@ARTICLE{Carvalho2007,
  author = {Benilton Carvalho and Henrik Bengtsson and Terence P Speed and Rafael
	A Irizarry},
  title = {{E}xploration, normalization, and genotype calls of high-density
	oligonucleotide {SNP} array data.},
  journal = {Biostatistics},
  year = {2007},
  volume = {8},
  pages = {485--499},
  number = {2},
  month = {Apr},
  abstract = {In most microarray technologies, a number of critical steps are required
	to convert raw intensity measurements into the data relied upon by
	data analysts, biologists, and clinicians. These data manipulations,
	referred to as preprocessing, can influence the quality of the ultimate
	measurements. In the last few years, the high-throughput measurement
	of gene expression is the most popular application of microarray
	technology. For this application, various groups have demonstrated
	that the use of modern statistical methodology can substantially
	improve accuracy and precision of the gene expression measurements,
	relative to ad hoc procedures introduced by designers and manufacturers
	of the technology. Currently, other applications of microarrays are
	becoming more and more popular. In this paper, we describe a preprocessing
	methodology for a technology designed for the identification of DNA
	sequence variants in specific genes or regions of the human genome
	that are associated with phenotypes of interest such as disease.
	In particular, we describe a methodology useful for preprocessing
	Affymetrix single-nucleotide polymorphism chips and obtaining genotype
	calls with the preprocessed data. We demonstrate how our procedure
	improves existing approaches using data from 3 relatively large studies
	including the one in which large numbers of independent calls are
	available. The proposed methods are implemented in the package oligo
	available from Bioconductor.},
  doi = {10.1093/biostatistics/kxl042},
  keywords = {17189563},
  owner = {rscharpf},
  pii = {kxl042},
  pmid = {17189563},
  timestamp = {2007.07.07},
  url = {http://dx.doi.org/10.1093/biostatistics/kxl042},
}

@ARTICLE{Scharpf2007,
  author = {Robert B Scharpf and Jason C Ting and Jonathan Pevsner and Ingo Ruczinski},
  title = {{SNP}chip: {R} classes and methods for {SNP} array data.},
  journal = {Bioinformatics},
  year = {2007},
  volume = {23},
  pages = {627--628},
  number = {5},
  month = {Mar},
  abstract = {High-density single nucleotide polymorphism microarrays (SNP chips)
	provide information on a subject's genome, such as copy number and
	genotype (heterozygosity/homozygosity) at a SNP. While fluorescence
	in situ hybridization and karyotyping reveal many abnormalities,
	SNP chips provide a higher resolution map of the human genome that
	can be used to detect, e.g., aneuploidies, microdeletions, microduplications
	and loss of heterozygosity (LOH). As a variety of diseases are linked
	to such chromosomal abnormalities, SNP chips promise new insights
	for these diseases by aiding in the discovery of such regions, and
	may suggest targets for intervention. The R package SNPchip contains
	classes and methods useful for storing, visualizing and analyzing
	high density SNP data. Originally developed from the SNPscan web-tool,
	SNPchip utilizes S4 classes and extends other open source R tools
	available at Bioconductor. This has numerous advantages, including
	the ability to build statistical models for SNP-level data that operate
	on instances of the class, and to communicate with other R packages
	that add additional functionality. AVAILABILITY: The package is available
	from the Bioconductor web page at www.bioconductor.org. SUPPLEMENTARY
	INFORMATION: The supplementary material as described in this article
	(case studies, installation guidelines and R code) is available from
	http://biostat.jhsph.edu/~iruczins/publications/sm/},
  doi = {10.1093/bioinformatics/btl638},
  keywords = {Models, Oligonucleotide Array Sequence Analysis, Polymorphism, Single
	Nucleotide, Software, Statistical, 17204461},
  owner = {rscharpf},
  pii = {btl638},
  pmid = {17204461},
  timestamp = {2007.05.21},
  url = {http://dx.doi.org/10.1093/bioinformatics/btl638},
}

