Normalization of Mass Spectrometry Data (NOMAD)

Abstract

MotivationiTRAQ reagent-based mass spectrometry (MS) is a commonly used technology for identification and quantification of proteins in biological samples. Such studies are often performed over multiple MS runs, potentially resulting in introduction of MS run bias that could affect downstream analysis. iTRAQ MS data have therefore commonly been normalized using a reference sample which is included in each MS run. We show, however, that such normalization does not efficiently remove systematic MS run bias. A linear model approach was previously proposed to improve on the reference normalization approach but does not computationally scale to larger data. Here we describe the NOMAD (normalization of mass spectrometry data) R package which implements a computationally efficient ANOVA normalization approach with protein assembly functionality.\n\nResultsNOMAD provides the same advantages as the linear regression solution but is more computationally efficient which allows superior scaling to larger sample sizes. Moreover, NOMAD efficiently removes bias which allows for valid across MS run comparisons.\n\nAvailabilityThe NOMAD Bioconductor package: www.bioconductor.org\n\nContactola.larsson@ki.se; carl.murie@ki.se