refs.bib

@Article{Gatto:2012,
  author =       {Gatto, L and Lilley, K S},
  title =        {\texttt{MSnbase} - an {R/Bioconductor} package for isobaric
		  tagged mass spectrometry data visualization,
		  processing and quantitation.},
  journal =      {Bioinformatics},
  year =         {2012},
  month =        {Jan},
  number =       {2},
  volume =       {28},
  pages =        {288-9},
  doi =          {10.1093/bioinformatics/btr645},
  PMID = {22113085}}

@Article{Chambers:2012,
  author =       {Chambers, M C and Maclean, B and Burke, R and
		 Amodei, D and Ruderman, D L and Neumann, S and
		 Gatto, L and Fischer, B and Pratt, B and Egertson,
		 J and Hoff, K and Kessner, D and Tasman, N and
		 Shulman, N and Frewen, B and Baker, T A and
		 Brusniak, M Y and Paulse, C and Creasy, D and
		 Flashner, L and Kani, K and Moulding, C and
		 Seymour, S L and Nuwaysir, L M and Lefebvre, B and
		 Kuhlmann, F and Roark, J and Rainer, P and Detlev,
		 S and Hemenway, T and Huhmer, A and Langridge, J
		 and Connolly, B and Chadick, T and Holly, K and
		 Eckels, J and Deutsch, E W and Moritz, R L and
		 Katz, J E and Agus, D B and MacCoss, M and Tabb, D
		 L and Mallick, P},
  title =        {A cross-platform toolkit for mass spectrometry and
		  proteomics.},
  journal =      {Nat Biotechnol},
  year =         {2012},
  month =        {Oct},
  number =       {10},
  volume =       {30},
  pages =        {918-20},
  doi =          {10.1038/nbt.2377},
  PMID = {23051804}}

@Article{Gatto:2014,
  author =       {Gatto, L and Christoforou, A},
  title =        {Using {R} and {Bioconductor} for proteomics data
		  analysis.},
  journal =      {Biochim Biophys Acta},
  year =         {2014},
  month =        {Jan},
  number =       {1 Pt A},
  volume =       {1844},
  pages =        {42-51},
  doi =          {10.1016/j.bbapap.2013.04.032},
  PMID = {23692960}}

@Article{Huber:2015,
  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 Pagès, H and Reyes, A and Shannon, P and
		 Smyth, G K and Tenenbaum, D and Waldron, L and
		 Morgan, M},
  title =        {Orchestrating high-throughput genomic analysis
		 with {Bioconductor}.},
  journal =      {Nat Methods},
  year =         {2015},
  month =        {Jan},
  number =       {2},
  volume =       {12},
  pages =        {115-21},
  doi =          {10.1038/nmeth.3252},
  PMID = {25633503}}

@Article{Gatto:2015,
   author = {Gatto, Laurent and Breckels, Lisa M. and Naake, Thomas
		  and Gibb, Sebastian},
   title = {Visualization of proteomics data using {R} and
		  {Bioconductor}},
   journal = {PROTEOMICS},
   volume = {15},
   number = {8},
   issn = {1615-9861},
   url = {http://dx.doi.org/10.1002/pmic.201400392},
   doi = {10.1002/pmic.201400392},
   pages = {1375-1389},
   keywords = {Bioconductor, Bioinformatics, Data analysis, Programming, R, Visualization},
   year = {2015}
}

@Article{Griss:2019,
  author =       {Griss, J and Vinterhalter, G and Schw\"ammle, V},
  title =        {IsoProt: A Complete and Reproducible Workflow To
		  Analyze {iTRAQ/TMT} Experiments.},
  journal =      {J Proteome Res},
  year =         {2019},
  month =        {Apr},
  number =       {4},
  volume =       {18},
  pages =        {1751-1759},
  doi =          {10.1021/acs.jproteome.8b00968},
  PMID =         {30855969}}

@Article{Wieczorek:2017,
  author =       {Wieczorek, S and Combes, F and Lazar, C and Giai
		 Gianetto, Q and Gatto, L and Dorffer, A and Hesse,
		 A M and Cout\'e, Y and Ferro, M and Bruley, C and
		 Burger, T},
  title =        {\texttt{DAPAR} \& \texttt{ProStaR}: software to
		  perform statistical analyses in quantitative
		  discovery proteomics.},
  journal =      {Bioinformatics},
  year =         {2017},
  month =        {01},
  number =       {1},
  volume =       {33},
  pages =        {135-136},
  doi =          {10.1093/bioinformatics/btw580},
  PMID =         {27605098}}

