%% synapter package references

@Article{Gatto2012,
  author =       {Gatto, Laurent and Lilley, Kathryn S},
  title =        {{MSnbase} -- an {R}/{B}ioconductor 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},
  url =          {http://bioinformatics.oxfordjournals.org/content/28/2/288},
  PMID =         {22113085}
}


@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 = {2012},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org/},
}

@ARTICLE{Gentleman2004,
  author = {Robert C Gentleman and Vincent J Carey and Douglas M Bates and Ben
	Bolstad and Marcel Dettling and Sandrine Dudoit and Byron Ellis and
	Laurent Gautier and Yongchao Ge and Jeff Gentry and Kurt Hornik and
	Torsten Hothorn and Wolfgang Huber and Stefano Iacus and Rafael Irizarry
	and Friedrich Leisch and Cheng Li and Martin Maechler and Anthony
	J Rossini and Gunther Sawitzki and Colin Smith and Gordon Smyth and
	Luke Tierney and Jean Y H Yang and Jianhua Zhang},
  title = {Bioconductor: open software development for computational biology
	and bioinformatics.},
  journal = {Genome Biol},
  year = {2004},
  volume = {5},
  pages = {R80},
  number = {10},
  abstract = {The Bioconductor project is an initiative for the collaborative creation
	of extensible software for computational biology and bioinformatics.
	The goals of the project include: fostering collaborative development
	and widespread use of innovative software, reducing barriers to entry
	into interdisciplinary scientific research, and promoting the achievement
	of remote reproducibility of research results. We describe details
	of our aims and methods, identify current challenges, compare Bioconductor
	to other open bioinformatics projects, and provide working examples.},
  doi = {10.1186/gb-2004-5-10-r80},
  file = {Gentleman_et_al_GenomeBiology_2004.pdf:/home/lgatto/Biblio/Gentleman_et_al_GenomeBiology_2004.pdf:PDF},
  keywords = {Computational Biology; Internet; Repro; Software; ducibility of Results},
  owner = {lgatto},
  pii = {gb-2004-5-10-r80},
  pmid = {15461798},
  timestamp = {2006.09.27},
  url = {http://dx.doi.org/10.1186/gb-2004-5-10-r80}
}

@InCollection{limma,
    title = {Limma: linear models for microarray data},
    author = {Gordon K. Smyth},
    booktitle = {Bioinformatics and Computational Biology Solutions using R and Bioconductor},
    editor = {R. Gentleman and V. Carey and S. Dudoit and R. Irizarry and W. Huber},
    publisher = {Springer},
    address = {New York},
    year = {2005},
    pages = {397--420},
}

@article{Storey2003,
	author = {Storey, John D. and Tibshirani, Robert},
	title = {Statistical significance for genomewide studies},
	journal = {Proceedings of the National Academy of Sciences of the United States
	of America},
	year = {2003},
	volume = {100},
	pages = {9440-9445},
	number = {16},
	abstract = {With the increase in genomewide experiments and the sequencing of
	multiple genomes, the analysis of large data sets has become commonplace
	in biology. It is often the case that thousands of features in a
	genomewide data set are tested against some null hypothesis, where
	a number of features are expected to be significant. Here we propose
	an approach to measuring statistical significance in these genomewide
	studies based on the concept of the false discovery rate. This approach
	offers a sensible balance between the number of true and false positives
	that is automatically calibrated and easily interpreted. In doing
	so, a measure of statistical significance called the value is associated
	with each tested feature. The value is similar to the well known
	value, except it is a measure of significance in terms of the false
	discovery rate rather than the false positive rate. Our approach
	avoids a flood of false positive results, while offering a more liberal
	criterion than what has been used in genome scans for linkage. },
	doi = {10.1073/pnas.1530509100},
	eprint = {http://www.pnas.org/content/100/16/9440.full.pdf+html},
	tags = {R, software, statistics},
	url = {http://www.pnas.org/content/100/16/9440.abstract}
}
@Article{PLGS2009,
  author =       {Li, G Z and Vissers, J P and Silva, J C and
                 Golick, D and Gorenstein, M V and Geromanos, S J},
  title =        {Database searching and accounting of multiplexed
                 precursor and product ion spectra from the data
                 independent analysis of simple and complex peptide
                 mixtures.},
  journal =      {Proteomics},
  year =         {2009},
  month =        {Mar},
  number =       {6},
  volume =       {9},
  pages =        {1696-719},
  doi =          {10.1002/pmic.200800564},
  PMID =         {19294629}
}

@article{Bond2013,
   author = {Bond, Nicholas James and Shliaha, Pavel Vyacheslavovich and Lilley, Kathryn S. and Gatto, Laurent},
   title = {Improving qualitative and quantitative performance for MS$^E$-based label free proteomics},
   journal = {Journal of Proteome Research},
   volume = {12},
   issue = {6},
   year = {2013},
   doi = {10.1021/pr300776t},
   URL = {http://pubs.acs.org/doi/abs/10.1021/pr300776t},
   eprint = {http://pubs.acs.org/doi/pdf/10.1021/pr300776t}
}

@article{Shliaha2013,
   author = {Shliaha, Pavel Vyacheslavovich and Bond, Nicholas James and Gatto, Laurent and Lilley, Kathryn S.},
   title = {The Effects of Travelling Wave Ion Mobility Separation on Data Independent Acquisition in Proteomics Studies},
   journal = {Journal of Proteome Research},
   volume = {2},
   issue = {6},
   year = {2013},
   doi = {10.1021/pr300775k},
   URL = {http://pubs.acs.org/doi/abs/10.1021/pr300775k},
   eprint = {http://pubs.acs.org/doi/pdf/10.1021/pr300775k}
}


