refs.bib

@ARTICLE{Huber:2015,
  title   = "Orchestrating high-throughput genomic analysis with {Bioconductor}",
  author  = "Huber, W and Carey, V J and Gentleman, R and Anders, S and
             Carlson, M and Carvalho, B S and Bravo, H C and Davis, S and
             Gatto, L and Girke, T and Gottardo, R and Hahne, F and Hansen, K D
             and Irizarry, R A and Lawrence, M and Love, M I and MacDonald, J
             and Obenchain, V and Ole{\'s}, A K and Pag{\`e}s, H and Reyes, A
             and Shannon, P and Smyth, G K and Tenenbaum, D and Waldron, L and
             Morgan, M",
  journal = "Nat. Methods",
  volume  =  12,
  number  =  2,
  pages   = "115--121",
  month   =  jan,
  year    =  2015
}

@ARTICLE{Gatto:2014,
  title   = "Using {R} and {Bioconductor} for proteomics data analysis",
  author  = "Gatto, L and Christoforou, A",
  journal = "Biochim. Biophys. Acta",
  volume  =  1844,
  number  = "1 Pt A",
  pages   = "42--51",
  month   =  jan,
  year    =  2014
}

@ARTICLE{Gatto:2012,
  title    = "{MSnbase-an} {R/Bioconductor} package for isobaric tagged mass
              spectrometry data visualization, processing and quantitation",
  author   = "Gatto, Laurent and Lilley, Kathryn S",
  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 $\geq$ 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.",
  journal  = "Bioinformatics",
  volume   =  28,
  number   =  2,
  pages    = "288--289",
  month    =  jan,
  year     =  2012,
  language = "en"
}

@Manual{biocmsprot,
  title =	 {Bioconductor tools for mass spectrometry and proteomics},
  author =	 {Gatto, Laurent},
  year =         2019,
  url =		 {https://rawgit.com/lgatto/bioc-ms-prot/master/lab.html}
}

@Article{Lazar:2016,
  author =       {Lazar, C and Gatto, L and Ferro, M and Bruley, C
                 and Burger, T},
  title =        {Accounting for the Multiple Natures of Missing
                 Values in Label-Free Quantitative Proteomics Data
                 Sets to Compare Imputation Strategies.},
  journal =      {J Proteome Res},
  year =         {2016},
  month =        {Apr},
  number =       {4},
  volume =       {15},
  pages =        {1116-25},
  doi =          {10.1021/acs.jproteome.5b00981},
  PMID =         {26906401}}

@Article{Breckels:2016,
  author =       {Breckels, L M and Mulvey, C M and Lilley, K S and
                 Gatto, L},
  title =        {A Bioconductor workflow for processing and
                 analysing spatial proteomics data.},
  journal =      {F1000Res},
  year =         {2016},
  month =        {},
  number =       {},
  volume =       {5},
  pages =        {2926},
  doi =          {10.12688/f1000research.10411.2},
  PMID =         {30079225}}

@Article{Gatto:2014a,
  author =       {Gatto, L and Breckels, L M and Wieczorek, S and
                 Burger, T and Lilley, K S},
  title =        {Mass-spectrometry-based spatial proteomics data
                 analysis using pRoloc and pRolocdata.},
  journal =      {Bioinformatics},
  year =         {2014},
  month =        {May},
  number =       {9},
  volume =       {30},
  pages =        {1322-4},
  doi =          {10.1093/bioinformatics/btu013},
  PMID =         {24413670}}

@Article{Christoforou:2016,
  author =       {Christoforou, A and Mulvey, C M and Breckels, L M
                 and Geladaki, A and Hurrell, T and Hayward, P C
                 and Naake, T and Gatto, L and Viner, R and
                 Martinez Arias, A and Lilley, K S},
  title =        {A draft map of the mouse pluripotent stem cell
                 spatial proteome.},
  journal =      {Nat Commun},
  year =         {2016},
  month =        {Jan},
  number =       {},
  volume =       {7},
  pages =        {8992},
  doi =          {10.1038/ncomms9992},
  PMID =         {26754106}}

@Article{Nesvizhskii:2005,
  author =       {Nesvizhskii, A I and Aebersold, R},
  title =        {Interpretation of shotgun proteomic data: the
                 protein inference problem.},
  journal =      {Mol Cell Proteomics},
  year =         {2005},
  month =        {Oct},
  number =       {10},
  volume =       {4},
  pages =        {1419-40},
  doi =          {10.1074/mcp.R500012-MCP200},
  PMID =         {16009968}}

@Article{Kall:2008,
  author =       {Käll, L and Storey, J D and MacCoss, M J and
                 Noble, W S},
  title =        {Posterior error probabilities and false discovery
                 rates: two sides of the same coin.},
  journal =      {J Proteome Res},
  year =         {2008},
  month =        {Jan},
  number =       {1},
  volume =       {7},
  pages =        {40-4},
  doi =          {10.1021/pr700739d},
  PMID =         {18052118}}

@article{Sinha:2020,
    author = {Sinha, Ankit and Mann, Matthias},
    title = "{A beginner’s guide to mass spectrometry–based proteomics}",
    journal = {The Biochemist},
    year = {2020},
    month = {09},
    abstract = "{Mass spectrometry (MS)-based proteomics is the most
                  comprehensive approach for the quantitative
                  profiling of proteins, their interactions and
                  modifications. It is a challenging topic as a firm
                  grasp requires expertise in biochemistry for sample
                  preparation, analytical chemistry for
                  instrumentation and computational biology for data
                  analysis. In this short guide, we highlight the
                  various components of a mass spectrometer, the
                  sample preparation process for conversion of
                  proteins into peptides, and quantification and
                  analysis strategies. The advancing technology of
                  MS-based proteomics now opens up opportunities in
                  clinical applications and single-cell analysis.}",
    issn = {0954-982X},
    doi = {10.1042/BIO20200057},
    url = {https://doi.org/10.1042/BIO20200057},
    note = {BIO20200057},
    eprint = {https://portlandpress.com/biochemist/article-pdf/doi/10.1042/BIO20200057/892770/bio20200057.pdf},
}


@article{Gatto:2020,
        author = {Gatto, Laurent and Gibb, Sebastian and Rainer, Johannes},
        title = {{MSnbase}, efficient and elegant {R}-based processing and visualisation of raw mass spectrometry data},
        elocation-id = {2020.04.29.067868},
        year = {2020},
        doi = {10.1101/2020.04.29.067868},
        publisher = {Cold Spring Harbor Laboratory},
        abstract = {We present version 2 of the MSnbase R/Bioconductor package. MSnbase provides infrastructure for the manipulation, processing and visualisation of mass spectrometry data. We focus on the new on-disk infrastructure, that allows the handling of large raw mass spectrometry experiment on commodity hardware and illustrate how the package is used for elegant data processing, method development and visualisation.Competing Interest StatementThe authors have declared no competing interest.},
        URL = {https://www.biorxiv.org/content/early/2020/05/09/2020.04.29.067868},
        eprint = {https://www.biorxiv.org/content/early/2020/05/09/2020.04.29.067868.full.pdf},
        journal = {bioRxiv}
}