bibliography.bib

@article{hyper,
	Author = {Christoforou, A and Mulvey, CM and Breckels, LM and
		  Hayward, PC and Geladaki, E and Hurrell, T and
		  Naake, T and Gatto, L and Viner, R and Martinez
		  Arias, A and Lilley, KS},
	doi = {10.1038/ncomms9992},
	url = {http://dx.doi.org/10.1038/ncomms9992},
	Journal = {Nat Commun},
	Journal-Full = {Nature communications},
	Title = {A draft map of the mouse pluripotent stem cell spatial proteome},
	volume = {7},
	pages = {9992},
	year = {2016},
	month = {jan}}

@article{Gatto:2014,
	Author = {Gatto, L and Christoforou, A},
	Date-Modified = {2015-10-05 08:37:19 +0000},
	Doi = {10.1016/j.bbapap.2013.04.032},
	Journal = {Biochim Biophys Acta},
	Month = {Jan},
	Number = {1 Pt A},
	Pages = {42-51},
	Pmid = {23692960},
	Title = {Using {R} and {Bioconductor} for proteomics data analysis.},
	Volume = {1844},
	Year = {2014},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.bbapap.2013.04.032}}


@article{Gatto:2010,
	Author = {Gatto, L and Vizca\'ino, JA and Hermjakob, H and Huber, W and Lilley, KS},
	Doi = {10.1002/pmic.201000244},
	Journal = {Proteomics},
	Month = {Nov},
	Number = {22},
	Pages = {3957-69},
	Pmid = {21080489},
	Title = {Organelle proteomics experimental designs and analysis.},
	Volume = {10},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.doi.org/10.1002/pmic.201000244}}

@article{McAlister:2014,
	Author = {McAlister, GC and Nusinow, DP and Jedrychowski, MP
		  and W{\"u}hr, M and Huttlin, EL and Erickson, BK and
		  Rad, R and Haas, W and Gygi, SP},
	Doi = {10.1021/ac502040v},
	Journal = {Anal Chem},
	Month = {Jul},
	Number = {14},
	Pages = {7150-8},
	Pmid = {24927332},
	Title = {MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes.},
	Volume = {86},
	Year = {2014},
	Bdsk-Url-1 = {http://dx.doi.org/10.1021/ac502040v}}

@manual{shiny,
	Author = {Chang, W and Cheng, Jo and Allaire, JJ and Xie, Y and McPherson, J},
	Note = {R package version 0.11.1},
	Title = {shiny: Web Application Framework for R},
	Url = {http://CRAN.R-project.org/package=shiny},
	Year = {2015},
	Bdsk-Url-1 = {http://CRAN.R-project.org/package=shiny}}

@article{Trotter:2010,
	Author = {Trotter, MWB and Sadowski, PG and Dunkley, TPJ and Groen, AJ and Lilley, KS},
	Doi = {10.1002/pmic.201000359},
	Issn = {1615-9861},
	Journal = {Proteomics},
	Keywords = {Bioinformatics, Organelle proteomics, Protein localisation, Statistical models, Support vector machines},
	Number = {23},
	Pages = {4213--4219},
	Publisher = {WILEY-VCH Verlag},
	Title = {Improved sub-cellular resolution via simultaneous analysis of organelle proteomics data across varied experimental conditions},
	Url = {http://dx.doi.org/10.1002/pmic.201000359},
	Volume = {10},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.doi.org/10.1002/pmic.201000359}}

@article{DeDuve:1981,
	Author = {De Duve, C and Beaufay, H},
	Date-Added = {2015-07-24 15:42:48 +0000},
	Date-Modified = {2015-07-24 15:43:24 +0000},
	Journal = {J Cell Biol},
	Journal-Full = {The Journal of cell biology},
	Mesh = {Cell Biology; Cell Fractionation; Centrifugation, Density Gradient; History, 20th Century; Microscopy, Electron; Organoids; Subcellular Fractions; Ultracentrifugation; United States},
	Month = {Dec},
	Number = {3 Pt 2},
	Pages = {293s-299s},
	Pmc = {PMC2112816},
	Pmid = {7033241},
	Pst = {ppublish},
	Title = {A short history of tissue fractionation},
	Volume = {91},
	Year = {1981}}

