@ARTICLE{Klambauer:11,
  author = {Klambauer, G. and Schwarzbauer, K. and Mitterecker, A. and Mayr, A.
  and Clevert, D.-A. and Bodenhofer, U. and Hochreiter, S.},
  title = {{cn.MOPS: Mixture of Poissons for Discovering Copy Number Variations 
		in Next Generation Sequencing Data with a Low False Discovery Rate}},
  journal = {Nucleic Acids Research},
  year = {2012},
  volume = {40},
  pages = {e69},
  URL = {http://nar.oxfordjournals.org/content/40/9/e69.abstract}, 
  eprint = {http://nar.oxfordjournals.org/content/40/9/e69.full.pdf+html},
  doi = {10.1093/nar/gks003},
  number = {9},
}


@ARTICLE{Chiang:09,
  author = {Derek Y. Chiang and Gad Getz and David B. Jaffe and Xiaojun Zhao
	and Scott L. Carter and Carsten Russ and Chad Nusbaum and Matthew
	Meyerson and Eric S. Lander},
  title = {High-resolution mapping of copy-number alterations with massively
	parallel sequencing},
  journal = {Nat. Methods},
  year = {2008},
  volume = {6},
  pages = {99--103},
  abstract = {Cancer results from somatic alterations in key genes, including point
	mutations, copy number alterations and structural rearrangements.
	A powerful way to discover cancer-causing genes is to identify genomic
	regions that show recurrent copy-number alterations (gains and losses)
	in tumor genomes. The analysis of copy-number alterations has traditionally
	been performed by using DNA microarrays, but recent advances in sequencing
	technologies suggest that random shotgun sequencing may provide a
	powerful alternative. Here, we present (i) a statistical analysis
	of the power to detect copy-number alterations of a given size; (ii)
	an algorithm to identify breakpoints, using massively parallel sequence
	data; and (iii) analysis of experimental data from three matched
	pairs of tumor and normal cell lines. We show that a collection of
	10 million shotgun sequence reads has comparable power to detect
	events as the current generation of DNA microarrays and has considerably
	better power to localize breakpoints (typically, to within ~1 kb).
	Our experimental analysis confirms these analytical predictions.
	As sequencing costs continue to drop, massively parallel sequencing
	will be increasingly feasible and will likely become the preferred
	approach for analyzing cancer genomes. },
  file = {:home/klambaue/Dropbox/Research/PapersEtc/Research/Chiang2009.pdf:PDF;:home/klambaue/Dropbox/Research/PapersEtc/Research/Medvedev2009.pdf:PDF}
}



@ARTICLE{Alkan:09,
  author = {Alkan, Can and Kidd, Jeffrey M. and Marques-Bonet, Tomas and Aksay,
	Gozde and Antonacci, Francesca and Hormozdiari, Fereydoun and Kitzman,
	Jacob O. and Baker, Carl and Malig, Maika and Mutlu, Onur and Sahinalp,
	S. Cenk and Gibbs, Richard A. and Eichler, Evan E.},
  title = {Personalized copy number and segmental duplication maps using next-generation
	sequencing},
  journal = {Nat. Genet.},
  year = {2009},
  volume = {41},
  pages = {1061--1067},
  number = {10},
  month = {October},
  abstract = {Despite their importance in gene innovation and phenotypic variation,
	duplicated regions have remained largely intractable owing to difficulties
	in accurately resolving their structure, copy number and sequence
	content. We present an algorithm (mrFAST) to comprehensively map
	next-generation sequence reads, which allows for the prediction of
	absolute copy-number variation of duplicated segments and genes.
	We examine three human genomes and experimentally validate genome-wide
	copy number differences. We estimate that, on average, 73-87 genes
	vary in copy number between any two individuals and find that these
	genic differences overwhelmingly correspond to segmental duplications
	(odds ratio = 135; P < 2.2 x 10(-16)). Our method can distinguish
	between different copies of highly identical genes, providing a more
	accurate assessment of gene content and insight into functional constraint
	without the limitations of array-based technology.},
  citeulike-article-id = {5687044},
  citeulike-linkout-0 = {http://dx.doi.org/10.1038/ng.437},
  citeulike-linkout-1 = {http://dx.doi.org/10.1038/ng.437},
  citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/19718026},
  citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=19718026},
  day = {30},
  doi = {10.1038/ng.437},
  file = {:/home/klambaue/Dropbox/Research/Papers/Research/Alkan2009.pdf:PDF},
  issn = {1546-1718},
  keywords = {gcb752},
  posted-at = {2010-03-11 16:55:34},
  priority = {2},
  publisher = {Nature Publishing Group},
  url = {http://dx.doi.org/10.1038/ng.437}
}


@ARTICLE{Quackenbush:11,
  author = {Fah J. Sathirapongsasuti and Lee, Hane and Horst, Basil A.J. and
	Brunner, Georg and Cochran, Alistair J. and Binder, Scott and Quackenbush,
	John and Nelson, Stanley F.},
  title = {{Exome Sequencing-Based Copy-Number Variation and Loss of Heterozygosity
	Detection: ExomeCNV}},
	volume = {27}, 
  number = {19}, 
  pages = {2648-2654}, 
  year = {2011}, 
  journal = {Bioinformatics},
  year = {2011},
  abstract = {Motivation: The ability to detect copy-number variation (CNV) and
	loss of heterozygosity (LOH) from exome sequencing data extends the
	utility of this powerful approach that has mainly been used for point
	or small insertion/deletion detection.Results: We present ExomeCNV,
	a statistical method to detect CNV and LOH using depth-of-coverage
	and B-allele frequencies, from mapped short sequence reads, and we
	assess both the method’s power and the effects of confounding variables.
	We apply our method to a cancer exome resequencing dataset. As expected,
	accuracy and resolution are dependent on depth-of-coverage and capture
	probe design.Availability: CRAN package “ExomeCNV”Supplementary information:
	Supplementary data are available at Bioinformatics online.},
  doi = {10.1093/bioinformatics/btr462},
  eprint = {http://bioinformatics.oxfordjournals.org/content/early/2011/08/09/bioinformatics.btr462.full.pdf+html},
  url = {http://bioinformatics.oxfordjournals.org/content/early/2011/08/09/bioinformatics.btr462.abstract}
}
