Meena Choi1, Ching-Yun Chang1, Timothy Clough1, Daniel Broudy1, Trevor Killeen1, Brendan MacLean1, Olga Vitek2. 1. Department of Statistics, Purdue University, West Lafayette, IN, Department of Genome Sciences, University of Washington, Seattle, WA 98195 and Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA. 2. Department of Statistics, Purdue University, West Lafayette, IN, Department of Genome Sciences, University of Washington, Seattle, WA 98195 and Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA Department of Statistics, Purdue University, West Lafayette, IN, Department of Genome Sciences, University of Washington, Seattle, WA 98195 and Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA.
Abstract
UNLABELLED: MSstats is an R package for statistical relative quantification of proteins and peptides in mass spectrometry-based proteomics. Version 2.0 of MSstats supports label-free and label-based experimental workflows and data-dependent, targeted and data-independent spectral acquisition. It takes as input identified and quantified spectral peaks, and outputs a list of differentially abundant peptides or proteins, or summaries of peptide or protein relative abundance. MSstats relies on a flexible family of linear mixed models. AVAILABILITY AND IMPLEMENTATION: The code, the documentation and example datasets are available open-source at www.msstats.org under the Artistic-2.0 license. The package can be downloaded from www.msstats.org or from Bioconductor www.bioconductor.org and used in an R command line workflow. The package can also be accessed as an external tool in Skyline (Broudy et al., 2014) and used via graphical user interface.
UNLABELLED: MSstats is an R package for statistical relative quantification of proteins and peptides in mass spectrometry-based proteomics. Version 2.0 of MSstats supports label-free and label-based experimental workflows and data-dependent, targeted and data-independent spectral acquisition. It takes as input identified and quantified spectral peaks, and outputs a list of differentially abundant peptides or proteins, or summaries of peptide or protein relative abundance. MSstats relies on a flexible family of linear mixed models. AVAILABILITY AND IMPLEMENTATION: The code, the documentation and example datasets are available open-source at www.msstats.org under the Artistic-2.0 license. The package can be downloaded from www.msstats.org or from Bioconductor www.bioconductor.org and used in an R command line workflow. The package can also be accessed as an external tool in Skyline (Broudy et al., 2014) and used via graphical user interface.
Authors: Alberto Lleó; Raúl Núñez-Llaves; Daniel Alcolea; Cristina Chiva; Daniel Balateu-Paños; Martí Colom-Cadena; Gemma Gomez-Giro; Laia Muñoz; Marta Querol-Vilaseca; Jordi Pegueroles; Lorena Rami; Albert Lladó; José L Molinuevo; Mikel Tainta; Jordi Clarimón; Tara Spires-Jones; Rafael Blesa; Juan Fortea; Pablo Martínez-Lage; Raquel Sánchez-Valle; Eduard Sabidó; Àlex Bayés; Olivia Belbin Journal: Mol Cell Proteomics Date: 2019-01-03 Impact factor: 5.911
Authors: Josue Baeza; Alexis J Lawton; Jing Fan; Michael J Smallegan; Ian Lienert; Tejas Gandhi; Oliver M Bernhardt; Lukas Reiter; John M Denu Journal: J Proteome Res Date: 2020-04-27 Impact factor: 4.466
Authors: Kristin L Patrick; Jason A Wojcechowskyj; Samantha L Bell; Morgan N Riba; Tao Jing; Sara Talmage; Pengbiao Xu; Ana L Cabello; Jiewei Xu; Michael Shales; David Jimenez-Morales; Thomas A Ficht; Paul de Figueiredo; James E Samuel; Pingwei Li; Nevan J Krogan; Robert O Watson Journal: Cell Syst Date: 2018-08-01 Impact factor: 10.304
Authors: Allen M Andres; Joel A Kooren; Sarah J Parker; Kyle C Tucker; Nandini Ravindran; Bruce R Ito; Chengqun Huang; Vidya Venkatraman; Jennifer E Van Eyk; Roberta A Gottlieb; Robert M Mentzer Journal: Am J Physiol Heart Circ Physiol Date: 2016-05-06 Impact factor: 4.733