@ARTICLE{Scharpf2008,
  author = {Robert B Scharpf and Giovanni Parmigiani and Jonathan Pevsner and
	Ingo Ruczinski},
  title = {Hidden {M}arkov models for the assessment of chromosomal alterations
	using high-throughput {SNP} arrays},
  journal = {Annals of Applied Statistics},
  year = {2008},
  volume = {2},
  pages = {687--713},
  number = {2},
  url = {http://dx.doi.org/10.1214/07-AOAS155}
}

@ARTICLE{Wang2007a,
  author = {Kai Wang and Mingyao Li and Dexter Hadley and Rui Liu and Joseph
	Glessner and Struan F A Grant and Hakon Hakonarson and Maja Bucan},
  title = {PennCNV: an integrated hidden Markov model designed for high-resolution
	copy number variation detection in whole-genome SNP genotyping data.},
  journal = {Genome Res},
  year = {2007},
  volume = {17},
  pages = {1665--1674},
  number = {11},
  month = {Nov},
  abstract = {Comprehensive identification and cataloging of copy number variations
	(CNVs) is required to provide a complete view of human genetic variation.
	The resolution of CNV detection in previous experimental designs
	has been limited to tens or hundreds of kilobases. Here we present
	PennCNV, a hidden Markov model (HMM) based approach, for kilobase-resolution
	detection of CNVs from Illumina high-density SNP genotyping data.
	This algorithm incorporates multiple sources of information, including
	total signal intensity and allelic intensity ratio at each SNP marker,
	the distance between neighboring SNPs, the allele frequency of SNPs,
	and the pedigree information where available. We applied PennCNV
	to genotyping data generated for 112 HapMap individuals; on average,
	we detected approximately 27 CNVs for each individual with a median
	size of approximately 12 kb. Excluding common rearrangements in lymphoblastoid
	cell lines, the fraction of CNVs in offspring not detected in parents
	(CNV-NDPs) was 3.3\%. Our results demonstrate the feasibility of
	whole-genome fine-mapping of CNVs via high-density SNP genotyping.},
  doi = {10.1101/gr.6861907},
  keywords = {Gene Dosage; Genome, Human; Genotype; Humans; Markov Chains; Models,
	Statistical; Polymorphism, Single Nucleotide; Variation (Genetics)},
  owner = {gp},
  pii = {gr.6861907},
  pmid = {17921354},
  timestamp = {2008.03.20},
  url = {http://dx.doi.org/10.1101/gr.6861907}
}

@ARTICLE{Colella2007,
  author = {Stefano Colella and Christopher Yau and Jennifer M Taylor and Ghazala
	Mirza and Helen Butler and Penny Clouston and Anne S Bassett and
	Anneke Seller and Christopher C Holmes and Jiannis Ragoussis},
  title = {{Q}uanti{SNP}: an {O}bjective {B}ayes {H}idden-{M}arkov {M}odel to
	detect and accurately map copy number variation using {SNP} genotyping
	data.},
  journal = {Nucleic Acids Res},
  year = {2007},
  volume = {35},
  pages = {2013--2025},
  number = {6},
  abstract = {Array-based technologies have been used to detect chromosomal copy
	number changes (aneuploidies) in the human genome. Recent studies
	identified numerous copy number variants (CNV) and some are common
	polymorphisms that may contribute to disease susceptibility. We developed,
	and experimentally validated, a novel computational framework (QuantiSNP)
	for detecting regions of copy number variation from BeadArray SNP
	genotyping data using an Objective Bayes Hidden-Markov Model (OB-HMM).
	Objective Bayes measures are used to set certain hyperparameters
	in the priors using a novel re-sampling framework to calibrate the
	model to a fixed Type I (false positive) error rate. Other parameters
	are set via maximum marginal likelihood to prior training data of
	known structure. QuantiSNP provides probabilistic quantification
	of state classifications and significantly improves the accuracy
	of segmental aneuploidy identification and mapping, relative to existing
	analytical tools (Beadstudio, Illumina), as demonstrated by validation
	of breakpoint boundaries. QuantiSNP identified both novel and validated
	CNVs. QuantiSNP was developed using BeadArray SNP data but it can
	be adapted to other platforms and we believe that the OB-HMM framework
	has widespread applicability in genomic research. In conclusion,
	QuantiSNP is a novel algorithm for high-resolution CNV/aneuploidy
	detection with application to clinical genetics, cancer and disease
	association studies.},
  doi = {10.1093/nar/gkm076},
  keywords = {Algorithms, Aneuploidy, Bayes Theorem, Chromosome Breakage, Chromosome
	Mapping, Computational Biology, Genome, Genomics, Genotype, Human,
	Humans, Loss of Heterozygosity, Markov Chains, Models, Polymorphism,
	Singl, Statistical, e Nucleotide, 17341461},
  owner = {rscharpf},
  pii = {gkm076},
  pmid = {17341461},
  timestamp = {2007.07.10},
  url = {http://dx.doi.org/10.1093/nar/gkm076}
}

