academic and commercial groups. <span class="anchor" id="line-13"></span><span class="anchor" id="line-14"></span><p class="line862">Fun link: <a class="http" href="http://www.youtube.com/watch?v=yEzv1hihmNU">Controlling Cytoscape using the Nintendo Wiimote</a> <span class="anchor" id="line-15"></span><span class="anchor" id="line-16"></span><p class="line867"> <h2 id="NetMatch">NetMatch</h2> <span class="anchor" id="line-17"></span><p class="line867">NetMatch is a Cytoscape plugin that finds user defined network motifs in any Cytoscape network. Node and edge attributes of any type and paths of unknown length can be specified in the search. <span class="anchor" id="line-18"></span><span class="anchor" id="line-19"></span><p class="line874">Released by: Ferro, Giugno, Pulvirenti group, University of Catania, Bader group, University of Toronto and Shasha group, New York University. <span class="anchor" id="line-20"></span><span class="anchor" id="line-21"></span><p class="line867"><a href="/Software/NetMatch">Available from the Bader Lab mirror of the NetMatch homepage</a> <span class="anchor" id="line-22"></span><span class="anchor" id="line-23"></span><p class="line867"> <h2 id="MCODE">MCODE</h2> <span class="anchor" id="line-24"></span><p class="line874">MCODE is a Cytoscape plugin that finds clusters (highly interconnected regions) in a network. Clusters mean different things in different types of networks. For instance, clusters in a protein-protein interaction network are often protein complexes and parts of pathways, while clusters in a protein similarity network represent protein families. <span class="anchor" id="line-25"></span><span class="anchor" id="line-26"></span><p class="line867"><a href="/Software/MCODE">Available from the MCODE homepage</a> <span class="anchor" id="line-27"></span><span class="anchor" id="line-28"></span><p class="line867"> <h2 id="BRAIN">BRAIN</h2> <span class="anchor" id="line-29"></span><p class="line874">The Biologically Relevant Analysis of Interaction Networks (BRAIN) is a set of algorithms for predicting and analyzing protein domain-peptide ligand interactions based on experimentally known binding evidence (e.g. from protein chip or phage display experiments). BRAIN can be accessed as a Cytoscape plugin which reads peptide binding profiles and generates interactions displayed as a Cytoscape network. <span class="anchor" id="line-30"></span><span class="anchor" id="line-31"></span><p class="line867"><a href="/Software/BRAIN">Available from the BRAIN homepage</a> <span class="anchor" id="line-32"></span><span class="anchor" id="line-33"></span><p class="line867"> <h2 id="LOLA">LOLA</h2> <span class="anchor" id="line-34"></span><p class="line874">LOLA (LOgos Look Amazing) is a tool for generating sequence logos using Position Weight Matrix based protein profiles. LOLA allows you to generate custom sequence logos by setting parameters such as logo height, trim percentage, and residue colour scheme. You can also generate "logo trees" based on clustered protein profiles to analyze binding motif classes. Sequence logos and logo trees can be saved in various formats including PDF, PNG, and JPEG. <span class="anchor" id="line-35"></span><span class="anchor" id="line-36"></span><p class="line867"><a href="/Software/LOLA">Available from the LOLA homepage</a> <span class="anchor" id="line-37"></span><span class="anchor" id="line-38"></span><p class="line867"> <h2 id="DoMo-Pred_Yeast">DoMo-Pred Yeast</h2> <span class="anchor" id="line-39"></span><p class="line867"><a class="nonexistent" href="/DoMo">DoMo</a>-Pred Yeast is a novel method for predicting physiologically relevant SH3 domain-peptide mediated protein-protein interactions in S. cerevisae using phage display data. This method uses position weight matrix models of protein linear motif preference for individual SH3 domains to scan the proteome for potential hits and then filters these hits using a range of evidence sources related to sequence-based and cellular constraints on protein interactions. By combining different peptide and protein features using multiple Bayesian models we are able to