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@article{Gitan2002,
author = {Raad S Gitan and Huidong Shi and Chuan-Mu Chen and Pearlly S Yan and Tim Hui-Ming Huang}, 
journal = {Genome Res},
title = {Methylation-specific oligonucleotide microarray: a new potential for high-throughput methylation analysis.},
abstract = {Oligonucleotide microarray-based hybridization is an emerging technology for genome-wide detection of DNA variations. We have extended this principle and developed a novel approach, called methylation-specific oligonucleotide (MSO) microarray, for detecting changes of DNA methylation in cancer. The method uses bisulfite-modified DNA as a template for PCR amplification, resulting in conversion of unmethylated cytosine, but not methylated cytosine, into thymine within CpG islands of interest. The amplified product, therefore, may contain a pool of DNA fragments with altered nucleotide sequences due to differential methylation status. A test sample is hybridized to a set of oligonucleotide (19-23 nucleotides in length) arrays that discriminate methylated and unmethylated cytosine at specific nucleotide positions, and quantitative differences in hybridization are determined by fluorescence analysis. A unique control system is also implemented to test the accuracy and reproducibility of oligonucleotides designed for microarray hybridization. This MSO microarray was applied to map methylated CpG sites within the human estrogen receptor alpha (ERalpha) gene CpG island in breast cancer cell lines, normal fibroblasts, breast tumors, and normal controls. Methylation patterns of the breast cancer cell lines, determined by MSO microarray, were further validated by bisulfite nucleotide sequencing (P <0.001). This proof-of-principle study shows that MSO microarray is a promising technique for mapping methylation changes in multiple CpG island loci and for generating epigenetic profiles in cancer.},
affiliation = {Department of Pathology, Ellis Fischel Cancer Center, University of Missouri School of Medicine, Columbia 65203, USA.},
number = {1},
pages = {158--164},
volume = {12},
year = {2002},
month = {Jan},
keywords = {analysis/genetics; Tumor Cells, Cultured, Neoplasm, methods; Receptors, chemistry/genetics; Cell Line; CpG Islands, Estrogen, analysis; Estrogen Receptor alpha; Female; Fibroblasts; Humans; Molecular Sequence Data; Oligonucleotide Array Sequence Analysis, genetics; DNA Methylation; DNA, Base Sequence; Breast Neoplasms}, 
date-added = {2008-06-23 13:27:04 -0300},
date-modified = {2008-06-23 13:27:04 -0300},
doi = {10.1101/gr.202801},
pmid = {11779841},
URL = {http://dx.doi.org/10.1101/gr.202801},
local-url = {file://localhost/Users/bcarvalh/Documents/Academic/ScientificPapers/Papers/2002/Gitan/Genome%20Res%202002%20Gitan.pdf},
uri = {papers://344BCCBF-F0AF-44EA-862D-4E1AB7B7B64D/Paper/p238},
read = {Yes},
rating = {0}
}

@article{Scacheri:2006p2564,
author = {Peter C Scacheri and Gregory E Crawford and Sean Davis}, 
journal = {Meth Enzymol},
title = {Statistics for ChIP-chip and DNase hypersensitivity experiments on NimbleGen arrays},
abstract = {Data obtained from high-density oligonucleotide tiling arrays present new computational challenges for users. This chapter presents ACME (Algorithm for Capturing Microarray Enrichment), a computer program developed for the analysis of data obtained using NimbleGen-tiled microarrays. ACME identifies signals or "peaks" in tiled array data using a simple sliding window and threshold strategy and assigns a probability value (p value) to each and every probe on the array. We present data indicating that this approach can be applied successfully to at least two different genomic applications involving tiled arrays: ChIP-chip and DNase-chip. In addition to highlighting previously described methods for analyzing tiled array data, we provide recommendations for assessing the quality of ChIP-chip and DNase-chip data, suggestions for optimizing the use of ACME, and descriptions of several of ACME features designed to facilitate interpretation of processed tiled array data. ACME is written in R language and is freely available upon request or through Bioconductor.},
affiliation = {Department of Genetics, Case Western Reserve University, Cleveland, OH, USA.},
pages = {270--82},
volume = {411},
year = {2006},
month = {Jan},
language = {eng},
keywords = {Data Interpretation: Statistical, Algorithms, Humans, Animals, DNA, Deoxyribonucleases, Software, Oligonucleotide Array Sequence Analysis}, 
date-added = {2009-11-05 15:06:15 -0200},
date-modified = {2009-11-05 15:06:15 -0200},
doi = {10.1016/S0076-6879(06)11014-9},
pii = {S0076-6879(06)11014-9},
pmid = {16939795},
URL = {http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B7CV2-4KS5C9T-G&_user=75682&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_acct=C000006078&_version=1&_urlVersion=0&_userid=75682&md5=cbf1dfb60fc5b34fbc671d3da49b993a},
local-url = {file://localhost/Users/bcarvalh/Documents/Academic/ScientificPapers/Papers/2006/Scacheri/Meth%20Enzymol%202006%20Scacheri.pdf},
uri = {papers://344BCCBF-F0AF-44EA-862D-4E1AB7B7B64D/Paper/p2564},
rating = {0}
}

