@string{AS =    {Annals of Statistics}}
@string{BI =    {Bioinformatics}}
@string{CancRes={Cancer Research}}
@string{ClinCh ={Clinical Chemistry}}
@string{PSB =   {Pacific Symposium on Biocomputing}}
@string{EMBOR = {EMBO Reports}}
@string{FEBSL=  {FEBS Letters}}
@string{GB =    {Genome Biology}}
@string{GR =    {Genome Research}}
@string{IJC =   {International Journal of Cancer}}
@string{JC =    {Journal of Classification}}
@string{JCB =   {Journal of Computational Biology}}
@string{JMVA =  {Journal of Multivariate Analysis}}
@string{JSPI =  {Journal of Statistical Planning and Inference}}
@string{JASA=   {Journal of the American Statistical Association}}
@string{JCGS=   {Journal of Computational and Graphical Statistics}}
@string{JRSSB=	{Journal of the Royal Statistical Society, Series B}}
@string{MBC =   {Molecular Biology of the Cell}}
@string{MC =    {Molecular Cell}}
@string{ML =    {Machine Learning}}
@string{NAR =   {Nucleic Acids Res}}
@string{NCB =   {Nature Cell Biology}}
@string{NBT =   {Nature Biotechnology}}
@string{NG  =   {Nature Genetics}}
@string{PRL =   {Physical Review Letters}}
@string{PSB =   {Pacific Symposium on Biocomputing}}
@string{PNAS =  {Proc. Natl. Acad. Sci. of the U.S.A.}}
@string{SAGMB = {Statistical Applications in Genetics and Molecular Biology}}
@string{SM =    {Statistics in Medicine}}
@string{SN =    {Social Networks}}

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  volume =	 {40},
}

@incollection{Yang:speed:2003,
  author =	 {Yang, Y.\ H. and Speed, T.\ P.},
  year =	 {2003},
  title =	 {Design and analysis of comparative microarray experiments},
  booktitle =	 {Statistical Analysis of Gene Expression Microarray Data},
  editor =	 {T.\ P.\ Speed},
  publisher =	 {Chapman \& Hall/CRC Press},
	address = {Boca Raton},
  pages =	 {35--91}
}

@article{Yasui03,
  Author =	 {Y. Yasui and M. Pepe and M. L. Thompson and others},
  Title =	 {A data-analytic strategy for protein biomarker discovery:
                  profiling of high-dimensional proteomic data for cancer
                  detection },
  Year =	 2004,
  Journal =	 BI,
  Volume =	 20,
  Pages =	 {777--785},
}

@article{Ye03,
  Author =	 {B. Ye and D. W. Cramer and S. J. Skates and others},
  Title =	 {Haptoglobin-$\alpha$ subunit as potential serum biomarker in
                  ovarian cancer: identification and characterization using
                  proteomic profiling and mass spectrometry },
  Year =	 2003,
  Journal =	 {Clinical Cancer Research},
  Volume =	 9,
  Pages =	 {2904--2911},
}

@article{YeeIA,
  author =	 "Yee Hwa Yang and M. J. Buckley and Sandrine Dudoit and
                  others",
  title =	 "Comparison of methods for image analysis on {cDNA}
                  microarray data",
  journal =	 JCGS,
  year =	 2002,
  volume =	 11,
  pages =	 {108--136},
}

@Article{Zhan:mile:alda:2004,
  author =	 {Li Zhang and Michael F. Miles and Kenneth D. Aldape},
  title =	 {A model of molecular interactions on short oligonucleotide
                  mic roarrays: implications for probe design and data
                  analysis},
  journal =	 NBT,
  year =	 {2004},
  volume =	 {21},
  number =	 {7},
  pages =	 {818-821},
}

@Article{Zhang_etal03,
  author =	 "J. Zhang and V. Carey and R. Gentleman",
  title =	 {An extensible application for assembling annotation for
                  genomic data},
  journal =	 BI,
  volume =	 {19},
  number =	 {1},
  pages =	 {155-56},
  year =	 {2003}
}

@Article{Zhou_etal02,
  author =	 "X. Zhou and M. C. Kao and W. H. Wong",
  title =	 {Transitive functional annotation by shortest-path analysis
                  of gene expression data},
  journal =	 PNAS,
  volume =	 {99},
  pages =	 {12783-8},
  year =	 {2002}
}

@Article{ade4,
  author =	 {Daniel Chessel and Anne B. Dufour and Jean Thioulouse},
  title =	 {The ade4 Package --- {I}: One-table Methods},
  journal =	 {R News},
  year =	 2004,
  volume =	 4,
  number =	 1,
  pages =	 {5--10},
  url =		 {http://CRAN.R-project.org/doc/Rnews},
}

@Article{ape,
  author =	 {E. Paradis and J. Claude and K. Strimmer},
  title =	 {{APE}: Analyses of phylogenetics and evolution in {R}
                  language},
  journal =	 BI,
  year =	 {2004},
  volume =	 {20},
  pages =	 {289--290}
}

@book{compModelNetwork,
  title =	 {Computational Modeling of Genetic and Biochemical Networks},
  editor =	 {J. M. Bower and H. Bolouri},
  publisher =	 {MIT Press},
	address = {Cambridge},
  year =	 {2001}
}

@book{geneExpression,
  TITLE =	 {The Analysis of Gene Expression Data: Methods and Software},
  EDITOR =	 {G. Parmigiani and E. Garrett and R. Irizarry and others},
  PUBLISHER =	 {Springer, New York},
  YEAR =	 {2003},
}

@article{genomewide,
  author =	 {A. E. Carpenter and D. M. Sabatini},
  title =	 {Systematic Genome-wide screens of gene function},
  journal =	 {Nature Reviews in Genetics},
  volume =	 {5},
  year =	 {2004},
  pages =	 {11--22}
}

@article{ggobi,
  author =	 {D. F. Swayne and D. Temple Lang and A. Buja and others},
  title =	 {{GG}obi: Evolving from XGobi into an Extensible Framework
                  for Interactive Data Visualization},
  year =	 {2003},
  journal =	 {Computational Statistics and Data Analysis},
  volume =	 {43},
  pages =	 {423--444},
}

@article{go:2000,
  Author =	 {Consortium, Gene Ontology},
  Title =	 {Gene ontology: tool for the unification of biology},
  Year =	 2000,
  Journal =	 NG,
  Volume =	 25,
  Pages =	 {25-29}
}

@article{go:2001,
  author =	 {The gene ontology consortium},
  year =	 {2001},
  title =	 {Creating the gene ontology resource: design and
                  implementation},
  journal =	 GR,
  volume =	 {11},
  pages =	 {1425--1433}
}

@article{graphviz,
  title =	 {An open graph visualization system and its applications to
                  software engineering},
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}

@article {hierpam,
  AUTHOR =	 {van der Laan, M.J. and Pollard, K.S.},
  TITLE =	 {Hybrid clustering of gene expression data with visualization
                  and the bootstrap},
  JOURNAL =	 {Journal of Statistical Planning and Inference},
  VOLUME =	 {117},
  YEAR =	 {2003},
  PAGES =	 {275--303}
}

@article {image,
  AUTHOR =	 {G. Lennon and C. Auffray and M. Polymeropoulos and others},
  TITLE =	 {The {IMAGE} Consortium: an integrated molecular analysis of
                  genomes and their expression},
  JOURNAL =	 {Genomics},
  VOLUME =	 {33},
  YEAR =	 {1996},
  PAGES =	 {151--152}
}

@manual{mas5.0:2001,
  Key =		 "Affymetrix",
  TITLE =	 "Affymetrix Microarray Suite Users Guide",
  YEAR =	 "2001",
  EDITION =	 "Version 5.0",
  ORGANIZATION = "Affymetrix",
  ADDRESS =	 {Santa Clara, CA}
}

@article{metagenes,
  AUTHOR =	 {J.R. Nevins and E.S. Huang and H. Dressman and others},
  TITLE =	 {Towards integrated clinico-genomic models for personalized
                  medicine: combining gene expression signatures and clinical
                  factors in breast cancer outcomes prediction},
  JOURNAL =	 {Human Molecular Genetics},
  VOLUME =	 {12},
  YEAR =	 {2003},
  PAGES =	 {R153--R157}
}

@book{microarrayDE,
  author =	 {R. M. Simon and E. L. Korn and L. M. McShane and others},
  title =	 {Design and Analysis of {DNA} Microarray Investigations},
  publisher =	 {Springer},
  year =	 {2004}
}

@book{microarrayQC,
  author =	 {W. Zhang and I. Shmulevich and J. Astola},
  title =	 {Microarray Quality Control},
  publisher =	 {Wiley},
  year =	 {2004}
}

@article{mincut,
  author =	 {S. Klamt and E. D. Gilles},
  title =	 {Minimal cut sets in biochemical reaction networks},
  journal =	 {Bioinformatics},
  year =	 {2004},
  pages =	 {226--234},
  volume =	 {20}
}

@Article{nimblegen,
  author =	 {Sangeet Singh-Gasson and Roland D. Green and Yongian Yue and
                  others},
  title =	 {Maskless Fabrication of Light-Directed Oligonucleotide
                  Microarrays Using a Digital Micromirror Array},
  journal =	 NBT,
  year =	 {1999},
  volume =	 {17},
  pages =	 {974--978},
  annote =	 {Nimblegen}
}

@article{ppi:networks,
  author =	 {Gagneur, J. and Krause, R. and Bouwmeester, T. and others},
  title =	 {Modular decomposition of protein-protein interaction
                  networks},
  journal =	 GB,
  volume =	 {5},
  year =	 {2004},
  pages =	 {R57},
  url =		 {http://genomebiology.com/2004/5/8/R57}
}

@book{ripley:1996,
  author =	 {Ripley, B.D.},
  year =	 {1996},
  title =	 {Pattern Recognition and Neural Networks},
  publisher =	 {Cambridge University Press},
	address={Cambridge}
}

@Article{vdLaanetalMT2SAGMB04,
  author =	 {M. J. van der Laan and S. Dudoit and K. S. Pollard},
  title =	 {Multiple testing. {P}art {II}. {S}tep-down procedures for
                  control of the family-wise error rate},
  journal =	 SAGMB,
  year =	 {2004},
  volume =	 {3},
  number =	 {1},
  pages =	 {Article 14},
  url =		 {www.bepress.com/sagmb/vol3/iss1/art14}
}

@Article{vdLaanetalMT3SAGMB04,
  author =	 {M. J. van der Laan and S. Dudoit and K. S. Pollard},
  title =	 {Augmentation Procedures for Control of the Generalized
                  Family-Wise Error Rate and Tail Probabilities for the
                  Proportion of False Positives},
  journal =	 SAGMB,
  year =	 {2004},
  volume =	 {3},
  number =	 {1},
  pages =	 {Article 15},
  url =		 {www.bepress.com/sagmb/vol3/iss1/art15}
}

@Article{GentTL,
author = {R. Gentleman and D. {Temple Lang}},
title = {Statistical Analyses and Reproducible Research},
journal = JCGS,
year = {2005, to appear}
}

@InCollection{RGVCen,
  author =       {R. Gentleman and V. Carey},
  title =        {Bioconductor: software and development strategies for statistical genomics},
  booktitle =    {Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics},
  publisher =    {Wiley, New York},
  year =         {2005},
  editor =       {M.J. Dunne and L.B. Jorde and P.F.R. Little and S. Subramaniam},
  note =         {(To appear)}
}
@InCollection{AWRDiff,
  author =       {A. v. Heydebreck and W. Huber and R. Gentleman},
  title =        {Differential expression in the Bioconductor project},
  booktitle =    {Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics},
  publisher =    {Wiley, New York},
  year =         {2005},
  editor =       {M.J. Dunne and L.B. Jorde and P.F.R. Little and S. Subramaniam},
  note =         {(To appear)}
}

@article{Golub, 
 author = {R. Gentleman}, 
 title = {Reproducible Research: A Bioinformatics
    Case Study}, 
 journal = SAGMB,
 volume = {4},
  pages = {Article 2}, 
  year = {2005},
  url = {http://www.bepress.com/sagmb/vol4/iss1/art2}
}

@book{BioCBook,
 title = {Bioinformatics and Computational Biology Solutions Using R and Bioconductor},
 editor = {R. Gentleman and V. Carey and W. Huber and R. Irizarry and S. Dudoit},
 year = {2005},
 publisher = {Springer, New York}
}

@book{useR,
  title = {Bioconductor Case Studies},
  editor = {F. Hahne and W. Huber and R. Gentleman and S. Falcon},
  year = {2009},
  publisher = {Springer, New York}
}

@Article{mecham,
author ={ Mecham, B. H. and  Wetmore, D. Z. and Szallasi, Z and others},
title = {Increased measurement accuracy for
sequence-verified microarray probes},
journal = {Physiol Genomics},
volume =  18,   
pages = {308-15},
year = 2004 
}

@Article{altcdf,
title = { Alternative mapping of probes to genes for {\it Affymetrix} chips},
author = {Gautier, L and  Mooller, M and Friis-Hansen, L and others},
journal = {BMC Bioinformatics},
year = { 2004},
volume = {5},
page = 111
}

