% This file was created with JabRef 2.5.
% Encoding: UTF8

@ARTICLE{Akalin2012,
  author = {Altuna Akalin and Matthias Kormaksson and Sheng Li and Francine E
	Garrett-Bakelman and Maria E Figueroa and Ari Melnick and Christopher
	E Mason},
  title = {methylKit: a comprehensive R package for the analysis of genome-wide
	DNA methylation profiles.},
  journal = {Genome Biol},
  year = {2012},
  volume = {13},
  pages = {R87},
  number = {10},
  month = {Oct},
  abstract = {ABSTRACT: DNA methylation is a chemical modification of cytosine bases
	that is pivotal for gene regulation, cellular specification and cancer
	development. Here, we describe an R package, methylKit, that rapidly
	analyzes genome-wide cytosine epigenetic profiles from high-throughput
	methylation and hydroxymethylation sequencing experiments. methylKit
	includes functions for clustering, sample quality visualization,
	differential methylation analysis and annotation features, thus automating
	and simplifying many of the steps for discerning statistically significant
	bases or regions of DNA methylation. Finally, we demonstrate methylKit
	on breast cancer data, in which we find statistically significant
	regions of differential methylation and stratify tumor subtypes.
	methylKit is available at http://code.google.com/p/methylkit.},
  doi = {10.1186/gb-2012-13-10-r87},
  file = {Akalin2012.pdf:Akalin2012.pdf:PDF},
  institution = {Department of Physiology and Biophysics, 1305 York Ave,, Weill Cornell
	Medical College, New York, NY 10065, USA. ala2027@med.cornell.edu.},
  language = {eng},
  medline-pst = {aheadofprint},
  owner = {hebestrk},
  pii = {gb-2012-13-10-r87},
  pmid = {23034086},
  timestamp = {2012.11.09},
  url = {http://dx.doi.org/10.1186/gb-2012-13-10-r87}
}

@ARTICLE{Benjamini2007,
  author = {Yoav Benjamini and Ruth Heller},
  title = {False Discovery Rates for Spatial Signals},
  journal = {Journal of the American Statistical Association},
  year = {2007},
  volume = {102},
  pages = {1272-1281},
  file = {Benjamini2007.pdf:Benjamini2007.pdf:PDF},
  owner = {hebestrk},
  timestamp = {2012.08.09}
}

@ARTICLE{Benjamini1997,
  author = {Yoav Benjamini and Yosef Hochberg},
  title = {Multiple Hypotheses Testing with Weights},
  journal = {Scandinavian Journal of Statistics},
  year = {1997},
  volume = {24},
  pages = {407-418},
  owner = {hebestrk},
  timestamp = {2012.12.05}
}

@ARTICLE{Benjamini2006,
  author = {Yoav Benjamini and Abba M. Krieger and Daniel Yekutieli},
  title = {Adaptive linear step-up procedures that control the false discovery
	rate},
  journal = {Biometrika},
  year = {2006},
  volume = {93},
  pages = {491-507},
  number = {3},
  abstract = {The linear step-up multiple testing procedure controls the false discovery
	rate at the
	
	desired level q for independent and positively dependent test statistics.
	When all null
	
	hypotheses are true, and the test statistics are independent and continuous,
	the bound is
	
	sharp. When some of the null hypotheses are not true, the procedure
	is conservative by
	
	a factor which is the proportion m /m of the true null hypotheses
	among the hypotheses.
	
	We provide a new two-stage procedure in which the linear step-up procedure
	is used in
	
	stage one to estimate m , providing a new level q∞ which is used in
	the linear step-up
	
	procedure in the second stage. We prove that a general form of the
	two-stage procedure
	
	controls the false discovery rate at the desired level q. This framework
	enables us to study
	
	analytically the properties of other procedures that exist in the
	literature. A simulation
	
	study is presented that shows that two-stage adaptive procedures improve
	in power over
	
	the original procedure, mainly because they provide tighter control
	of the false discovery
	
	rate. We further study the performance of the current suggestions,
	some variations of the
	
	procedures, and previous suggestions, in the case where the test statistics
	are positively
	
	dependent, a case for which the original procedure controls the false
	discovery rate. In
	
	the setting studied here the newly proposed two-stage procedure is
	the only one that
	
	controls the false discovery rate. The procedures are illustrated
	with two examples of
	
	biological importance.},
  file = {Benjamini2006.pdf:Benjamini2006.pdf:PDF},
  owner = {hebestrk},
  timestamp = {2012.08.10}
}

@ARTICLE{Benjamini2001,
  author = {Yoav Benjamini and Daniel Yekutieli},
  title = {The Control of the False Discovery Rate in Multiple Testing Under
	Dependency},
  journal = {The Annals of Statistics},
  year = {2001},
  volume = {29},
  pages = {1165-1188},
  number = {4},
  file = {Benjamini2001.pdf:Benjamini2001.pdf:PDF},
  owner = {hebestrk},
  timestamp = {2012.08.09}
}

@ARTICLE{Bock2012,
  author = {Christoph Bock},
  title = {Analysing and interpreting DNA methylation data.},
  journal = {Nat Rev Genet},
  year = {2012},
  volume = {13},
  pages = {705--719},
  number = {10},
  month = {Sep},
  abstract = {DNA methylation is an epigenetic mark that has suspected regulatory
	roles in a broad range of biological processes and diseases. The
	technology is now available for studying DNA methylation genome-wide,
	at a high resolution and in a large number of samples. This Review
	discusses relevant concepts, computational methods and software tools
	for analysing and interpreting DNA methylation data. It focuses not
	only on the bioinformatic challenges of large epigenome-mapping projects
	and epigenome-wide association studies but also highlights software
	tools that make genome-wide DNA methylation mapping more accessible
	for laboratories with limited bioinformatics experience.},
  doi = {10.1038/nrg3273},
  file = {Bock2012.pdf:Bock2012.pdf:PDF},
  institution = {CeMM Research Center for Molecular Medicine of the Austrian Academy
	of Sciences, 1090 Vienna, Austria; Department of Laboratory Medicine,
	Medical University of Vienna, 1090 Vienna, Austria; and Max Planck
	Institute for Informatics, 66123 Saarbrücken, Germany.},
  language = {eng},
  medline-pst = {ppublish},
  owner = {hebestrk},
  pii = {nrg3273},
  pmid = {22986265},
  timestamp = {2012.11.09},
  url = {http://dx.doi.org/10.1038/nrg3273}
}

@ARTICLE{Boks2009,
  author = {Marco P Boks and Eske M Derks and Daniel J Weisenberger and Erik
	Strengman and Esther Janson and Iris E Sommer and René S Kahn and
	Roel A Ophoff},
  title = {The relationship of DNA methylation with age, gender and genotype
	in twins and healthy controls.},
  journal = {PLoS One},
  year = {2009},
  volume = {4},
  pages = {e6767},
  number = {8},
  abstract = {Cytosine-5 methylation within CpG dinucleotides is a potentially important
	mechanism of epigenetic influence on human traits and disease. In
	addition to influences of age and gender, genetic control of DNA
	methylation levels has recently been described. We used whole blood
	genomic DNA in a twin set (23 MZ twin-pairs and 23 DZ twin-pairs,
	N = 92) as well as healthy controls (N = 96) to investigate heritability
	and relationship with age and gender of selected DNA methylation
	profiles using readily commercially available GoldenGate bead array
	technology. Despite the inability to detect meaningful methylation
	differences in the majority of CpG loci due to tissue type and locus
	selection issues, we found replicable significant associations of
	DNA methylation with age and gender. We identified associations of
	genetically heritable single nucleotide polymorphisms with large
	differences in DNA methylation levels near the polymorphism (cis
	effects) as well as associations with much smaller differences in
	DNA methylation levels elsewhere in the human genome (trans effects).
	Our results demonstrate the feasibility of array-based approaches
	in studies of DNA methylation and highlight the vast differences
	between individual loci. The identification of CpG loci of which
	DNA methylation levels are under genetic control or are related to
	age or gender will facilitate further studies into the role of DNA
	methylation and disease.},
  doi = {10.1371/journal.pone.0006767},
  file = {Boks2009.pdf:Boks2009.pdf:PDF},
  institution = {Rudolf Magnus Institute of Neuroscience, Department of Psychiatry,
	University Medical Center Utrecht, Utrecht, The Netherlands.},
  keywords = {Age Factors; Case-Control Studies; DNA Methylation; Female; Genotype;
	Humans; Linkage Disequilibrium; Male; Twins},
  language = {eng},
  medline-pst = {epublish},
  owner = {hebestrk},
  pmid = {19774229},
  timestamp = {2012.10.22},
  url = {http://dx.doi.org/10.1371/journal.pone.0006767}
}