@Article{Smith:2006,
  author =       {Smith, C A and Want, E J and O'Maille, G and
		 Abagyan, R and Siuzdak, G},
  title =        {XCMS: processing mass spectrometry data for
		 metabolite profiling using nonlinear peak
		 alignment, matching, and identification.},
  journal =      {Anal Chem},
  year =         {2006},
  month =        {Feb},
  number =       {3},
  volume =       {78},
  pages =        {779-87},
  doi =          {10.1021/ac051437y},
  PMID =         {16448051}}

@article{Meier:2018,
  title = "BoxCar acquisition method enables single-shot proteomics at
		  a depth of 10,000 proteins in 100 minutes",
  abstract = "Great advances have been made in sensitivity and
		    acquisition speed on the Orbitrap mass analyzer,
		    enabling increasingly deep proteome
		    coverage. However, these advances have been mainly
		    limited to the MS2 level, whereas ion beam sampling
		    for the MS1 scans remains extremely
		    inefficient. Here we report a data-acquisition
		    method, termed BoxCar, in which filling multiple
		    narrow mass-to-charge segments increases the mean
		    ion injection time more than tenfold as compared to
		    that of a standard full scan. In 1-h analyses, the
		    method provided MS1-level evidence for more than
		    90{\%} of the proteome of a human cancer cell line
		    that had previously been identified in 24 fractions,
		    and it quantified more than 6,200 proteins in ten of
		    ten replicates. In mouse brain tissue, we detected
		    more than 10,000 proteins in only 100 min, and
		    sensitivity extended into the low-attomolar range.",
  author = "Meier, Florian and Geyer, {Philipp E.} and {Virreira
		  Winter}, Sebastian and Juergen, Cox and Matthias,
		  Mann",
  year = "2018",
  doi = "10.1038/s41592-018-0003-5",
  language = "English",
  volume = "15",
  pages = "440-448",
  journal = "Nature Methods",
  issn = "1548-7091",
  publisher = "nature publishing group",
  number = "6"
}

@Manual{MSnbaseBoxCar,
  title = {\texttt{MSnbaseBoxCar}: BoxCar Data Processing with {MSnbase}},
  author = {Laurent Gatto},
  year = {2020},
  note = {R package version 0.2.0},
  url = {https://github.com/lgatto/MSnbaseBoxCar},
}

@Manual{protViz,
  title = {\texttt{protViz}: Visualizing and Analyzing Mass
		  Spectrometry Related Data in Proteomics},
  author = {Christian Panse and Jonas Grossmann},
  year = {2020},
  note = {R package version 0.6},
  url  = {https://CRAN.R-project.org/package=protViz},
}

@Manual{Pviz,
  title = {\texttt{Pviz}: Peptide Annotation and Data Visualization using {Gviz}},
  author = {Renan Sauteraud and Mike Jiang and Raphael Gottardo},
  year = {2019},
  note = {R package version 1.21.0},
}

@Article{Bemis:2015,
  title = {\texttt{Cardinal}: an {R} package for statistical analysis
		  of mass spectrometry-based imaging experiments},
  author = {Kyle D. Bemis and April Harry and Livia S. Eberlin and
		Christina Ferreira and Stephanie M. {van de Ven} and
		Parag Mallick and Mark Stolowitz and Olga Vitek},
  journal = {Bioinformatics},
  year = {2015},
  doi = {10.1093/bioinformatics/btv146},
}

@Manual{magrittr,
  title = {\texttt{magrittr}: A Forward-Pipe Operator for {R}},
  author = {Stefan Milton Bache and Hadley Wickham},
  year = 2014,
  note = {R package version 1.5},
  url = {https://CRAN.R-project.org/package=magrittr},
}

@Misc{contribs,
  author =  {Laurent Gatto},
  title  =  {\texttt{MSnbase} contributors 2010 - 2020},
  month  =  {April},
  year   =  {2020},
  url    =  {https://lgatto.github.io/msnbase-contribs-2/},
}