predict high confidence interactions with an overall accuracy accuracy of 0.97 <span class="anchor" id="line-40"></span><span class="anchor" id="line-41"></span><p class="line867"><a href="/Software/DoMo-Pred">Available from the Domo-Pred Yeast homepage</a> <span class="anchor" id="line-42"></span><span class="anchor" id="line-43"></span><span class="anchor" id="line-44"></span><p class="line867"> <h2 id="DoMo-Pred_Human">DoMo-Pred Human</h2> <span class="anchor" id="line-45"></span><p class="line867"><a class="nonexistent" href="/DoMo">DoMo</a>-Pred Human is a novel method of predicting SH3 domain-peptide mediated protein-protein interactions in humans using phage display data. This method builds upon our previously published work of combining multiple binding site and full length protein features using naive Bayesian models for predicting PRM mediated interactions. This work is a novel algorithm for predicting protein interactions using network topology. We have also extended the semi-supervised training regime of multinomial naive Bayesian classifier developed for text classification to Gaussian naive Bayesian models for PPI prediction. <span class="anchor" id="line-46"></span><span class="anchor" id="line-47"></span><p class="line867"><a href="/Software/DoMo-Pred-human">Available from the Domo-Pred Human homepage</a> <span class="anchor" id="line-48"></span><span class="anchor" id="line-49"></span><p class="line867"> <h2 id="Enrichment_Map">Enrichment Map</h2> <span class="anchor" id="line-50"></span><p class="line874">Enrichment Map is a Cytoscape plugin for functional enrichment visualization. Enrichment results have to be generated outside Enrichment Map, using any of the available methods. Gene-sets, such as pathways and Gene Ontology terms, are organized into a network (i.e. the "enrichment map"). In this way, mutually overlapping gene-sets cluster together, making interpretation easier. Enrichment Map also enables the comparison of two different enrichment results in the same map. <span class="anchor" id="line-51"></span><span class="anchor" id="line-52"></span><p class="line867"><a href="/Software/EnrichmentMap">Available from Enrichment Map homepage</a> <span class="anchor" id="line-53"></span><span class="anchor" id="line-54"></span><p class="line867"> <h2 id="WordCloud">WordCloud</h2> <span class="anchor" id="line-55"></span><p class="line867"><a href="/WordCloud">WordCloud</a> is a Cytoscape app that generates a visual summary of a network. It displays string attributes associated with nodes in the network as a tag cloud, where more frequent words are displayed using a larger font size. Word co-occurence in a phrase can be visualized by arranging words in clusters or as a network. <span class="anchor" id="line-56"></span><span class="anchor" id="line-57"></span><p class="line867"><a href="/Software/WordCloudPlugin">Available from WordCloud homepage</a> <span class="anchor" id="line-58"></span><span class="anchor" id="line-59"></span><span class="anchor" id="line-60"></span><p class="line867"> <h2 id="AutoAnnotate">AutoAnnotate</h2> <span class="anchor" id="line-61"></span><p class="line867"><a href="/AutoAnnotate">AutoAnnotate</a> is a Cytoscape app that finds node clusters and visually annotates them with labels and groups. These visualizations provide a concise visual summary which is helpful for network analysis and interpretation. <span class="anchor" id="line-62"></span><span class="anchor" id="line-63"></span><p class="line867"><a href="/Software/AutoAnnotate">Available from AutoAnnotate homepage</a> <span class="anchor" id="line-64"></span><span class="anchor" id="line-65"></span><span class="anchor" id="line-66"></span><p class="line867"> <h2 id="SIREN">SIREN</h2> <span class="anchor" id="line-67"></span><p class="line874">The Signing of Regulatory Networks (SIREN) algorithm can infer the regulatory type (positive or negative regulation) of interactions in a known gene regulatory network given corresponding genome-wide gene expression data. <span class="anchor" id="line-68"></span><span class="anchor" id="line-69"></span><p class="line867"><a href="/Software/SIREN">Available