@ARTICLE{Challen2012,
  author = {Grant A Challen and Deqiang Sun and Mira Jeong and Min Luo and Jaroslav
	Jelinek and Jonathan S Berg and Christoph Bock and Aparna Vasanthakumar
	and Hongcang Gu and Yuanxin Xi and Shoudan Liang and Yue Lu and Gretchen
	J Darlington and Alexander Meissner and Jean-Pierre J Issa and Lucy
	A Godley and Wei Li and Margaret A Goodell},
  title = {Dnmt3a is essential for hematopoietic stem cell differentiation.},
  journal = {Nature Genetics},
  year = {2012},
  volume = {44},
  pages = {23--31},
  number = {1},
  month = {Jan},
  note = {Fisher exact test for each CpG},
  abstract = {Loss of the de novo DNA methyltransferases Dnmt3a and Dnmt3b in embryonic
	stem cells obstructs differentiation; however, the role of these
	enzymes in somatic stem cells is largely unknown. Using conditional
	ablation, we show that Dnmt3a loss progressively impairs hematopoietic
	stem cell (HSC) differentiation over serial transplantation, while
	simultaneously expanding HSC numbers in the bone marrow. Dnmt3a-null
	HSCs show both increased and decreased methylation at distinct loci,
	including substantial CpG island hypermethylation. Dnmt3a-null HSCs
	upregulate HSC multipotency genes and downregulate differentiation
	factors, and their progeny exhibit global hypomethylation and incomplete
	repression of HSC-specific genes. These data establish Dnmt3a as
	a critical participant in the epigenetic silencing of HSC regulatory
	genes, thereby enabling efficient differentiation.},
  doi = {10.1038/ng.1009},
  institution = {Stem Cells and Regenerative Medicine Center, Baylor College of Medicine,
	Houston, Texas, USA.},
  keywords = {Animals; Cell Differentiation, genetics; Cell Division; DNA (Cytosine-5-)-Methyltransferase,
	genetics; DNA Methylation; Hematopoietic Stem Cells, physiology;
	Humans; Mice; Mice, Inbred C57BL; Mice, Knockout; Mutation},
  language = {eng},
  medline-pst = {epublish},
  owner = {hebestrk},
  pii = {ng.1009},
  pmid = {22138693},
  timestamp = {2012.09.25},
  url = {http://dx.doi.org/10.1038/ng.1009}
}

@ARTICLE{Chen2010,
  author = {Pao-Yang Chen and Shawn J Cokus and Matteo Pellegrini},
  title = {BS Seeker: precise mapping for bisulfite sequencing.},
  journal = {BMC Bioinformatics},
  year = {2010},
  volume = {11},
  pages = {203},
  abstract = {Bisulfite sequencing using next generation sequencers yields genome-wide
	measurements of DNA methylation at single nucleotide resolution.
	Traditional aligners are not designed for mapping bisulfite-treated
	reads, where the unmethylated Cs are converted to Ts. We have developed
	BS Seeker, an approach that converts the genome to a three-letter
	alphabet and uses Bowtie to align bisulfite-treated reads to a reference
	genome. It uses sequence tags to reduce mapping ambiguity. Post-processing
	of the alignments removes non-unique and low-quality mappings.We
	tested our aligner on synthetic data, a bisulfite-converted Arabidopsis
	library, and human libraries generated from two different experimental
	protocols. We evaluated the performance of our approach and compared
	it to other bisulfite aligners. The results demonstrate that among
	the aligners tested, BS Seeker is more versatile and faster. When
	mapping to the human genome, BS Seeker generates alignments significantly
	faster than RMAP and BSMAP. Furthermore, BS Seeker is the only alignment
	tool that can explicitly account for tags which are generated by
	certain library construction protocols.BS Seeker provides fast and
	accurate mapping of bisulfite-converted reads. It can work with BS
	reads generated from the two different experimental protocols, and
	is able to efficiently map reads to large mammalian genomes. The
	Python program is freely available at http://pellegrini.mcdb.ucla.edu/BS_Seeker/BS_Seeker.html.},
  doi = {10.1186/1471-2105-11-203},
  institution = {Department of Molecular, Cell, and Developmental Biology, University
	of California, Los Angeles, CA, USA.},
  keywords = {Base Sequence; Genome; Genome, Human; Genomics, methods; Humans; Sequence
	Alignment; Sequence Analysis, DNA, methods; Software; Sulfites, chemistry},
  language = {eng},
  medline-pst = {epublish},
  owner = {hebestrk},
  pii = {1471-2105-11-203},
  pmid = {20416082},
  timestamp = {2012.11.09},
  url = {http://dx.doi.org/10.1186/1471-2105-11-203}
}

@BOOK{Cressie1993,
  title = {Statistics for Spatial Data},
  publisher = {Wiley},
  year = {1993},
  author = {Noel A. C. Cressie},
  owner = {hebestrk},
  timestamp = {2012.12.06}
}

@ARTICLE{Cribari-Neto2010,
  author = {Francisco Cribari-Neto and Achim Zeileis},
  title = {Beta Regression in R},
  journal = {Journal of Statistical Software},
  year = {2010},
  volume = {34},
  pages = {1-24},
  number = {2},
  file = {Cribari-Neto2010.pdf:Cribari-Neto2010.pdf:PDF},
  owner = {hebestrk},
  timestamp = {2012.12.05}
}

@ARTICLE{Das2004,
  author = {Partha M Das and Rakesh Singal},
  title = {DNA methylation and cancer.},
  journal = {J Clin Oncol},
  year = {2004},
  volume = {22},
  pages = {4632--4642},
  number = {22},
  month = {Nov},
  abstract = {DNA methylation is an important regulator of gene transcription, and
	its role in carcinogenesis has been a topic of considerable interest
	in the last few years. Alterations in DNA methylation are common
	in a variety of tumors as well as in development. Of all epigenetic
	modifications, hypermethylation, which represses transcription of
	the promoter regions of tumor suppressor genes leading to gene silencing,
	has been most extensively studied. However, global hypomethylation
	has also been recognized as a cause of oncogenesis. New information
	concerning the mechanism of methylation and its control has led to
	the discovery of many regulatory proteins and enzymes. The contribution
	of dietary folate and methylene terahydrofolate reductase polymorphisms
	to methylation patterns in normal and cancer tissues is under intense
	investigation. As methylation occurs early and can be detected in
	body fluids, it may be of potential use in early detection of tumors
	and for determining the prognosis. Because DNA methylation is reversible,
	drugs like 5'-azacytidine, decitabine, and histone deacetylase inhibitors
	are being used to treat a variety of tumors. Novel demethylating
	agents such as antisense DNA methyl transferase and small interference
	RNA are being developed, making the field of DNA methylation wider
	and more exciting.},
  doi = {10.1200/JCO.2004.07.151},
  institution = {Department of Medicine, Miami VA Medical Center, Miami, FL, USA.},
  keywords = {Antineoplastic Agents, pharmacology; Cell Transformation, Neoplastic;
	DNA Methylation; Gene Expression Regulation, Neoplastic; Genetic
	Markers; Humans; Neoplasms, genetics/physiopathology; Prognosis;
	Risk Factors},
  language = {eng},
  medline-pst = {ppublish},
  owner = {hebestrk},
  pii = {22/22/4632},
  pmid = {15542813},
  timestamp = {2012.11.09},
  url = {http://dx.doi.org/10.1200/JCO.2004.07.151}
}

@ARTICLE{Eckhardt2006,
  author = {Florian Eckhardt and Joern Lewin and Rene Cortese and Vardhman K
	Rakyan and John Attwood and Matthias Burger and John Burton and Tony
	V Cox and Rob Davies and Thomas A Down and Carolina Haefliger and
	Roger Horton and Kevin Howe and David K Jackson and Jan Kunde and
	Christoph Koenig and Jennifer Liddle and David Niblett and Thomas
	Otto and Roger Pettett and Stefanie Seemann and Christian Thompson
	and Tony West and Jane Rogers and Alex Olek and Kurt Berlin and Stephan
	Beck},
  title = {DNA methylation profiling of human chromosomes 6, 20 and 22.},
  journal = {Nat Genet},
  year = {2006},
  volume = {38},
  pages = {1378--1385},
  number = {12},
  month = {Dec},
  abstract = {DNA methylation is the most stable type of epigenetic modification
	modulating the transcriptional plasticity of mammalian genomes. Using
	bisulfite DNA sequencing, we report high-resolution methylation profiles
	of human chromosomes 6, 20 and 22, providing a resource of about
	1.9 million CpG methylation values derived from 12 different tissues.
	Analysis of six annotation categories showed that evolutionarily
	conserved regions are the predominant sites for differential DNA
	methylation and that a core region surrounding the transcriptional
	start site is an informative surrogate for promoter methylation.
	We find that 17\% of the 873 analyzed genes are differentially methylated
	in their 5' UTRs and that about one-third of the differentially methylated
	5' UTRs are inversely correlated with transcription. Despite the
	fact that our study controlled for factors reported to affect DNA
	methylation such as sex and age, we did not find any significant
	attributable effects. Our data suggest DNA methylation to be ontogenetically
	more stable than previously thought.},
  doi = {10.1038/ng1909},
  file = {Eckhardt2006.pdf:Eckhardt2006.pdf:PDF},
  institution = {Epigenomics AG, Kleine Präsidentstrasse 1, 10178 Berlin, Germany.},
  keywords = {5' Untranslated Regions; Adult; Age Factors; Aged; Animals; Chromosomes,
	Human, Pair 20, genetics/metabolism; Chromosomes, Human, Pair 22,
	genetics/metabolism; Chromosomes, Human, Pair 6, genetics/metabolism;
	CpG Islands; DNA Methylation; Epigenesis, Genetic; Evolution, Molecular;
	Female; Humans; Male; Mice; Middle Aged; Organ Specificity; Promoter
	Regions, Genetic; Sex Characteristics; Species Specificity; Transcription,
	Genetic},
  language = {eng},
  medline-pst = {ppublish},
  owner = {hebestrk},
  pii = {ng1909},
  pmid = {17072317},
  timestamp = {2012.11.21},
  url = {http://dx.doi.org/10.1038/ng1909}
}

@ARTICLE{Ferrari2004,
  author = {Silvia L.P. Ferrari and Francisco Cribari-Neto},
  title = {Beta Regression for Modelling Rates and Proportions},
  journal = {Journal of Applied Statistics},
  year = {2004},
  volume = {31},
  pages = {799-815},
  number = {7},
  file = {Ferrari2004.pdf:Ferrari2004.pdf:PDF},
  owner = {hebestrk},
  timestamp = {2012.10.16}
}

