Ranathunge C, Patel SS, Pinky L, Correll VL, Chen S, Semmes OJ, Armstrong RK, Combs CD, Nyalwidhe JO. promor: a comprehensive R package for label-free proteomics data analysis and predictive modeling.
BIOINFORMATICS ADVANCES 2023;
3:vbad025. [PMID:
36922981 PMCID:
PMC10010602 DOI:
10.1093/bioadv/vbad025]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 02/22/2023] [Accepted: 03/06/2023] [Indexed: 03/09/2023]
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.
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