@article{Lin:2008p33,
author = {S Lin and Benilton S Carvalho and D Cutler and D Arking and A Chakravarti and Rafael Irizarry}, 
journal = {Genome Biol},
title = {Validation and extension of an empirical Bayes method for SNP calling on Affymetrix microarrays},
abstract = {ABSTRACT: Multiple algorithms have been developed for the purpose of calling single nucleotide polymorphisms (SNPs) from Affymetrix microarrays. We extend and validate the algorithm CRLMM, which incorporates HapMap information within an empirical Bayes framework. We find CRLMM to be more accurate than the Affymetrix default programs (BRLMM and Birdseed). Also, we tie our call confidence metric to percent accuracy. We intend that our validation datasets and methods, refered to as SNPaffycomp, serve as standard benchmarks for future SNP calling algorithms.},
affiliation = {Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, North Wolfe St, E3035, Baltimore, MD 21205, USA. rafa@jhu.edu.},
number = {4},
pages = {R63},
volume = {9},
year = {2008},
month = {Apr},
language = {ENG},
date-added = {2008-06-23 13:09:24 -0300},
date-modified = {2009-10-21 02:51:22 -0200},
doi = {10.1186/gb-2008-9-4-r63},
pii = {gb-2008-9-4-r63},
pmid = {18387188},
URL = {http://genomebiology.com/2008/9/4/R63},
local-url = {file://localhost/Users/bcarvalh/Documents/Academic/ScientificPapers/Papers/2008/Lin/Genome%20Biol%202008%20Lin.pdf},
uri = {papers://344BCCBF-F0AF-44EA-862D-4E1AB7B7B64D/Paper/p33},
read = {Yes},
rating = {0}
}

@article{Irizarry2003b,
author = {Rafael Irizarry and Siew Loon Ooi and Zhijin Wu and Jef D Boeke}, 
journal = {Stat Appl Genet Mol Biol},
title = {Use of mixture models in a microarray-based screening procedure for detecting differentially represented yeast mutants.},
abstract = {We describe the use of a statistical model in a genome-wide microarray-based yeast genetic screen performed by imposing different genetic selections on thousands of yeast mutants in parallel. A mixture model is fitted to data obtained from oligonucleotide arrays hybridized to 20-mer oligonucleotide "barcodes'' and a procedure based on the fitted model is used to search for mutants differentially represented under experimental and control conditions. The fitted stochastic model provides a way to assess uncertainty. We demonstrate the usefulness of the model by applying it to the problem of screening for components of the nonhomologous end joining (NHEJ) pathway and identified known components of the NHEJ pathway.},
affiliation = {Johns Hopkins University, USA. rafa@jhu.edu},
pages = {Article1},
volume = {2},
year = {2003},
date-added = {2008-06-23 13:27:04 -0300},
date-modified = {2009-10-21 02:51:22 -0200},
doi = {10.2202/1544-6115.1002},
pmid = {16646779},
URL = {http://dx.doi.org/10.2202/1544-6115.1002},
local-url = {file://localhost/Users/bcarvalh/Documents/Academic/ScientificPapers/Papers/2003/Irizarry/Stat%20Appl%20Genet%20Mol%20Biol%202003%20Irizarry.pdf},
uri = {papers://344BCCBF-F0AF-44EA-862D-4E1AB7B7B64D/Paper/p227},
read = {Yes},
rating = {0}
}