from SIREN homepage</a> <span class="anchor" id="line-70"></span><span class="anchor" id="line-71"></span><p class="line867"><a class="http" href="http://apps.cytoscape.org/apps/siren">http://apps.cytoscape.org/apps/siren</a> <span class="anchor" id="line-72"></span><span class="anchor" id="line-73"></span><p class="line867"> <h2 id="SocialNetworkApp">SocialNetworkApp</h2> <span class="anchor" id="line-74"></span><p class="line874">The Social Network Cytoscape app creates a visual summary of how individuals are connected. Biological networks can be visualized and analyzed using Cytoscape. Often researchers want to go beyond the network of proteins or genes and also look at the inter-connectedness between colleagues and institutions. Who tends to publish together? What institutions are most collaborative? Are there inter-disciplinary connections in my institution? The app addresses these questions by building co-publication networks where the nodes represent authors, edges represent co-authorship and edge thickness represents how frequently co-authors collaborate. <span class="anchor" id="line-75"></span><span class="anchor" id="line-76"></span><p class="line867"><a href="/Software/SocialNetworkApp">Available from SocialNetworkApp homepage</a> <span class="anchor" id="line-77"></span><span class="anchor" id="line-78"></span><p class="line867"> <h1 id="Biological_Pathway_and_Network_Database_Software">Biological Pathway and Network Database Software</h1> <span class="anchor" id="line-79"></span><span class="anchor" id="line-80"></span><p class="line867"> <h2 id="GeneMANIA">GeneMANIA</h2> <span class="anchor" id="line-81"></span><span class="anchor" id="line-82"></span><p class="line874">GeneMANIA helps you predict the function of your favourite genes and gene sets. <span class="anchor" id="line-83"></span><span class="anchor" id="line-84"></span><p class="line874">GeneMANIA finds other genes that are related to a set of input genes, using a very large set of functional association data. Association data include protein and genetic interactions, pathways, co-expression, co-localization and protein domain similarity. You can use GeneMANIA to find new members of a pathway or complex, find additional genes you may have missed in your screen or find new genes with a specific function, such as protein kinases. Your question is defined by the set of genes you input. <span class="anchor" id="line-85"></span><span class="anchor" id="line-86"></span><p class="line867"><a class="http" href="http://www.genemania.org/">http://www.genemania.org/</a> <span class="anchor" id="line-87"></span><span class="anchor" id="line-88"></span><p class="line862">This work is done in collaboration with <a class="https" href="https://www.morrislab.ai/">Quaid Morris' lab</a> at the University of Toronto. <span class="anchor" id="line-89"></span><span class="anchor" id="line-90"></span><p class="line867"> <h2 id="cPath:_Pathway_Commons_data_integration_and_web_services">cPath: Pathway Commons data integration and web services</h2> <span class="anchor" id="line-91"></span><p class="line874">cPath, an open source database and web service application for collecting, storing, browsing and querying biological pathway data. cPath makes it easy to aggregate custom pathway data sets available in standard exchange formats from multiple databases, present pathway data to biologists, and export pathway data via a web service to third-party software for visualization and analysis. cPath is software only, and does not include new pathway information. Key features include: a built-in identity and reference service for identifying and annotating interactors; built-in support for PSI and BioPAX standard pathway exchange formats; and a web service interface for searching and retrieving biological network data sets. The cPath and related software is freely available under the LGPL or MIT open source license for academic and commercial use. <span class="anchor" id="line-92"></span><span class="anchor" id="line-93"></span><p class="line867"><a class="https" href="https://github.com/PathwayCommons/">https://github.com/PathwayCommons/</a> <br> <a