@Article{bagg,
title = {Signal in noise: evaluating reported reproducibility of
proteomic tests for ovarian cancer},
author = {Baggerly, K and Morris, J and Edmondson, S and others},
journal ={Journal of the NCI},
year = 2005,
volume = 97,
pages={315--319}
}


%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Stegle:2008p1260,
author = {O Stegle and A Kannan and R Durbin and J Winn}, 
journal = {Lecture Notes in Computer Science},
title = {Accounting for non-genetic factors improves the power of eqtl studies},
abstract = {Abstract. The recent availability of large scale data sets profiling sin- gle nucleotide polymorphisms (SNPs) and gene expression across differ- ent human populations, has directed much attention towards discovering patterns of },
pages = {411--422},
volume = {4955},
year = {2008},
month = {Jan},
date-added = {2009-11-20 08:16:02 -0500},
date-modified = {2010-03-07 05:47:18 -0500},
doi = {10.1007/978-3-540-78839-3},
pmid = {16984794572437161067related:a4QZoawctusJ},
URL = {http://www.springerlink.com/index/6851m28355141009.pdf},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2008/Stegle/Lecture%20Notes%20in%20Computer%20Science%202008%20Stegle.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p1260},
read = {Yes},
rating = {0}
}


%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Leek:2007p1723,
author = {Jeffrey T Leek and John D Storey}, 
journal = {PLoS Genet},
title = {Capturing heterogeneity in gene expression studies by surrogate variable analysis},
abstract = {It has unambiguously been shown that genetic, environmental, demographic, and technical factors may have substantial effects on gene expression levels. In addition to the measured variable(s) of interest, there will tend to be sources of signal due to factors that are unknown, unmeasured, or too complicated to capture through simple models. We show that failing to incorporate these sources of heterogeneity into an analysis can have widespread and detrimental effects on the study. Not only can this reduce power or induce unwanted dependence across genes, but it can also introduce sources of spurious signal to many genes. This phenomenon is true even for well-designed, randomized studies. We introduce "surrogate variable analysis" (SVA) to overcome the problems caused by heterogeneity in expression studies. SVA can be applied in conjunction with standard analysis techniques to accurately capture the relationship between expression and any modeled variables of interest. We apply SVA to disease class, time course, and genetics of gene expression studies. We show that SVA increases the biological accuracy and reproducibility of analyses in genome-wide expression studies.},
affiliation = {Department of Biostatistics, University of Washington, Seattle, Washington, USA.},
number = {9},
pages = {1724--35},
volume = {3},
year = {2007},
month = {Sep},
language = {eng},
keywords = {Humans, Algorithms, Genetic Heterogeneity, Epigenesis: Genetic, Data Interpretation: Statistical, Female, Quantitative Trait: Heritable, Genes: BRCA1, Genome: Fungal, Genes: BRCA2, Kidney, Linkage (Genetics), Breast Neoplasms, Linear Models, Genome: Human, Saccharomyces cerevisiae, Reproducibility of Results, Gene Expression, Mutation, Computer Simulation, Oligonucleotide Array Sequence Analysis, Time Factors}, 
date-added = {2010-05-27 12:10:21 -0400},
date-modified = {2010-05-27 12:10:39 -0400},
doi = {10.1371/journal.pgen.0030161},
pii = {07-PLGE-RA-0237},
pmid = {17907809},
URL = {http://www.plosgenetics.org/article/info%253Adoi%252F10.1371%252Fjournal.pgen.0030161},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2007/Leek/PLoS%20Genet%202007%20Leek.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p1723},
read = {Yes},
rating = {0}
}


%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Pepke:2009p1246,
author = {Shirley Pepke and Barbara Wold and Ali Mortazavi}, 
journal = {Nat Meth},
title = {Computation for ChIP-seq and RNA-seq studies},
abstract = {Genome-wide measurements of protein-DNA interactions and transcriptomes are increasingly done by deep DNA sequencing methods (ChIP-seq and RNA-seq). The power and richness of these counting-based measurements comes at the cost of routinely handling tens to hundreds of millions of reads. Whereas early adopters necessarily developed their own custom computer code to analyze the first ChIP-seq and RNA-seq datasets, a new generation of more sophisticated algorithms and software tools are emerging to assist in the analysis phase of these projects. Here we describe the multilayered analyses of ChIP-seq and RNA-seq datasets, discuss the software packages currently available to perform tasks at each layer and describe some upcoming challenges and features for future analysis tools. We also discuss how software choices and uses are affected by specific aspects of the underlying biology and data structure, including genome size, positional clustering of transcription factor binding sites, transcript discovery and expression quantification.},
affiliation = {Center for Advanced Computing Research, California Institute of Technology, Pasadena, California, USA.},
number = {11 Suppl},
pages = {S22--32},
volume = {6},
year = {2009},
month = {Nov},
language = {eng},
keywords = {RNA, Base Sequence, Sequence Alignment, Sequence Analysis: DNA, Gene Expression Profiling, Chromatin Immunoprecipitation, Algorithms}, 
date-added = {2009-11-18 10:14:05 -0500},
date-modified = {2009-11-18 10:14:05 -0500},
doi = {10.1038/nmeth.1371},
pii = {nmeth.1371},
pmid = {19844228},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2009/Pepke/Nat%20Meth%202009%20Pepke.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p1246},
read = {Yes},
rating = {0}
}


%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Cope:2004p1873,
author = {Leslie M Cope and Rafael A Irizarry and Harris A Jaffee and Zhijin Wu and Terence P Speed}, 
journal = {Bioinformatics (Oxford, England)},
title = {A benchmark for Affymetrix GeneChip expression measures},
abstract = {MOTIVATION: The defining feature of oligonucleotide expression arrays is the use of several probes to assay each targeted transcript. This is a bonanza for the statistical geneticist, who can create probeset summaries with specific characteristics. There are now several methods available for summarizing probe level data from the popular Affymetrix GeneChips, but it is difficult to identify the best method for a given inquiry. RESULTS: We have developed a graphical tool to evaluate summaries of Affymetrix probe level data. Plots and summary statistics offer a picture of how an expression measure performs in several important areas. This picture facilitates the comparison of competing expression measures and the selection of methods suitable for a specific investigation. The key is a benchmark data set consisting of a dilution study and a spike-in study. Because the truth is known for these data, we can identify statistical features of the data for which the expected outcome is known in advance. Those features highlighted in our suite of graphs are justified by questions of biological interest and motivated by the presence of appropriate data.},
affiliation = {Department of Mathematical Sciences, Johns Hopkins University, 104 Whitehead Hall, 3400 North Charles Street, Baltimore, MD 21218, USA.},
number = {3},
pages = {323--31},
volume = {20},
year = {2004},
month = {Feb},
language = {eng},
keywords = {Algorithms, Sequence Analysis: DNA, Oligonucleotide Array Sequence Analysis, Computer Graphics, Benchmarking, Reproducibility of Results, Reference Standards, Software, Gene Expression Profiling, United States, Information Storage and Retrieval, Sensitivity and Specificity, User-Computer Interface}, 
date-added = {2010-09-29 22:38:09 -0400},
date-modified = {2010-09-29 22:39:16 -0400},
doi = {10.1093/bioinformatics/btg410},
pii = {20/3/323},
pmid = {14960458},
URL = {http://bioinformatics.oxfordjournals.org/content/20/3/323.long},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2004/Cope/Bioinformatics%20(Oxford%20England)%202004%20Cope.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p1873},
rating = {0}
}

% 14960456 
@Article{pmid14960456,
   Author="Gautier, L.  and Cope, L.  and Bolstad, B. M.  and Irizarry, R. A. ",
   Title="{affy--analysis of {A}ffymetrix {G}ene{C}hip data at the probe level}",
   Journal="Bioinformatics",
   Year="2004",
   Volume="20",
   Pages="307--315",
   Month="Feb"
}

%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Li:2008p1783,
author = {Bingshan Li and Suzanne M Leal}, 
journal = {Am J Hum Genet},
title = {Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data},
abstract = {Although whole-genome association studies using tagSNPs are a powerful approach for detecting common variants, they are underpowered for detecting associations with rare variants. Recent studies have demonstrated that common diseases can be due to functional variants with a wide spectrum of allele frequencies, ranging from rare to common. An effective way to identify rare variants is through direct sequencing. The development of cost-effective sequencing technologies enables association studies to use sequence data from candidate genes and, in the future, from the entire genome. Although methods used for analysis of common variants are applicable to sequence data, their performance might not be optimal. In this study, it is shown that the collapsing method, which involves collapsing genotypes across variants and applying a univariate test, is powerful for analyzing rare variants, whereas multivariate analysis is robust against inclusion of noncausal variants. Both methods are superior to analyzing each variant individually with univariate tests. In order to unify the advantages of both collapsing and multiple-marker tests, we developed the Combined Multivariate and Collapsing (CMC) method and demonstrated that the CMC method is both powerful and robust. The CMC method can be applied to either candidate-gene or whole-genome sequence data.},
affiliation = {Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.},
number = {3},
pages = {311--21},
volume = {83},
year = {2008},
month = {Sep},
language = {eng},
keywords = {Genome: Human, Genetic Variation, Models: Genetic, Polymorphism: Single Nucleotide, Linkage Disequilibrium, Genetic Markers, Genetic Predisposition to Disease, Haplotypes, Humans}, 
date-added = {2010-09-20 12:27:14 -0400},
date-modified = {2010-09-20 12:27:14 -0400},
doi = {10.1016/j.ajhg.2008.06.024},
pii = {S0002-9297(08)00408-4},
pmid = {18691683},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2008/Li/Am%20J%20Hum%20Genet%202008%20Li.PDF},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p1783},
read = {Yes},
rating = {0}
}

%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Bodmer:2008p1009,
author = {Walter Bodmer and Carolina Bonilla}, 
journal = {Nat Genet},
title = {Common and rare variants in multifactorial susceptibility to common diseases},
abstract = {Here, we give a historical overview of the search for genetic variants that influence the susceptibility of an individual to a chronic disease, from RA Fisher's seminal work to the current excitement of whole-genome association studies (WGAS). We then discuss the concepts behind the identification of common variants as disease causal factors and contrast them to the basic ideas that underlie the rare variant hypothesis. The identification of rare variants involves the careful selection of candidate genes to examine, the availability of highly efficient resequencing techniques and the appropriate assessment of the functional consequences of the implicated variant. We believe that this strategy can be successfully applied at present in order to unravel the contribution of rare variants to the multifactorial inheritance of common diseases, which could lead to the implementation of much needed preventative screening schemes.},
affiliation = {Cancer and Immunogenetics Laboratory, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DS, UK. walter.bodmer@hertford.ox.ac.uk},
number = {6},
pages = {695--701},
volume = {40},
year = {2008},
month = {Jun},
language = {eng},
keywords = {Multifactorial Inheritance, Disease Susceptibility, Quantitative Trait: Heritable, Linkage (Genetics), Humans, Genetic Variation}, 
date-added = {2009-09-10 14:35:51 -0400},
date-modified = {2010-09-21 12:00:13 -0400},
doi = {10.1038/ng.f.136},
pii = {ng.f.136},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2008/Bodmer/Nat%20Genet%202008%20Bodmer.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p1009},
read = {Yes},
rating = {2}
}

%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Clayton:2007p437,
author = {David Clayton and Hin-Tak Leung}, 
journal = {Hum Hered},
title = {An {R} package for analysis of whole-genome association studies},
abstract = {OBJECTIVE: To provide data classes and methods to facilitate the analysis of whole genome association studies in the R language for statistical computing. METHODS: We have implemented data classes in which each genotype call is stored as a single byte. At this density, data for single chromosomes derived from large studies and new high-throughput gene chip platforms can be handled in memory. We use the object-oriented programming model introduced with version 4 of the S-plus package, usually termed 'S4 methods'. RESULTS: At the current state of development the package only supports population-based studies, although we would hope to provide support for family-based studies soon. Both quantitative and qualitative phenotypes may be analysed. Flexible association testing functions are provided which can carry out single SNP tests which control for potential confounding by quantitative and qualitative covariates. Tests involving several SNPs taken together as 'tags' are also supported. Efficient calculation of pair-wise linkage disequilibrium measures is implemented and data input functions include a function which can download data directly from the international HapMap project website.},
affiliation = {Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK. david.clayton@cimr.cam.ac.uk},
number = {1},
pages = {45--51},
volume = {64},
year = {2007},
month = {Jan},
language = {eng},
keywords = {Linkage Disequilibrium, Phenotype, Models: Genetic, Databases: Genetic, Genome: Human, Polymorphism: Single Nucleotide, Software, Genotype, Humans}, 
date-added = {2009-07-02 10:50:50 -0400},
date-modified = {2009-07-22 16:12:35 -0400},
doi = {10.1159/000101422},
pii = {000101422},
pmid = {17483596},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2007/Clayton/Hum%20Hered%202007%20Clayton-1.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p437},
read = {Yes},
rating = {0}
}