@article{Ashburner:2000,
	Author = {Ashburner, M and Ball, CA and Blake, JA and
		  Botstein, D and Butler, H and Cherry, JM and Davis,
		  AP and Dolinski, K and Dwight, SS and Eppig, JT and
		  Harris, MA. and Hill, DP and Issel-Tarver, L and
		  Kasarskis, A and Lewis, S and Matese, JC and
		  Richardson, JE and Ringwald, M and Rubin, GM and
		  Sherlock, G},
	Doi = {10.1038/75556},
	Journal = {Nat Genet},
	Month = {May},
	Number = {1},
	Pages = {25-9},
	Pmid = {10802651},
	Title = {Gene ontology: tool for the unification of
		  biology. The Gene Ontology Consortium.},
	Volume = {25},
	Year = {2000},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/75556}}

@article{Breckels:2013,
	Author = {Breckels, LM and Gatto, L and Christoforou, A and
		  Groen, AJ and Lilley, KS and Trotter, MW},
	Date-Modified = {2015-10-05 08:37:19 +0000},
	Doi = {10.1016/j.jprot.2013.02.019},
	Journal = {J Proteomics},
	Month = {Aug},
	Pages = {129-40},
	Pmid = {23523639},
	Title = {The effect of organelle discovery upon sub-cellular protein localisation.},
	Volume = {88},
	Year = {2013},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.jprot.2013.02.019}}

@article{Tan:2009,
	Author = {Tan, DJ and Dvinge, H and Christoforou, A and
		  Bertone, P and Martinez Arias, A and Lilley, KS},
	Date-Modified = {2015-10-05 08:37:19 +0000},
	Doi = {10.1021/pr800866n},
	Journal = {J Proteome Res},
	Month = {Jun},
	Number = {6},
	Pages = {2667-78},
	Pmid = {19317464\cite{Tan:2009}},
	Title = {Mapping organelle proteins and protein complexes in Drosophila melanogaster.},
	Volume = {8},
	Year = {2009},
	Bdsk-Url-1 = {http://dx.doi.org/10.1021/pr800866n}}

@article{Dunkley:2006,
	Abstract = {A challenging task in the study of the secretory pathway is the identification
	and localization of new proteins to increase our understanding of
	the functions of different organelles. Previous proteomic studies
	of the endomembrane system have been hindered by contaminating proteins,
	making it impossible to assign proteins to organelles. Here we have
	used the localization of organelle proteins by the isotope tagging
	technique in conjunction with isotope tags for relative and absolute
	quantitation and 2D liquid chromatography for the simultaneous assignment
	of proteins to multiple subcellular compartments. With this approach,
	the density gradient distributions of 689 proteins from Arabidopsis
	thaliana were determined, enabling confident and simultaneous localization
	of 527 proteins to the endoplasmic reticulum, Golgi apparatus, vacuolar
	membrane, plasma membrane, or mitochondria and plastids. This parallel
	analysis of endomembrane components has enabled protein steady-state
	distributions to be determined. Consequently, genuine organelle residents
	have been distinguished from contaminating proteins and proteins
	in transit through the secretory pathway.},
	Author = {Dunkley, TPJ and Hester, S and Shadforth, IP and
		  Runions, J and Weimar, T and Hanton, SL and Griffin,
		  JL and Bessant, C and Brandizzi, F and Hawes, C and
		  Watson, RB and Dupree, P and Lilley, KS},
	Doi = {10.1073/pnas.0506958103},
	File = {:Dunkley2006.pdf:PDF},
	Journal = {Proc Natl Acad Sci USA},
	Language = {eng},
	Medline-Pst = {ppublish},
	Month = {Apr},
	Number = {17},
	Owner = {lgatto},
	Pages = {6518-6523},
	Pii = {0506958103},
	Pmid = {16618929},
	Tags = {Arabidopsis, LOPIT, labelled, mass spectrometry, proteomics, quantitative, iTRAQ},
	Timestamp = {2010.01.08},
	Title = {Mapping the Arabidopsis organelle proteome.},
	Url = {http://dx.doi.org/10.1073/pnas.0506958103},
	Volume = {103},
	Year = {2006},
	Bdsk-Url-1 = {http://dx.doi.org/10.1073/pnas.0506958103}}

