promor: a comprehensive R package for label-free proteomics data analysis and predictive modeling

Chathurani Ranathunge; Sagar S Patel; Lubna Pinky; Vanessa L Correll; Shimin Chen; O John Semmes; Robert K Armstrong; C Donald Combs; Julius O Nyalwidhe

doi:10.1093/bioadv/vbad025

promor: a comprehensive R package for label-free proteomics data analysis and predictive modeling

Bioinform Adv. 2023 Mar 7;3(1):vbad025. doi: 10.1093/bioadv/vbad025. eCollection 2023.

Authors

Chathurani Ranathunge¹, Sagar S Patel¹, Lubna Pinky¹, Vanessa L Correll², Shimin Chen², O John Semmes², Robert K Armstrong^{1

3}, C Donald Combs¹, Julius O Nyalwidhe²

Affiliations

¹ Eastern Virginia Medical School, School of Health Professions, Norfolk, VA 23501, USA.
² The Leroy T. Canoles Jr. Cancer Research Center, Eastern Virginia Medical School, Norfolk, VA 23501, USA.
³ Sentara Center for Simulation and Immersive Learning, Eastern Virginia Medical School, Norfolk, VA 23501, USA.

Abstract

Summary: We present promor, a comprehensive, user-friendly R package that streamlines label-free quantification proteomics data analysis and building machine learning-based predictive models with top protein candidates.

Availability and implementation: promor is freely available as an open source R package on the Comprehensive R Archive Network (CRAN) (https://CRAN.R-project.org/package=promor) and distributed under the Lesser General Public License (version 2.1 or later). Development version of promor is maintained on GitHub (https://github.com/caranathunge/promor) and additional documentation and tutorials are provided on the package website (https://caranathunge.github.io/promor/).

Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Grants and funding

U2C CA271894/CA/NCI NIH HHS/United States