%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Price:2010p1781,
author = {Alkes L Price and Gregory V Kryukov and Paul I W de Bakker and Shaun M Purcell and Jeff Staples and Lee-Jen Wei and Shamil R Sunyaev}, 
journal = {Am J Hum Genet},
title = {Pooled association tests for rare variants in exon-resequencing studies},
abstract = {Deep sequencing will soon generate comprehensive sequence information in large disease samples. Although the power to detect association with an individual rare variant is limited, pooling variants by gene or pathway into a composite test provides an alternative strategy for identifying susceptibility genes. We describe a statistical method for detecting association of multiple rare variants in protein-coding genes with a quantitative or dichotomous trait. The approach is based on the regression of phenotypic values on individuals' genotype scores subject to a variable allele-frequency threshold, incorporating computational predictions of the functional effects of missense variants. Statistical significance is assessed by permutation testing with variable thresholds. We used a rigorous population-genetics simulation framework to evaluate the power of the method, and we applied the method to empirical sequencing data from three disease studies.},
affiliation = {Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.},
number = {6},
pages = {832--8},
volume = {86},
year = {2010},
month = {Jun},
language = {eng},
keywords = {Models: Statistical, Models: Genetic, Humans, Genetic Variation, Gene Frequency, Genome-Wide Association Study, Genetics: Population, Exons}, 
date-added = {2010-09-03 16:12:53 -0400},
date-modified = {2010-09-03 16:12:53 -0400},
doi = {10.1016/j.ajhg.2010.04.005},
pii = {S0002-9297(10)00207-7},
pmid = {20471002},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2010/Price/Am%20J%20Hum%20Genet%202010%20Price.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p1781},
read = {Yes},
rating = {0}
}


@article{katz2006saa,
  title={{A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database}},
  author={Katz, S. and Irizarry, R.A. and Lin, X. and Tripputi, M. and Porter, M.W.},
  journal={BMC Bioinformatics},
  volume={7},
  number={1},
  pages={464},
  year={2006},
  publisher={BioMed Central}
}

@article{mccall2008consolidated,
  title={{Consolidated strategy for the analysis of microarray spike-in data}},
  author={McCall, M.N. and Irizarry, R.A.},
  journal={Nucleic Acids Research},
  volume={36},
  number={17},
  pages={e108},
  year={2008},
  publisher={Oxford Univ Press}
}


% ben bolstad's dissertation
@phdthesis{bolstad2004lla,
  title={{Low-level analysis of high-density oligonucleotide array data: background, normalization and summarization}},
  author={Bolstad, B.M.},
  year={2004},
  school={University of California}
}

@article{irizarry2003ena,
  title={{Exploration, normalization, and summaries of high density oligonucleotide array probe level data.}},
  author={Irizarry, R.A. and Hobbs, B. and Collin, F. and Beazer-Barclay, Y.D. and Antonellis, K.J. and Scherf, UWE and Speed, T.P.},
  journal={Biostatistics},
  volume={4},
  number={2},
  pages={249},
  year={2003}
}

% gcRMA
@article{wu2004mbb,
  title={{A model-based background adjustment for oligonucleotide expression arrays}},
  author={Wu, Z. and Irizarry, R.A. and Gentleman, R. and Martinez-Murillo, F. and Spencer, F.},
  journal={Journal of the American Statistical Association},
  volume={99},
  number={468},
  pages={909--917},
  year={2004},
  publisher={ASA}
}

@article{affymetrix2005gpl,
  title={{Guide to probe logarithmic intensity error (PLIER) estimation}},
  author={Affymetrix, I.},
  journal={Technical Note},
  year={2005}
}

@article{irizarry2005multiple,
  title={{Multiple-laboratory comparison of microarray platforms}},
  author={Irizarry, R.A. and Warren, D. and Spencer, F. and Kim, I.F. and Biswal, S. and Frank, B.C. and Gabrielson, E. and Garcia, J.G.N. and Geoghegan, J. and Germino, G. and others},
  journal={Nature methods},
  volume={2},
  number={5},
  pages={345--350},
  year={2005},
  publisher={Nature Publishing Group}
}
@article{li2001mba,
  title={{Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection}},
  author={Li, C. and Wong, W.H.},
  journal={Proceedings of the National Academy of Sciences},
  volume={98},
  number={1},
  pages={31},
  year={2001},
  publisher={National Acad Sciences}
}

@article{zilliox2007gene,
  title={{A gene expression bar code for microarray data}},
  author={Zilliox, M.J. and Irizarry, R.A.},
  journal={Nature methods},
  volume={4},
  number={11},
  pages={911--913},
  year={2007},
  publisher={Nature Publishing Group}
}
@article{potti2006genomic,
  title={{Genomic signatures to guide the use of chemotherapeutics}},
  author={Potti, A. and Dressman, H.K. and Bild, A. and Riedel, R.F. and Chan, G. and Sayer, R. and Cragun, J. and Cottrill, H. and Kelley, M.J. and Petersen, R. and others},
  journal={Nature medicine},
  volume={12},
  number={11},
  pages={1294--1300},
  year={2006},
  publisher={Nature Publishing Group}
}
@article{langmead2009ultrafast,
  title={{Ultrafast and memory-efficient alignment of short DNA sequences to the human genome}},
  author={Langmead, B. and Trapnell, C. and Pop, M. and Salzberg, S.L.},
  journal={Genome Biol},
  volume={10},
  number={3},
  pages={R25},
  year={2009}
}
@article{langmead2010cloud,
  title={{Cloud-scale RNA-sequencing differential expression analysis with Myrna}},
  author={Langmead, B. and Hansen, K.D. and Leek, J.T.},
  journal={Genome biology},
  volume={11},
  number={8},
  pages={R83},
  year={2010},
  publisher={BioMed Central Ltd}
}

@article{scharpf2010multilevel,
  title={{A multilevel model to address batch effects in copy number estimation using snp arrays}},
  author={Scharpf, R.B. and Ruczinski, I. and Carvalho, B. and Doan, B. and Chakravarti, A. and Irizarry, R.A.},
  journal={Submitted Revised Manuscript},
  year={2010}
}

@article{irizarry2009model,
  title={{Model-Based Quality Assessment and Base-Calling for Second-Generation Sequencing Data}},
  author={Irizarry, R.A. and Bravo, H.C.},
  journal={Johns Hopkins University, Dept. of Biostatistics Working Papers},
  pages={184},
  year={2009},
  publisher={bepress}
}

@article{wu2010intensity,
  title={{Intensity normalization improves color calling in SOLiD sequencing}},
  author={Wu, H. and Irizarry, R.A. and Bravo, H.C.},
  journal={Nature Methods},
  volume={7},
  number={5},
  pages={336--337},
  year={2010},
  publisher={Nature Publishing Group}
}
@article{ghosh2003statistical,
  title={{Statistical issues and methods for meta-analysis of microarray data: a case study in prostate cancer}},
  author={Ghosh, D. and Barette, T.R. and Rhodes, D. and Chinnaiyan, A.M.},
  journal={Functional \& integrative genomics},
  volume={3},
  number={4},
  pages={180--188},
  issn={1438-793X},
  year={2003},
  publisher={Springer}
}
@article{miclaus2010variability,
  title={{Variability in GWAS analysis: the impact of genotype calling algorithm inconsistencies}},
  author={Miclaus, K. and Chierici, M. and Lambert, C. and Zhang, L. and Vega, S. and Hong, H. and Yin, S. and Furlanello, C. and Wolfinger, R. and Goodsaid, F.},
  journal={The Pharmacogenomics Journal},
  volume={10},
  number={4},
  pages={324--335},
  year={2010},
  publisher={Nature Publishing Group}
}
@article{irizarry2008comprehensive,
  title={{Comprehensive high-throughput arrays for relative methylation (CHARM)}},
  author={Irizarry, R.A. and Ladd-Acosta, C. and Carvalho, B. and Wu, H. and Brandenburg, S.A. and Jeddeloh, J.A. and Wen, B. and Feinberg, A.P.},
  journal={Genome research},
  volume={18},
  number={5},
  pages={780},
  year={2008},
  publisher={Cold Spring Harbor Lab}
}

@article{zhang2010assessment,
  title={{Assessment of variability in GWAS with CRLMM genotyping algorithm on WTCCC coronary artery disease}},
  author={Zhang, L. and Yin, S. and Miclaus, K. and Chierici, M. and Vega, S. and Lambert, C. and Hong, H. and Wolfinger, RD and Furlanello, C. and Goodsaid, F.},
  journal={The Pharmacogenomics Journal},
  volume={10},
  number={4},
  pages={347--354},
  year={2010},
  publisher={Nature Publishing Group}
}
@article{baggerly2009deriving,
  title={{Deriving chemosensitivity from cell lines: Forensic bioinformatics and reproducible research in high-throughput biology}},
  author={Baggerly, K.A. and Coombes, K.R.},
  journal={Annals},
  volume={3},
  number={4},
  pages={1309--1334},
  year={2009}
}
@article{coombes2007microarrays,
  title={{Microarrays: retracing steps}},
  author={Coombes, K.R. and Wang, J. and Baggerly, K.A.},
  journal={Nature medicine},
  volume={13},
  number={11},
  pages={1276--1277},
  year={2007},
  publisher={Nature Publishing Group}
}


%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Sean:2007p1963,
author = {Sean Davis and Paul S Meltzer}, 
journal = {Bioinformatics (Oxford, England)},
title = {GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor},
abstract = {Microarray technology has become a standard molecular biology tool. Experimental data have been generated on a huge number of organisms, tissue types, treatment conditions and disease states. The Gene Expression Omnibus (Barrett et al., 2005), developed by the National Center for Bioinformatics (NCBI) at the National Institutes of Health is a repository of nearly 140,000 gene expression experiments. The BioConductor project (Gentleman et al., 2004) is an open-source and open-development software project built in the R statistical programming environment (R Development core Team, 2005) for the analysis and comprehension of genomic data. The tools contained in the BioConductor project represent many state-of-the-art methods for the analysis of microarray and genomics data. We have developed a software tool that allows access to the wealth of information within GEO directly from BioConductor, eliminating many the formatting and parsing problems that have made such analyses labor-intensive in the past. The software, called GEOquery, effectively establishes a bridge between GEO and BioConductor. Easy access to GEO data from BioConductor will likely lead to new analyses of GEO data using novel and rigorous statistical and bioinformatic tools. Facilitating analyses and meta-analyses of microarray data will increase the efficiency with which biologically important conclusions can be drawn from published genomic data. Availability: GEOquery is available as part of the BioConductor project.},
affiliation = {Genetics Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. sdavis2@mail.nih.gov},
number = {14},
pages = {1846--7},
volume = {23},
year = {2007},
month = {Jul},
language = {eng},
keywords = {Cluster Analysis, Computers, Oligonucleotide Array Sequence Analysis, Software, Databases: Genetic, Gene Expression Profiling, Algorithms, Computational Biology, Data Interpretation: Statistical, Programming Languages, Information Storage and Retrieval, Databases: Factual}, 
date-added = {2010-10-05 11:37:05 -0400},
date-modified = {2010-10-05 11:37:29 -0400},
doi = {10.1093/bioinformatics/btm254},
pii = {btm254},
pmid = {17496320},
URL = {http://bioinformatics.oxfordjournals.org/content/23/14/1846.long},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2007/Sean/Bioinformatics%20(Oxford%20England)%202007%20Sean.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p1963},
rating = {0}
}


%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Zhu:2008p1948,
author = {Yuelin Zhu and Sean Davis and Robert Stephens and Paul S Meltzer and Yidong Chen}, 
journal = {Bioinformatics (Oxford, England)},
title = {GEOmetadb: powerful alternative search engine for the Gene Expression Omnibus},
abstract = {The NCBI Gene Expression Omnibus (GEO) represents the largest public repository of microarray data. However, finding data in GEO can be challenging. We have developed GEOmetadb in an attempt to make querying the GEO metadata both easier and more powerful. All GEO metadata records as well as the relationships between them are parsed and stored in a local MySQL database. A powerful, flexible web search interface with several convenient utilities provides query capabilities not available via NCBI tools. In addition, a Bioconductor package, GEOmetadb that utilizes a SQLite export of the entire GEOmetadb database is also available, rendering the entire GEO database accessible with full power of SQL-based queries from within R. AVAILABILITY: The web interface and SQLite databases available at http://gbnci.abcc.ncifcrf.gov/geo/. The Bioconductor package is available via the Bioconductor project. The corresponding MATLAB implementation is also available at the same website.},
affiliation = {Genetics Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.},
number = {23},
pages = {2798--800},
volume = {24},
year = {2008},
month = {Dec},
language = {eng},
keywords = {Gene Expression Profiling, Gene Expression, Databases: Genetic, Software, Information Storage and Retrieval, Oligonucleotide Array Sequence Analysis, User-Computer Interface}, 
date-added = {2010-10-05 11:36:17 -0400},
date-modified = {2010-10-05 11:36:17 -0400},
doi = {10.1093/bioinformatics/btn520},
pii = {btn520},
pmid = {18842599},
URL = {http://bioinformatics.oxfordjournals.org/content/24/23/2798.long},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p1948},
rating = {0}
}