@article{Hall:2009,
	Abstract = {A major challenge in eukaryotic cell biology is to understand the
	roles of individual proteins and the subcellular compartments in
	which they reside. Here, we use the localization of organelle proteins
	by isotope tagging technique to complete the first proteomic analysis
	of the major organelles of the DT40 lymphocyte cell line. This cell
	line is emerging as an important research tool because of the ease
	with which gene knockouts can be generated. We identify 1090 proteins
	through the analysis of preparations enriched for integral membrane
	or soluble and peripherally associated proteins and localize 223
	proteins to the endoplasmic reticulum, Golgi, lysosome, mitochondrion,
	or plasma membrane by matching their density gradient distributions
	to those of known organelle residents. A striking finding is that
	within the secretory and endocytic pathway a high proportion of proteins
	are not uniquely localized to a single organelle, emphasizing the
	dynamic steady-state nature of intracellular compartments in eukaryotic
	cells.},
	Author = {Hall, SL and Hester, S and Griffin, JL and Lilley, KS and Jackson, AP},
	Doi = {10.1074/mcp.M800394-MCP200},
	File = {Hall2009.pdf:Hall2009.pdf:PDF},
	Journal = {Mol Cell Proteomics},
	Keywords = {Animals; Cell Line; Chickens; Endoplasmic Reticulum; Lymphocytes; Mass Spectrometry; Organelles; Proteome},
	Month = {Jun},
	Number = {6},
	Owner = {lgatto},
	Pages = {1295-1305},
	Pii = {M800394-MCP200},
	Pmid = {19181659},
	Tags = {LOPIT, mass spectrometry, labelled, iTRAQ, organelle, quantitative, automation, proteomics, subcellular},
	Timestamp = {2010.02.03},
	Title = {The organelle proteome of the DT40 lymphocyte cell line.},
	Url = {http://dx.doi.org/10.1074/mcp.M800394-MCP200},
	Volume = {8},
	Year = {2009},
	Bdsk-Url-1 = {http://dx.doi.org/10.1074/mcp.M800394-MCP200}}

@article{Groen:2014,
	Abstract = {Knowledge of protein subcellular localization
		  assists in the elucidation of protein function and
		  understanding of different biological mechanisms
		  that occur at discrete subcellular
		  niches. Organelle-centric proteomics enables
		  localization of thousands of proteins
		  simultaneously. Although such techniques have
		  successfully allowed organelle protein catalogues to
		  be achieved, they rely on the purification or
		  significant enrichment of the organelle of interest,
		  which is not achievable for many
		  organelles. Incomplete separation of organelles
		  leads to false discoveries, with erroneous
		  assignments. Proteomics methods that measure the
		  distribution patterns of specific organelle markers
		  along density gradients are able to assign proteins
		  of unknown localization based on comigration with
		  known organelle markers, without the need for
		  organelle purification. These methods are greatly
		  enhanced when coupled to sophisticated computational
		  tools. Here we apply and compare multiple approaches
		  to establish a high-confidence data set of
		  Arabidopsis root tissue trans-Golgi network (TGN)
		  proteins. The method employed involves
		  immunoisolations of the TGN, coupled to
		  probability-based organelle proteomics
		  techniques. Specifically, the technique known as
		  LOPIT (localization of organelle protein by isotope
		  tagging), couples density centrifugation with
		  quantitative mass-spectometry-based proteomics using
		  isobaric labeling and targeted methods with
		  semisupervised machine learning methods. We
		  demonstrate that while the immunoisolation method
		  gives rise to a significant data set, the approach
		  is unable to distinguish cargo proteins and
		  persistent contaminants from full-time residents of
		  the TGN. The LOPIT approach, however, returns
		  information about many subcellular niches
		  simultaneously and the steady-state location of
		  proteins. Importantly, therefore, it is able to
		  dissect proteins present in more than one organelle
		  and cargo proteins en route to other cellular
		  destinations from proteins whose steady-state
		  location favors the TGN. Using this approach, we
		  present a robust list of Arabidopsis TGN proteins.},
	Author = {Groen, AJ and Sancho-Andr{\'e}s, G and Breckels, LM
		  and Gatto, L and Aniento, F and Lilley, KS},
	Date-Added = {2014-12-08 08:51:32 +0000},
	Date-Modified = {2014-12-08 08:51:52 +0000},
	Doi = {10.1021/pr4008464},
	Journal = {J Proteome Res},
	Journal-Full = {Journal of proteome research},
	Month = {Feb},
	Number = {2},
	Pages = {763-76},
	Pmc = {PMC3929368},
	Pmid = {24344820},
	Pst = {ppublish},
	Title = {Identification of trans-golgi network proteins in Arabidopsis thaliana root tissue},
	Volume = {13},
	Year = {2014},
	Bdsk-Url-1 = {http://dx.doi.org/10.1021/pr4008464}}