class="http" href="http://www.pathwaycommons.org/">http://www.pathwaycommons.org/</a> (Pathways and molecular interactions) <br> PC2 (cPath2): <a class="http" href="http://www.pathwaycommons.org/pc2/">http://www.pathwaycommons.org/pc2/</a> <br> <span class="anchor" id="line-94"></span><span class="anchor" id="line-95"></span><p class="line862">This work is done in collaboration with the Sander group of the Computational Biology Center at Memorial Sloan-Kettering Cancer Center in New York City, then at the Memorial Slon the Dana-Farber Cancer Institute and Harvard Medical School, and the Demir Lab at Oregon Health & Science University. <span class="anchor" id="line-96"></span><span class="anchor" id="line-97"></span><p class="line867"> <h2 id="netDx">netDx</h2> <span class="anchor" id="line-98"></span><span class="anchor" id="line-99"></span><p class="line874">netDx is a patient classifier algorithm that can integrate several types of patient data into a single model. It does this by converting each type of data into a view of patient similarity; i.e. by converting the data into a graph in which more similar patients are tightly linked, while less similar patients are not so tightly linked. <span class="anchor" id="line-100"></span><span class="anchor" id="line-101"></span><p class="line867"><a class="http" href="http://netdx.org">http://netdx.org</a> <span class="anchor" id="line-102"></span><span class="anchor" id="line-103"></span><p class="line867"> <h1 id="Pathway_and_Network_Data">Pathway and Network Data</h1> <span class="anchor" id="line-104"></span><span class="anchor" id="line-105"></span><p class="line867"> <h2 id="Pathway_Commons">Pathway Commons</h2> <span class="anchor" id="line-106"></span><p class="line874">Pathway Commons is a convenient point of access to biological pathway information collected from public pathway databases, which you can browse or search. Pathways include biochemical reactions, complex assembly, transport and catalysis events, and physical interactions involving proteins, DNA, RNA, small molecules and complexes. <span class="anchor" id="line-107"></span><span class="anchor" id="line-108"></span><p class="line867"><a class="http" href="http://www.pathwaycommons.org/">http://www.pathwaycommons.org/</a> <span class="anchor" id="line-109"></span><span class="anchor" id="line-110"></span><p class="line862">This work is done in collaboration with the Sander group of the <a class="http" href="http://cbio.mskcc.org/">Computational Biology Center</a> at Memorial Sloan-Kettering Cancer Center in New York City. <span class="anchor" id="line-111"></span><span class="anchor" id="line-112"></span><p class="line867"> <h2 id="The_Cancer_Cell_Map">The Cancer Cell Map</h2> <span class="anchor" id="line-113"></span><p class="line874">The Cancer Cell Map contains selected cancer related signaling pathways which you can browse or search. Biologists can browse and search the Cancer Cell Map pathways. View gene expression data on any pathway. Computational biologists can download all pathways in BioPAX format for global analysis. Software developers can build software on top of the Cancer Cell Map using the web service API. Download and install the cPath software to create a local mirror. <span class="anchor" id="line-114"></span><span class="anchor" id="line-115"></span><p class="line867"><a class="http" href="http://netpath.org/browse">http://netpath.org/browse</a> <span class="anchor" id="line-116"></span><span class="anchor" id="line-117"></span><p class="line862">This work is done in collaboration with the Sander group of the <a class="http" href="http://cbio.mskcc.org/">Computational Biology Center</a> at Memorial Sloan-Kettering Cancer Center in New York City. <span class="anchor" id="line-118"></span><span class="anchor" id="line-119"></span><p class="line867"> <h1 id="Pathway_Data_Exchange_Standards">Pathway Data Exchange Standards</h1> <span class="anchor" id="line-120"></span><p class="line867"> <h2 id="BioPAX">BioPAX</h2> <span class="anchor" id="line-121"></span><p class="line874">BioPAX (Biological Pathway Exchange) is a