%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Stegle:2010p2015,
author = {Oliver Stegle and Leopold Parts and Richard Durbin and John Winn}, 
journal = {PLoS Comput Biol},
title = {A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies},
abstract = {Gene expression measurements are influenced by a wide range of factors, such as the state of the cell, experimental conditions and variants in the sequence of regulatory regions. To understand the effect of a variable of interest, such as the genotype of a locus, it is important to account for variation that is due to confounding causes. Here, we present VBQTL, a probabilistic approach for mapping expression quantitative trait loci (eQTLs) that jointly models contributions from genotype as well as known and hidden confounding factors. VBQTL is implemented within an efficient and flexible inference framework, making it fast and tractable on large-scale problems. We compare the performance of VBQTL with alternative methods for dealing with confounding variability on eQTL mapping datasets from simulations, yeast, mouse, and human. Employing Bayesian complexity control and joint modelling is shown to result in more precise estimates of the contribution of different confounding factors resulting in additional associations to measured transcript levels compared to alternative approaches. We present a threefold larger collection of cis eQTLs than previously found in a whole-genome eQTL scan of an outbred human population. Altogether, 27% of the tested probes show a significant genetic association in cis, and we validate that the additional eQTLs are likely to be real by replicating them in different sets of individuals. Our method is the next step in the analysis of high-dimensional phenotype data, and its application has revealed insights into genetic regulation of gene expression by demonstrating more abundant cis-acting eQTLs in human than previously shown. Our software is freely available online at http://www.sanger.ac.uk/resources/software/peer/.},
affiliation = {Max Planck Institutes T{\"u}bingen, T{\"u}bingen, Germany. oliver.stegle@tuebingen.mpg.de},
number = {5},
pages = {e1000770},
volume = {6},
year = {2010},
month = {May},
language = {eng},
keywords = {Markov Chains, Reproducibility of Results, Software, Databases: Genetic, Gene Expression, Humans, Models: Statistical, Phenotype, Quantitative Trait Loci, Mice, Animals, Models: Genetic, Bayes Theorem, Yeasts, Internet}, 
date-added = {2010-11-26 12:54:54 -0500},
date-modified = {2010-11-26 12:55:01 -0500},
doi = {10.1371/journal.pcbi.1000770},
pmid = {20463871},
URL = {http://www.ploscompbiol.org/article/info%253Adoi%252F10.1371%252Fjournal.pcbi.1000770},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2010/Stegle/PLoS%20Comput%20Biol%202010%20Stegle.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p2015},
read = {Yes},
rating = {0}
}


%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{CareyGG2,
author = {Vincent J Carey and Adam R Davis and Michael F Lawrence and Robert Gentleman and Benjamin A Raby}, 
journal = {Bioinformatics},
title = {Data structures and algorithms for analysis of genetics of gene expression with Bioconductor: GGtools 3.x},
abstract = {SUMMARY: Associations between DNA polymorphisms and mRNA abundance are a natural target of genetic investigations, and microarrays facilitate genome-wide and transcriptome-wide surveys of these associations. This work is motivated by emerging requirements for data architectures and algorithm interfaces to allow flexible exploration of public and private archives of genotyping and expression arrays. Using R/Bioconductor facilities, Phase II HapMap genotypes and Illumina 47K expression assay results archived on multiple populations may be interactively explored and analyzed using commodity hardware. AVAILABILITY AND IMPLEMENTATION: Open Source. Bioconductor 2.3 packages GGtools, GGBase, GGdata, hmyriB36. Freely available on the web at (http://www.bioconductor.org).},
affiliation = {Channing Laboratory, Department of Medicine, I2B2 National Center for Biocomputing, Brigham and Women's Hospital, Boston, MA 02115, USA. stvjc@channing.harvard.edu},
number = {11},
pages = {1447--8},
volume = {25},
year = {2009},
month = {Jun},
language = {eng},
date-added = {2009-07-02 06:26:07 -0400},
date-modified = {2009-07-22 16:12:35 -0400},
doi = {10.1093/bioinformatics/btp169},
pii = {btp169},
pmid = {19349284},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p429},
rating = {0}
}

@article{CareyGG1,
author = {Vincent J Carey and Martin Morgan and Seth Falcon and Ross Lazarus and Robert Gentleman}, 
journal = {Bioinformatics},
title = {GGtools: analysis of genetics of gene expression in bioconductor},
abstract = {This paper reviews the central concepts and implementation of data structures and methods for studying genetics of gene expression with the GGtools package of Bioconductor. Illustration with a HapMap+expression dataset is provided.},
affiliation = {Brigham {\&} Womens Hosp, Channing Lab, Boston, MA 02115 USA},
number = {4},
pages = {522--523},
volume = {23},
year = {2007},
month = {Jan},
language = {English},
keywords = {Genomics}, 
date-added = {2010-11-29 21:45:06 -0500},
date-modified = {2010-11-29 21:45:09 -0500},
doi = {10.1093/bioinformatics/btl628},
pmid = {000244259200027},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p2046},
rating = {0}
}


%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Carey:2009p426,
author = {V J Carey and R Gentleman}, 
journal = {Pacific Symposium on Biocomputing Pacific Symposium on Biocomputing},
title = {Interpreting genetics of gene expression: integrative architecture in Bioconductor},
abstract = {Several influential studies of genotypic determinants of gene expression in humans have now been published based on various populations including HapMap cohorts. The magnitude of the analytic task (transcriptome vs. SNP-genome) is a hindrance to dissemination of efficient, thorough, and auditable inference methods for this project. We describe the structure and use of Bioconductor facilities for inference in genetics of gene expression, with simultaneous application to multiple HapMap cohorts. Tools distributed for this purpose are readily adapted for the structure and analysis of privately-generated data in expression genetics.},
affiliation = {Channing Laboratory, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Ave., Boston, MA 02115, USA. stvjc@channing.harvard.edu},
pages = {380--90},
year = {2009},
month = {Jan},
language = {eng},
keywords = {Humans, Polymorphism: Single Nucleotide, Genetics: Population, Urotensins, Biometry, Carrier Proteins, Software, Forkhead Transcription Factors, Databases: Genetic, Regulatory Elements: Transcriptional, HLA-DR Antigens, Cohort Studies, Gene Expression Profiling, Quantitative Trait Loci}, 
date-added = {2009-07-02 06:26:07 -0400},
date-modified = {2009-07-22 16:12:35 -0400},
pmid = {19209716},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p426},
rating = {0}
}


%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Montgomery:2010p1661,
author = {Stephen B Montgomery and Micha Sammeth and Maria Gutierrez-Arcelus and Radoslaw P Lach and Catherine Ingle and James Nisbett and Roderic Guigo and Emmanouil T Dermitzakis}, 
journal = {Nature},
title = {Transcriptome genetics using second generation sequencing in a Caucasian population},
abstract = {Gene expression is an important phenotype that informs about genetic and environmental effects on cellular state. Many studies have previously identified genetic variants for gene expression phenotypes using custom and commercially available microarrays. Second generation sequencing technologies are now providing unprecedented access to the fine structure of the transcriptome. We have sequenced the mRNA fraction of the transcriptome in 60 extended HapMap individuals of European descent and have combined these data with genetic variants from the HapMap3 project. We have quantified exon abundance based on read depth and have also developed methods to quantify whole transcript abundance. We have found that approximately 10 million reads of sequencing can provide access to the same dynamic range as arrays with better quantification of alternative and highly abundant transcripts. Correlation with SNPs (small nucleotide polymorphisms) leads to a larger discovery of eQTLs (expression quantitative trait loci) than with arrays. We also detect a substantial number of variants that influence the structure of mature transcripts indicating variants responsible for alternative splicing. Finally, measures of allele-specific expression allowed the identification of rare eQTLs and allelic differences in transcript structure. This analysis shows that high throughput sequencing technologies reveal new properties of genetic effects on the transcriptome and allow the exploration of genetic effects in cellular processes.},
affiliation = {Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211 Switzerland. stephen.montgomery@unige.ch},
number = {7289},
pages = {773--7},
volume = {464},
year = {2010},
month = {Apr},
language = {eng},
date-added = {2010-05-05 10:00:12 -0400},
date-modified = {2010-07-08 22:35:22 -0400},
doi = {10.1038/nature08903},
pii = {nature08903},
pmid = {20220756},
URL = {http://www.nature.com/nature/journal/v464/n7289/full/nature08903.html},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2010/Montgomery/Nature%202010%20Montgomery.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p1661},
read = {Yes},
rating = {0}
}


%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Li:2009p1165,
author = {Yun Li and Cristen Willer and Serena Sanna and Gon{\c c}alo Abecasis}, 
journal = {Annual review of genomics and human genetics},
title = {Genotype imputation},
abstract = {Genotype imputation is now an essential tool in the analysis of genome-wide association scans. This technique allows geneticists to accurately evaluate the evidence for association at genetic markers that are not directly genotyped. Genotype imputation is particularly useful for combining results across studies that rely on different genotyping platforms but also increases the power of individual scans. Here, we review the history and theoretical underpinnings of the technique. To illustrate performance of the approach, we summarize results from several gene mapping studies. Finally, we preview the role of genotype imputation in an era when whole genome resequencing is becoming increasingly common.},
affiliation = {Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109-2029, USA. ylwtx@umich.edu},
pages = {387--406},
volume = {10},
year = {2009},
month = {Jan},
language = {eng},
date-added = {2009-09-23 14:13:15 -0400},
date-modified = {2009-09-23 14:13:15 -0400},
doi = {10.1146/annurev.genom.9.081307.164242},
pmid = {19715440},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2009/Li/Annual%20review%20of%20genomics%20and%20human%20genetics%202009%20Li.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p1165},
read = {Yes},
rating = {0}
}


%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Williams:2007p21,
author = {R. B.H Williams and E. K.F Chan and M. J Cowley and P. F.R Little}, 
journal = {Genome Research},
title = {The influence of genetic variation on gene expression},
number = {12},
pages = {1707--1716},
volume = {17},
year = {2007},
month = {Dec},
date-added = {2009-06-01 12:30:54 -0400},
date-modified = {2009-07-22 16:12:35 -0400},
doi = {10.1101/gr.6981507},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2007/Williams/Genome%20Research%202007%20Williams.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p21},
read = {Yes},
rating = {0}
}


%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Ronald:2005p2597,
author = {James Ronald and Rachel B Brem and Jacqueline Whittle and Leonid Kruglyak}, 
journal = {PLoS Genet},
title = {Local regulatory variation in Saccharomyces cerevisiae},
abstract = {Naturally occurring sequence variation that affects gene expression is an important source of phenotypic differences among individuals within a species. We and others have previously shown that such regulatory variation can occur both at the same locus as the gene whose expression it affects (local regulatory variation) and elsewhere in the genome at trans-acting factors. Here we present a detailed analysis of genome-wide local regulatory variation in Saccharomyces cerevisiae. We used genetic linkage analysis to show that nearly a quarter of all yeast genes contain local regulatory variation between two divergent strains. We measured allele-specific expression in a diploid hybrid of the two strains for 77 genes showing strong self-linkage and found that in 52%-78% of these genes, local regulatory variation acts directly in cis. We also experimentally confirmed one example in which local regulatory variation in the gene AMN1 acts in trans through a feedback loop. Genome-wide sequence analysis revealed that genes subject to local regulatory variation show increased polymorphism in the promoter regions, and that some but not all of this increase is due to polymorphisms in predicted transcription factor binding sites. Increased polymorphism was also found in the 3' untranslated regions of these genes. These findings point to the importance of cis-acting variation, but also suggest that there is a diverse set of mechanisms through which local variation can affect gene expression levels.},
affiliation = {Department of Genome Sciences, University of Washington, Seattle, Washington, USA.},
number = {2},
pages = {e25},
volume = {1},
year = {2005},
month = {Aug},
language = {eng},
keywords = {Regulatory Elements: Transcriptional, Cell Cycle Proteins, Genetic Linkage, 3' Untranslated Regions, Promoter Regions: Genetic, Saccharomyces cerevisiae Proteins, Polymorphism: Genetic, Saccharomyces cerevisiae, Genome: Fungal, Gene Expression Regulation: Fungal}, 
date-added = {2011-03-04 11:57:26 -0500},
date-modified = {2011-03-04 11:57:34 -0500},
doi = {10.1371/journal.pgen.0010025},
pmid = {16121257},
URL = {http://www.plosgenetics.org/article/info%253Adoi%252F10.1371%252Fjournal.pgen.0010025},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2005/Ronald/PLoS%20Genet%202005%20Ronald.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p2597},
rating = {0}
}


%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Lee:2010p2570,
author = {Eunjee Lee and Harmen J Bussemaker}, 
journal = {Mol Syst Biol},
title = {Identifying the genetic determinants of transcription factor activity},
abstract = {Analysis of parallel genotyping and expression profiling data has shown that mRNA expression levels are highly heritable. Currently, only a tiny fraction of this genetic variance can be mechanistically accounted for. The influence of trans-acting polymorphisms on gene expression traits is often mediated by transcription factors (TFs). We present a method that exploits prior knowledge about the in vitro DNA-binding specificity of a TF in order to map the loci ('aQTLs') whose inheritance modulates its protein-level regulatory activity. Genome-wide regression of differential mRNA expression on predicted promoter affinity is used to estimate segregant-specific TF activity, which is subsequently mapped as a quantitative phenotype. In budding yeast, our method identifies six times as many locus-TF associations and more than twice as many trans-acting loci as all existing methods combined. Application to mouse data from an F2 intercross identified an aQTL on chromosome VII modulating the activity of Zscan4 in liver cells. Our method has greatly improved statistical power over existing methods, is mechanism based, strictly causal, computationally efficient, and generally applicable.},
affiliation = {Department of Biological Sciences, Columbia University, New York, NY 10027, USA.},
pages = {412},
volume = {6},
year = {2010},
month = {Sep},
language = {eng},
keywords = {Animals, Liver, Regression Analysis, Humans, Saccharomycetales, Transcription Factors, Chromosomes, Mice: Inbred C57BL, Quantitative Trait Loci, Mice: Inbred DBA, Alleles, Genome, Mice, Crosses: Genetic, Models: Genetic}, 
date-added = {2011-03-04 11:55:28 -0500},
date-modified = {2011-03-04 11:55:28 -0500},
doi = {10.1038/msb.2010.64},
pii = {msb201064},
pmid = {20865005},
URL = {http://www.nature.com/msb/journal/v6/n1/full/msb201064.html},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2010/Lee/Mol%20Syst%20Biol%202010%20Lee.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p2570},
rating = {0}
}


