Literature DB >> 22113085

MSnbase-an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation.

Laurent Gatto1, Kathryn S Lilley.   

Abstract

UNLABELLED: 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.

Mesh:

Year:  2011        PMID: 22113085     DOI: 10.1093/bioinformatics/btr645

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  96 in total

1.  Using hyperLOPIT to perform high-resolution mapping of the spatial proteome.

Authors:  Claire M Mulvey; Lisa M Breckels; Aikaterini Geladaki; Nina Kočevar Britovšek; Daniel J H Nightingale; Andy Christoforou; Mohamed Elzek; Michael J Deery; Laurent Gatto; Kathryn S Lilley
Journal:  Nat Protoc       Date:  2017-05-04       Impact factor: 13.491

2.  psims - A Declarative Writer for mzML and mzIdentML for Python.

Authors:  Joshua Klein; Joseph Zaia
Journal:  Mol Cell Proteomics       Date:  2018-12-18       Impact factor: 5.911

3.  Deciphering thylakoid sub-compartments using a mass spectrometry-based approach.

Authors:  Martino Tomizioli; Cosmin Lazar; Sabine Brugière; Thomas Burger; Daniel Salvi; Laurent Gatto; Lucas Moyet; Lisa M Breckels; Anne-Marie Hesse; Kathryn S Lilley; Daphné Seigneurin-Berny; Giovanni Finazzi; Norbert Rolland; Myriam Ferro
Journal:  Mol Cell Proteomics       Date:  2014-05-28       Impact factor: 5.911

4.  Detecting Significant Changes in Protein Abundance.

Authors:  Kai Kammers; Robert N Cole; Calvin Tiengwe; Ingo Ruczinski
Journal:  EuPA Open Proteom       Date:  2015-06

5.  Proteome-wide identification of ubiquitin interactions using UbIA-MS.

Authors:  Xiaofei Zhang; Arne H Smits; Gabrielle Ba van Tilburg; Huib Ovaa; Wolfgang Huber; Michiel Vermeulen
Journal:  Nat Protoc       Date:  2018-02-15       Impact factor: 13.491

6.  Robust Summarization and Inference in Proteome-wide Label-free Quantification.

Authors:  Adriaan Sticker; Ludger Goeminne; Lennart Martens; Lieven Clement
Journal:  Mol Cell Proteomics       Date:  2020-04-22       Impact factor: 5.911

7.  Mapping the Saccharomyces cerevisiae Spatial Proteome with High Resolution Using hyperLOPIT.

Authors:  Daniel J H Nightingale; Stephen G Oliver; Kathryn S Lilley
Journal:  Methods Mol Biol       Date:  2019

8.  Quantitative proteomics reveals rapid divergence in the postmating response of female reproductive tracts among sibling species.

Authors:  Erin L McCullough; Caitlin E McDonough; Scott Pitnick; Steve Dorus
Journal:  Proc Biol Sci       Date:  2020-06-24       Impact factor: 5.349

9.  Proteome Response of a Metabolically Flexible Anoxygenic Phototroph to Fe(II) Oxidation.

Authors:  Casey Bryce; Mirita Franz-Wachtel; Nicolas C Nalpas; Jennyfer Miot; Karim Benzerara; James M Byrne; Sara Kleindienst; Boris Macek; Andreas Kappler
Journal:  Appl Environ Microbiol       Date:  2018-08-01       Impact factor: 4.792

Review 10.  Identification of small molecules using accurate mass MS/MS search.

Authors:  Tobias Kind; Hiroshi Tsugawa; Tomas Cajka; Yan Ma; Zijuan Lai; Sajjan S Mehta; Gert Wohlgemuth; Dinesh Kumar Barupal; Megan R Showalter; Masanori Arita; Oliver Fiehn
Journal:  Mass Spectrom Rev       Date:  2017-04-24       Impact factor: 10.946

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