@ARTICLE{BengtssonH_etal_2010,
  author = {Henrik Bengtsson and Pierre Neuvial and Terence P Speed},
  title = {{TumorBoost}: Normalization of allele-specific tumor copy numbers
	from a single pair of tumor-normal genotyping microarrays},
  journal = {BMC Bioinformatics},
  year = {2010},
  volume = {11},
  pages = {245},
  number = {1},
  month = {May},
  abstract = {ABSTRACT: BACKGROUND: High-throughput genotyping microarrays assess
	both total DNA copy number and allelic composition, which makes them
	a tool of choice for copy number studies in cancer, including total
	copy number and loss of heterozygosity (LOH) analyses. Even after
	state of the art preprocessing methods, allelic signal estimates
	from genotyping arrays still suffer from systematic effects that
	make them difficult to use effectively for such downstream analyses.
	RESULTS: We propose a method, TumorBoost, for normalizing allelic
	estimates of one tumor sample based on estimates from a single matched
	normal. The method applies to any paired tumor-normal estimates from
	any microarray-based technology, combined with any preprocessing
	method. We demonstrate that it increases the signal-to-noise ratio
	of allelic signals, making it significantly easier to detect allelic
	imbalances. CONCLUSIONS: TumorBoost increases the power to detect
	somatic copy-number events (including copy-neutral LOH) in the tumor
	from allelic signals of Affymetrix or Illumina origin. We also conclude
	that high-precision allelic estimates can be obtained from a single
	pair of tumor-normal hybridizations, if TumorBoost is combined with
	single-array preprocessing methods such as (allele-specific) CRMA
	v2 for Affymetrix or BeadStudio's (proprietary) XY-normalization
	method for Illumina. A bounded-memory implementation is available
	in the open-source and cross-platform R package aroma.cn, which is
	part of the Aroma Project (http://www.aroma-project.org/).},
  doi = {10.1186/1471-2105-11-245},
  file = {:BengtssonH_etal_2010-TumorBoost.pdf:PDF},
  language = {eng},
  medline-pst = {aheadofprint},
  owner = {hb},
  pii = {1471-2105-11-245},
  pmid = {20462408},
  timestamp = {2010.05.15},
  url = {http://dx.doi.org/10.1186/1471-2105-11-245}
}

@ARTICLE{OlshenA_etal_2011,
  author = {Adam B. Olshen and Henrik Bengtsson and Pierre Neuvial and Paul Spellman
	and Richard A. Olshen and Venkatraman E. Seshan},
  title = {Parent-specific copy number in paired tumor-normal studies using
	circular binary segmentation},
  journal = {Bioinformatics},
  year = {2011},
  volume = {27},
  pages = {2038-2046},
  number = {15},
  doi = {10.1093/bioinformatics/btr329},
  pmid = {21666266},
  url = {http://bioinformatics.oxfordjournals.org/content/27/15/2038}
}


@ARTICLE{VenkatramanOlshen_2007,
  author = {E. S. Venkatraman and Adam B Olshen},
  title = {{A} faster circular binary segmentation algorithm for the analysis
	of array {CGH} data.},
  journal = {Bioinformatics},
  year = {2007},
  volume = {23},
  pages = {657--663},
  number = {6},
  month = {Mar},
  abstract = {MOTIVATION: Array CGH technologies enable the simultaneous measurement
	of DNA copy number for thousands of sites on a genome. We developed
	the circular binary segmentation (CBS) algorithm to divide the genome
	into regions of equal copy number. The algorithm tests for change-points
	using a maximal t-statistic with a permutation reference distribution
	to obtain the corresponding P-value. The number of computations required
	for the maximal test statistic is O(N2), where N is the number of
	markers. This makes the full permutation approach computationally
	prohibitive for the newer arrays that contain tens of thousands markers
	and highlights the need for a faster algorithm. RESULTS: We present
	a hybrid approach to obtain the P-value of the test statistic in
	linear time. We also introduce a rule for stopping early when there
	is strong evidence for the presence of a change. We show through
	simulations that the hybrid approach provides a substantial gain
	in speed with only a negligible loss in accuracy and that the stopping
	rule further increases speed. We also present the analyses of array
	CGH data from breast cancer cell lines to show the impact of the
	new approaches on the analysis of real data. AVAILABILITY: An R version
	of the CBS algorithm has been implemented in the "DNAcopy" package
	of the Bioconductor project. The proposed hybrid method for the P-value
	is available in version 1.2.1 or higher and the stopping rule for
	declaring a change early is available in version 1.5.1 or higher.},
  doi = {10.1093/bioinformatics/btl646},
  file = {VenkatramanOlshen_2007-A faster circular binary segmentation algorithm for the analysis of array CGH data.pdf:VenkatramanOlshen_2007-A faster circular binary segmentation algorithm for the analysis of array CGH data.pdf:PDF},
  keywords = {Algorithms; Chromosome Mapping, methods; Gene Dosage, genetics; Oligonucleotide
	Array Sequence Analysis, methods; Programming Languages; Sequence
	Alignment, methods; Sequence Analysis, DNA, methods; Software; Time
	Factors},
  owner = {hb},
  pii = {btl646},
  pmid = {17234643},
  timestamp = {2007.02.02},
  url = {http://dx.doi.org/10.1093/bioinformatics/btl646}
}


@ARTICLE{OlshenA_etal_2004,
  author = {Adam B Olshen and E. S. Venkatraman and Robert Lucito and Michael
	Wigler},
  title = {Circular binary segmentation for the analysis of array-based DNA
	copy number data.},
  journal = {Biostatistics},
  year = {2004},
  volume = {5},
  pages = {557--572},
  number = {4},
  month = {Oct},
  abstract = {DNA sequence copy number is the number of copies of DNA at a region
	of a genome. Cancer progression often involves alterations in DNA
	copy number. Newly developed microarray technologies enable simultaneous
	measurement of copy number at thousands of sites in a genome. We
	have developed a modification of binary segmentation, which we call
	circular binary segmentation, to translate noisy intensity measurements
	into regions of equal copy number. The method is evaluated by simulation
	and is demonstrated on cell line data with known copy number alterations
	and on a breast cancer cell line data set.},
  doi = {10.1093/biostatistics/kxh008},
  file = {OlshenA_etal_2004-Circular binary segmentation for the analysis of array-based DNA copy number data.pdf:OlshenA_etal_2004-Circular binary segmentation for the analysis of array-based DNA copy number data.pdf:PDF},
  keywords = {Breast Neoplasms; Cell Line, Tumor; Computer Simulation; Female; Gene
	Dosage; Genome, Human; Humans; Olig; e Analysis; onucleotide Array
	Sequence;

	CBS},
  owner = {hb},
  pii = {5/4/557},
  pmid = {15475419},
  timestamp = {2007.07.09},
  url = {http://dx.doi.org/10.1093/biostatistics/kxh008}
}