collaborative effort to create a data exchange format for biological pathway data. BioPAX covers metabolic pathways, molecular interactions and protein post-translational modifications. Future versions will expand support for signaling pathways, gene regulatory networks and genetic interactions. <span class="anchor" id="line-122"></span><span class="anchor" id="line-123"></span><p class="line867"><a class="http" href="http://www.biopax.org/">http://www.biopax.org/</a> <span class="anchor" id="line-124"></span><span class="anchor" id="line-125"></span><p class="line867"> <h2 id="PSI-MI">PSI-MI</h2> <span class="anchor" id="line-126"></span><p class="line874">PSI-MI (The Proteomics Standards Initiative Molecular Interactions) format allows exchange of molecular interaction data, focusing on protein-protein interactions. <span class="anchor" id="line-127"></span><span class="anchor" id="line-128"></span><p class="line867"><a class="http" href="http://psidev.sourceforge.net/">http://psidev.sourceforge.net/</a> <span class="anchor" id="line-129"></span><span class="anchor" id="line-130"></span><p class="line874">Both of these projects are large international collaborative efforts involving many different academic and commercial groups. <span class="anchor" id="line-131"></span><span class="anchor" id="line-132"></span><p class="line867"> <h1 id="Community_Web_Sites">Community Web Sites</h1> <span class="anchor" id="line-133"></span><p class="line867"> <h2 id="Pathguide">Pathguide</h2> <span class="anchor" id="line-134"></span><p class="line874">Pathguide, the Pathway Resource List, contains information about hundreds of online biological pathway resources. Databases that are free and those supporting BioPAX, CellML, PSI-MI or SBML standards are highlighted. <span class="anchor" id="line-135"></span><span class="anchor" id="line-136"></span><p class="line867"><a class="http" href="http://www.pathguide.org/">http://www.pathguide.org/</a> <span class="anchor" id="line-137"></span><span class="anchor" id="line-138"></span><p class="line867"> <h2 id="DV-IMPACT">DV-IMPACT</h2> <span class="anchor" id="line-139"></span><p class="line867"><a class="https" href="https://dvimpact.baderlab.org"><img alt="https://dvimpact.baderlab.org" class="attachment" src="/Software?action=AttachFile&do=get&target=dvimpact.png" title="https://dvimpact.baderlab.org" width="500" /></a> <span class="anchor" id="line-140"></span><span class="anchor" id="line-141"></span><p class="line874">Database of disease variants and their impacts on domain-peptide protein interaction networks. <span class="anchor" id="line-142"></span><span class="anchor" id="line-143"></span><p class="line867"><a class="https" href="https://dvimpact.baderlab.org">https://dvimpact.baderlab.org</a> <span class="anchor" id="line-144"></span><span class="anchor" id="line-145"></span><p class="line867"> <h2 id="HuRI_-_Human_Reference_Interactome">HuRI - Human Reference Interactome</h2> <span class="anchor" id="line-146"></span><span class="anchor" id="line-147"></span><p class="line867"><a class="http" href="http://www.interactome-atlas.org"><img alt="http://www.interactome-atlas.org" class="attachment" src="/Software?action=AttachFile&do=get&target=huri.png" title="http://www.interactome-atlas.org" width="500" /></a> <span class="anchor" id="line-148"></span><span class="anchor" id="line-149"></span><p class="line874">Pairwise combinations of human protein-coding genes are tested systematically using high throughput yeast two-hybrid screens to detect protein-protein interactions. The quality of these interactions is further validated in multiple orthogonal assays. Currently 64006 PPIs involving 9094 proteins have been identified using this framework. <span class="anchor" id="line-150"></span><span class="anchor" id="line-151"></span><p class="line874">In addition to systematically identifying PPIs experimentally, this web portal also includes PPIs of comparable high quality extracted from literature. This subset of literature-curated PPIs currently comprises 13441 PPIs involving 6047 proteins. <span class="anchor" id="line-152"></span><span