%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Suchard:2010p2600,
author = {M Suchard and Q Wang and C Chan and J Frelinger{\ldots}}, 
journal = {Journal of Computational {\ldots}},
title = {Understanding GPU programming for statistical computation: Studies in massively parallel massive mixtures},
abstract = {This article describes advances in statistical computation for large-scale data analy- sis in structured Bayesian mixture models via graphics processing unit (GPU) pro- gramming. The developments are partly motivated by computational challenges arising in fitting models of increasing ...},
year = {2010},
month = {Jan},
date-added = {2011-03-04 13:31:42 -0500},
date-modified = {2011-03-04 13:31:42 -0500},
pmid = {6342425456743321642related:KpCUa1nSBFgJ},
URL = {http://pubs.amstat.org/doi/abs/10.1198/jcgs.2010.10016},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2010/Suchard/Journal%20of%20Computational%20%E2%80%A6%202010%20Suchard.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p2600},
rating = {0}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%
%%%%%
%%%%%  Added by Rafa, there might be duplicates!
%%%%%
%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

@article{li2008mapping,
  title={{Mapping short DNA sequencing reads and calling variants using mapping quality scores}},
  author={Li, H. and Ruan, J. and Durbin, R.},
  journal={Genome research},
  volume={18},
  number={11},
  pages={1851},
  issn={1088-9051},
  year={2008},
  publisher={Cold Spring Harbor Lab}
}
@article{li2009soap2,
  title={{SOAP2: an improved ultrafast tool for short read alignment}},
  author={Li, R. and Yu, C. and Li, Y. and Lam, T.W. and Yiu, S.M. and Kristiansen, K. and Wang, J.},
  journal={Bioinformatics},
  volume={25},
  number={15},
  pages={1966},
  issn={1367-4803},
  year={2009},
  publisher={Oxford Univ Press}
}
@article{li2010fast,
  title={{Fast and accurate long-read alignment with Burrows-Wheeler transform}},
  author={Li, H. and Durbin, R.},
  journal={Bioinformatics},
  volume={26},
  number={5},
  pages={589},
  issn={1367-4803},
  year={2010},
  publisher={Oxford Univ Press}
}
@article{mortazavi2008mapping,
  title={{Mapping and quantifying mammalian transcriptomes by RNA-Seq}},
  author={Mortazavi, A. and Williams, B.A. and McCue, K. and Schaeffer, L. and Wold, B.},
  journal={Nature methods},
  volume={5},
  number={7},
  pages={621--628},
  year={2008},
  publisher={Nature Publishing Group}
}
@article{parmigiani2002statistical,
  title={{A statistical framework for expression-based molecular classification in cancer}},
  author={Parmigiani, G. and Garrett, E.S. and Anbazhagan, R. and Gabrielson, E.},
  journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  volume={64},
  number={4},
  pages={717--736},
  issn={1467-9868},
  year={2002},
  publisher={Wiley Online Library}
}
@article{workman:2002,
  title={{A new non-linear normalization method for reducing variability in DNA microarray experiments}},
  author={Workman, C. and Jensen, L.J. and Jarmer, H.{\~A}. and Berka, R. and Gautier, L. and Nielsen, H.B.{\~A}. and Saxild, H.H. and Nielsen, C. and Brunak, S.{\~A}. and Knudsen, S.},
  journal={Genome Biology},
  volume={3},
  number={9},
  pages={1--0048},
  issn={1465-6906},
  year={2002},
  publisher={unknown}
}

@Article{astr:2003,
  author = 	 {Magnus {\AA}strand},
  title = 	 {Contrast Normalization of Oligonucleotide Arrays},
  journal = 	 {Journal of Computational Biology},
  year = 	 {2003},
  volume =	 {10},
  number =	 {1},
  pages =	 {95--102},
  annote =	 {Affymetrix arrays. Normalization.}
}



@article{edgar2002geo,
  title={{Gene Expression Omnibus: NCBI gene expression and hybridization array data repository}},
  author={Edgar, R. and Domrachev, M. and Lash, A.E.},
  journal={Nucleic Acids Research},
  volume={30},
  number={1},
  pages={207},
  year={2002},
  publisher={Oxford Univ Press}
}
@article{shah2009ontology,
  title={{Ontology-driven indexing of public datasets for translational bioinformatics}},
  author={Shah, N. and Jonquet, C. and Chiang, A. and Butte, A. and Chen, R. and Musen, M.},
  journal={BMC bioinformatics},
  volume={10},
  number={Suppl 2},
  pages={S1},
  issn={1471-2105},
  year={2009},
  publisher={BioMed Central Ltd}
}
@article{taub2010overcoming,
  title={{Overcoming bias and systematic errors in next generation sequencing data}},
  author={Taub, M.A. and Bravo, H.C. and Irizarry, R.A.},
  journal={Genome Medicine},
  volume={2},
  number={12},
  pages={1--5},
  year={2010},
  publisher={Springer}
}

@article{mccall2011gene,
  title={{The Gene Expression Barcode: leveraging public data repositories to begin cataloging the human and murine transcriptomes}},
  author={McCall, M.N. and Uppal, K. and Jaffee, H.A. and Zilliox, M.J. and Irizarry, R.A.},
  journal={Nucleic Acids Research},
  volume={39},
  number={suppl 1},
  pages={D1011},
  issn={0305-1048},
  year={2011},
  publisher={Oxford Univ Press}
}

@article{moreau2003comparison,
  title={{Comparison and meta-analysis of microarray data: from the bench to the computer desk}},
  author={Moreau, Y. and Aerts, S. and Moor, B.D. and Strooper, B.D. and Dabrowski, M.},
  journal={TRENDS in Genetics},
  volume={19},
  number={10},
  pages={570--577},
  issn={0168-9525},
  year={2003},
  publisher={Elsevier}
}
@article{dudley2009disease,
  title={{Disease signatures are robust across tissues and experiments}},
  author={Dudley, J.T. and Tibshirani, R. and Deshpande, T. and Butte, A.J.},
  journal={Molecular systems biology},
  volume={5},
  number={1},
  year={2009},
  publisher={Nature Publishing Group}
}
@article{mccall2008consolidated,
  title={{Consolidated strategy for the analysis of microarray spike-in data}},
  author={McCall, M.N. and Irizarry, R.A.},
  journal={Nucleic Acids Research},
  volume={36},
  number={17},
  pages={e108},
  year={2008},
  publisher={Oxford Univ Press}
}

@article{parkinson2008aua,
  title={{ArrayExpress update--from an archive of functional genomics experiments to the atlas of gene expression}},
  author={Parkinson, H. and Kapushesky, M. and Kolesnikov, N. and Rustici, G. and Shojatalab, M. and Abeygunawardena, N. and Berube, H. and Dylag, M. and Emam, I. and Farne, A. and others},
  journal={Nucleic Acids Research},
  year={2008},
  publisher={Oxford Univ Press}
}

@ARTICLE{Pradervand2009,
  author = {Sylvain Pradervand and Johann Weber and Jérôme Thomas and Manuel
	Bueno and Pratyaksha Wirapati and Karine Lefort and G. Paolo Dotto
	and Keith Harshman},
  title = {Impact of normalization on miRNA microarray expression profiling.},
  journal = {RNA},
  year = {2009},
  volume = {15},
  pages = {493--501},
  number = {3},
  month = {Mar},
  __markedentry = {[rscharpf]},
  abstract = {Profiling miRNA levels in cells with miRNA microarrays is becoming
	a widely used technique. Although normalization methods for mRNA
	gene expression arrays are well established, miRNA array normalization
	has so far not been investigated in detail. In this study we investigate
	the impact of normalization on data generated with the Agilent miRNA
	array platform. We have developed a method to select nonchanging
	miRNAs (invariants) and use them to compute linear regression normalization
	coefficients or variance stabilizing normalization (VSN) parameters.
	We compared the invariants normalization to normalization by scaling,
	quantile, and VSN with default parameters as well as to no normalization
	using samples with strong differential expression of miRNAs (heart-brain
	comparison) and samples where only a few miRNAs are affected (by
	p53 overexpression in squamous carcinoma cells versus control). All
	normalization methods performed better than no normalization. Normalization
	procedures based on the set of invariants and quantile were the most
	robust over all experimental conditions tested. Our method of invariant
	selection and normalization is not limited to Agilent miRNA arrays
	and can be applied to other data sets including those from one color
	miRNA microarray platforms, focused gene expression arrays, and gene
	expression analysis using quantitative PCR.},
  doi = {10.1261/rna.1295509},
  institution = {Lausanne DNA Array Facility, Center for Integrative Genomics, University
	of Lausanne, CH-1015 Lausanne, Switzerland. Sylvain.Pradervand@unil.ch},
  keywords = {Gene Expression Profiling, methods; Humans; Linear Models; MicroRNAs;
	Microarray Analysis, methods; Sensitivity and Specificity},
  language = {eng},
  medline-pst = {ppublish},
  owner = {rscharpf},
  pii = {rna.1295509},
  pmid = {19176604},
  timestamp = {2011.02.22},
}

@ARTICLE{Johnson2006,
  author = {W. Evan Johnson and Wei Li and Clifford A Meyer and Raphael Gottardo
	and Jason S Carroll and Myles Brown and X. Shirley Liu},
  title = {Model-based analysis of tiling-arrays for ChIP-chip.},
  journal = {Proc Natl Acad Sci U S A},
  year = {2006},
  volume = {103},
  pages = {12457--12462},
  number = {33},
  month = {Aug},
  __markedentry = {[rscharpf]},
  abstract = {We propose a fast and powerful analysis algorithm, titled Model-based
	Analysis of Tiling-arrays (MAT), to reliably detect regions enriched
	by transcription factor chromatin immunoprecipitation (ChIP) on Affymetrix
	tiling arrays (ChIP-chip). MAT models the baseline probe behavior
	by considering probe sequence and copy number on each array. It standardizes
	the probe value through the probe model, eliminating the need for
	sample normalization. MAT uses an innovative function to score regions
	for ChIP enrichment, which allows robust P value and false discovery
	rate calculations. MAT can detect ChIP regions from a single ChIP
	sample, multiple ChIP samples, or multiple ChIP samples with controls
	with increasing accuracy. The single-array ChIP region detection
	feature minimizes the time and monetary costs for laboratories newly
	adopting ChIP-chip to test their protocols and antibodies and allows
	established ChIP-chip laboratories to identify samples with questionable
	quality that might contaminate their data. MAT is developed in open-source
	Python and is available at http://chip.dfci.harvard.edu/ approximately
	wli/MAT. The general framework presented here can be extended to
	other oligonucleotide microarrays and tiling array platforms.},
  doi = {10.1073/pnas.0601180103},
  institution = {Department of Biostatistics and Computational Biology, Dana-Farber
	Cancer Institute, 44 Binney Street, Boston, MA 02115, USA.},
  keywords = {Algorithms; Humans; Internet; Models, Theoretical; Oligonucleotide
	Array Sequence Analysis, instrumentation/methods; Receptors, Estrogen,
	genetics/metabolism; Software; Transcription Factors, genetics/metabolism},
  language = {eng},
  medline-pst = {ppublish},
  owner = {rscharpf},
  pii = {0601180103},
  pmid = {16895995},
  timestamp = {2011.02.17},

}


% ben bolstad's dissertation
@phdthesis{bolstad2004lla,
  title={{Low-level analysis of high-density oligonucleotide array data: background, normalization and summarization}},
  author={Bolstad, B.M.},
  year={2004},
  school={University of California}
}
@article{bolstad2003cnm,
  title={{A comparison of normalization methods for high density oligonucleotide array data based on variance and bias}},
  author={Bolstad, BM and Irizarry, RA and Astrand, M. and Speed, TP},
  journal={Bioinformatics},
  volume={19},
  number={2},
  pages={185--193},
  year={2003}
}


% gcRMA
@article{wu2004mbb,
  title={{A model-based background adjustment for oligonucleotide expression arrays}},
  author={Wu, Z. and Irizarry, R.A. and Gentleman, R. and Martinez-Murillo, F. and Spencer, F.},
  journal={Journal of the American Statistical Association},
  volume={99},
  number={468},
  pages={909--917},
  year={2004},
  publisher={ASA}
}

@article{affymetrix2005gpl,
  title={{Guide to probe logarithmic intensity error (PLIER) estimation}},
  author={Affymetrix, I.},
  journal={Technical Note},
  year={2005}
}