@ARTICLE{Genovese2002,
  author = {Christopher R Genovese and Nicole A Lazar and Thomas Nichols},
  title = {Thresholding of statistical maps in functional neuroimaging using
	the false discovery rate.},
  journal = {Neuroimage},
  year = {2002},
  volume = {15},
  pages = {870--878},
  number = {4},
  month = {Apr},
  abstract = {Finding objective and effective thresholds for voxelwise statistics
	derived from neuroimaging data has been a long-standing problem.
	With at least one test performed for every voxel in an image, some
	correction of the thresholds is needed to control the error rates,
	but standard procedures for multiple hypothesis testing (e.g., Bonferroni)
	tend to not be sensitive enough to be useful in this context. This
	paper introduces to the neuroscience literature statistical procedures
	for controlling the false discovery rate (FDR). Recent theoretical
	work in statistics suggests that FDR-controlling procedures will
	be effective for the analysis of neuroimaging data. These procedures
	operate simultaneously on all voxelwise test statistics to determine
	which tests should be considered statistically significant. The innovation
	of the procedures is that they control the expected proportion of
	the rejected hypotheses that are falsely rejected. We demonstrate
	this approach using both simulations and functional magnetic resonance
	imaging data from two simple experiments.},
  doi = {10.1006/nimg.2001.1037},
  file = {Genovese2002.pdf:Genovese2002.pdf:PDF},
  institution = {Department of Statistics, Carnegie Mellon University, Pittsburgh,
	Pennsylvania 15213, USA.},
  keywords = {Adult; Artifacts; Brain Mapping, methods; Cerebral Cortex, physiology;
	Computer Simulation; Female; Humans; Image Processing, Computer-Assisted;
	Magnetic Resonance Imaging, methods; Male; Mathematical Computing;
	Motor Activity, physiology; Reference Values},
  language = {eng},
  medline-pst = {ppublish},
  owner = {hebestrk},
  pii = {S1053811901910377},
  pmid = {11906227},
  timestamp = {2012.08.09},
  url = {http://dx.doi.org/10.1006/nimg.2001.1037}
}

@ARTICLE{Goldman2012,
  author = {Mary Goldman and Brian Craft and Teresa Swatloski and Kyle Ellrott
	and Melissa Cline and Mark Diekhans and Singer Ma and Chris Wilks
	and Josh Stuart and David Haussler and Jingchun Zhu},
  title = {The UCSC Cancer Genomics Browser: update 2013.},
  journal = {Nucleic Acids Res},
  year = {2012},
  month = {Oct},
  abstract = {The UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu/)
	is a set of web-based tools to display, investigate and analyse cancer
	genomics data and its associated clinical information. The browser
	provides whole-genome to base-pair level views of several different
	types of genomics data, including some next-generation sequencing
	platforms. The ability to view multiple datasets together allows
	users to make comparisons across different data and cancer types.
	Biological pathways, collections of genes, genomic or clinical information
	can be used to sort, aggregate and zoom into a group of samples.
	We currently display an expanding set of data from various sources,
	including 201 datasets from 22 TCGA (The Cancer Genome Atlas) cancers
	as well as data from Cancer Cell Line Encyclopedia and Stand Up To
	Cancer. New features include a completely redesigned user interface
	with an interactive tutorial and updated documentation. We have also
	added data downloads, additional clinical heatmap features, and an
	updated Tumor Image Browser based on Google Maps. New security features
	allow authenticated users access to private datasets hosted by several
	different consortia through the public website.},
  doi = {10.1093/nar/gks1008},
  institution = {Center for Biomolecular Science and Engineering and Howard Hughes
	Medical Institute, University of California at Santa Cruz, Santa
	Cruz, CA 95064, USA.},
  language = {eng},
  medline-pst = {aheadofprint},
  owner = {hebestrk},
  pii = {gks1008},
  pmid = {23109555},
  timestamp = {2012.12.12},
  url = {http://dx.doi.org/10.1093/nar/gks1008}
}

@ARTICLE{Gopalakrishnan2008,
  author = {Suhasni Gopalakrishnan and Beth O Van Emburgh and Keith D Robertson},
  title = {DNA methylation in development and human disease.},
  journal = {Mutat Res},
  year = {2008},
  volume = {647},
  pages = {30--38},
  number = {1-2},
  month = {Dec},
  abstract = {DNA methylation is a heritable and stable epigenetic mark associated
	with transcriptional repression. Changes in the patterns and levels
	of global and regional DNA methylation regulate development and contribute
	directly to disease states such as cancer. Recent findings provide
	intriguing insights into the epigenetic crosstalk between DNA methylation,
	histone modifications, and small interfering RNAs in the control
	of cell development and carcinogenesis. In this review, we summarize
	the recent studies in DNA methylation primarily focusing on the interplay
	between different epigenetic modifications and their potential role
	in gene silencing in development and disease. Although the molecular
	mechanisms involved in the epigenetic crosstalk are not fully understood,
	unraveling their precise regulation is important not only for understanding
	the underpinnings of cellular development and cancer, but also for
	the design of clinically relevant and efficient therapeutics using
	stem cells and anticancer drugs that target tumor initiating cells.},
  doi = {10.1016/j.mrfmmm.2008.08.006},
  institution = { Molecular Biology, University of Florida, Gainesville, FL 32610,
	USA.},
  keywords = {Animals; Cell Differentiation; DNA Methylation; Epigenesis, Genetic;
	Gene Silencing; Growth and Development, genetics; Humans; Models,
	Genetic; Neoplasms, genetics; Pluripotent Stem Cells, physiology;
	Repressor Proteins, metabolism},
  language = {eng},
  medline-pst = {ppublish},
  owner = {hebestrk},
  pii = {S0027-5107(08)00179-6},
  pmid = {18778722},
  timestamp = {2012.11.09},
  url = {http://dx.doi.org/10.1016/j.mrfmmm.2008.08.006}
}

@MANUAL{Gruen2012,
  title = {Extended Beta Regression in R: Shaken, Stirred,
	
	Mixed, and Partitioned},
  author = {Bettina Gr\"{u}n and Ioannis Kosmidis and Achim Zeileis},
  year = {2012},
  file = {Gruen2012.pdf:Gruen2012.pdf:PDF},
  owner = {hebestrk},
  timestamp = {2012.10.19}
}

@ARTICLE{Gu2010,
  author = {Hongcang Gu and Christoph Bock and Tarjei S Mikkelsen and Natalie
	Jäger and Zachary D Smith and Eleni Tomazou and Andreas Gnirke and
	Eric S Lander and Alexander Meissner},
  title = {Genome-scale DNA methylation mapping of clinical samples at single-nucleotide
	resolution.},
  journal = {Nat Methods},
  year = {2010},
  volume = {7},
  pages = {133--136},
  number = {2},
  month = {Feb},
  abstract = {Bisulfite sequencing measures absolute levels of DNA methylation at
	single-nucleotide resolution, providing a robust platform for molecular
	diagnostics. We optimized bisulfite sequencing for genome-scale analysis
	of clinical samples: here we outline how restriction digestion targets
	bisulfite sequencing to hotspots of epigenetic regulation and describe
	a statistical method for assessing significance of altered DNA methylation
	patterns. Thirty nanograms of DNA was sufficient for genome-scale
	analysis and our protocol worked well on formalin-fixed, paraffin-embedded
	samples.},
  doi = {10.1038/nmeth.1414},
  institution = {Broad Institute, Cambridge, Massachusetts, USA.},
  keywords = {Base Sequence; Chromosome Mapping, methods; Colonic Neoplasms, diagnosis/genetics;
	DNA Methylation, genetics; DNA, Neoplasm, genetics; Genetic Testing,
	methods; Humans; Molecular Sequence Data; Nucleotides, genetics;
	Reproducibility of Results; Sensitivity and Specificity},
  language = {eng},
  medline-pst = {ppublish},
  owner = {hebestrk},
  pii = {nmeth.1414},
  pmid = {20062050},
  timestamp = {2012.11.21},
  url = {http://dx.doi.org/10.1038/nmeth.1414}
}

@ARTICLE{Hansen2012,
  author = {Kasper D Hansen and Benjamin Langmead and Rafael A Irizarry},
  title = {BSmooth: from whole genome bisulfite sequencing reads to differentially
	methylated regions.},
  journal = {Genome Biol},
  year = {2012},
  volume = {13},
  pages = {R83},
  number = {10},
  month = {Oct},
  abstract = {ABSTRACT: DNA methylation is an important epigenetic modification
	involved in gene regulation, which can now be measured using whole-genome
	bisulfite sequencing. However, cost, complexity of the data, and
	lack of comprehensive analytical tools are major challenges that
	keep this technology from becoming widely applied. Here we present
	BSmooth, an alignment, quality control and analysis pipeline that
	provides accurate and precise results even with low coverage data,
	appropriately handling biological replicates. BSmooth is open source
	software, and can be downloaded from http://rafalab.jhsph.edu/bsmooth.},
  doi = {10.1186/gb-2012-13-10-r83},
  file = {Hansen2012.pdf:Hansen2012.pdf:PDF},
  institution = {Department of Biostatistics, Johns Hopkins Bloomberg School of Public
	Health, Baltimore, MD 21205, USA. khansen@jhsph.edu.},
  language = {eng},
  medline-pst = {aheadofprint},
  owner = {hebestrk},
  pii = {gb-2012-13-10-r83},
  pmid = {23034175},
  timestamp = {2012.10.22},
  url = {http://dx.doi.org/10.1186/gb-2012-13-10-r83}
}

