Powerful and flexible omics data analysis and visualization in the context of high-quality biological systems content
Solution partner: Qlucore
Advance your discovery research with faster, precise analysis through the powerful combination of Qlucore Omics Explorer and MetaCore, a Cortellis solution. Qlucore Omics Explorer’s bioinformatics software delivers data analysis and visualization instantly using MetaCore’s high-quality, 100% manually curated drug development content, including comprehensive, validated pathway data. The tools are so easy to use that you can easily interpret and explore your multi-omics data, so complete and accurate that you can put them in the optimal biological context with confidence.
You no longer need to choose between speed and accuracy for your genomic data and pathway analysis
Because Qlucore Omics Explorer is so easy to use, you don’t need to be an expert in analysis to use it. This frees up your bioinformaticians for more complex projects and gives you the power to do your own analyses, in an instant.
Fast, simple and visual analysis of measured data using the high-quality scientist-curated information in MetaCore to support your wide range of omics research including RNA-seq pathway analysis, proteomic analysis, genome sequence analysis and others.
Transform a list of molecules into a story, connect protein abundance changes and determine how they affect pathways, and interpret networks using experimental results identified using an integration with MetaCore.
Gain access to a unified, consistent set of over 50 leading-edge algorithms for protein-protein interaction networks and pathway analyses of molecular datasets for target/biomarker discovery, patient stratification and multi-OMICS analysis.
Collaborate with the best scientists in industry and academia to curate and map the biological pathways underlying diseases and develop both a literature base of understanding and an analytics platform to study protein-protein interactions and pathways in hot disease areas.
Systematically prioritize the repositioning of all known drugs for an indication of interest using a unique computational model that combines extensive bioinformatics and commercial evidence with advanced proprietary analytics.