@article{li2001mba,
  title={{Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection}},
  author={Li, C. and Wong, W.H.},
  journal={Proceedings of the National Academy of Sciences},
  volume={98},
  number={1},
  pages={31},
  year={2001},
  publisher={National Acad Sciences}
}


@ARTICLE{Scharpf2011,
  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},
  journal = {Biostatistics},
  year = {2011},
volume =	 {12},
number = {1},
    pages = {33-50},
  month = {Jan},
  doi = {10.1093/biostatistics/kxq043},
  issue = {1},
  pmcid = {PMC3006124},
  vol = {12}
}


@ARTICLE{Lopez-Romero2010,
  author = {Pedro Lopez-Romero and Manuel A González and Sergio Callejas and
	Ana Dopazo and Rafael A Irizarry},
  title = {Processing of Agilent microRNA array data.},
  journal = {BMC Res Notes},
  year = {2010},
  volume = {3},
  pages = {18},
  __markedentry = {[rscharpf]},
  abstract = {ABSTRACT:The Agilent microRNA microarray platform interrogates each
	microRNA with several copies of distinct oligonucleotide probes and
	integrates the results into a total gene signal (TGS), using a proprietary
	algorithm that makes use of the background subtracted signal. The
	TGS can be normalized between arrays, and the Agilent recommendation
	is either not to normalize or to normalize to the 75th percentile
	signal intensity. The robust multiarray average algorithm (RMA) is
	an alternative method, originally developed to obtain a summary measure
	of mRNA Affymetrix gene expression arrays by using a linear model
	that takes into account the probe affinity effect. The RMA method
	has been shown to improve the accuracy and precision of expression
	measurements relative to other competing methods. There is also evidence
	that it might be preferable to use non-corrected signals for the
	processing of microRNA data, rather than background-corrected signals.
	In this study we assess the use of the RMA method to obtain a summarized
	microRNA signal for the Agilent arrays.We have adapted the RMA method
	to obtain a processed signal for the Agilent arrays and have compared
	the RMA summarized signal to the TGS generated with the image analysis
	software provided by the vendor. We also compared the use of the
	RMA algorithm with uncorrected and background-corrected signals,
	and compared quantile normalization with the normalization method
	recommended by the vendor. The pre-processing methods were compared
	in terms of their ability to reduce the variability (increase precision)
	of the signals between biological replicates. Application of the
	RMA method to non-background corrected signals produced more precise
	signals than either the RMA-background-corrected signal or the quantile-normalized
	Agilent TGS. The Agilent TGS normalized to the 75\% percentile showed
	more variation than the other measures.Used without background correction,
	a summarized signal that takes into account the probe effect might
	provide a more precise estimate of microRNA expression. The variability
	of quantile normalization was lower compared with the normalization
	method recommended by the vendor.},
  doi = {10.1186/1756-0500-3-18},
  institution = {Department of Cardiovascular Epidemiology and Population Genetics,
	Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC),
	Madrid, Spain. plromero1@gmail.com.},
  language = {eng},
  medline-pst = {epublish},
  owner = {rscharpf},
  pii = {1756-0500-3-18},
  pmid = {20205787},
  timestamp = {2011.02.22},
}

@article{carvalho2007exploration,
  title={{Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data}},
  author={Carvalho, B. and Bengtsson, H. and Speed, T.P. and Irizarry, R.A.},
  journal={Biostatistics},
  volume={8},
  number={2},
  pages={485},
  year={2007},
  publisher={Biometrika Trust}
}

@article{mccall2008csa,
  title={{Consolidated strategy for the analysis of microarray spike-in data}},
  author={McCall, M.N. and Irizarry, R.A.},
  journal={Nucleic Acids Research},
  volume={36},
  number={17},
  pages={e108},
  year={2008},
  publisher={Oxford Univ Press}
}
@article{leek2010tackling,
  title={{Tackling the widespread and critical impact of batch effects in high-throughput data}},
  author={Leek, J.T. and Scharpf, R.B. and Bravo, H.C. and Simcha, D. and Langmead, B. and Johnson, W.E. and Geman, D. and Baggerly, K. and Irizarry, R.A.},
  journal={Nature Reviews Genetics},
  volume={11},
  number={10},
  pages={733--739},
  issn={1471-0056},
  year={2010},
  publisher={Nature Publishing Group}
}
@article{mccall2010frozen,
  title={{Frozen robust multiarray analysis (fRMA)}},
  author={McCall, M.N. and Bolstad, B.M. and Irizarry, R.A.},
  journal={Biostatistics},
  issn={1465-4644},
  year={2010},
  publisher={Biometrika Trust}
}
@article{baggerly2009deriving,
  title={{Deriving chemosensitivity from cell lines: Forensic bioinformatics and reproducible research in high-throughput biology}},
  author={Baggerly, K.A. and Coombes, K.R.},
  journal={Annals},
  volume={3},
  number={4},
  pages={1309--1334},
  year={2009}
}
%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/


%% Created for Kasper Daniel Hansen at 2011-03-04 13:26:36 -0500 


%% Saved with string encoding Unicode (UTF-8) 



@article{Robinson:2010nx,
	Abstract = {The fine detail provided by sequencing-based transcriptome surveys suggests that RNA-seq is likely to become the platform of choice for interrogating steady state RNA. In order to discover biologically important changes in expression, we show that normalization continues to be an essential step in the analysis. We outline a simple and effective method for performing normalization and show dramatically improved results for inferring differential expression in simulated and publicly available data sets.},
	Author = {Robinson, Mark D and Oshlack, Alicia},
	Date-Added = {2011-03-04 13:19:18 -0500},
	Date-Modified = {2011-03-04 13:19:18 -0500},
	Doi = {10.1186/gb-2010-11-3-r25},
	Journal = {Genome Biol},
	Journal-Full = {Genome biology},
	Mesh = {Base Sequence; Computer Simulation; Gene Expression Profiling; Gene Library; Models, Statistical; RNA},
	Number = {3},
	Pages = {R25},
	Pmc = {PMC2864565},
	Pmid = {20196867},
	Pst = {ppublish},
	Title = {A scaling normalization method for differential expression analysis of RNA-seq data},
	Volume = {11},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.doi.org/10.1186/gb-2010-11-3-r25}}

@article{Bullard:2010cr,
	Abstract = {High-throughput sequencing technologies, such as the Illumina Genome Analyzer, are powerful new tools for investigating a wide range of biological and medical questions. Statistical and computational methods are key for drawing meaningful and accurate conclusions from the massive and complex datasets generated by the sequencers. We provide a detailed evaluation of statistical methods for normalization and differential expression (DE) analysis of Illumina transcriptome sequencing (mRNA-Seq) data.},
	Author = {Bullard, James H and Purdom, Elizabeth and Hansen, Kasper D and Dudoit, Sandrine},
	Date-Added = {2011-03-04 13:19:09 -0500},
	Date-Modified = {2011-03-04 13:19:09 -0500},
	Doi = {10.1186/1471-2105-11-94},
	Journal = {BMC Bioinformatics},
	Journal-Full = {BMC bioinformatics},
	Mesh = {Computational Biology; Databases, Genetic; RNA, Messenger; Sequence Analysis, RNA},
	Pages = {94},
	Pmc = {PMC2838869},
	Pmid = {20167110},
	Pst = {epublish},
	Title = {Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments},
	Volume = {11},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.doi.org/10.1186/1471-2105-11-94}}

@article{Young:2010dq,
	Abstract = {We present GOseq, an application for performing Gene Ontology (GO) analysis on RNA-seq data. GO analysis is widely used to reduce complexity and highlight biological processes in genome-wide expression studies, but standard methods give biased results on RNA-seq data due to over-detection of differential expression for long and highly expressed transcripts. Application of GOseq to a prostate cancer data set shows that GOseq dramatically changes the results, highlighting categories more consistent with the known biology.},
	Author = {Young, Matthew D and Wakefield, Matthew J and Smyth, Gordon K and Oshlack, Alicia},
	Date-Added = {2011-03-04 13:18:20 -0500},
	Date-Modified = {2011-03-04 13:18:20 -0500},
	Doi = {10.1186/gb-2010-11-2-r14},
	Journal = {Genome Biol},
	Journal-Full = {Genome biology},
	Mesh = {Androgens; Bias (Epidemiology); Cell Line, Tumor; Gene Expression Regulation, Neoplastic; Genome-Wide Association Study; Humans; Male; Prostatic Neoplasms; Sequence Analysis, RNA},
	Number = {2},
	Pages = {R14},
	Pmc = {PMC2872874},
	Pmid = {20132535},
	Pst = {ppublish},
	Title = {Gene ontology analysis for RNA-seq: accounting for selection bias},
	Volume = {11},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.doi.org/10.1186/gb-2010-11-2-r14}}

@article{Oshlack:2009bh,
	Abstract = {Several recent studies have demonstrated the effectiveness of deep sequencing for transcriptome analysis (RNA-seq) in mammals. As RNA-seq becomes more affordable, whole genome transcriptional profiling is likely to become the platform of choice for species with good genomic sequences. As yet, a rigorous analysis methodology has not been developed and we are still in the stages of exploring the features of the data.},
	Author = {Oshlack, Alicia and Wakefield, Matthew J},
	Date-Added = {2011-03-04 13:18:04 -0500},
	Date-Modified = {2011-03-04 13:18:04 -0500},
	Doi = {10.1186/1745-6150-4-14},
	Journal = {Biol Direct},
	Journal-Full = {Biology direct},
	Mesh = {Base Sequence; Bias (Epidemiology); Gene Expression Profiling; Humans; Oligonucleotide Array Sequence Analysis; RNA, Messenger; Systems Biology; Transcription, Genetic},
	Pages = {14},
	Pmc = {PMC2678084},
	Pmid = {19371405},
	Pst = {epublish},
	Title = {Transcript length bias in RNA-seq data confounds systems biology},
	Volume = {4},
	Year = {2009},
	Bdsk-Url-1 = {http://dx.doi.org/10.1186/1745-6150-4-14}}

@article{Li:2010qf,
	Abstract = {MOTIVATION: RNA-Seq is a promising new technology for accurately measuring gene expression levels. Expression estimation with RNA-Seq requires the mapping of relatively short sequencing reads to a reference genome or transcript set. Because reads are generally shorter than transcripts from which they are derived, a single read may map to multiple genes and isoforms, complicating expression analyses. Previous computational methods either discard reads that map to multiple locations or allocate them to genes heuristically. RESULTS: We present a generative statistical model and associated inference methods that handle read mapping uncertainty in a principled manner. Through simulations parameterized by real RNA-Seq data, we show that our method is more accurate than previous methods. Our improved accuracy is the result of handling read mapping uncertainty with a statistical model and the estimation of gene expression levels as the sum of isoform expression levels. Unlike previous methods, our method is capable of modeling non-uniform read distributions. Simulations with our method indicate that a read length of 20-25 bases is optimal for gene-level expression estimation from mouse and maize RNA-Seq data when sequencing throughput is fixed.},
	Author = {Li, Bo and Ruotti, Victor and Stewart, Ron M and Thomson, James A and Dewey, Colin N},
	Date-Added = {2011-03-04 13:17:38 -0500},
	Date-Modified = {2011-03-04 13:17:38 -0500},
	Doi = {10.1093/bioinformatics/btp692},
	Journal = {Bioinformatics},
	Journal-Full = {Bioinformatics (Oxford, England)},
	Mesh = {Algorithms; Animals; Base Sequence; Computational Biology; Databases, Genetic; Gene Expression; Gene Expression Profiling; Genome; Mice; Sequence Analysis, RNA; Software; Zea mays},
	Month = {Feb},
	Number = {4},
	Pages = {493-500},
	Pmc = {PMC2820677},
	Pmid = {20022975},
	Pst = {ppublish},
	Title = {RNA-Seq gene expression estimation with read mapping uncertainty},
	Volume = {26},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/bioinformatics/btp692}}

@article{Mortazavi:2008ve,
	Abstract = {We have mapped and quantified mouse transcriptomes by deeply sequencing them and recording how frequently each gene is represented in the sequence sample (RNA-Seq). This provides a digital measure of the presence and prevalence of transcripts from known and previously unknown genes. We report reference measurements composed of 41-52 million mapped 25-base-pair reads for poly(A)-selected RNA from adult mouse brain, liver and skeletal muscle tissues. We used RNA standards to quantify transcript prevalence and to test the linear range of transcript detection, which spanned five orders of magnitude. Although >90\% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3' untranscribed regions, as well as new candidate microRNA precursors. RNA splice events, which are not readily measured by standard gene expression microarray or serial analysis of gene expression methods, were detected directly by mapping splice-crossing sequence reads. We observed 1.45 x 10(5) distinct splices, and alternative splices were prominent, with 3,500 different genes expressing one or more alternate internal splices.},
	Author = {Mortazavi, Ali and Williams, Brian A and McCue, Kenneth and Schaeffer, Lorian and Wold, Barbara},
	Date-Added = {2011-03-04 13:17:25 -0500},
	Date-Modified = {2011-03-04 13:17:25 -0500},
	Doi = {10.1038/nmeth.1226},
	Journal = {Nat Methods},
	Journal-Full = {Nature methods},
	Mesh = {3' Untranslated Regions; Algorithms; Alternative Splicing; Animals; Brain; Chromosome Mapping; Databases, Nucleic Acid; Exons; Gene Expression Profiling; Liver; Mice; Mice, Inbred C57BL; Muscle, Skeletal; Oligonucleotide Array Sequence Analysis; Promoter Regions, Genetic; RNA; RNA Splicing; RNA, Messenger; Sensitivity and Specificity; Sequence Analysis, RNA; Software},
	Month = {Jul},
	Number = {7},
	Pages = {621-8},
	Pmid = {18516045},
	Pst = {ppublish},
	Title = {Mapping and quantifying mammalian transcriptomes by RNA-Seq},
	Volume = {5},
	Year = {2008},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/nmeth.1226}}