@ARTICLE{Hansen2011,
  author = {Kasper Daniel Hansen and Winston Timp and Héctor Corrada Bravo and
	Sarven Sabunciyan and Benjamin Langmead and Oliver G McDonald and
	Bo Wen and Hao Wu and Yun Liu and Dinh Diep and Eirikur Briem and
	Kun Zhang and Rafael A Irizarry and Andrew P Feinberg},
  title = {Increased methylation variation in epigenetic domains across cancer
	types.},
  journal = {Nature Genetics},
  year = {2011},
  volume = {43},
  pages = {768--775},
  number = {8},
  month = {Aug},
  abstract = {Tumor heterogeneity is a major barrier to effective cancer diagnosis
	and treatment. We recently identified cancer-specific differentially
	DNA-methylated regions (cDMRs) in colon cancer, which also distinguish
	normal tissue types from each other, suggesting that these cDMRs
	might be generalized across cancer types. Here we show stochastic
	methylation variation of the same cDMRs, distinguishing cancer from
	normal tissue, in colon, lung, breast, thyroid and Wilms' tumors,
	with intermediate variation in adenomas. Whole-genome bisulfite sequencing
	shows these variable cDMRs are related to loss of sharply delimited
	methylation boundaries at CpG islands. Furthermore, we find hypomethylation
	of discrete blocks encompassing half the genome, with extreme gene
	expression variability. Genes associated with the cDMRs and large
	blocks are involved in mitosis and matrix remodeling, respectively.
	We suggest a model for cancer involving loss of epigenetic stability
	of well-defined genomic domains that underlies increased methylation
	variability in cancer that may contribute to tumor heterogeneity.},
  doi = {10.1038/ng.865},
  file = {Hansen2011.pdf:Hansen2011.pdf:PDF},
  institution = {Department of Biostatistics, Johns Hopkins Bloomberg School of Public
	Health, Baltimore, Maryland, USA.},
  keywords = {CpG Islands, genetics; DNA Methylation; DNA, Neoplasm, genetics; Epigenomics;
	Gene Expression Profiling; Gene Expression Regulation, Neoplastic;
	Genetic Variation, genetics; Humans; Neoplasms, classification/genetics;
	Oligonucleotide Array Sequence Analysis; Promoter Regions, Genetic;
	Sulfites; Tumor Markers, Biological, genetics},
  language = {eng},
  medline-pst = {epublish},
  owner = {hebestrk},
  pii = {ng.865},
  pmid = {21706001},
  timestamp = {2012.08.15},
  url = {http://dx.doi.org/10.1038/ng.865}
}

@ARTICLE{Hebestreit2013,
  author = {Katja Hebestreit and Martin Dugas and Hans-Ulrich Klein},
  title = {Detection of significantly differentially methylated regions in targeted
	bisulfite sequencing data.},
  journal = {Bioinformatics},
  year = {2013},
  volume = {29},
  pages = {1647--1653},
  number = {13},
  month = {Jul},
  __markedentry = {[hebestrk]},
  abstract = {Bisulfite sequencing is currently the gold standard to obtain genome-wide
	DNA methylation profiles in eukaryotes. In contrast to the rapid
	development of appropriate pre-processing and alignment software,
	methods for analyzing the resulting methylation profiles are relatively
	limited so far. For instance, an appropriate pipeline to detect DNA
	methylation differences between cancer and control samples is still
	required.We propose an algorithm that detects significantly differentially
	methylated regions in data obtained by targeted bisulfite sequencing
	approaches, such as reduced representation bisulfite sequencing.
	In a first step, this approach tests all target regions for methylation
	differences by taking spatial dependence into account. A false discovery
	rate procedure controls the expected proportion of incorrectly rejected
	regions. In a second step, the significant target regions are trimmed
	to the actually differentially methylated regions. This hierarchical
	procedure detects differentially methylated regions with increased
	power compared with existing methods.R/Bioconductor package BiSeq.katja.hebestreit@uni-muenster.deSupplementary
	Data are available at Bioinformatics online.},
  doi = {10.1093/bioinformatics/btt263},
  institution = {Institute of Medical Informatics, University of Münster, Albert-Schweitzer-Campus
	1, 48149 Münster, Germany.},
  language = {eng},
  medline-pst = {ppublish},
  owner = {hebestrk},
  pii = {btt263},
  pmid = {23658421},
  timestamp = {2013.07.18},
  url = {http://dx.doi.org/10.1093/bioinformatics/btt263}
}

@ARTICLE{Heller2006,
  author = {Ruth Heller and Damian Stanley and Daniel Yekutieli and Nava Rubin
	and Yoav Benjamini},
  title = {Cluster-based analysis of FMRI data.},
  journal = {Neuroimage},
  year = {2006},
  volume = {33},
  pages = {599--608},
  number = {2},
  month = {Nov},
  abstract = {We propose a method for the statistical analysis of fMRI data that
	tests cluster units rather than voxel units for activation. The advantages
	of this analysis over previous ones are both conceptual and statistical.
	Recognizing that the fundamental units of interest are the spatially
	contiguous clusters of voxels that are activated together, we set
	out to approximate these cluster units from the data by a clustering
	algorithm especially tailored for fMRI data. Testing the cluster
	units has a two-fold statistical advantage over testing each voxel
	separately: the signal to noise ratio within the unit tested is higher,
	and the number of hypotheses tests compared is smaller. We suggest
	controlling FDR on clusters, i.e., the proportion of clusters rejected
	erroneously out of all clusters rejected and explain the meaning
	of controlling this error rate. We introduce the powerful adaptive
	procedure to control the FDR on clusters. We apply our cluster-based
	analysis (CBA) to both an event-related and a block design fMRI vision
	experiment and demonstrate its increased power over voxel-by-voxel
	analysis in these examples as well as in simulations.},
  doi = {10.1016/j.neuroimage.2006.04.233},
  file = {Heller2006.pdf:Heller2006.pdf:PDF},
  institution = {Department of Statistics and Operations Research, the Sackler Faculty
	of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel. rheller@post.tau.ac.il},
  keywords = {Algorithms; Brain Mapping, methods; Brain, anatomy /&/ histology;
	Cluster Analysis; Functional Laterality; Humans; Magnetic Resonance
	Imaging, methods; Models, Neurological; Occipital Lobe, anatomy /&/
	histology},
  language = {eng},
  medline-pst = {ppublish},
  owner = {hebestrk},
  pii = {S1053-8119(06)00528-3},
  pmid = {16952467},
  timestamp = {2012.08.09},
  url = {http://dx.doi.org/10.1016/j.neuroimage.2006.04.233}
}

@ARTICLE{Jaffe2012,
  author = {Andrew E Jaffe and Peter Murakami and Hwajin Lee and Jeffrey T Leek
	and M. Daniele Fallin and Andrew P Feinberg and Rafael A Irizarry},
  title = {Bump hunting to identify differentially methylated regions in epigenetic
	epidemiology studies.},
  journal = {Int J Epidemiol},
  year = {2012},
  volume = {41},
  pages = {200--209},
  number = {1},
  month = {Feb},
  abstract = {During the past 5 years, high-throughput technologies have been successfully
	used by epidemiology studies, but almost all have focused on sequence
	variation through genome-wide association studies (GWAS). Today,
	the study of other genomic events is becoming more common in large-scale
	epidemiological studies. Many of these, unlike the single-nucleotide
	polymorphism studied in GWAS, are continuous measures. In this context,
	the exercise of searching for regions of interest for disease is
	akin to the problems described in the statistical 'bump hunting'
	literature.New statistical challenges arise when the measurements
	are continuous rather than categorical, when they are measured with
	uncertainty, and when both biological signal, and measurement errors
	are characterized by spatial correlation along the genome. Perhaps
	the most challenging complication is that continuous genomic data
	from large studies are measured throughout long periods, making them
	susceptible to 'batch effects'. An example that combines all three
	characteristics is genome-wide DNA methylation measurements. Here,
	we present a data analysis pipeline that effectively models measurement
	error, removes batch effects, detects regions of interest and attaches
	statistical uncertainty to identified regions.We illustrate the usefulness
	of our approach by detecting genomic regions of DNA methylation associated
	with a continuous trait in a well-characterized population of newborns.
	Additionally, we show that addressing unexplained heterogeneity like
	batch effects reduces the number of false-positive regions.Our framework
	offers a comprehensive yet flexible approach for identifying genomic
	regions of biological interest in large epidemiological studies using
	quantitative high-throughput methods.},
  doi = {10.1093/ije/dyr238},
  file = {Jaffe2012.pdf:Jaffe2012.pdf:PDF},
  institution = {Department of Biostatistics, Johns Hopkins Bloomberg School of Public
	Health, Baltimore, MD, USA.},
  keywords = {DNA Methylation, genetics; Epigenesis, Genetic; Genome-Wide Association
	Study, methods; Humans; Models, Statistical},
  language = {eng},
  medline-pst = {ppublish},
  owner = {hebestrk},
  pii = {dyr238},
  pmid = {22422453},
  timestamp = {2012.11.28},
  url = {http://dx.doi.org/10.1093/ije/dyr238}
}

@ARTICLE{Krueger2011,
  author = {Felix Krueger and Simon R Andrews},
  title = {Bismark: a flexible aligner and methylation caller for Bisulfite-Seq
	applications.},
  journal = {Bioinformatics},
  year = {2011},
  volume = {27},
  pages = {1571--1572},
  number = {11},
  month = {Jun},
  abstract = {SUMMARY: A combination of bisulfite treatment of DNA and high-throughput
	sequencing (BS-Seq) can capture a snapshot of a cell's epigenomic
	state by revealing its genome-wide cytosine methylation at single
	base resolution. Bismark is a flexible tool for the time-efficient
	analysis of BS-Seq data which performs both read mapping and methylation
	calling in a single convenient step. Its output discriminates between
	cytosines in CpG, CHG and CHH context and enables bench scientists
	to visualize and interpret their methylation data soon after the
	sequencing run is completed. Availability and implementation: Bismark
	is released under the GNU GPLv3+ licence. The source code is freely
	available from www.bioinformatics.bbsrc.ac.uk/projects/bismark/.},
  doi = {10.1093/bioinformatics/btr167},
  institution = {Bioinformatics Group, The Babraham Institute, CB22 3AT, Cambridge,
	UK. felix.krueger@bbsrc.ac.uk},
  keywords = {Cytosine, metabolism; DNA Methylation; DNA, chemistry; Sequence Analysis,
	DNA; Software; Sulfites},
  language = {eng},
  medline-pst = {ppublish},
  owner = {hebestrk},
  pii = {btr167},
  pmid = {21493656},
  timestamp = {2012.11.09},
  url = {http://dx.doi.org/10.1093/bioinformatics/btr167}
}

