MALDIrppa: quality control and robust analysis for mass spectrometry data

Javier Palarea-Albaladejo; Kevin Mclean; Frank Wright; David G E Smith

doi:10.1093/bioinformatics/btx628

MALDIrppa: quality control and robust analysis for mass spectrometry data

Bioinformatics. 2018 Feb 1;34(3):522-523. doi: 10.1093/bioinformatics/btx628.

Authors

Javier Palarea-Albaladejo¹, Kevin Mclean², Frank Wright¹, David G E Smith³

Affiliations

¹ Biomathematics and Statistics Scotland, JCMB, The King's Buildings, Peter Guthrie Tait Road, Edinburgh, EH9 3FD, UK.
² Proteomics Facility Services, Moredun Research Institute, Pentland Science Park, Bush Loan, Penicuik, Mid Lothian, EH26 0PZ, UK.
³ Institute of Biological Chemistry, Biophysics and Bioengineering, Heriot-Watt University, Edinburgh, EH14 4AS, UK.

PMID: 29028890
DOI: 10.1093/bioinformatics/btx628

Abstract

Summary: This R package helps to implement a robust approach to deal with mass spectrometry (MS) data. It is aimed at alleviating reproducibility issues and pernicious effects of deviating signals on both data pre-processing and downstream data analysis. Based on robust statistical methods, it facilitates the identification and filtering of low-quality mass spectra and atypical peak profiles as well as monitoring and data handling through pre-processing, which extends existing computational tools for high-throughput data.

Availability and implementation: MALDIrppa is implemented as a package for the R environment for data analysis and it is freely available to download from the CRAN repository at https://CRAN.R-project.org/package=MALDIrppa.

Contact: javier.palarea@bioss.ac.uk.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Computational Biology / methods
High-Throughput Screening Assays / methods
High-Throughput Screening Assays / standards
Mass Spectrometry / methods
Mass Spectrometry / standards*
Quality Control*
Reproducibility of Results
Software*