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      Using R and Bioconductor for proteomics data analysis

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          Abstract

          This review presents how R, the popular statistical environment and programming language, can be used in the frame of proteomics data analysis. A short introduction to R is given, with special emphasis on some of the features that make R and its add-on packages a premium software for sound and reproducible data analysis. The reader is also advised on how to find relevant R software for proteomics. Several use cases are then presented, illustrating data input/output, quality control, quantitative proteomics and data analysis. Detailed code and additional links to extensive documentation are available in the freely available companion package RforProteomics.

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          Most cited references15

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          TANDEM: matching proteins with tandem mass spectra.

          Tandem mass spectra obtained from fragmenting peptide ions contain some peptide sequence specific information, but often there is not enough information to sequence the original peptide completely. Several proprietary software applications have been developed to attempt to match the spectra with a list of protein sequences that may contain the sequence of the peptide. The application TANDEM was written to provide the proteomics research community with a set of components that can be used to test new methods and algorithms for performing this type of sequence-to-data matching. The source code and binaries for this software are available at http://www.proteome.ca/opensource.html, for Windows, Linux and Macintosh OSX. The source code is made available under the Artistic License, from the authors.
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            R: A Language for Data Analysis and Graphics

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              Reproducible research in computational science.

              Roger Peng (2011)
              Computational science has led to exciting new developments, but the nature of the work has exposed limitations in our ability to evaluate published findings. Reproducibility has the potential to serve as a minimum standard for judging scientific claims when full independent replication of a study is not possible.
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                Author and article information

                Journal
                2013-05-28
                Article
                10.1016/j.bbapap.2013.04.032
                1305.6559
                eee303fb-1a7d-47aa-abf8-6c5f0e76d064

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                q-bio.GN

                Genetics
                Genetics

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