@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 \texttt{pRoloc} and
		  \texttt{pRolocdata}.},
  journal =      {Bioinformatics},
  year =         {2014},
  month =        {May},
  number =       {9},
  volume =       {30},
  pages =        {1322-4},
  doi =          {10.1093/bioinformatics/btu013},
  PMID =         {24413670}
}

@Manual{msmsTests,
    title = {msmsTests: LC-MS/MS Differential Expression Tests},
    author = {Josep Gregori and Alex Sanchez and Josep Villanueva},
    year = {2019},
    note = {R package version 1.25.0},
}

@Article{Zhang:2018,
    title = {Proteome-wide identification of ubiquitin interactions
		  using {UbIA-MS}},
    author = {Zhang, Xiaofei and Smits, Arne H and {van Tilburg},
		  Gabrielle BA and Ovaa, Huib and Huber, Wolfgang and
		  Vermeulen, Michiel },
    journal = {Nature Protocols},
    year = {2018},
    volume = {13},
    pages = {530-550},
}


@Article{Stravs:2013,
  author =	 {Stravs, Michael A and Schymanski, Emma L and Singer,
		  Heinz P and Hollender, Juliane},
  title =	 {Automatic Recalibration and Processing of Tandem
		  Mass Spectra using Formula Annotation},
  journal =	 {Journal of Mass Spectrometry},
  year =	 {2013},
  volume =	 {48},
  number =	 {1},
  pages =	 {89-99},
  month =	 {Jan},
  doi =          {10.1002/jms.3131},
}

@Article{Dogu:2017,
  author =	 {Dogu, Eralp and Mohammad-Taheri, Sara and
		  Abbatiello, Susan E and Bereman, Michael S and
		  MacLean, Brendan and Schilling, Birgit and Vitek, Olga},
  title =	 {\texttt{MSstatsQC}: Longitudinal System Suitability
		  Monitoring and Quality Control for Targeted
		  Proteomic Experiments.},
  journal =	 {Mol Cell Proteomics},
  year =	 {2017},
  volume =	 {16},
  number =	 {7},
  pages =	 {1335-1347},
  month =	 {July},
  doi =          {10.1074/mcp.M116.064774},
}

@Manual{msdata,
  title = {\texttt{msdata}: Various Mass Spectrometry raw data example files},
  author = {Neumann, Steffen and Gatto, Laurent and Rainer, Johannes},
  year = {2019},
  note = {R package version 0.27.0},
}


@Article{Cote:2010,
  author =	 {C\^ot\'e Richard G, Reisinger Florian, Martens Lennart},
  title =	 {jmzML, an open-source Java API for mzML, the PSI standard for MS data.},
  journal =	 {Proteomics},
  year =	 {2010},
  volume =	 {10},
  number =	 {7},
  pages =	 {1332-1335},
  doi =          {10.1002/pmic.200900719},
}


@Article{Bittremieux:2020,
  author =	 {Bittremieux, Wout},
  title =	 {{spectrum\_utils}: A Python package for mass
		  spectrometry data processing and visualization},
  journal =	 {Analytical Chemistry},
  year =	 {2020},
  volume =	 {92},
  number =	 {1},
  pages =	 {659-661},
  doi =          {10.1021/acs.analchem.9b04884},
}


@Article{Cox:2008,
  author =	 {Cox, J and Mann, M},
  title =	 {{MaxQuant} enables high peptide identification
		  rates, individualized p.p.b.-range mass accuracies
		  and proteome-wide protein quantification},
  journal =	 {Nat Biotechnol},
  year =	 {2008},
  volume =	 {26},
  pages =	 {1367-1372},
  doi =          {10.1038/nbt.1511},
}


@Article{Vaudel:2015,
  author =	 {Vaudel, Marc and Burkhart, Julia M and Zahedi,
		  Ren\'e P and Oveland, Eystein and Berven, Frode S
		  and Sickmann, Albert and Martens, Lennart and
		  Barsnes, Harald},
  title =	 {{PeptideShaker} enables reanalysis of MS-derived
		  proteomics data sets},
  journal =	 {Nat Biotechnol},
  year =	 {2015},
  volume =	 {33},
  pages =	 {22-24},
  doi =          {10.1038/nbt.3109},
}


@Manual{R,
    title = {{R}: A Language and Environment for Statistical
		  Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2020},
    url = {https://www.R-project.org/},
  }