@ARTICLE{Korn2008,
  author = {Joshua M Korn and Finny G Kuruvilla and Steven A McCarroll and Alec
	Wysoker and James Nemesh and Simon Cawley and Earl Hubbell and Jim
	Veitch and Patrick J Collins and Katayoon Darvishi and Charles Lee
	and Marcia M Nizzari and Stacey B Gabriel and Shaun Purcell and Mark
	J Daly and David Altshuler},
  title = {Integrated genotype calling and association analysis of SNPs, common
	copy number polymorphisms and rare CNVs.},
  journal = {Nat Genet},
  year = {2008},
  volume = {40},
  pages = {1253--1260},
  number = {10},
  month = {Oct},
  abstract = {Accurate and complete measurement of single nucleotide (SNP) and copy
	number (CNV) variants, both common and rare, will be required to
	understand the role of genetic variation in disease. We present Birdsuite,
	a four-stage analytical framework instantiated in software for deriving
	integrated and mutually consistent copy number and SNP genotypes.
	The method sequentially assigns copy number across regions of common
	copy number polymorphisms (CNPs), calls genotypes of SNPs, identifies
	rare CNVs via a hidden Markov model (HMM), and generates an integrated
	sequence and copy number genotype at every locus (for example, including
	genotypes such as A-null, AAB and BBB in addition to AA, AB and BB
	calls). Such genotypes more accurately depict the underlying sequence
	of each individual, reducing the rate of apparent mendelian inconsistencies.
	The Birdsuite software is applied here to data from the Affymetrix
	SNP 6.0 array. Additionally, we describe a method, implemented in
	PLINK, to utilize these combined SNP and CNV genotypes for association
	testing with a phenotype.},
  doi = {10.1038/ng.237},
  institution = {Broad Institute of Harvard and Massachusetts Institute of Technology,
	Cambridge, Massachusetts 02142, USA. jkorn@broad.mit.edu},
  owner = {rscharpf},
  pii = {ng.237},
  pmid = {18776909},
  timestamp = {2008.10.07},
  url = {http://dx.doi.org/10.1038/ng.237}
}

@UNPUBLISHED{ScharpfCopyNumber,
  author = {Robert B Scharpf and Ingo Ruczinski and Benilton Carvalho and Betty
	Doan and Aravinda Chakravarti and Rafael Irizarry},
  title = {A multilevel model to address batch effects in copy number estimation
	using SNP arrays},
  note = {Submitted Revised Manuscript},
  month = {Jan},
  year = {2010}
}

@ARTICLE{Olshen2004,
  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 = {557-72},
  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},
  keywords = {Breast Neoplasms, Cell Line, Computer Simulation, Female, Gene Dosage,
	Genome, Human, Humans, Non-U.S. Gov't, Oligonucleotide Array Sequence
	Analysis, P.H.S., Research Support, Tumor, U.S. Gov't, 15475419},
  owner = {robscharpf},
  pii = {5/4/557},
  url = {http://dx.doi.org/10.1093/biostatistics/kxh008}
}