class="anchor" id="line-153"></span><p class="line867"><a class="http" href="http://www.interactome-atlas.org/">http://www.interactome-atlas.org/</a> <span class="anchor" id="line-154"></span><span class="anchor" id="line-155"></span><p class="line867"> <h2 id="YeRI_-_Yeast_Reference_Interactome">YeRI - Yeast Reference Interactome</h2> <span class="anchor" id="line-156"></span><span class="anchor" id="line-157"></span><p class="line867"><a class="http" href="http://yeast.interactome-atlas.org/"><img alt="http://yeast.interactome-atlas.org/" class="attachment" src="/Software?action=AttachFile&do=get&target=yeri.png" title="http://yeast.interactome-atlas.org/" width="500" /></a> <span class="anchor" id="line-158"></span><span class="anchor" id="line-159"></span><p class="line874">Pairwise combinations of yeast protein-coding genes were tested systematically using high throughput yeast two-hybrid (Y2H) screens to detect protein-protein interactions. All four systematic maps were generated using different assay versions of the canonical yeast two-hybrid system, described in table 1. For maps generated at CCSB (Yu et al and YI-II), the precision of the dataset was tested by validating some of the interactions in multiple orthogonal assays (Venkatesan et al). In total, 4,556 high-quality protein-protein interactions (PPIs) involving 2,552 proteins have been identified by the four maps. <span class="anchor" id="line-160"></span><span class="anchor" id="line-161"></span><p class="line874">In addition to systematically identifying PPIs experimentally, the yeast interactome map also contains PPIs of comparable high quality from literature curation databases. This subset of literature-curated PPIs currently comprises 5,589 PPIs. <span class="anchor" id="line-162"></span><span class="anchor" id="line-163"></span><p class="line867"><a class="http" href="http://yeast.interactome-atlas.org">http://yeast.interactome-atlas.org</a> <span class="anchor" id="line-164"></span><span class="anchor" id="line-165"></span><p class="line867"> <h2 id="MIMP">MIMP</h2> <span class="anchor" id="line-166"></span><span class="anchor" id="line-167"></span><p class="line867"><a class="http" href="http://mimp.baderlab.org/"><img alt="http://mimp.baderlab.org/" class="attachment" src="/Software?action=AttachFile&do=get&target=mimp.png" title="http://mimp.baderlab.org/" width="100" /></a> <span class="anchor" id="line-168"></span><span class="anchor" id="line-169"></span><p class="line874">Mutation IMpact on Phosphorylation <span class="anchor" id="line-170"></span><ul><li>Predicting loss and gain of phosphorylation - MIMP characterizes genetic variants such as cancer mutations that specifically alter kinase-binding sites in proteins. As these residues are changed in disease mutations, alterations in kinase binding specificity potentially lead to rewiring of kinase-substrate interaction networks <span class="anchor" id="line-171"></span></li><li>Comprehensive kinase-substrate data - MIMP makes use of phosphorylation data for a wide range of kinases to comprehensively predict kinase-rewiring mutations. Kinase specificity models are refined to remove sequences not matching the general motif and improve prediction power. <span class="anchor" id="line-172"></span><span class="anchor" id="line-173"></span></li></ul><p class="line867"><a class="http" href="http://mimp.baderlab.org/">http://mimp.baderlab.org/</a> <span class="anchor" id="line-174"></span><span class="anchor" id="line-175"></span><p class="line867"> <h2 id="POW">POW</h2> <span class="anchor" id="line-176"></span><span class="anchor" id="line-177"></span><p class="line867"><a class="http" href="http://pow.baderlab.org"><img alt="http://pow.baderlab.org" class="attachment" src="/Software?action=AttachFile&do=get&target=pow.png" title="http://pow.baderlab.org" width="100" /></a> <span class="anchor" id="line-178"></span><span class="anchor" id="line-179"></span><p class="line874">POW is a website that allows users to predict PDZ domain-peptide interactions for human, mouse, worm and fly PDZ domains. Predictions are made using a support vector machine (SVM) that was trained