@Article{Goloborodko:2013,
  author =	 {Goloborodko, Anton A and Levitsky, Lev I and Ivanov,
		  Mark V and Gorshkov, Mikhail V},
  title =	 {{Pyteomics} - a Python Framework for Exploratory
		  Data Analysis and Rapid Software Prototyping in
		  Proteomics},
  journal =	 {J. Am. Soc. Mass Spectrom.},
  year =	 {2013},
  volume =	 {24},
  pages =	 {301-304},
  doi =          {10.1007/s13361-012-0516-6},
}

@Article{Rost:2014,
  author =	 {Rost, Hannes L and Schmitt, Uwe and Aebersold, Ruedi and Malmstrom Lars},
  title =	 {{pyOpenMS}: a Python-based interface to the OpenMS
		  mass-spectrometry algorithm library},
  journal =	 {Proteomics},
  year =	 {2014},
  volume =	 {14},
  number =	 {1},
  pages =	 {74-77},
  doi =          {10.1002/pmic.201300246},
}

@article{Stanstrup:2019,
  title = {The {{metaRbolomics Toolbox}} in {{Bioconductor}} and Beyond.},
  author = {Stanstrup, Jan and Broeckling, Corey D. and Helmus, Rick and Hoffmann, Nils and Math{\'e}, Ewy and Naake, Thomas and Nicolotti, Luca and Peters, Kristian and Rainer, Johannes and Salek, Reza M. and Schulze, Tobias and Schymanski, Emma L. and Stravs, Michael A. and Th{\'e}venot, Etienne A. and Treutler, Hendrik and Weber, Ralf J. M. and Willighagen, Egon and Witting, Michael and Neumann, Steffen},
  year = {2019},
  month = sep,
  volume = {9},
  pages = {200},
  doi = {10.3390/metabo9100200},
  abstract = {Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.},
  file = {/Users/jo/Zotero/storage/MPW2DQQL/Stanstrup et al_2019_The metaRbolomics Toolbox in Bioconductor and beyond.pdf},
  journal = {Metabolites},
  keywords = {Mass Spectrometry,Metabolomics,Methodology,my papers},
  language = {English},
  number = {10}
}

@article{Nothias:2020pre,
	author = {Nothias, Louis Felix and Petras, Daniel and Schmid,
		  Robin and D{\"u}hrkop, Kai and Rainer, Johannes and
		  Sarvepalli, Abinesh and Protsyuk, Ivan and Ernst,
		  Madeleine and Tsugawa, Hiroshi and Fleischauer,
		  Markus and Aicheler, Fabian and Aksenov, Alexander
		  and Alka, Oliver and Allard, Pierre-Marie and
		  Barsch, Aiko and Cachet, Xavier and Caraballo,
		  Mauricio and Da Silva, Ricardo R. and Dang, Tam and
		  Garg, Neha and Gauglitz, Julia M. and Gurevich,
		  Alexey and Isaac, Giorgis and Jarmusch, Alan K. and
		  Kamen{\'\i}k, Zden{\v e}k and Kang, Kyo Bin and
		  Kessler, Nikolas and Koester, Irina and Korf, Ansgar
		  and Gouellec, Audrey Le and Ludwig, Marcus and
		  Christian, Martin H. and McCall, Laura-Isobel and
		  McSayles, Jonathan and Meyer, Sven W. and Mohimani,
		  Hosein and Morsy, Mustafa and Moyne, Oriane and
		  Neumann, Steffen and Neuweger, Heiko and Nguyen,
		  Ngoc Hung and Nothias-Esposito, Melissa and Paolini,
		  Julien and Phelan, Vanessa V. and Pluskal,
		  Tom{\'a}{\v s} and Quinn, Robert A. and Rogers,
		  Simon and Shrestha, Bindesh and Tripathi, Anupriya
		  and van der Hooft, Justin J.J. and Vargas, Fernando
		  and Weldon, Kelly C. and Witting, Michael and Yang,
		  Heejung and Zhang, Zheng and Zubeil, Florian and
		  Kohlbacher, Oliver and B{\"o}cker, Sebastian and
		  Alexandrov, Theodore and Bandeira, Nuno and Wang,
		  Mingxun and Dorrestein, Pieter C.},
	title = {Feature-based Molecular Networking in the GNPS Analysis Environment},
	elocation-id = {812404},
	year = {2019},
	doi = {10.1101/812404},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {Molecular networking has become a key method used
		  to visualize and annotate the chemical space in
		  non-targeted mass spectrometry-based
		  experiments. However, distinguishing isomeric
		  compounds and quantitative interpretation are
		  currently limited. Therefore, we created
		  Feature-based Molecular Networking (FBMN) as a new
		  analysis method in the Global Natural Products
		  Social Molecular Networking (GNPS)
		  infrastructure. FBMN leverages feature detection and
		  alignment tools to enhance quantitative analyses and
		  isomer distinction, including from ion-mobility
		  spectrometry experiments, in molecular networks.},
	URL = {https://www.biorxiv.org/content/early/2019/10/20/812404},
	eprint = {https://www.biorxiv.org/content/early/2019/10/20/812404.full.pdf},
	journal = {bioRxiv}
}

