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bioRxiv2018

adductomicsR: A package for detection and quantification of protein adducts from mass spectra of tryptic digests

SummaryLiquid chromatography-high resolution mass spectrometry (LC-HRMS) has been used to establish a method, referred to as adductomics, for characterisation of putative protein adducts at selected loci in human serum albumin (HSA). Applications of this method have been limited by the lack of software for untargeted analysis of modified peptides in protein digests. Here we present adductomicsR, an open-source R package for processing LC-HRMS data from analysis of adducted HSA peptides. The software interrogates mass spectra to correct for retention-time drift, and to discover and quantify putative adducts along with those for a housekeeping peptide and internal standard.\n\nAvailability and implementationadductomicsR is written in R and publicly available at https://github.com/JosieLHayes/adductomicsR, which includes a vignette with example data.\n\nSupplementary informationmzXML files for the vignette and test dataset are available in an associated data package adductData (https://github.com/JosieLHayes/adductData).\n\nContactjosie.hayes@berkeley.edu\n\nIssue SectionAPPLICATIONS NOTE

bioinformatics↗

bioRxiv2018

Data-adaptive pipeline for filtering and normalizing metabolomics data.

IntroductionUntargeted metabolomics datasets contain large proportions of uninformative features and are affected by a variety of nuisance technical effects that can bias subsequent statistical analyses. Thus, there is a need for versatile and data-adaptive methods for filtering and normalizing data prior to investigating the underlying biological phenomena.\n\nObjectivesHere, we propose and evaluate a data-adaptive pipeline for metabolomics data that are generated by liquid chromatography-mass spectrometry platforms.\n\nMethodsOur data-adaptive pipeline includes novel methods for filtering features based on blank samples, proportions of missing values, and estimated intra-class correlation coefficients. It also incorporates a variant of k-nearest-neighbor imputation of missing values. Finally, we adapted an RNA-Seq approach and R package, scone, to select an appropriate normalization scheme for removing unwanted variation from metabolomics datasets.\n\nResultsUsing two metabolomics datasets that were generated in our laboratory from samples of human blood serum and neonatal blood spots, we compared our data-adaptive pipeline with a traditional filtering and normalization scheme. The data-adaptive approach outperformed the traditional pipeline in almost all metrics related to removal of unwanted variation and maintenance of biologically relevant signatures. The R code for running the data-adaptive pipeline is provided with an example dataset at https://github.com/courtneyschiffman/Data-adaptive-metabolomics.\n\nConclusionOur proposed data-adaptive pipeline is intuitive and effectively reduces technical noise from untargeted metabolomics datasets. It is particularly relevant for interrogation of biological phenomena in data derived from complex matrices associated with biospecimens.

bioinformatics↗