@article{Irizarry2006a,
author = {Rafael Irizarry and Zhijin Wu and Harris A Jaffee}, 
journal = {Bioinformatics},
title = {Comparison of Affymetrix GeneChip expression measures.},
abstract = {MOTIVATION: In the Affymetrix GeneChip system, preprocessing occurs before one obtains expression level measurements. Because the number of competing preprocessing methods was large and growing we developed a benchmark to help users identify the best method for their application. A webtool was made available for developers to benchmark their procedures. At the time of writing over 50 methods had been submitted. RESULTS: We benchmarked 31 probe set algorithms using a U95A dataset of spike in controls. Using this dataset, we found that background correction, one of the main steps in preprocessing, has the largest effect on performance. In particular, background correction appears to improve accuracy but, in general, worsen precision. The benchmark results put this balance in perspective. Furthermore, we have improved some of the original benchmark metrics to provide more detailed information regarding precision and accuracy. A handful of methods stand out as providing the best balance using spike-in data with the older U95A array, although different experiments on more current arrays may benchmark differently. AVAILABILITY: The affycomp package, now version 1.5.2, continues to be available as part of the Bioconductor project (http://www.bioconductor.org). The webtool continues to be available at http://affycomp.biostat.jhsph.edu CONTACT: rafa@jhu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
affiliation = {Department of Biostatistics, Johns Hopkins University, 615 N. Wolfe Street, Baltimore, MD 21205, USA. rafa@jhu.edu},
number = {7},
pages = {789--794},
volume = {22},
year = {2006},
month = {Apr},
keywords = {instrumentation/methods; Reproducibility of Results; Software, methods; Oligonucleotide Array Sequence Analysis, Algorithms; Benchmarking; Gene Expression Profiling}, 
date-added = {2008-06-23 13:27:04 -0300},
date-modified = {2009-10-21 02:51:22 -0200},
doi = {10.1093/bioinformatics/btk046},
pii = {btk046},
pmid = {16410320},
URL = {http://dx.doi.org/10.1093/bioinformatics/btk046},
local-url = {file://localhost/Users/bcarvalh/Documents/Academic/ScientificPapers/Papers/2006/Irizarry/Bioinformatics%202006%20Irizarry.pdf},
uri = {papers://344BCCBF-F0AF-44EA-862D-4E1AB7B7B64D/Paper/p220},
read = {Yes},
rating = {0}
}

@article{Wu:2004p1634,
author = {Zhijin Wu and Rafael Irizarry and Robert C Gentleman and F Martinez-Murillo {\ldots}}, 
journal = {Journal of the American Statistical Association},
title = {A Model-Based Background Adjustment for Oligonucleotide Expression Arrays},
abstract = {High-density oligonucleotide expression arrays are widely used in many areas of biomedical research. Affymetrix GeneChip arrays are the most popular. In the Affymetrix system, a fair amount of further preprocessing and data reduction occurs after the image-processing step. Statistical procedures developed by academic groups have been successful in improving the default algorithms provided by the Affymetrix system. In this article we present a solution to one of the preprocessing steps---background adjustment---based on a formal statistical framework. Our solution greatly improves the performance of the technology in various practical applications. These arrays use short oligonucleotides to probe for genes in an RNA sample. Typically, each gene is represented by 11--20 pairs of oligonucleotide probes. The first component of these pairs is referred to as a perfect match probe and is designed to hybridize only with transcripts from the intended gene (i. e., specific hybridization). However, hybridization by other sequences (i. e., nonspecific hybridization) is unavoidable. Furthermore, hybridization strengths are measured by a scanner that introduces optical noise. Therefore, the observed intensities need to be adjusted to give accurate measurements of specific hybridization. We have found that the default ad hoc adjustment, provided as part of the Affymetrix system, can be improved through the use of estimators derived from a statistical model that uses probe sequence information. A final step in preprocessing is to summarize the probe-level data for each gene to define a measure of expression that represents the amount of the corresponding mRNA species. In this article we illustrate the practical consequences of not adjusting appropriately for the presence of nonspecific hybridization and provide a solution based on our background adjustment procedure. Software that computes our adjustment is available as part of the Bioconductor Project (http://www. bioconductor.org).},
number = {468},
pages = {909--917},
volume = {99},
year = {2004},
month = {Dec},
language = {English},
date-added = {2009-01-07 15:38:12 -0200},
date-modified = {2009-10-21 02:51:22 -0200},
doi = {10.1198/016214504000000683},
pmid = {18322185620852199218},
URL = {http://pubs.amstat.org/doi/pdf/10.1198/016214504000000683},
local-url = {file://localhost/Users/bcarvalh/Documents/Academic/ScientificPapers/Papers/2004/Wu/Journal%20of%20the%20American%20Statistical%20Association%202004%20Wu.pdf},
uri = {papers://344BCCBF-F0AF-44EA-862D-4E1AB7B7B64D/Paper/p1634},
rating = {0}
}