@Manual{MSnbaseVignettes,
  title = {\texttt{MSnbase} Base Functions and Classes For Mass Spectrometry and Proteomics},
  author = {Laurent Gatto},
  year = {2020},
  note = {R package version 2.14.2},
  url = {https://lgatto.github.io/MSnbase/},
  doi = {10.18129/B9.bioc.MSnbase}
}

@Manual{xcmsWorkflow,
  title = {Metabolomics data pre-processing using \texttt{xcms}},
  author = {Johannes Rainer},
  year = {2020},
  url = {https://github.com/jorainer/metabolomics2018},
  doi = {10.5281/zenodo.3909299}
}

@Book{ggplot2,
    author = {Hadley Wickham},
    title = {{ggplot2}: Elegant Graphics for Data Analysis},
    publisher = {Springer-Verlag New York},
    year = {2016},
    isbn = {978-3-319-24277-4},
    url = {https://ggplot2.tidyverse.org/},
}

@Article{da_Veiga_Leprevost:2017,
  author =       {da Veiga Leprevost, F and Grüning, B A and Alves
		 Aflitos, S and Röst, H L and Uszkoreit, J and
		 Barsnes, H and Vaudel, M and Moreno, P and Gatto,
		 L and Weber, J and Bai, M and Jimenez, R C and
		 Sachsenberg, T and Pfeuffer, J and Vera Alvarez, R
		 and Griss, J and Nesvizhskii, A I and
		 Perez-Riverol, Y},
  title =        {BioContainers: an open-source and community-driven
		 framework for software standardization.},
  journal =      {Bioinformatics},
  year =         {2017},
  month =        {Aug},
  number =       {16},
  volume =       {33},
  pages =        {2580-2582},
  doi =          {10.1093/bioinformatics/btx192},
  PMID =         {28379341}}


@Article{Nothias:2020,
  author =       {Nothias, L F and Petras, D and Schmid, R and
		 Dührkop, K and Rainer, J and Sarvepalli, A and
		 Protsyuk, I and Ernst, M and Tsugawa, H and
		 Fleischauer, M and Aicheler, F and Aksenov, A A
		 and Alka, O and Allard, P M and Barsch, A and
		 Cachet, X and Caraballo-Rodriguez, A M and Da
		 Silva, R R and Dang, T and Garg, N and Gauglitz, J
		 M and Gurevich, A and Isaac, G and Jarmusch, A K
		 and Kameník, Z and Kang, K B and Kessler, N and
		 Koester, I and Korf, A and Le Gouellec, A and
		 Ludwig, M and Martin H, C and McCall, L I and
		 McSayles, J and Meyer, S W and Mohimani, H and
		 Morsy, M and Moyne, O and Neumann, S and Neuweger,
		 H and Nguyen, N H and Nothias-Esposito, M and
		 Paolini, J and Phelan, V V and Pluskal, T and
		 Quinn, R A and Rogers, S and Shrestha, B and
		 Tripathi, A and van der Hooft, JJJ and Vargas, F
		 and Weldon, K C and Witting, M and Yang, H and
		 Zhang, Z and Zubeil, F and Kohlbacher, O and
		 Böcker, S and Alexandrov, T and Bandeira, N and
		 Wang, M and Dorrestein, P C},
  title =        {Feature-based molecular networking in the GNPS
		 analysis environment.},
  journal =      {Nat Methods},
  year =         {2020},
  month =        {Aug},
  number =       {},
  volume =       {},
  pages =        {},
  doi =          {10.1038/s41592-020-0933-6},
  PMID =         {32839597}}