Facilitates the easy interrogation of omics datasets holistically to improve ‘findability’ of relevant data. Omicseq uses an algorithm for ranking omics datasets that fully uses the numerical content of the dataset to determine relevance to the query entity. It aims to enhance findability of omics datasets, to facilitate re-utilization, or re-purposing of existing data for secondary, tertiary analyses. The tool can facilitate discoveries using existing and emerging genomic datasets.
Department of Mathematics and Computer Science, Emory University, Atlanta, GA, USA; Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA; Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA; Health Science Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA; Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA; Department of Computer Science, Stony Brook University, Computer Science Building, Stony Brook, NY, USA; Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
Omicseq funding source(s)
Supported by Emory Integrated Computational Core (EICC), one of the Emory Integrated Core Facilities, which is subsidized by the Emory University School of Medicine and by the National Institutes of Health [UL1TR000454]; Patient-Centered Outcomes Research Institute [ME-1310-07058]; National Institute of Health [R01HG008802, R01GM114612, R01GM118574, R01GM118609, R21LM012060, U01EB023685]; National Science Foundation [ACI 1443054, IIS 1350885]; National Institute of Health [P01GM085354].