@article{Scharpf2007,
author = {Robert B Scharpf and Jason C Ting and Jonathan Pevsner and Ingo Ruczinski}, 
journal = {Bioinformatics},
title = {SNPchip: R classes and methods for SNP array data.},
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/},
affiliation = {Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.},
number = {5},
pages = {627--628},
volume = {23},
year = {2007},
month = {Mar},
keywords = {Statistical; Oligonucleotide Array Sequence Analysis; Software; leotide, Models}, 
date-added = {2008-06-23 13:27:04 -0300},
date-modified = {2008-06-23 13:27:04 -0300},
doi = {10.1093/bioinformatics/btl638},
pii = {btl638},
pmid = {17204461},
URL = {http://dx.doi.org/10.1093/bioinformatics/btl638},
local-url = {file://localhost/Users/bcarvalh/Documents/Academic/ScientificPapers/Papers/2007/Scharpf/Bioinformatics%202007%20Scharpf.pdf},
uri = {papers://344BCCBF-F0AF-44EA-862D-4E1AB7B7B64D/Paper/p235},
read = {Yes},
rating = {0}
}

@article{Clark2007,
author = {Tyson A Clark and Anthony C Schweitzer and Tina X Chen and Michelle K Staples and Gang Lu and Hui Wang and Alan Williams and John E Blume}, 
journal = {Genome Biol},
title = {Discovery of tissue-specific exons using comprehensive human exon microarrays.},
abstract = {BACKGROUND: Higher eukaryotes express a diverse population of messenger RNAs generated by alternative splicing. Large-scale methods for monitoring gene expression must adapt in order to accurately detect the transcript variation generated by this splicing. RESULTS: We have designed a high-density oligonucleotide microarray with probesets for more than one million annotated and predicted exons in the human genome. Using these arrays and a simple algorithm that normalizes exon signal to signal from the gene as a whole, we have identified tissue-specific exons from a panel of 16 different normal adult tissues. RT-PCR validation confirms approximately 86% of the predicted tissue-enriched probesets. Pair-wise comparisons between the tissues suggest that as many as 73% of detected genes are differentially alternatively spliced. We also demonstrate how an inclusive exon microarray can be used to discover novel alternative splicing events. As examples, 17 new tissue-specific exons from 11 genes were validated by RT-PCR and sequencing. CONCLUSION: In conjunction with a conceptually simple algorithm, comprehensive exon microarrays can detect tissue-specific alternative splicing events. Our data suggest significant expression outside of known exons and well annotated genes and a high frequency of alternative splicing events. In addition, we identified and validated a number of novel exons with tissue-specific splicing patterns. The tissue map data will likely serve as a valuable source of information on the regulation of alternative splicing.},
affiliation = {Affymetrix, Inc, Santa Clara, CA 95051, USA. tyson_clark@affymetrix.com},
number = {4},
pages = {R64},
volume = {8},
year = {2007},
keywords = {Messenger, Algorithms; Alternative Splicing; Brain, metabolism; Exons; Gene Expression Profiling; Humans; Oligonucleotide Array Sequence Analysis, metabolism, methods; RNA}, 
date-added = {2008-06-23 13:27:04 -0300},
date-modified = {2008-06-23 13:27:04 -0300},
doi = {10.1186/gb-2007-8-4-r64},
pii = {gb-2007-8-4-r64},
pmid = {17456239},
URL = {http://dx.doi.org/10.1186/gb-2007-8-4-r64},
local-url = {file://localhost/Users/bcarvalh/Documents/Academic/ScientificPapers/Papers/2007/Clark/Genome%20Biol%202007%20Clark.pdf},
uri = {papers://344BCCBF-F0AF-44EA-862D-4E1AB7B7B64D/Paper/p234},
read = {Yes},
rating = {0}
}