@ARTICLE{Krueger2012,
  author = {Felix Krueger and Benjamin Kreck and Andre Franke and Simon R Andrews},
  title = {DNA methylome analysis using short bisulfite sequencing data.},
  journal = {Nat Methods},
  year = {2012},
  volume = {9},
  pages = {145--151},
  number = {2},
  month = {Feb},
  abstract = {Bisulfite conversion of genomic DNA combined with next-generation
	sequencing (BS-seq) is widely used to measure the methylation state
	of a whole genome, the methylome, at single-base resolution. However,
	analysis of BS-seq data still poses a considerable challenge. Here
	we summarize the challenges of BS-seq mapping as they apply to both
	base and color-space data. We also explore the effect of sequencing
	errors and contaminants on inferred methylation levels and recommend
	the most appropriate way to analyze this type of data.},
  doi = {10.1038/nmeth.1828},
  institution = {Bioinformatics Group, The Babraham Institute, Cambridge, UK.},
  keywords = {DNA Methylation; DNA, genetics/metabolism; Sequence Analysis, DNA;
	Sulfites, chemistry},
  language = {eng},
  medline-pst = {epublish},
  owner = {hebestrk},
  pii = {nmeth.1828},
  pmid = {22290186},
  timestamp = {2013.01.10},
  url = {http://dx.doi.org/10.1038/nmeth.1828}
}

@ARTICLE{Kuan2012,
  author = {Pei Fen Kuan and Derek Y Chiang},
  title = {Integrating Prior Knowledge in Multiple Testing under Dependence
	with Applications to Detecting Differential DNA Methylation.},
  journal = {Biometrics},
  year = {2012},
  volume = {68},
  pages = {774--783},
  number = {3},
  month = {Sep},
  abstract = {Summary DNA methylation has emerged as an important hallmark of epigenetics.
	Numerous platforms including tiling arrays and next generation sequencing,
	and experimental protocols are available for profiling DNA methylation.
	Similar to other tiling array data, DNA methylation data shares the
	characteristics of inherent correlation structure among nearby probes.
	However, unlike gene expression or protein DNA binding data, the
	varying CpG density which gives rise to CpG island, shore and shelf
	definition provides exogenous information in detecting differential
	methylation. This article aims to introduce a robust testing and
	probe ranking procedure based on a nonhomogeneous hidden Markov model
	that incorporates the above-mentioned features for detecting differential
	methylation. We revisit the seminal work of Sun and Cai (2009, Journal
	of the Royal Statistical Society: Series B (Statistical Methodology)?71,
	393-424) and propose modeling the nonnull using a nonparametric symmetric
	distribution in two-sided hypothesis testing. We show that this model
	improves probe ranking and is robust to model misspecification based
	on extensive simulation studies. We further illustrate that our proposed
	framework achieves good operating characteristics as compared to
	commonly used methods in real DNA methylation data that aims to detect
	differential methylation sites.},
  doi = {10.1111/j.1541-0420.2011.01730.x},
  institution = {Department of Biostatistics and Lineberger Comprehensive Cancer Center,
	University of North Carolina, Chapel Hill, North Carolina 27599,
	U.S.A. Department of Genetics and Lineberger Comprehensive Cancer
	Center, University of North Carolina, Chapel Hill, North Carolina
	27599, U.S.A.},
  language = {eng},
  medline-pst = {ppublish},
  owner = {hebestrk},
  pmid = {22260651},
  timestamp = {2012.11.09},
  url = {http://dx.doi.org/10.1111/j.1541-0420.2011.01730.x}
}

@ARTICLE{Kuan2010,
  author = {Pei Fen Kuan and Sijian Wang and Xin Zhou and Haitao Chu},
  title = {A statistical framework for Illumina DNA methylation arrays.},
  journal = {Bioinformatics},
  year = {2010},
  volume = {26},
  pages = {2849--2855},
  number = {22},
  month = {Nov},
  doi = {10.1093/bioinformatics/btq553},
  file = {Kuan2010.pdf:Kuan2010.pdf:PDF},
  institution = {Department of Biostatistics, Lineberger Comprehensive Cancer Center,
	University of North Carolina, Chapel Hill, NC 27599, USA. pfkuan@bios.unc.edu},
  keywords = {DNA Methylation; Data Interpretation, Statistical; Databases, Genetic;
	Oligonucleotide Array Sequence Analysis, methods},
  language = {eng},
  medline-pst = {ppublish},
  owner = {hebestrk},
  pii = {btq553},
  pmid = {20880956},
  timestamp = {2012.11.23},
  url = {http://dx.doi.org/10.1093/bioinformatics/btq553}
}

@ARTICLE{Li2010,
  author = {Yingrui Li and Jingde Zhu and Geng Tian and Ning Li and Qibin Li
	and Mingzhi Ye and Hancheng Zheng and Jian Yu and Honglong Wu and
	Jihua Sun and Hongyu Zhang and Quan Chen and Ruibang Luo and Minfeng
	Chen and Yinghua He and Xin Jin and Qinghui Zhang and Chang Yu and
	Guangyu Zhou and Jinfeng Sun and Yebo Huang and Huisong Zheng and
	Hongzhi Cao and Xiaoyu Zhou and Shicheng Guo and Xueda Hu and Xin
	Li and Karsten Kristiansen and Lars Bolund and Jiujin Xu and Wen
	Wang and Huanming Yang and Jian Wang and Ruiqiang Li and Stephan
	Beck and Jun Wang and Xiuqing Zhang},
  title = {The DNA methylome of human peripheral blood mononuclear cells.},
  journal = {PLoS Biol},
  year = {2010},
  volume = {8},
  pages = {e1000533},
  number = {11},
  abstract = {DNA methylation plays an important role in biological processes in
	human health and disease. Recent technological advances allow unbiased
	whole-genome DNA methylation (methylome) analysis to be carried out
	on human cells. Using whole-genome bisulfite sequencing at 24.7-fold
	coverage (12.3-fold per strand), we report a comprehensive (92.62\%)
	methylome and analysis of the unique sequences in human peripheral
	blood mononuclear cells (PBMC) from the same Asian individual whose
	genome was deciphered in the YH project. PBMC constitute an important
	source for clinical blood tests world-wide. We found that 68.4\%
	of CpG sites and <0.2\% of non-CpG sites were methylated, demonstrating
	that non-CpG cytosine methylation is minor in human PBMC. Analysis
	of the PBMC methylome revealed a rich epigenomic landscape for 20
	distinct genomic features, including regulatory, protein-coding,
	non-coding, RNA-coding, and repeat sequences. Integration of our
	methylome data with the YH genome sequence enabled a first comprehensive
	assessment of allele-specific methylation (ASM) between the two haploid
	methylomes of any individual and allowed the identification of 599
	haploid differentially methylated regions (hDMRs) covering 287 genes.
	Of these, 76 genes had hDMRs within 2 kb of their transcriptional
	start sites of which >80\% displayed allele-specific expression (ASE).
	These data demonstrate that ASM is a recurrent phenomenon and is
	highly correlated with ASE in human PBMCs. Together with recently
	reported similar studies, our study provides a comprehensive resource
	for future epigenomic research and confirms new sequencing technology
	as a paradigm for large-scale epigenomics studies.},
  doi = {10.1371/journal.pbio.1000533},
  institution = {BGI-Shenzhen, Shenzhen, Guangdong, China.},
  keywords = {Alleles; CpG Islands; DNA Methylation; Haploidy; Humans; Leukocytes,
	Mononuclear, metabolism; RNA, Untranslated, genetics; Sequence Alignment},
  language = {eng},
  medline-pst = {epublish},
  owner = {hebestrk},
  pmid = {21085693},
  timestamp = {2012.11.21},
  url = {http://dx.doi.org/10.1371/journal.pbio.1000533}
}

@ARTICLE{Lutsik2011,
  author = {Pavlo Lutsik and Lars Feuerbach and Julia Arand and Thomas Lengauer
	and J\"{o}rn Walter and Christoph Bock},
  title = {BiQ Analyzer HT: locus-specific analysis of DNA methylation by high-throughput
	bisulfite sequencing.},
  journal = {Nucleic Acids Res},
  year = {2011},
  volume = {39},
  pages = {W551--W556},
  number = {Web Server issue},
  month = {Jul},
  abstract = {Bisulfite sequencing is a widely used method for measuring DNA methylation
	in eukaryotic genomes. The assay provides single-base pair resolution
	and, given sufficient sequencing depth, its quantitative accuracy
	is excellent. High-throughput sequencing of bisulfite-converted DNA
	can be applied either genome wide or targeted to a defined set of
	genomic loci (e.g. using locus-specific PCR primers or DNA capture
	probes). Here, we describe BiQ Analyzer HT (http://biq-analyzer-ht.bioinf.mpi-inf.mpg.de/),
	a user-friendly software tool that supports locus-specific analysis
	and visualization of high-throughput bisulfite sequencing data. The
	software facilitates the shift from time-consuming clonal bisulfite
	sequencing to the more quantitative and cost-efficient use of high-throughput
	sequencing for studying locus-specific DNA methylation patterns.
	In addition, it is useful for locus-specific visualization of genome-wide
	bisulfite sequencing data.},
  doi = {10.1093/nar/gkr312},
  institution = { Germany.},
  keywords = {DNA Methylation; Genetic Loci; High-Throughput Nucleotide Sequencing;
	Sequence Analysis, DNA; Software; Sulfites},
  language = {eng},
  medline-pst = {ppublish},
  owner = {hebestrk},
  pii = {gkr312},
  pmid = {21565797},
  timestamp = {2012.11.09},
  url = {http://dx.doi.org/10.1093/nar/gkr312}
}

