MSnbase-an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation
Open Access
- 22 November 2011
- journal article
- research article
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 28 (2), 288-289
- https://doi.org/10.1093/bioinformatics/btr645
Abstract
Summary:MSnbase is an R/Bioconductor package for the analysis of quantitative proteomics experiments that use isobaric tagging. It provides an exploratory data analysis framework for reproducible research, allowing raw data import, quality control, visualization, data processing and quantitation. MSnbase allows direct integration of quantitative proteomics data with additional facilities for statistical analysis provided by the Bioconductor project. Availability:MSnbase is implemented in R (version ≥2.13.0) and available at the Bioconductor web site (http://www.bioconductor.org/). Vignettes outlining typical workflows, input/output capabilities and detailing underlying infrastructure are included in the package. Contact:lg390@cam.ac.uk Supplementary information: Supplementary data are available from Bioinformatics online.Keywords
This publication has 9 references indexed in Scilit:
- Challenges for proteomics core facilitiesProteomics, 2011
- Addressing Accuracy and Precision Issues in iTRAQ QuantitationMolecular & Cellular Proteomics, 2010
- Mass spectrometry in high-throughput proteomics: ready for the big timeNature Methods, 2010
- The minimum information about a proteomics experiment (MIAPE)Nature Biotechnology, 2007
- Multiplexed Protein Quantitation in Saccharomyces cerevisiae Using Amine-reactive Isobaric Tagging ReagentsMolecular & Cellular Proteomics, 2004
- Bioconductor: open software development for computational biology and bioinformaticsGenome Biology, 2004
- Tandem Mass Tags: A Novel Quantification Strategy for Comparative Analysis of Complex Protein Mixtures by MS/MSAnalytical Chemistry, 2003
- A comparison of normalization methods for high density oligonucleotide array data based on variance and biasBioinformatics, 2003
- Variance stabilization applied to microarray data calibration and to the quantification of differential expressionBioinformatics, 2002