@article{Irizarry:2003p1260,
author = {Rafael Irizarry and Benjamin M Bolstad and Francois Collin and Leslie M Cope and Bridget Hobbs and Terence P Speed}, 
journal = {Nucleic Acids Res},
title = {Summaries of Affymetrix GeneChip probe level data},
abstract = {High density oligonucleotide array technology is widely used in many areas of biomedical research for quantitative and highly parallel measurements of gene expression. Affymetrix GeneChip arrays are the most popular. In this technology each gene is typically represented by a set of 11-20 pairs of probes. In order to obtain expression measures it is necessary to summarize the probe level data. Using two extensive spike-in studies and a dilution study, we developed a set of tools for assessing the effectiveness of expression measures. We found that the performance of the current version of the default expression measure provided by Affymetrix Microarray Suite can be significantly improved by the use of probe level summaries derived from empirically motivated statistical models. In particular, improvements in the ability to detect differentially expressed genes are demonstrated.},
affiliation = {Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA. rafa@jhu.edu},
number = {4},
pages = {e15},
volume = {31},
year = {2003},
month = {Feb},
language = {eng},
keywords = {Reproducibility of Results, Gene Expression Profiling, Humans, Liver, DNA Probes, Oligonucleotide Array Sequence Analysis, Central Nervous System, RNA: Messenger, Software}, 
date-added = {2008-08-14 14:33:11 -0300},
date-modified = {2009-10-21 02:51:22 -0200},
pmid = {12582260},
URL = {http://nar.oxfordjournals.org/cgi/content/full/31/4/e15},
local-url = {file://localhost/Users/bcarvalh/Documents/Academic/ScientificPapers/Papers/2003/Irizarry/Nucleic%20Acids%20Res%202003%20Irizarry.pdf},
uri = {papers://344BCCBF-F0AF-44EA-862D-4E1AB7B7B64D/Paper/p1260},
read = {Yes},
rating = {0}
}

@article{Irizarry2006,
author = {Rafael Irizarry and Leslie M Cope and Zhijin Wu}, 
journal = {Genome Biol},
title = {Feature-level exploration of a published Affymetrix GeneChip control dataset.},
abstract = {A comment on Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset by SE Choe, M Boutros, AM Michelson, GM Church and MS Halfon. Genome Biology 2005, 6:R16.},
number = {8},
pages = {404},
volume = {7},
year = {2006},
keywords = {methods; Oligonucleotide Array Sequence Analysis, Messenger, methods; Oligonucleotide Probes; RNA, Algorithms; Animals; Drosophila; Gene Expression Profiling}, 
date-added = {2008-06-23 13:27:04 -0300},
date-modified = {2009-10-21 02:51:22 -0200},
doi = {10.1186/gb-2006-7-8-404},
pii = {gb-2006-7-8-404},
pmid = {16953902},
URL = {http://dx.doi.org/10.1186/gb-2006-7-8-404},
local-url = {file://localhost/Users/bcarvalh/Documents/Academic/ScientificPapers/Papers/2006/Irizarry/Genome%20Biol%202006%20Irizarry.pdf},
uri = {papers://344BCCBF-F0AF-44EA-862D-4E1AB7B7B64D/Paper/p229},
read = {Yes},
rating = {0}
}

@article{Irizarry2003a,
author = {Rafael Irizarry and Bridget Hobbs and Francois Collin and Yasmin D Beazer-Barclay and Kristen J Antonellis and Uwe Scherf and Terence P Speed}, 
journal = {Biostatistics},
title = {Exploration, normalization, and summaries of high density oligonucleotide array probe level data.},
abstract = {In this paper we report exploratory analyses of high-density oligonucleotide array data from the Affymetrix GeneChip system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of five MGU74A mouse GeneChip arrays, part of the data from an extensive spike-in study conducted by Gene Logic and Wyeth's Genetics Institute involving 95 HG-U95A human GeneChip arrays; and part of a dilution study conducted by Gene Logic involving 75 HG-U95A GeneChip arrays. We display some familiar features of the perfect match and mismatch probe (PM and MM) values of these data, and examine the variance-mean relationship with probe-level data from probes believed to be defective, and so delivering noise only. We explain why we need to normalize the arrays to one another using probe level intensities. We then examine the behavior of the PM and MM using spike-in data and assess three commonly used summary measures: Affymetrix's (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model-based expression index (MBEI). The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike-in data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities.},
affiliation = {Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA. rafa@jhu.edu},
number = {2},
pages = {249--264},
volume = {4},
year = {2003},
month = {Apr},
keywords = {Nonparametric, methods; Reproducibility of Results; Statistics, Statistical; Gene Expression Profiling, Algorithms; Animals; DNA Probes, genetics; Data Interpretation, statistics /{\&}/ numerical data; Humans; Linear Models; Mice; Normal Distribution; Oligonucleotide Array Sequence Analysis}, 
date-added = {2008-06-23 13:27:04 -0300},
date-modified = {2009-10-21 02:51:22 -0200},
doi = {10.1093/biostatistics/4.2.249},
pii = {4/2/249},
pmid = {12925520},
URL = {http://dx.doi.org/10.1093/biostatistics/4.2.249},
local-url = {file://localhost/Users/bcarvalh/Documents/Academic/ScientificPapers/Papers/2003/Irizarry/Biostatistics%202003%20Irizarry.pdf},
uri = {papers://344BCCBF-F0AF-44EA-862D-4E1AB7B7B64D/Paper/p222},
read = {Yes},
rating = {0}
}

