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Download Knowledge Engine for Genomics (KnowEnG): Cloud
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Knowledge Engine for Genomics (KnowEnG): Cloud-based Environment for Scalable Analyses of Genomic Signatures Charles Blatti Postdoctoral Research Associate KnowEnG Center of Excellence in Big Data Computing, University of Illinois at Urbana-Champaign National Center for Supercomputing Applications November,University 2016 of Illinois at Urbana-Champaign Genomic Data Analysis Using Prior Knowledge in a Scalable Cloud Knowledge Network User Interface Analysis Pipelines Network Smoothing Consensus Clustering Genes Samples NMF Clustering User Spreadsheet 2 Genomic Data Analysis Using Prior Knowledge in a Scalable Cloud Knowledge Network User Interface Analysis Pipelines Network Smoothing Samples NMF Clustering Consensus Clustering Genes 3 User Spreadsheet Gene Prioritization Research Application Amin Emad Knowledge-Guided Prioritization of Genes Determinant of Drug Resistance Using ProGENI Research Highlights Session 2 4 Genomics of Drug Sensitivity in Cancer Data GDSC Data 13,000 Genes Basal Expression Data 600 Cell Lines 139 Drugs Drug Sensitivity Data 600 Cell Lines 5 Gene Prioritization Measure 6 Incorporating the Knowledge Network Network Transform Prioritization Measure Network Ranking 7 Robust Prioritization by Resampling Network Transform Prioritization Measure Network Ranking 8 Running the Pipeline Docker Containers Scheduled by Chronos Managed by Mesos Synced to Cloud Storage 9 Visualizing the Results 10 Porting Results to Gene Set Characterization 11 Choosing Public Gene Sets Standard GSC Method Popular Webtools Annotation Gene Sets Characteristic Gene Sets Experimental Gene Sets 12 Incorporating the Knowledge Network P1 P2 Heterogeneous Edge Types GO_edge KEGG_edge HumanNet_edge P3 G1 G6 G2 G7 G3 G8 G4 G9 G5 G10 P4 P5 13 Visualizing the GSC Results 14 Sample Clustering / Subtype Stratification 15 Upcoming Features Integration with Other Clouds Import user spreadsheets directly from other cloud-based datasets like TCGA, LINCS New Workflows Gene Regulatory Networks – Model interactions between transcripts and transcription factors Text Mining – Find genes most specifically related to different disease terminology Phenotype Prediction – Create model that predicts phenotypic outcomes from genomic data 16 Thank You! Thank You! Come see our demo at Poster #75 KnowEnG Development Team • Research and Design: Saurabh Sinha, Colleen Bushell, Matt Berry, Lisa Gatzke, Amin Emad, Charles Blatti, and Sheng Wang • Pipelines and Infrastructure: Nahil Sobh, Dan Lanier, Milt Epstein, Xi Chen, Suyang Chen, Jing Ge, Pramod Rizal, Omar Sobh, Aidan Epstein, Corey Post 17