using experimentally determined PDZ interaction data from protein microarray and phage display experiments for mouse and human [1,2]. Two types of predictors are available for use. The first is sequence-based (trained using domain and peptide sequence features) while the other structure-based (trained using domain structure and peptide sequence features). <span class="anchor" id="line-180"></span><span class="anchor" id="line-181"></span><p class="line867"><a class="http" href="http://pow.baderlab.org/">http://pow.baderlab.org/</a> <span class="anchor" id="line-182"></span><span class="anchor" id="line-183"></span><p class="line867"> <h2 id="PRM-DB">PRM-DB</h2> <span class="anchor" id="line-184"></span><span class="anchor" id="line-185"></span><p class="line867"><a class="http" href="http://www.prm-db.org/"><img alt="http://www.prm-db.org/" class="attachment" src="/Software?action=AttachFile&do=get&target=prmdb.png" title="http://www.prm-db.org/" width="300" /></a> <span class="anchor" id="line-186"></span><span class="anchor" id="line-187"></span><p class="line874">The database of Peptide Recognition Modultes <span class="anchor" id="line-188"></span><span class="anchor" id="line-189"></span><p class="line867"><a class="http" href="http://www.prm-db.org/">http://www.prm-db.org/</a> <span class="anchor" id="line-190"></span><span class="anchor" id="line-191"></span><p class="line867"> <h2 id="RShiny_apps">RShiny apps</h2> <span class="anchor" id="line-192"></span><span class="anchor" id="line-193"></span><p class="line867"><a class="http" href="http://shiny.baderlab.org/">http://shiny.baderlab.org/</a> <span class="anchor" id="line-194"></span><span class="anchor" id="line-195"></span><ul><li><p class="line891"><a class="http" href="http://shiny.baderlab.org/RetinaSCs">Mouse Retinal Stem Cells</a> - Data portal for the paper A microfluidic platform enables comprehensive gene expression profiling of mouse retinal stem cells by Coles, Labib, et al. Lab on a Chip, 2021. This is an RShiny app based on <a class="https" href="https://baderlab.github.io/scClustViz/">scClustViz</a> for exploring the single-cell transcriptomes of cells isolated from the mouse retina and enriched for stem cells using the published microfluidics platform. <span class="anchor" id="line-196"></span></li><li><p class="line891"><a class="http" href="http://shiny.baderlab.org/AgingMouseBrain">Aging Mouse Brain</a> - Data portal for the paper Single-cell transcriptomic profiling of the aging mouse brain by Ximerakis, Lipnick, et al. Nat Neurosci, 2019. This data portal includes RShiny apps based on both scClustViz for exploring scRNAseq data and <a class="https" href="https://baderlab.github.io/CCInx/">CCInx</a> for predicting interactions between cell types. <span class="anchor" id="line-197"></span></li><li><p class="line891"><a class="http" href="http://shiny.baderlab.org/hPSCLSECsAndHumanLiver">hPSC-derived LSECs</a> - Data portal for the paper Generation of functional liver sinusoidal endothelial cells from human pluripotent stem-cell-derived venous angioblasts by Gage et al. Cell Stem Cell, 2020. This data portal includes RShiny apps based on <a class="https" href="https://baderlab.github.io/scClustViz/">scClustViz</a> for exploring scRNAseq data of hPSC-derived LSECs generated from the maturation of venous angioblasts in NSG mice, as well as a comparison between these cells and the cells of the human liver atlas presented below. <span class="anchor" id="line-198"></span></li><li><p class="line891"><a class="http" href="http://shiny.baderlab.org/HumanLiverAtlas">Human Liver Atlas</a> - Data portal for the paper Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations by MacParland et al. Nat Commun, 2018. This is an RShiny app based on <a class="https" href="https://baderlab.github.io/scClustViz/">scClustViz</a> for exploring the scRNAseq data presented in the paper. <span class="anchor" id="line-199"></span></li><li><p class="line862">Mouse Cerebral Cortex - Data portal for the paper Developmental emergence of adult neural stem cells as revealed by single cell transcriptional profiling by Yuzwa, Borrett, et