Susan K Van Riper1, LeeAnn Higgins2, John V Carlis3, Timothy J Griffin2. 1. Department of Biomedical Informatics and Computational Biology, University of Minnesota, Rochester University of Minnesota Informatics Institute, University of Minnesota, St Paul. 2. Department of Biochemistry, Molecular Biology, and Biophysics. 3. Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
Abstract
UNLABELLED: RIPPER is a framework for mass-spectrometry-based label-free relative quantification for proteomics and metabolomics studies. RIPPER combines a series of previously described algorithms for pre-processing, analyte quantification, retention time alignment, and analyte grouping across runs. It is also the first software framework to implement proximity-based intensity normalization. RIPPER produces lists of analyte signals with their unnormalized and normalized intensities that can serve as input to statistical and directed mass spectrometry (MS) methods for detecting quantitative differences between biological samples using MS. AVAILABILITY AND IMPLEMENTATION: http://www.z.umn.edu/ripper CONTACT: vanr0014@umn.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
UNLABELLED: RIPPER is a framework for mass-spectrometry-based label-free relative quantification for proteomics and metabolomics studies. RIPPER combines a series of previously described algorithms for pre-processing, analyte quantification, retention time alignment, and analyte grouping across runs. It is also the first software framework to implement proximity-based intensity normalization. RIPPER produces lists of analyte signals with their unnormalized and normalized intensities that can serve as input to statistical and directed mass spectrometry (MS) methods for detecting quantitative differences between biological samples using MS. AVAILABILITY AND IMPLEMENTATION: http://www.z.umn.edu/ripper CONTACT: vanr0014@umn.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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