@Article{Gatto:2015,
   author = {Gatto, L and Breckels, LM and Naake, T and Gibb, S},
   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{Thompson:2003,
	author = {Thompson, A and Sch\"afer, J and Kuhn, K and Kienle,
		  S and Schwarz, J and Schmidt, G and Neumann, T and
		  Johnstone, R and Mohammed, AK and Hamon, C},
	title = {Tandem mass tags: a novel quantification strategy for comparative
	analysis of complex protein mixtures by {MS/MS}.},
	journal = {Anal. Chem.},
	year = {2003},
	volume = {75},
	pages = {1895-904},
	number = {8},
	pmid = {12713048},
	tags = {mass spectrometry, labelled, quantitative, TMT}
}

@Article{Gatto:2014b,
  author =       {Gatto, L and Breckels, L M and Burger, T and
		  Nightingale, DJ and Groen, AJ and Campbell, C and
		  Nikolovski, N and Mulvey, CM and Christoforou, A and
		  Ferro, M and Lilley, KS},
  title =        {A foundation for reliable spatial proteomics data
		  analysis.},
  journal =      {Mol Cell Proteomics},
  year =         {2014},
  month =        {Aug},
  number =       {8},
  volume =       {13},
  pages =        {1937-52},
  doi =          {10.1074/mcp.M113.036350},
  PMID =         {24846987}}

@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{Webb-Robertson:2015,
  author =       {Webb-Robertson, BJ and Wiberg, HK and Matzke, MM and
		  Brown, JN and Wang, J and McDermott, JE and Smith,
		  RD and Rodland, KD and Metz, TO and Pounds, JG and
		  Waters, KM},
  title =        {Review, evaluation, and discussion of the
		 challenges of missing value imputation for mass
		 spectrometry-based label-free global proteomics.},
  journal =      {J Proteome Res},
  year =         {2015},
  month =        {May},
  number =       {5},
  volume =       {14},
  pages =        {1993-2001},
  doi =          {10.1021/pr501138h},
  PMID =         {25855118}}

@article{Breckels:2016,
  doi = {10.1371/journal.pcbi.1004920},
  url = {http://dx.doi.org/10.1371/journal.pcbi.1004920},
  year  = {2016},
  month = {may},
  publisher = {Public Library of Science ({PLoS})},
  volume = {12},
  number = {5},
  pages = {e1004920},
  author = {Breckels, LM and Holden, SB and Wojnar, D and Mulvey, CM
		  and Christoforou, A and Groen, AJ and Trotter, MWB
		  and Kohlbacher, O and Lilley, KS and Gatto, L},
  editor = {Thomas Dandekar},
  title = {Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics},
  journal = {{PLOS} Computational Biology}
}

@Manual{mclust,
    title = {\texttt{mclust} Version 4 for R: Normal Mixture Modeling for
      Model-Based Clustering, Classification, and Density Estimation},
    author = {Chris Fraley and Adrian E. Raftery and Thomas Brendan
      Murphy and Luca Scrucca},
    year = {2012},
    type = {Technical Report},
    number = {597},
    institution = {Department of Statistics, University of Washington},
  }