@ARTICLE{Marchini2004,
  author = {Jonathan Marchini and Anne Presanis},
  title = {Comparing methods of analyzing fMRI statistical parametric maps.},
  journal = {Neuroimage},
  year = {2004},
  volume = {22},
  pages = {1203--1213},
  number = {3},
  month = {Jul},
  abstract = {Approaches for the analysis of statistical parametric maps (SPMs)
	can be crudely grouped into three main categories in which different
	philosophies are applied to delineate activated regions. These being
	type I error control thresholding, false discovery rate (FDR) control
	thresholding and posterior probability thresholding. To better understand
	the properties of these main approaches, we carried out a simulation
	study to compare the approaches as they would be used on real data
	sets. Using default settings, we find that posterior probability
	thresholding is the most powerful approach, and type I error control
	thresholding provides the lowest levels of type I error. False discovery
	rate control thresholding performs in between the other approaches
	for both these criteria, although for some parameter settings this
	approach can approximate the performance of posterior probability
	thresholding. Based on these results, we discuss the relative merits
	of the three approaches in an attempt to decide upon an optimal approach.
	We conclude that viewing the problem of delineating areas of activation
	as a classification problem provides a highly interpretable framework
	for comparing the methods. Within this framework, we highlight the
	role of the loss function, which explicitly penalizes the types of
	errors that may occur in a given analysis.},
  doi = {10.1016/j.neuroimage.2004.03.030},
  file = {Marchini2004.pdf:Marchini2004.pdf:PDF},
  institution = {Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.
	marchini@stats.ox.ac.uk},
  keywords = {Bayes Theorem; Computer Simulation; Data Interpretation, Statistical;
	Differential Threshold; Linear Models; Magnetic Resonance Imaging;
	Probability},
  language = {eng},
  medline-pst = {ppublish},
  owner = {hebestrk},
  pii = {S105381190400165X},
  pmid = {15219592},
  timestamp = {2012.08.09},
  url = {http://dx.doi.org/10.1016/j.neuroimage.2004.03.030}
}

@ARTICLE{Meissner2005,
  author = {Alexander Meissner and Andreas Gnirke and George W Bell and Bernard
	Ramsahoye and Eric S Lander and Rudolf Jaenisch},
  title = {Reduced representation bisulfite sequencing for comparative high-resolution
	DNA methylation analysis.},
  journal = {Nucleic Acids Res},
  year = {2005},
  volume = {33},
  pages = {5868--5877},
  number = {18},
  abstract = {We describe a large-scale random approach termed reduced representation
	bisulfite sequencing (RRBS) for analyzing and comparing genomic methylation
	patterns. BglII restriction fragments were size-selected to 500-600
	bp, equipped with adapters, treated with bisulfite, PCR amplified,
	cloned and sequenced. We constructed RRBS libraries from murine ES
	cells and from ES cells lacking DNA methyltransferases Dnmt3a and
	3b and with knocked-down (kd) levels of Dnmt1 (Dnmt[1(kd),3a-/-,3b-/-]).
	Sequencing of 960 RRBS clones from Dnmt[1(kd),3a-/-,3b-/-] cells
	generated 343 kb of non-redundant bisulfite sequence covering 66212
	cytosines in the genome. All but 38 cytosines had been converted
	to uracil indicating a conversion rate of >99.9\%. Of the remaining
	cytosines 35 were found in CpG and 3 in CpT dinucleotides. Non-CpG
	methylation was >250-fold reduced compared with wild-type ES cells,
	consistent with a role for Dnmt3a and/or Dnmt3b in CpA and CpT methylation.
	Closer inspection revealed neither a consensus sequence around the
	methylated sites nor evidence for clustering of residual methylation
	in the genome. Our findings indicate random loss rather than specific
	maintenance of methylation in Dnmt[1(kd),3a-/-,3b-/-] cells. Near-complete
	bisulfite conversion and largely unbiased representation of RRBS
	libraries suggest that random shotgun bisulfite sequencing can be
	scaled to a genome-wide approach.},
  doi = {10.1093/nar/gki901},
  institution = {Whitehead Institute for Biomedical Research and Massachusetts Institute
	of Technology, Nine Cambridge Center, Cambridge, MA 02142, USA.},
  keywords = {Animals; DNA (Cytosine-5-)-Methyltransferase, genetics; DNA Methylation;
	Embryo, Mammalian, cytology; Genomic Library; Genomics, methods;
	Mice; Mice, Knockout; RNA Interference; Stem Cells, metabolism; Sulfites,
	chemistry},
  language = {eng},
  medline-pst = {epublish},
  owner = {hebestrk},
  pii = {33/18/5868},
  pmid = {16224102},
  timestamp = {2012.11.09},
  url = {http://dx.doi.org/10.1093/nar/gki901}
}

@ARTICLE{Otto2012,
  author = {Christian Otto and Peter F Stadler and Steve Hoffmann},
  title = {Fast and sensitive mapping of bisulfite-treated sequencing data.},
  journal = {Bioinformatics},
  year = {2012},
  volume = {28},
  pages = {1698--1704},
  number = {13},
  month = {Jul},
  abstract = {Cytosine DNA methylation is one of the major epigenetic modifications
	and influences gene expression, developmental processes, X-chromosome
	inactivation, and genomic imprinting. Aberrant methylation is furthermore
	known to be associated with several diseases including cancer. The
	gold standard to determine DNA methylation on genome-wide scales
	is 'bisulfite sequencing': DNA fragments are treated with sodium
	bisulfite resulting in the conversion of unmethylated cytosines into
	uracils, whereas methylated cytosines remain unchanged. The resulting
	sequencing reads thus exhibit asymmetric bisulfite-related mismatches
	and suffer from an effective reduction of the alphabet size in the
	unmethylated regions, rendering the mapping of bisulfite sequencing
	reads computationally much more demanding. As a consequence, currently
	available read mapping software often fails to achieve high sensitivity
	and in many cases requires unrealistic computational resources to
	cope with large real-life datasets.In this study, we present a seed-based
	approach based on enhanced suffix arrays in conjunction with Myers
	bit-vector algorithm to efficiently extend seeds to optimal semi-global
	alignments while allowing for bisulfite-related substitutions. It
	outperforms most current approaches in terms of sensitivity and performs
	time-competitive in mapping hundreds of millions of sequencing reads
	to vertebrate genomes.The software segemehl is freely available at
	http://www.bioinf.uni-leipzig.de/Software/segemehl.},
  doi = {10.1093/bioinformatics/bts254},
  institution = {Interdisciplinary Center for Bioinformatics and Bioinformatics Group,
	Department of Computer Science, University Leipzig, 04107 Leipzig,
	Germany.},
  language = {eng},
  medline-pst = {ppublish},
  owner = {hebestrk},
  pii = {bts254},
  pmid = {22581174},
  timestamp = {2012.11.09},
  url = {http://dx.doi.org/10.1093/bioinformatics/bts254}
}

@ARTICLE{Pavlicova2008,
  author = {Pavlicova, Martina and Santner, Thomas J. and Cressie, Noel},
  title = {Detecting signals in FMRI data using powerful FDR procedures},
  journal = {Statistics and its Inference},
  year = {2008},
  volume = {1},
  pages = {23-32},
  number = {{1}},
  note = {Referenziert Benjamini2007},
  abstract = {{Functional magnetic resonance imaging (FMRI) has revolutionized the
	study of linking physical stimuli with localized brain activity.
	Among the challenges of working with FMRI data, they are noisy, they
	exhibit spatial correlation, and they are usually large containing
	tens of thousands of voxels of information. The notion of False Discovery
	Rate (FDR) has made a great impact on how to perform powerful multiple
	hypothesis tests to detect signals in such large multivariate data.
	The spatial dependence in FMRI data requires special care since,
	if ignored, it can lead to a loss of control of size as well as a
	deterioration in power of FDR procedures. This article advocates
	transforming the voxel-wise test statistics to wavelet space, where
	the coefficients are approximately uncorrelated. We demonstrate,
	through a series of experiments, that an FDR procedure in wavelet
	space enhanced by P-value adaptive thresholding (EPAT), maintains
	control of the size of the multiple-testing procedure and offers
	substantially increased power over an FDR procedure that is applied
	directly to the map of (spatially dependent) test statistics. The
	EPAT methodology, developed here for FMRI data, is generic and can
	be applied in other dependent data settings.}},
  address = {{PO BOX 43502, SOMERVILLE, MA 02143 USA}},
  affiliation = {{Pavlicova, M (Reprint Author), Columbia Univ, Dept Biostat, New York,
	NY 10032 USA.. Pavlicova, Martina, Columbia Univ, Dept Biostat, New
	York, NY 10032 USA. Santner, Thomas J.; Cressie, Noel, Ohio State
	Univ, Dept Stat, Columbus, OH 43210 USA.}},
  author-email = {{mp2370@columbia.edu santner.1@osu.edu ncressie@stat.osu.edu}},
  doc-delivery-number = {{V13GG}},
  file = {Pavlicova2008.pdf:Pavlicova2008.pdf:PDF},
  funding-acknowledgement = {{Office of Naval Research\{{[}\}N00014-05-1-0133]; National Science
	Foundation\{{[}\}DMS-0706997, DMS-0406026, 0112050]}},
  funding-text = {{This research was supported by an Office of Naval Research grant,
	N00014-05-1-0133, and by the National Science Foundation under grant
	DMS-0706997, grant DMS-0406026, and Agreement No. 0112050. The authors
	would like to thank Antonio Algaze and Petra Schmalbrock for providing
	the FMRI data and the members of FMRIB, Oxford UK, for initial consultation
	about simulating activation FMRI datasets. We would also like to
	thank the referees and Editor for helpful comments that led to improvements
	in the presentation and content of this paper.}},
  issn = {{1938-7989}},
  journal-iso = {{Stat. Interface}},
  keywords = {{Brain mapping; False discovery rate; Power; Sensitivity; Size; Spatial
	dependence; Specificity; Wavelets}},
  keywords-plus = {{FALSE DISCOVERY RATE; RATES}},
  language = {{English}},
  number-of-cited-references = {{25}},
  owner = {hebestrk},
  publisher = {{INT PRESS BOSTON, INC}},
  research-areas = {{Mathematical \& Computational Biology; Mathematics}},
  times-cited = {{1}},
  timestamp = {2012.08.10},
  type = {{Article}},
  unique-id = {{ISI:000207654700004}},
  web-of-science-categories = {{Mathematical \& Computational Biology; Mathematics, Interdisciplinary
	Applications}}
}