@article{Pickrell:2010ly,
	Abstract = {Understanding the genetic mechanisms underlying natural variation in gene expression is a central goal of both medical and evolutionary genetics, and studies of expression quantitative trait loci (eQTLs) have become an important tool for achieving this goal. Although all eQTL studies so far have assayed messenger RNA levels using expression microarrays, recent advances in RNA sequencing enable the analysis of transcript variation at unprecedented resolution. We sequenced RNA from 69 lymphoblastoid cell lines derived from unrelated Nigerian individuals that have been extensively genotyped by the International HapMap Project. By pooling data from all individuals, we generated a map of the transcriptional landscape of these cells, identifying extensive use of unannotated untranslated regions and more than 100 new putative protein-coding exons. Using the genotypes from the HapMap project, we identified more than a thousand genes at which genetic variation influences overall expression levels or splicing. We demonstrate that eQTLs near genes generally act by a mechanism involving allele-specific expression, and that variation that influences the inclusion of an exon is enriched within and near the consensus splice sites. Our results illustrate the power of high-throughput sequencing for the joint analysis of variation in transcription, splicing and allele-specific expression across individuals.},
	Author = {Pickrell, Joseph K and Marioni, John C and Pai, Athma A and Degner, Jacob F and Engelhardt, Barbara E and Nkadori, Everlyne and Veyrieras, Jean-Baptiste and Stephens, Matthew and Gilad, Yoav and Pritchard, Jonathan K},
	Date-Added = {2011-03-04 13:16:58 -0500},
	Date-Modified = {2011-03-04 13:16:58 -0500},
	Doi = {10.1038/nature08872},
	Journal = {Nature},
	Journal-Full = {Nature},
	Mesh = {African Continental Ancestry Group; Alleles; Consensus Sequence; DNA, Complementary; Exons; Gene Expression Profiling; Gene Expression Regulation; Genetic Variation; Humans; Nigeria; Polymorphism, Single Nucleotide; Quantitative Trait Loci; RNA Splice Sites; RNA, Messenger; Sequence Analysis, RNA; Transcription, Genetic},
	Month = {Apr},
	Number = {7289},
	Pages = {768-72},
	Pmid = {20220758},
	Pst = {ppublish},
	Title = {Understanding mechanisms underlying human gene expression variation with RNA sequencing},
	Volume = {464},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/nature08872}}

@article{Trapnell:2010zr,
	Abstract = {High-throughput mRNA sequencing (RNA-Seq) promises simultaneous transcript discovery and abundance estimation. However, this would require algorithms that are not restricted by prior gene annotations and that account for alternative transcription and splicing. Here we introduce such algorithms in an open-source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed >430 million paired 75-bp RNA-Seq reads from a mouse myoblast cell line over a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62\% of which are supported by independent expression data or by homologous genes in other species. Over the time series, 330 genes showed complete switches in the dominant transcription start site (TSS) or splice isoform, and we observed more subtle shifts in 1,304 other genes. These results suggest that Cufflinks can illuminate the substantial regulatory flexibility and complexity in even this well-studied model of muscle development and that it can improve transcriptome-based genome annotation.},
	Author = {Trapnell, Cole and Williams, Brian A and Pertea, Geo and Mortazavi, Ali and Kwan, Gordon and van Baren, Marijke J and Salzberg, Steven L and Wold, Barbara J and Pachter, Lior},
	Date-Added = {2011-03-04 13:15:35 -0500},
	Date-Modified = {2011-03-04 13:15:35 -0500},
	Doi = {10.1038/nbt.1621},
	Journal = {Nat Biotechnol},
	Journal-Full = {Nature biotechnology},
	Mesh = {Algorithms; Animals; Cell Differentiation; Cell Line; Gene Expression Profiling; Genome; Mice; Oligonucleotide Array Sequence Analysis; Protein Isoforms; Proto-Oncogene Proteins c-myc; RNA, Messenger; Sequence Analysis, RNA; Software},
	Month = {May},
	Number = {5},
	Pages = {511-5},
	Pmid = {20436464},
	Pst = {ppublish},
	Title = {Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation},
	Volume = {28},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/nbt.1621}}

@article{Trapnell:2009ys,
	Abstract = {A new protocol for sequencing the messenger RNA in a cell, known as RNA-Seq, generates millions of short sequence fragments in a single run. These fragments, or 'reads', can be used to measure levels of gene expression and to identify novel splice variants of genes. However, current software for aligning RNA-Seq data to a genome relies on known splice junctions and cannot identify novel ones. TopHat is an efficient read-mapping algorithm designed to align reads from an RNA-Seq experiment to a reference genome without relying on known splice sites.},
	Author = {Trapnell, Cole and Pachter, Lior and Salzberg, Steven L},
	Date-Added = {2011-03-04 13:15:28 -0500},
	Date-Modified = {2011-03-04 13:15:28 -0500},
	Doi = {10.1093/bioinformatics/btp120},
	Journal = {Bioinformatics},
	Journal-Full = {Bioinformatics (Oxford, England)},
	Mesh = {Algorithms; Gene Expression Profiling; Models, Genetic; RNA Splicing; RNA, Messenger; Sequence Alignment; Sequence Analysis, RNA; Software},
	Month = {May},
	Number = {9},
	Pages = {1105-11},
	Pmc = {PMC2672628},
	Pmid = {19289445},
	Pst = {ppublish},
	Title = {TopHat: discovering splice junctions with RNA-Seq},
	Volume = {25},
	Year = {2009},
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/bioinformatics/btp120}}

@article{Wang:2010vn,
	Abstract = {The accurate mapping of reads that span splice junctions is a critical component of all analytic techniques that work with RNA-seq data. We introduce a second generation splice detection algorithm, MapSplice, whose focus is high sensitivity and specificity in the detection of splices as well as CPU and memory efficiency. MapSplice can be applied to both short (<75 bp) and long reads (≥ 75 bp). MapSplice is not dependent on splice site features or intron length, consequently it can detect novel canonical as well as non-canonical splices. MapSplice leverages the quality and diversity of read alignments of a given splice to increase accuracy. We demonstrate that MapSplice achieves higher sensitivity and specificity than TopHat and SpliceMap on a set of simulated RNA-seq data. Experimental studies also support the accuracy of the algorithm. Splice junctions derived from eight breast cancer RNA-seq datasets recapitulated the extensiveness of alternative splicing on a global level as well as the differences between molecular subtypes of breast cancer. These combined results indicate that MapSplice is a highly accurate algorithm for the alignment of RNA-seq reads to splice junctions. Software download URL: http://www.netlab.uky.edu/p/bioinfo/MapSplice.},
	Author = {Wang, Kai and Singh, Darshan and Zeng, Zheng and Coleman, Stephen J and Huang, Yan and Savich, Gleb L and He, Xiaping and Mieczkowski, Piotr and Grimm, Sara A and Perou, Charles M and MacLeod, James N and Chiang, Derek Y and Prins, Jan F and Liu, Jinze},
	Date-Added = {2011-03-04 13:14:32 -0500},
	Date-Modified = {2011-03-04 13:14:32 -0500},
	Doi = {10.1093/nar/gkq622},
	Journal = {Nucleic Acids Res},
	Journal-Full = {Nucleic acids research},
	Mesh = {Algorithms; Alternative Splicing; Breast Neoplasms; Female; Gene Expression Profiling; Humans; RNA Splice Sites; Sequence Analysis, RNA; Software},
	Month = {Oct},
	Number = {18},
	Pages = {e178},
	Pmc = {PMC2952873},
	Pmid = {20802226},
	Pst = {ppublish},
	Title = {MapSplice: accurate mapping of RNA-seq reads for splice junction discovery},
	Volume = {38},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/nar/gkq622}}

@article{Guttman:2010kx,
	Abstract = {Massively parallel cDNA sequencing (RNA-Seq) provides an unbiased way to study a transcriptome, including both coding and noncoding genes. Until now, most RNA-Seq studies have depended crucially on existing annotations and thus focused on expression levels and variation in known transcripts. Here, we present Scripture, a method to reconstruct the transcriptome of a mammalian cell using only RNA-Seq reads and the genome sequence. We applied it to mouse embryonic stem cells, neuronal precursor cells and lung fibroblasts to accurately reconstruct the full-length gene structures for most known expressed genes. We identified substantial variation in protein coding genes, including thousands of novel 5' start sites, 3' ends and internal coding exons. We then determined the gene structures of more than a thousand large intergenic noncoding RNA (lincRNA) and antisense loci. Our results open the way to direct experimental manipulation of thousands of noncoding RNAs and demonstrate the power of ab initio reconstruction to render a comprehensive picture of mammalian transcriptomes.},
	Author = {Guttman, Mitchell and Garber, Manuel and Levin, Joshua Z and Donaghey, Julie and Robinson, James and Adiconis, Xian and Fan, Lin and Koziol, Magdalena J and Gnirke, Andreas and Nusbaum, Chad and Rinn, John L and Lander, Eric S and Regev, Aviv},
	Date-Added = {2011-03-04 13:13:54 -0500},
	Date-Modified = {2011-03-04 13:13:54 -0500},
	Doi = {10.1038/nbt.1633},
	Journal = {Nat Biotechnol},
	Journal-Full = {Nature biotechnology},
	Mesh = {Animals; Cell Line; Computational Biology; DNA, Intergenic; Embryonic Stem Cells; Gene Expression Profiling; Gene Library; Mice; Models, Genetic; RNA, Messenger; Sequence Analysis, RNA; Transcription, Genetic},
	Month = {May},
	Number = {5},
	Pages = {503-10},
	Pmc = {PMC2868100},
	Pmid = {20436462},
	Pst = {ppublish},
	Title = {Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs},
	Volume = {28},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/nbt.1633}}

@article{Li:2010uq,
	Abstract = {After mapping, RNA-Seq data can be summarized by a sequence of read counts commonly modeled as Poisson variables with constant rates along each transcript, which actually fit data poorly. We suggest using variable rates for different positions, and propose two models to predict these rates based on local sequences. These models explain more than 50\% of the variations and can lead to improved estimates of gene and isoform expressions for both Illumina and Applied Biosystems data.},
	Author = {Li, Jun and Jiang, Hui and Wong, Wing Hung},
	Date-Added = {2011-03-04 13:12:32 -0500},
	Date-Modified = {2011-03-04 13:12:32 -0500},
	Doi = {10.1186/gb-2010-11-5-r50},
	Journal = {Genome Biol},
	Journal-Full = {Genome biology},
	Mesh = {Animals; Apolipoproteins E; Base Sequence; Databases, Nucleic Acid; Embryo, Mammalian; Exons; Gene Expression Profiling; Gene Expression Regulation; Humans; Linear Models; Mice; Models, Genetic; Poisson Distribution; Protein Isoforms; RNA; Sequence Analysis, RNA; Statistics, Nonparametric},
	Number = {5},
	Pages = {R50},
	Pmc = {PMC2898062},
	Pmid = {20459815},
	Pst = {ppublish},
	Title = {Modeling non-uniformity in short-read rates in RNA-Seq data},
	Volume = {11},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.doi.org/10.1186/gb-2010-11-5-r50}}

@article{Hansen:2010fk,
	Abstract = {Generation of cDNA using random hexamer priming induces biases in the nucleotide composition at the beginning of transcriptome sequencing reads from the Illumina Genome Analyzer. The bias is independent of organism and laboratory and impacts the uniformity of the reads along the transcriptome. We provide a read count reweighting scheme, based on the nucleotide frequencies of the reads, that mitigates the impact of the bias.},
	Author = {Hansen, Kasper D and Brenner, Steven E and Dudoit, Sandrine},
	Date-Added = {2011-03-04 13:12:21 -0500},
	Date-Modified = {2011-03-04 13:12:21 -0500},
	Doi = {10.1093/nar/gkq224},
	Journal = {Nucleic Acids Res},
	Journal-Full = {Nucleic acids research},
	Mesh = {DNA Primers; Gene Expression Profiling; Nucleotides; Sequence Analysis, DNA},
	Month = {Jul},
	Number = {12},
	Pages = {e131},
	Pmc = {PMC2896536},
	Pmid = {20395217},
	Pst = {ppublish},
	Title = {Biases in Illumina transcriptome sequencing caused by random hexamer priming},
	Volume = {38},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/nar/gkq224}}

@article{Dai,
author = {Dai, Manhong and Wang, Pinglang and Boyd, Andrew D. and Kostov, Georgi and Athey, Brian and Jones, Edward G. and Bunney, William E. and Myers, Richard M. and Speed, Terry P. and Akil, Huda and Watson, Stanley J. and Meng, Fan}, 
title = {Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data}, 
volume = {33}, 
number = {20}, 
pages = {e175}, 
doi = {10.1093/nar/gni179}, 
URL = {http://nar.oxfordjournals.org/content/33/20/e175.abstract}, 
eprint = {http://nar.oxfordjournals.org/content/33/20/e175.full.pdf+html}, 
journal = {Nucleic Acids Research} 
}