@article{Carvalho:2007p32,
author = {Benilton S Carvalho and Henrik Bengtsson and Terence P Speed and Rafael Irizarry}, 
journal = {Biostatistics},
title = {Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data},
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.},
affiliation = {Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA.},
number = {2},
pages = {485--99},
volume = {8},
year = {2007},
month = {Apr},
language = {eng},
keywords = {Genotype, Data Interpretation: Statistical, Polymorphism: Single Nucleotide, Humans, Alleles, Oligonucleotides, Oligonucleotide Array Sequence Analysis, Algorithms}, 
date-added = {2008-06-23 13:09:24 -0300},
date-modified = {2009-10-21 02:51:22 -0200},
doi = {10.1093/biostatistics/kxl042},
pii = {kxl042},
pmid = {17189563},
URL = {http://biostatistics.oxfordjournals.org/cgi/content/full/8/2/485},
local-url = {file://localhost/Users/bcarvalh/Documents/Academic/ScientificPapers/Papers/2007/Carvalho/Biostatistics%202007%20Carvalho.pdf},
uri = {papers://344BCCBF-F0AF-44EA-862D-4E1AB7B7B64D/Paper/p32},
read = {Yes},
rating = {5}
}

@article{Bengtsson:2008p31,
author = {Henrik Bengtsson and Rafael Irizarry and Benilton S Carvalho and Terence P Speed}, 
journal = {Bioinformatics},
title = {Estimation and assessment of raw copy numbers at the single locus level},
abstract = {MOTIVATION: Although copy-number aberrations are known to contribute to the diversity of the human DNA and cause various diseases, many aberrations and their phenotypes are still to be explored. The recent development of single-nucleotide polymorphism (SNP) arrays provides researchers with tools for calling genotypes and identifying chromosomal aberrations at an order-of-magnitude greater resolution than possible a few years ago. The fundamental problem in array-based copy-number (CN) analysis is to obtain CN estimates at a single-locus resolution with high accuracy and precision such that downstream segmentation methods are more likely to succeed. RESULTS: We propose a preprocessing method for estimating raw CNs from Affymetrix SNP arrays. Its core utilizes a multichip probe-level model analogous to that for high-density oligonucleotide expression arrays. We extend this model by adding an adjustment for sequence-specific allelic imbalances such as cross-hybridization between allele A and allele B probes. We focus on total CN estimates, which allows us to further constrain the probe-level model to increase the signal-to-noise ratio of CN estimates. Further improvement is obtained by controlling for PCR effects. Each part of the model is fitted robustly. The performance is assessed by quantifying how well raw CNs alone differentiate between one and two copies on Chromosome X (ChrX) at a single-locus resolution (27kb) up to a 200kb resolution. The evaluation is done with publicly available HapMap data. AVAILABILITY: The proposed method is available as part of an open-source R package named aroma.affymetrix. Because it is a bounded-memory algorithm, any number of arrays can be analyzed.},
affiliation = {Department of Statistics, University of California, Berkeley, USA, Department of Biostatistics, Johns Hopkins University, Baltimore, USA.},
number = {6},
pages = {759--67},
volume = {24},
year = {2008},
month = {Mar},
language = {eng},
keywords = {Quantitative Trait Loci, Gene Expression Profiling, DNA Mutational Analysis, Pattern Recognition: Automated, Chromosome Mapping, Markov Chains, Oligonucleotide Array Sequence Analysis, Software, Polymorphism: Single Nucleotide, Gene Dosage, Base Sequence, Artificial Intelligence, Molecular Sequence Data, Algorithms}, 
date-added = {2008-06-23 13:09:24 -0300},
date-modified = {2009-10-21 02:51:22 -0200},
doi = {10.1093/bioinformatics/btn016},
pii = {btn016},
pmid = {18204055},
URL = {http://bioinformatics.oxfordjournals.org/cgi/content/full/24/6/759},
local-url = {file://localhost/Users/bcarvalh/Documents/Academic/ScientificPapers/Papers/2008/Bengtsson/Bioinformatics%202008%20Bengtsson.pdf},
uri = {papers://344BCCBF-F0AF-44EA-862D-4E1AB7B7B64D/Paper/p31},
read = {Yes},
rating = {0}
}

