Summary: A generalized goal of many high-throughput data studies is to identify functional mecha-nisms that underlie observed biological phenomena, whether disease outcomes or metabolic out-put. Increasingly, studies that rely on multiple sources of high-throughput data (genomic, tran-scriptomic, proteomic, metabolomic) are faced with a challenge of utilizing the data in a way that maximizes utility. However, methods for integration of multiple forms of molecular data into a biolog-ically coherent frameworks are needed. We have developed a framework to assess biological pathway activity that relates to phenotypic outcome using multi-source data.
Availability and implementation: The leapR package with user manual and example workflow is available for download from GitHub (https://github.com/biodataganache/leapR).
Published: June 16, 2021
Danna V.G., H.D. Mitchell, L.N. Anderson, I.G. Godinez, S. Gosline, J.G. Teeguarden, and J.E. McDermott. 2021.leapR: An R Package for MultiOmic Pathway Analysis.Journal of Proteome Research 20, no. 4:2116-2121.PNNL-SA-159351.doi:10.1021/acs.jproteome.0c00963