al. Cell Rep, 2017. This data portal contains time-series data presented as a set of RShiny apps based on <a class="https" href="https://baderlab.github.io/scClustViz/">scClustViz</a>. Timepoints: <a class="http" href="http://shiny.baderlab.org/MouseCortex/e11">E11.5</a> — <a class="http" href="http://shiny.baderlab.org/MouseCortex/e13">E13.5</a> — <a class="http" href="http://shiny.baderlab.org/MouseCortex/e15">E15.5</a> — <a class="http" href="http://shiny.baderlab.org/MouseCortex/e17">E17.5</a> <span class="anchor" id="line-200"></span></li></ul><span class="anchor" id="bottom"></span></div>  <div class="clearfix"></div> </div>   </div>  </div>   <div id="pageinfo-container"> <div class="container"> <p id="pageinfo" class="info" lang="en" dir="ltr">Software (last edited 2023-07-06 18:51:27 by <span title="RuthIsserlin @ server2.baderlab.med.utoronto.ca[192.168.81.22]"><a href="/RuthIsserlin" title="RuthIsserlin @ server2.baderlab.med.utoronto.ca[192.168.81.22]">RuthIsserlin</a></span>)</p> </div> </div>   <div id="footer"> <div class="container text-right text-muted"> <ul id="credits"> <li><a href="http://moinmo.in/" title="This site uses the MoinMoin Wiki software.">MoinMoin Powered</a></li><li><a href="http://moinmo.in/Python" title="MoinMoin is written in Python.">Python Powered</a></li><li><a href="http://moinmo.in/GPL" title="MoinMoin is GPL licensed.">GPL licensed</a></li><li><a href="http://validator.w3.org/check?uri=referer" title="Click here to validate this page.">Valid HTML 4.01</a></li> </ul> <center><a href='http://host-tracker.com/' onMouseOver='this.href='http://host-tracker.com/website-uptime-statistics/474337/lvuc/';'><img width=80 height=15 border=0 alt='website tracker' src='http://ext.host-tracker.com/uptime-img/?s=15&t=474337&m=00.0&p=Total&src=lvuc'></a><noscript><a href='http://host-tracker.com/' >remote server monitor</a></noscript></center><script src='http://www.google-analytics.com/urchin.js' type='text/javascript'></script><script type='text/javascript'> _uacct = 'UA-387720-1';urchinTracker();</script> </div>  </div>     <script src="/moin_static198/memodump/js/jquery.min.js"></script>  <script src="/moin_static198/memodump/js/bootstrap.min.js"></script>  <script src="/moin_static198/memodump/js/toggle.js"></script>  <script> +function ($) { // Toggle minified navbar under mobile landscape view $('.navbar-collapse').on('show.bs.collapse', function () { $('.navbar-mobile-toggle').togglejs('show'); }); $('.navbar-collapse').on('hidden.bs.collapse', function () { $('.navbar-mobile-toggle').togglejs('hide'); }); //Scroll position fix for hash anchors var mdAnchorFix = { escapeRe: /[ !"#$%&'()*+,.\/:;<=>?@\[\\\]^`{|}~]/g, escape: function (str) { return str.replace(mdAnchorFix.escapeRe, '\\$&'); }, rgbRe: /^rgba$([ \t]*\d{1,3},){3}([ \t]*\d{1,3})$$/i, isTransparent: function (rgbstr) { if (rgbstr === 'transparent') { return true; } rgbMatch = rgbstr.match(mdAnchorFix.rgbRe); if (rgbMatch) { return (Number(rgbMatch[2]) ? false : true); } return false; }, navbarHeight: function () { var height = 0; var $navbar = $('.navbar'); if ( !mdAnchorFix.isTransparent($navbar.css('background-color')) && ($navbar.css('display') !== 'none') && ($navbar.css('visibility') !== 'hidden') ) { height = $navbar.height(); } return height; }, jump: function () { origin = $('#' + mdAnchorFix.escape(location.hash.substr(1))).offset().top; offset = mdAnchorFix.navbarHeight() + 15; setTimeout(function () { window.scrollTo(0, origin - offset); }, 1); }, clickWrapper: function () { if ( ($(this).attr('href') === location.hash) || !('onhashchange' in window.document.body) ) { setTimeout(function () { $(window).trigger("hashchange"); }, 1); } }, }; $('#pagebox a[href^="#"]:not([href="#"])').on("click", mdAnchorFix.clickWrapper); $(window).on("hashchange", mdAnchorFix.jump); if (location.hash) setTimeout(function () { mdAnchorFix.jump(); }, 100); }(jQuery); </script>  <div id="turnkey-credit"> <div style="clear: both;"> <a href="https://www.turnkeylinux.org/moinmoin">MoinMoin Appliance</a> - Powered by <a href="https://www.turnkeylinux.org">TurnKey Linux</a> </div> </div></body> </html>