@article{Gautier2004,
author = {Laurent Gautier and Leslie M Cope and Benjamin M Bolstad and Rafael Irizarry}, 
journal = {Bioinformatics},
title = {affy--analysis of Affymetrix GeneChip data at the probe level.},
abstract = {MOTIVATION: The processing of the Affymetrix GeneChip data has been a recent focus for data analysts. Alternatives to the original procedure have been proposed and some of these new methods are widely used. RESULTS: The affy package is an R package of functions and classes for the analysis of oligonucleotide arrays manufactured by Affymetrix. The package is currently in its second release, affy provides the user with extreme flexibility when carrying out an analysis and make it possible to access and manipulate probe intensity data. In this paper, we present the main classes and functions in the package and demonstrate how they can be used to process probe-level data. We also demonstrate the importance of probe-level analysis when using the Affymetrix GeneChip platform.},
affiliation = {Center for Biological Sequence Analysis (CBS), Technical University of Denmark, Building 208, 2800 Lyngby, Denmark. laurent@cbs.dtu.dk},
number = {3},
pages = {307--315},
volume = {20},
year = {2004},
month = {Feb},
keywords = {methods; Software; User-Computer Interface, methods; Oligonucleotide Array Sequence Analysis, Genetic; Gene Expression Profiling, Algorithms; DNA Probes, instrumentation/methods; Information Storage and Retrieval, DNA, instrumentation/methods; Sequence Alignment, chemistry; Databases, methods; Sequence Analysis, Bibliographic; Databases}, 
date-added = {2008-06-23 13:27:04 -0300},
date-modified = {2009-10-21 02:51:22 -0200},
doi = {10.1093/bioinformatics/btg405},
pii = {20/3/307},
pmid = {14960456},
URL = {http://dx.doi.org/10.1093/bioinformatics/btg405},
local-url = {file://localhost/Users/bcarvalh/Documents/Academic/ScientificPapers/Papers/2004/Gautier/Bioinformatics%202004%20Gautier.pdf},
uri = {papers://344BCCBF-F0AF-44EA-862D-4E1AB7B7B64D/Paper/p232},
read = {Yes},
rating = {0}
}

@manual{Bengtsson2008a,
author = {Henrik Bengtsson and James Bullard and Kasper Daniel Hansen}, 
journal = {Manual},
title = {affxparser: Affymetrix File Parsing SDK},
note = {R package version 1.13.2},
year = {2008},
date-added = {2008-06-23 13:27:04 -0300},
date-modified = {2008-07-22 12:04:58 -0300},
uri = {papers://344BCCBF-F0AF-44EA-862D-4E1AB7B7B64D/Paper/p224},
rating = {0}
}