% 17234643 
@Article{pmid17234643,
   Author="Venkatraman, E. S.  and Olshen, A. B. ",
   Title="{{A} faster circular binary segmentation algorithm for the analysis of array {C}{G}{H} data}",
   Journal="Bioinformatics",
   Year="2007",
   Volume="23",
   Pages="657--663",
   Month="Mar"
}

% 17098774 
@Article{pmid17098774,
   Author="Falcon, S.  and Gentleman, R. ",
   Title="{{U}sing {G}{O}stats to test gene lists for {G}{O} term association}",
   Journal="Bioinformatics",
   Year="2007",
   Volume="23",
   Pages="257--258",
   Month="Jan"
}

% 14597310 
@Article{pmid14597310,
   Author="Smyth, G. K.  and Speed, T. ",
   Title="{{N}ormalization of c{D}{N}{A} microarray data}",
   Journal="Methods",
   Year="2003",
   Volume="31",
   Pages="265--273",
   Month="Dec"
}

@article{Knuth01011984,
author = {Knuth, D. E.}, 
title = {Literate Programming}, 
volume = {27}, 
number = {2}, 
pages = {97-111}, 
year = {1984}, 
doi = {10.1093/comjnl/27.2.97}, 
abstract ={The author and his associates have been experimenting for the past several years with a programming language and documentation system called WEB. This paper presents WEB by example, and discusses why the new system appears to be an improvement over previous ones.}, 
URL = {http://comjnl.oxfordjournals.org/content/27/2/97.abstract}, 
eprint = {http://comjnl.oxfordjournals.org/content/27/2/97.full.pdf+html}, 
journal = {The Computer Journal} 
}

% 18551415 
@Article{pmid18551415,
   Author="Kuehn, H.  and Liberzon, A.  and Reich, M.  and Mesirov, J. P. ",
   Title="{{U}sing {G}ene{P}attern for gene expression analysis}",
   Journal="Curr Protoc Bioinformatics",
   Year="2008",
   Volume="Chapter 7",
   Pages="Unit 7.12",
   Month="Jun"
}
                  
% 19505943 
@Article{pmid19505943,
   Author="Li, H.  and Handsaker, B.  and Wysoker, A.  and Fennell, T.  and Ruan, J.  and Homer, N.  and Marth, G.  and Abecasis, G.  and Durbin, R. ",
   Title="{{T}he {S}equence {A}lignment/{M}ap format and {S}{A}{M}tools}",
   Journal="Bioinformatics",
   Year="2009",
   Volume="25",
   Pages="2078--2079",
   Month="Aug"
}
                  
@article{Barrett01012011,
author = {Barrett, Tanya and Troup, Dennis B. and Wilhite, Stephen E. and Ledoux, Pierre and Evangelista, Carlos and Kim, Irene F. and Tomashevsky, Maxim and Marshall, Kimberly A. and Phillippy, Katherine H. and Sherman, Patti M. and Muertter, Rolf N. and Holko, Michelle and Ayanbule, Oluwabukunmi and Yefanov, Andrey and Soboleva, Alexandra}, 
title = {NCBI GEO: archive for functional genomics data sets—10 years on}, 
volume = {39}, 
number = {suppl 1}, 
pages = {D1005-D1010}, 
year = {2011}, 
doi = {10.1093/nar/gkq1184}, 
abstract ={A decade ago, the Gene Expression Omnibus (GEO) database was established at the National Center for Biotechnology Information (NCBI). The original objective of GEO was to serve as a public repository for high-throughput gene expression data generated mostly by microarray technology. However, the research community quickly applied microarrays to non-gene-expression studies, including examination of genome copy number variation and genome-wide profiling of DNA-binding proteins. Because the GEO database was designed with a flexible structure, it was possible to quickly adapt the repository to store these data types. More recently, as the microarray community switches to next-generation sequencing technologies, GEO has again adapted to host these data sets. Today, GEO stores over 20 000 microarray- and sequence-based functional genomics studies, and continues to handle the majority of direct high-throughput data submissions from the research community. Multiple mechanisms are provided to help users effectively search, browse, download and visualize the data at the level of individual genes or entire studies. This paper describes recent database enhancements, including new search and data representation tools, as well as a brief review of how the community uses GEO data. GEO is freely accessible at http://www.ncbi.nlm.nih.gov/geo/.}, 
URL = {http://nar.oxfordjournals.org/content/39/suppl_1/D1005.abstract}, 
eprint = {http://nar.oxfordjournals.org/content/39/suppl_1/D1005.full.pdf+html}, 
journal = {Nucleic Acids Research} 
}

% 21071405 
@Article{pmid21071405,
   Author="Parkinson, H.  and Sarkans, U.  and Kolesnikov, N.  and Abeygunawardena, N.  and Burdett, T.  and Dylag, M.  and Emam, I.  and Farne, A.  and Hastings, E.  and Holloway, E.  and Kurbatova, N.  and Lukk, M.  and Malone, J.  and Mani, R.  and Pilicheva, E.  and Rustici, G.  and Sharma, A.  and Williams, E.  and Adamusiak, T.  and Brandizi, M.  and Sklyar, N.  and Brazma, A. ",
   Title="{{A}rray{E}xpress update--an archive of microarray and high-throughput sequencing-based functional genomics experiments}",
   Journal="Nucleic Acids Res.",
   Year="2011",
   Volume="39",
   Pages="D1002--1004",
   Month="Jan"
}

@article{Haider01072009,
author = {Haider, Syed and Ballester, Benoit and Smedley, Damian and Zhang, Junjun and Rice, Peter and Kasprzyk, Arek}, 
title = {BioMart Central Portal—unified access to biological data}, 
volume = {37}, 
number = {suppl 2}, 
pages = {W23-W27}, 
year = {2009}, 
doi = {10.1093/nar/gkp265}, 
abstract ={BioMart Central Portal (www.biomart.org) offers a one-stop shop solution to access a wide array of biological databases. These include major biomolecular sequence, pathway and annotation databases such as Ensembl, Uniprot, Reactome, HGNC, Wormbase and PRIDE; for a complete list, visit, http://www.biomart.org/biomart/martview. Moreover, the web server features seamless data federation making cross querying of these data sources in a user friendly and unified way. The web server not only provides access through a web interface (MartView), it also supports programmatic access through a Perl API as well as RESTful and SOAP oriented web services. The website is free and open to all users and there is no login requirement.}, 
URL = {http://nar.oxfordjournals.org/content/37/suppl_2/W23.abstract}, 
eprint = {http://nar.oxfordjournals.org/content/37/suppl_2/W23.full.pdf+html}, 
journal = {Nucleic Acids Research} 
}



%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Petretto:2010p2678,
author = {Enrico Petretto and Leonardo Bottolo and Sarah R Langley and Matthias Heinig and Chris McDermott-Roe and Rizwan Sarwar and Michal Pravenec and Norbert H{\"u}bner and Timothy J Aitman and Stuart A Cook and Sylvia Richardson}, 
journal = {PLoS Comput Biol},
title = {New insights into the genetic control of gene expression using a Bayesian multi-tissue approach},
abstract = {The majority of expression quantitative trait locus (eQTL) studies have been carried out in single tissues or cell types, using methods that ignore information shared across tissues. Although global analysis of RNA expression in multiple tissues is now feasible, few integrated statistical frameworks for joint analysis of gene expression across tissues combined with simultaneous analysis of multiple genetic variants have been developed to date. Here, we propose Sparse Bayesian Regression models for mapping eQTLs within individual tissues and simultaneously across tissues. Testing these on a set of 2,000 genes in four tissues, we demonstrate that our methods are more powerful than traditional approaches in revealing the true complexity of the eQTL landscape at the systems-level. Highlighting the power of our method, we identified a two-eQTL model (cis/trans) for the Hopx gene that was experimentally validated and was not detected by conventional approaches. We showed common genetic regulation of gene expression across four tissues for approximately 27% of transcripts, providing >5 fold increase in eQTLs detection when compared with single tissue analyses at 5% FDR level. These findings provide a new opportunity to uncover complex genetic regulatory mechanisms controlling global gene expression while the generality of our modelling approach makes it adaptable to other model systems and humans, with broad application to analysis of multiple intermediate and whole-body phenotypes.},
affiliation = {Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, London, United Kingdom.},
number = {4},
pages = {e1000737},
volume = {6},
year = {2010},
month = {Apr},
language = {eng},
keywords = {Algorithms, Animals, Adrenal Glands, Gene Regulatory Networks, Regression Analysis, Adipose Tissue, Humans, Kidney, Reproducibility of Results, Sensitivity and Specificity, Quantitative Trait Loci, Bayes Theorem, Gene Expression Profiling, Gene Expression Regulation, Heart Ventricles, Models: Genetic}, 
date-added = {2011-03-05 22:43:54 -0500},
date-modified = {2011-03-05 22:44:21 -0500},
doi = {10.1371/journal.pcbi.1000737},
pmid = {20386736},
URL = {http://www.ploscompbiol.org/article/info%253Adoi%252F10.1371%252Fjournal.pcbi.1000737},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2010/Petretto/PLoS%20Comput%20Biol%202010%20Petretto.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p2678},
rating = {0}
}


%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Bourgon:2010p1763,
author = {Richard Bourgon and Robert Gentleman and Wolfgang Huber}, 
journal = {Proc Natl Acad Sci USA},
title = {Independent filtering increases detection power for high-throughput experiments},
abstract = {With high-dimensional data, variable-by-variable statistical testing is often used to select variables whose behavior differs across conditions. Such an approach requires adjustment for multiple testing, which can result in low statistical power. A two-stage approach that first filters variables by a criterion independent of the test statistic, and then only tests variables which pass the filter, can provide higher power. We show that use of some filter/test statistics pairs presented in the literature may, however, lead to loss of type I error control. We describe other pairs which avoid this problem. In an application to microarray data, we found that gene-by-gene filtering by overall variance followed by a t-test increased the number of discoveries by 50%. We also show that this particular statistic pair induces a lower bound on fold-change among the set of discoveries. Independent filtering-using filter/test pairs that are independent under the null hypothesis but correlated under the alternative-is a general approach that can substantially increase the efficiency of experiments.},
affiliation = {European Bioinformatics Institute, Cambridge CB10 1SD, UK.},
number = {21},
pages = {9546--51},
volume = {107},
year = {2010},
month = {May},
language = {eng},
keywords = {Algorithms, Biometry, Computational Biology, Models: Genetic}, 
date-added = {2010-07-09 16:24:45 -0400},
date-modified = {2010-07-09 16:24:45 -0400},
doi = {10.1073/pnas.0914005107},
pii = {0914005107},
pmid = {20460310},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2010/Bourgon/Proc%20Natl%20Acad%20Sci%20USA%202010%20Bourgon.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p1763},
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@article{Stranger:2007p114,
author = {Barbara E Stranger and Alexandra C Nica and Matthew S Forrest and Antigone Dimas and Christine P Bird and Claude Beazley and Catherine E Ingle and Mark Dunning and Paul Flicek and Daphne Koller and Stephen Montgomery and Simon Tavar{\'e} and Panos Deloukas and Emmanouil T Dermitzakis}, 
journal = {Nat Genet},
title = {Population genomics of human gene expression},
number = {10},
pages = {1217--1224},
volume = {39},
year = {2007},
month = {Oct},
date-added = {2009-06-01 12:30:34 -0400},
date-modified = {2009-07-22 16:12:34 -0400},
doi = {10.1038/ng2142},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2007/Stranger/Nat%20Genet%202007%20Stranger.pdf},
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@article{Stranger:2007p162,
author = {B. E Stranger and M. S Forrest and M Dunning and C. E Ingle and C Beazley and N Thorne and R Redon and C. P Bird and A De Grassi and C Lee and C Tyler-Smith and N Carter and S. W Scherer and S Tavare and P Deloukas and M. E Hurles and E. T Dermitzakis}, 
journal = {Science},
title = {Relative Impact of Nucleotide and Copy Number Variation on Gene Expression Phenotypes},
number = {5813},
pages = {848--853},
volume = {315},
year = {2007},
month = {Feb},
date-added = {2009-06-01 12:30:33 -0400},
date-modified = {2009-07-22 16:12:35 -0400},
doi = {10.1126/science.1136678},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2007/Stranger/Science%202007%20Stranger.pdf},
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%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Cheung:2010p2828,
author = {Vivian G Cheung and Renuka R Nayak and Isabel Xiaorong Wang and Susannah Elwyn and Sarah M Cousins and Michael Morley and Richard S Spielman}, 
journal = {PLoS Biol},
title = {Polymorphic Cis- and Trans-Regulation of Human Gene Expression},
number = {9},
pages = {e1000480},
volume = {8},
year = {2010},
month = {Sep},
date-added = {2011-04-12 10:05:40 -0400},
date-modified = {2011-04-12 10:05:40 -0400},
doi = {10.1371/journal.pbio.1000480.t005},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2010/Cheung/PLoS%20Biol%202010%20Cheung.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p2828},
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