@ARTICLE{Pedersen2011,
  author = {Brent Pedersen and Tzung-Fu Hsieh and Christian Ibarra and Robert
	L Fischer},
  title = {MethylCoder: software pipeline for bisulfite-treated sequences.},
  journal = {Bioinformatics},
  year = {2011},
  volume = {27},
  pages = {2435--2436},
  number = {17},
  month = {Sep},
  abstract = {MOTIVATION: MethylCoder is a software program that generates per-base
	methylation data given a set of bisulfite-treated reads. It provides
	the option to use either of two existing short-read aligners, each
	with different strengths. It accounts for soft-masked alignments
	and overlapping paired-end reads. MethylCoder outputs data in text
	and binary formats in addition to the final alignment in SAM format,
	so that common high-throughput sequencing tools can be used on the
	resulting output. It is more flexible than existing software and
	competitive in terms of speed and memory use. AVAILABILITY: MethylCoder
	requires only a python interpreter and a C compiler to run. Extensive
	documentation and the full source code are available under the MIT
	license at: https://github.com/brentp/methylcode. CONTACT: bpederse@gmail.com.},
  doi = {10.1093/bioinformatics/btr394},
  institution = {Department of Plant and Microbial Biology, University of California,
	Berkeley, CA 94720, USA. bpederse@gmail.com},
  keywords = {Cytosine, metabolism; DNA Methylation; DNA, chemistry/metabolism;
	Genomics, methods; Sequence Alignment, methods; Sequence Analysis,
	DNA; Software; Sulfites},
  language = {eng},
  medline-pst = {ppublish},
  owner = {hebestrk},
  pii = {btr394},
  pmid = {21724594},
  timestamp = {2012.11.09},
  url = {http://dx.doi.org/10.1093/bioinformatics/btr394}
}

@ARTICLE{Schoofs2012,
  author = {Till Schoofs and Christian Rohde and Katja Hebestreit and Hans-Ulrich
	Klein and Stefanie G\"{o}llner and Isabell Schulze and Mads Lerdrup
	and Nikolaj Dietrich and Shuchi Agrawal-Singh and Anika Witten and
	Monika Stoll and Eva Lengfelder and Wolf-Karsten Hofmann and Peter
	Schlenke and Thomas B\"{u}chner and Klaus Hansen and Wolfgang E Berdel
	and Frank Rosenbauer and Martin Dugas and Carsten M\"{u}ller-Tidow},
  title = {DNA methylation changes are a late event in acute promyelocytic leukemia
	and coincide with loss of transcription factor binding.},
  journal = {Blood},
  year = {2012},
  month = {Nov},
  abstract = {The origin of aberrant DNA methylation in cancer remains largely unknown.
	In this study, we elucidated the DNA methylome in primary Acute Promyelocytic
	Leukemia (APL) and the role of PML-RARα in establishing these patterns.
	APL patients showed increased genome-wide DNA methylation with higher
	variability than healthy CD34+ cells, promyelocytes and remission
	bone marrow. A core set of differentially methylated regions in APL
	was identified. Age at diagnosis, Sanz score and Flt3-mutation status
	characterized methylation subtypes. Transcription factor binding
	sites, e.g. c-myc binding sites were associated with low methylation.
	SUZ12 and REST binding sites identified in embryonic stem cells were,
	however, preferentially DNA hypermethylated in APL. Unexpectedly,
	PML-RARα binding sites were also protected from aberrant DNA methylation
	in APL. In line, myeloid cells from pre-leukemic PML-RARα knock-in
	mice did not show altered DNA methylation and expression of PML-RARα
	in hematopoietic progenitor cells prevented differentiation without
	affecting DNA methylation. ATRA treatment of APL blasts did also
	not result in immediate DNA methylation changes. These results suggest
	that aberrant DNA methylation is associated with leukemia phenotype
	but not required for PML-RARα-mediated initiation of leukemogenesis.},
  doi = {10.1182/blood-2012-08-448860},
  institution = {Department of Medicine A - Hematology, Oncology and Pneumology, University
	of Muenster, Germany;},
  language = {eng},
  medline-pst = {aheadofprint},
  owner = {hebestrk},
  pii = {blood-2012-08-448860},
  pmid = {23152544},
  timestamp = {2012.12.12},
  url = {http://dx.doi.org/10.1182/blood-2012-08-448860}
}

@ARTICLE{Simas2010,
  author = {Alexandre B. Simas and Wagner Barreto-Souza and Andréa V. Rocha},
  title = {Improved estimators for a general class of beta regression models},
  journal = {Computational Statistics and Data Analysis},
  year = {2010},
  volume = {54},
  pages = {348–366},
  file = {Simas2010.pdf:Simas2010.pdf:PDF},
  owner = {hebestrk},
  timestamp = {2012.12.05}
}

@ARTICLE{Sun2012,
  author = {Zhifu Sun and Saurabh Baheti and Sumit Middha and Rahul Kanwar and
	Yuji Zhang and Xing Li and Andreas S Beutler and Eric Klee and Yan
	W Asmann and E. Aubrey Thompson and Jean-Pierre A Kocher},
  title = {SAAP-RRBS: streamlined analysis and annotation pipeline for reduced
	representation bisulfite sequencing.},
  journal = {Bioinformatics},
  year = {2012},
  volume = {28},
  pages = {2180--2181},
  number = {16},
  month = {Aug},
  abstract = {Reduced representation bisulfite sequencing (RRBS) is a cost-effective
	approach for genome-wide methylation pattern profiling. Analyzing
	RRBS sequencing data is challenging and specialized alignment/mapping
	programs are needed. Although such programs have been developed,
	a comprehensive solution that provides researchers with good quality
	and analyzable data is still lacking. To address this need, we have
	developed a Streamlined Analysis and Annotation Pipeline for RRBS
	data (SAAP-RRBS) that integrates read quality assessment/clean-up,
	alignment, methylation data extraction, annotation, reporting and
	visualization. This package facilitates a rapid transition from sequencing
	reads to a fully annotated CpG methylation report to biological interpretation.
	AVAILABILITY AND IMPLEMENTATION: SAAP-RRBS is freely available to
	non-commercial users at the web site http://ndc.mayo.edu/mayo/research/biostat/stand-alone-packages.cfm.},
  doi = {10.1093/bioinformatics/bts337},
  institution = {Division of Biomedical Statistics and Informatics, Department of
	Health Sciences Research, Mayo Clinic College of Medicine, Rochester,
	MN 55905, USA. sun.zhifu@mayo.edu},
  language = {eng},
  medline-pst = {ppublish},
  owner = {hebestrk},
  pii = {bts337},
  pmid = {22689387},
  timestamp = {2012.11.09},
  url = {http://dx.doi.org/10.1093/bioinformatics/bts337}
}

@ARTICLE{Thorvaldsdottir2012,
  author = {Helga Thorvaldsd\'{o}ttir and James T Robinson and Jill P Mesirov},
  title = {Integrative Genomics Viewer (IGV): high-performance genomics data
	visualization and exploration.},
  journal = {Brief Bioinform},
  year = {2012},
  month = {Apr},
  abstract = {Data visualization is an essential component of genomic data analysis.
	However, the size and diversity of the data sets produced by today's
	sequencing and array-based profiling methods present major challenges
	to visualization tools. The Integrative Genomics Viewer (IGV) is
	a high-performance viewer that efficiently handles large heterogeneous
	data sets, while providing a smooth and intuitive user experience
	at all levels of genome resolution. A key characteristic of IGV is
	its focus on the integrative nature of genomic studies, with support
	for both array-based and next-generation sequencing data, and the
	integration of clinical and phenotypic data. Although IGV is often
	used to view genomic data from public sources, its primary emphasis
	is to support researchers who wish to visualize and explore their
	own data sets or those from colleagues. To that end, IGV supports
	flexible loading of local and remote data sets, and is optimized
	to provide high-performance data visualization and exploration on
	standard desktop systems. IGV is freely available for download from
	http://www.broadinstitute.org/igv, under a GNU LGPL open-source license.},
  doi = {10.1093/bib/bbs017},
  language = {eng},
  medline-pst = {aheadofprint},
  owner = {hebestrk},
  pii = {bbs017},
  pmid = {22517427},
  timestamp = {2013.01.09},
  url = {http://dx.doi.org/10.1093/bib/bbs017}
}

@ARTICLE{Vasconcellos2005,
  author = {Klaus L.P. Vasconcellos and Francisco Cribari–Neto},
  title = {Improved maximum likelihood estimation in a new class of beta regression
	models},
  journal = {Brazilian Journal of Probability and Statistics},
  year = {2005},
  volume = {19},
  pages = {13-31},
  abstract = {We propose a class of regression models where the response
	
	is beta distributed and the two parameters that index the beta distribution
	
	are related to covariates and regression parameters. The proposed
	class of
	
	models is useful for modeling data that are restricted to the (0,
	1) interval.
	