@article{Bolstad:2003p282,
author = {Benjamin M Bolstad and Rafael Irizarry and M Astrand and Terence P Speed}, 
journal = {Bioinformatics},
title = {A comparison of normalization methods for high density oligonucleotide array data based on variance and bias},
abstract = {MOTIVATION: When running experiments that involve multiple high density oligonucleotide arrays, it is important to remove sources of variation between arrays of non-biological origin. Normalization is a process for reducing this variation. It is common to see non-linear relations between arrays and the standard normalization provided by Affymetrix does not perform well in these situations. RESULTS: We present three methods of performing normalization at the probe intensity level. These methods are called complete data methods because they make use of data from all arrays in an experiment to form the normalizing relation. These algorithms are compared to two methods that make use of a baseline array: a one number scaling based algorithm and a method that uses a non-linear normalizing relation by comparing the variability and bias of an expression measure. Two publicly available datasets are used to carry out the comparisons. The simplest and quickest complete data method is found to perform favorably. AVAILABILITY: Software implementing all three of the complete data normalization methods is available as part of the R package Affy, which is a part of the Bioconductor project http://www.bioconductor.org. SUPPLEMENTARY INFORMATION: Additional figures may be found at http://www.stat.berkeley.edu/~bolstad/normalize/index.html},
affiliation = {Group in Biostatistics, University of California, Berkeley, CA 94720, USA. bolstad@stat.berkeley.edu},
number = {2},
pages = {185--93},
volume = {19},
year = {2003},
month = {Jan},
language = {eng},
keywords = {Models: Genetic, Algorithms, Oligonucleotide Array Sequence Analysis, Nonlinear Dynamics, Stochastic Processes, Quality Control, Calibration, Molecular Probes, Sequence Analysis: DNA}, 
date-added = {2008-06-24 02:37:21 -0300},
date-modified = {2009-10-21 02:51:22 -0200},
pmid = {12538238},
URL = {http://bioinformatics.oxfordjournals.org/cgi/reprint/19/2/185},
local-url = {file://localhost/Users/bcarvalh/Documents/Academic/ScientificPapers/Papers/2003/Bolstad/Bioinformatics%202003%20Bolstad.pdf},
uri = {papers://344BCCBF-F0AF-44EA-862D-4E1AB7B7B64D/Paper/p282},
read = {Yes},
rating = {0}
}

@article{Consortium2003,
author = {TIH The International HapMap Consortium}, 
journal = {Nature},
title = {The International HapMap Project.},
abstract = {The goal of the International HapMap Project is to determine the common patterns of DNA sequence variation in the human genome and to make this information freely available in the public domain. An international consortium is developing a map of these patterns across the genome by determining the genotypes of one million or more sequence variants, their frequencies and the degree of association between them, in DNA samples from populations with ancestry from parts of Africa, Asia and Europe. The HapMap will allow the discovery of sequence variants that affect common disease, will facilitate development of diagnostic tools, and will enhance our ability to choose targets for therapeutic intervention.},
number = {6968},
pages = {789--796},
volume = {426},
year = {2003},
month = {Dec},
keywords = {human-dna-sequence-variation, europe, dna-samples, africa, community-engagement, individual-consent, international-hapmap-project, asia, public-consultation, human-genetic-variation}, 
date-added = {2008-06-23 13:27:04 -0300},
date-modified = {2009-01-14 18:37:24 -0200},
URL = {http://www.nature.com/nature/journal/v426/n6968/abs/nature02168.html},
local-url = {file://localhost/Users/bcarvalh/Documents/Academic/ScientificPapers/Papers/2003/The%20International%20HapMap%20Consortium/Nature%202003%20The%20International%20HapMap%20Consortium.pdf},
uri = {papers://344BCCBF-F0AF-44EA-862D-4E1AB7B7B64D/Paper/p231},
read = {Yes},
rating = {0}
}

@article{Gentleman2004,
author = {Robert C Gentleman and Vincent J Carey and Douglas M Bates and Benjamin M 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 K Smyth and Luke Tierney and Jean Y H Yang and Jianhua Zhang}, 
journal = {Genome Biol},
title = {Bioconductor: open software development for computational biology and bioinformatics.},
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.},
affiliation = {oston, MA 02115, USA. rgentlem@jimmy.harvard.edu},
number = {10},
pages = {R80},
volume = {5},
year = {2004},
keywords = {Computational Biology, instrumentation/methods; Internet; Reproducibility of Results; Software}, 
date-added = {2008-06-23 13:27:04 -0300},
date-modified = {2009-10-21 02:51:22 -0200},
doi = {10.1186/gb-2004-5-10-r80},
pii = {gb-2004-5-10-r80},
pmid = {15461798},
URL = {http://dx.doi.org/10.1186/gb-2004-5-10-r80},
local-url = {file://localhost/Users/bcarvalh/Documents/Academic/ScientificPapers/Papers/2004/Gentleman/Genome%20Biol%202004%20Gentleman.pdf},
uri = {papers://344BCCBF-F0AF-44EA-862D-4E1AB7B7B64D/Paper/p240},
read = {Yes},
rating = {0}
}