@misc{ghrepo,
  author = {Breckels, LM and Gatto, L},
  title = {A Bioconductor workflow for processing and analysing spatial proteomics data},
  year = {2016},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/lmsimp/bioc-pRoloc-hyperLOPIT-workflow}}
 }

@Article{Itzhak:2016,
  author =       {Itzhak, D N and Tyanova, S and Cox, J and Borner,
		 G H},
  title =        {Global, quantitative and dynamic mapping of
		 protein subcellular localization.},
  journal =      {Elife},
  year =         {2016},
  month =        {},
  number =       {},
  volume =       {5},
  pages =        {},
  doi =          {10.7554/eLife.16950},
  PMID =         {27278775\cite{Itzhak:2016}}}



@article{Uhlen:2010,
    author = {Uhlen, Mathias and Oksvold, Per and Fagerberg, Linn and
		  Lundberg, Emma and Jonasson, Kalle and Forsberg,
		  Mattias and Zwahlen, Martin and Kampf, Caroline and
		  Wester, Kenneth and Hober, Sophia and Wernerus,
		  Henrik and Bj\"{o}rling, Lisa and Ponten, Fredrik},
    citeulike-article-id = {8380545},
    citeulike-linkout-0 = {http://dx.doi.org/10.1038/nbt1210-1248},
    citeulike-linkout-1 = {http://dx.doi.org/10.1038/nbt1210-1248},
    citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/21139605},
    citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=21139605},
    comment = {Update of Protein Atlas. About 50\% of proteins are claimed to be assessed by immunostaining in various human tissues and neoplasms},
    doi = {10.1038/nbt1210-1248},
    issn = {1546-1696},
    journal = {Nature biotechnology},
    keywords = {proteomics, tissue},
    month = dec,
    number = {12},
    pages = {1248--1250},
    pmid = {21139605},
    posted-at = {2010-12-08 22:24:39},
    priority = {0},
    publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
    title = {{Towards a knowledge-based Human Protein Atlas.}},
    url = {http://dx.doi.org/10.1038/nbt1210-1248},
    volume = {28},
    year = {2010}
}

@Manual{hpar,
    title = {\texttt{hpar}: Human Protein Atlas in R},
    author = {Laurent Gatto},
    year = {2016},
    note = {R package version 1.16.0}
  }

@Article{Jean_Beltran:2016,
  author =       {Jean Beltran, P M and Mathias, R A and Cristea, I
		 M},
  title =        {A Portrait of the Human Organelle Proteome In
		 Space and Time during Cytomegalovirus Infection.},
  journal =      {Cell Syst},
  year =         {2016},
  month =        {Oct},
  number =       {4},
  volume =       {3},
  pages =        {361-373.e6},
  doi =          {10.1016/j.cels.2016.08.012},
  PMID =         {27641956}}

@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 =        {Feb},
  number =       {2},
  volume =       {12},
  pages =        {115-21},
  doi =          {10.1038/nmeth.3252},
  PMID =         {25633503}}

@Article{Itzhak:2017,
  author =       {Itzhak, D N and Davies, C and Tyanova, S and
		 Mishra, A and Williamson, J and Antrobus, R and
		 Cox, J and Weekes, M P and Borner, GHH},
  title =        {A Mass Spectrometry-Based Approach for Mapping
		 Protein Subcellular Localization Reveals the
		 Spatial Proteome of Mouse Primary Neurons.},
  journal =      {Cell Rep},
  year =         {2017},
  month =        {Sep},
  number =       {11},
  volume =       {20},
  pages =        {2706-2718},
  doi =          {10.1016/j.celrep.2017.08.063},
  PMID =         {28903049}}

@Article{Hirst:2018,
  author =       {Hirst, J and Itzhak, D N and Antrobus, R and
		 Borner, GHH and Robinson, M S},
  title =        {Role of the AP-5 adaptor protein complex in late
		 endosome-to-Golgi retrieval.},
  journal =      {PLoS Biol},
  year =         {2018},
  month =        {Jan},
  number =       {1},
  volume =       {16},
  pages =        {e2004411},
  doi =          {10.1371/journal.pbio.2004411},
  PMID =         {29381698}}