	We discuss maximum likelihood estimation of the parameters that define
	the
	
	regression structure of the model, and derive closed-form expressions
	for the
	
	second order biases of these estimators. The derived expressions are
	then used
	
	to define bias-corrected maximum likelihood estimators. Simulation
	results
	
	show that the bias correction scheme yields nearly unbiased estimators.},
  file = {Vasconcellos2005.pdf:Vasconcellos2005.pdf:PDF},
  owner = {hebestrk},
  timestamp = {2012.10.18}
}

@ARTICLE{WangD2012,
  author = {Dan Wang and Li Yan and Qiang Hu and Lara E. Sucheston and Michael
	J. Higgins and Christine B. Ambrosone and Candace S. Johnson and
	Dominic J. Smiraglia and Song Liu},
  title = {IMA: An R package for high-throughput analysis of Illumina’s 450K
	Infinium methylation data},
  journal = {Bioinformatics},
  year = {2012},
  volume = {28},
  pages = {729-730},
  number = {5},
  owner = {hebestrk},
  timestamp = {2012.12.05}
}

@ARTICLE{Wang2011,
  author = {Hong-Qiang Wang and Lindsey K Tuominen and Chung-Jui Tsai},
  title = {SLIM: a sliding linear model for estimating the proportion of true
	null hypotheses in datasets with dependence structures.},
  journal = {Bioinformatics},
  year = {2011},
  volume = {27},
  pages = {225--231},
  number = {2},
  month = {Jan},
  abstract = {The pre-estimate of the proportion of null hypotheses (π(0)) plays
	a critical role in controlling false discovery rate (FDR) in multiple
	hypothesis testing. However, hidden complex dependence structures
	of many genomics datasets distort the distribution of p-values, rendering
	existing π(0) estimators less effective.From the basic non-linear
	model of the q-value method, we developed a simple linear algorithm
	to probe local dependence blocks. We uncovered a non-static relationship
	between tests' p-values and their corresponding q-values that is
	influenced by data structure and π(0). Using an optimization framework,
	these findings were exploited to devise a Sliding Linear Model (SLIM)
	to more reliably estimate π(0) under dependence. When tested on a
	number of simulation datasets with varying data dependence structures
	and on microarray data, SLIM was found to be robust in estimating
	π(0) against dependence. The accuracy of its π(0) estimation suggests
	that SLIM can be used as a stand-alone tool for prediction of significant
	tests.The R code of the proposed method is available at http://aspendb.uga.edu/downloads
	for academic use.},
  doi = {10.1093/bioinformatics/btq650},
  institution = {Warnell School of Forestry and Natural Resources, University of Georgia,
	Athens, GA 30602, USA.},
  keywords = {Algorithms; Computer Simulation; Gene Expression Profiling; Linear
	Models; Oligonucleotide Array Sequence Analysis; Populus, genetics/metabolism},
  language = {eng},
  medline-pst = {ppublish},
  owner = {hebestrk},
  pii = {btq650},
  pmid = {21098430},
  timestamp = {2012.12.19},
  url = {http://dx.doi.org/10.1093/bioinformatics/btq650}
}

@ARTICLE{Wang2012,
  author = {Li Wang and Jihua Sun and Honglong Wu and Siyang Liu and Junwen Wang
	and Boxin Wu and Shujia Huang and Ning Li and Jun Wang and Xiuqing
	Zhang},
  title = {Systematic assessment of reduced representation bisulfite sequencing
	to human blood samples: A promising method for large-sample-scale
	epigenomic studies.},
  journal = {Journal of Biotechnology},
  year = {2012},
  volume = {157},
  pages = {1--6},
  number = {1},
  month = {Jan},
  abstract = {Complementary to the time- and cost-intensive direct bisulfite sequencing,
	we applied reduced representation bisulfite sequencing (RRBS) to
	the human peripheral blood mononuclear cells (PBMC) from YH, the
	Asian individual whose genome and epigenome has been deciphered in
	the YH project and systematically assessed the genomic coverage,
	coverage depth and reproducibility of this technology as well as
	the concordance of DNA methylation levels measured by RRBS and direct
	bisulfite sequencing for the detected CpG sites. Our result suggests
	that RRBS can cover more than half of CpG islands and promoter regions
	with a good coverage depth and the proportion of the CpG sites covered
	by the biological replicates reaches 80-90\%, indicating good reproducibility.
	Given a smaller data quantity, RRBS enjoys much better coverage depth
	than direct bisulfite sequencing and the concordance of DNA methylation
	levels between the two methods is high. It can be concluded that
	RRBS is a time and cost-effective sequencing method for unbiased
	DNA methylation profiling of CpG islands and promoter regions in
	a genome-wide scale and it is the method of choice to assay certain
	genomic regions for multiple samples in a rapid way.},
  doi = {10.1016/j.jbiotec.2011.06.034},
  file = {Wang2012.pdf:Wang2012.pdf:PDF},
  institution = {BGI-Shenzhen, Shenzhen, Guangdong, China.},
  keywords = {Computer Simulation; CpG Islands; DNA Methylation; DNA, blood/chemistry;
	Epigenomics, methods; Genome, Human; Humans; Leukocytes, Mononuclear,
	chemistry; Reproducibility of Results; Sequence Analysis, DNA, methods;
	Sulfites, chemistry},
  language = {eng},
  medline-pst = {ppublish},
  owner = {hebestrk},
  pii = {S0168-1656(11)00333-6},
  pmid = {21763364},
  timestamp = {2012.09.24},
  url = {http://dx.doi.org/10.1016/j.jbiotec.2011.06.034}
}

@ARTICLE{Worsley2002,
  author = {K. J. Worsley and C. H. Liao and J. Aston and V. Petre and G. H.
	Duncan and F. Morales and A. C. Evans},
  title = {A general statistical analysis for fMRI data.},
  journal = {Neuroimage},
  year = {2002},
  volume = {15},
  pages = {1--15},
  number = {1},
  month = {Jan},
  abstract = {We propose a method for the statistical analysis of fMRI data that
	seeks a compromise between efficiency, generality, validity, simplicity,
	and execution speed. The main differences between this analysis and
	previous ones are: a simple bias reduction and regularization for
	voxel-wise autoregressive model parameters; the combination of effects
	and their estimated standard deviations across different runs/sessions/subjects
	via a hierarchical random effects analysis using the EM algorithm;
	overcoming the problem of a small number of runs/session/subjects
	using a regularized variance ratio to increase the degrees of freedom.},
  doi = {10.1006/nimg.2001.0933},
  file = {Worsley2002.pdf:Worsley2002.pdf:PDF},
  institution = {Department of Mathematics and Statistics, McGill University, Montreal,
	Canada.},
  keywords = {Algorithms; Artifacts; Bias (Epidemiology); Brain, blood supply; Hemodynamics,
	physiology; Humans; Image Processing, Computer-Assisted, statistics
	/&/ numerical data; Imaging, Three-Dimensional, statistics /&/ numerical
	data; Linear Models; Magnetic Resonance Imaging, statistics /&/ numerical
	data; Mathematical Computing; Regression Analysis; Reproducibility
	of Results},
  language = {eng},
  medline-pst = {ppublish},
  owner = {hebestrk},
  pii = {S1053811901909334},
  pmid = {11771969},
  timestamp = {2012.08.09},
  url = {http://dx.doi.org/10.1006/nimg.2001.0933}
}

@ARTICLE{Worsley2004,
  author = {Keith J Worsley and Jonathan E Taylor and Francesco Tomaiuolo and
	Jason Lerch},
  title = {Unified univariate and multivariate random field theory.},
  journal = {Neuroimage},
  year = {2004},
  volume = {23 Suppl 1},
  pages = {S189--S195},
  abstract = {We report new random field theory P values for peaks of canonical
	correlation SPMs for detecting multiple contrasts in a linear model
	for multivariate image data. This completes results for all types
	of univariate and multivariate image data analysis. All other known
	univariate and multivariate random field theory results are now special
	cases, so these new results present a true unification of all currently
	known results. As an illustration, we use these results in a deformation-based
	morphometry (DBM) analysis to look for regions of the brain where
	vector deformations of nonmissile trauma patients are related to
	several verbal memory scores, to detect regions of changes in anatomical
	effective connectivity between the trauma patients and a group of
	age- and sex-matched controls, and to look for anatomical connectivity
	in cortical thickness.},
  doi = {10.1016/j.neuroimage.2004.07.026},
  file = {Worsley2004.pdf:Worsley2004.pdf:PDF},
  institution = {Department of Mathematics and Statistics, McGill University, Montreal,
	Canada H3A 2K6. keith.worsely@mcgill.ca},
  keywords = {Adult; Algorithms; Analysis of Variance; Brain Injuries, pathology;
	Brain, anatomy /&/ histology/pathology; Cerebral Cortex, injuries/pathology;
	Humans; Image Processing, Computer-Assisted, statistics /&/ numerical
	data; Linear Models; Magnetic Resonance Imaging; Models, Statistical},
  language = {eng},
  medline-pst = {ppublish},
  owner = {hebestrk},
  pii = {S1053-8119(04)00398-2},
  pmid = {15501088},
  timestamp = {2012.08.09},
  url = {http://dx.doi.org/10.1016/j.neuroimage.2004.07.026}
}

@ARTICLE{Xi2012,
  author = {Yuanxin Xi and Christoph Bock and Fabian M\"{u}ller and Deqiang Sun
	and Alexander Meissner and Wei Li},
  title = {RRBSMAP: a fast, accurate and user-friendly alignment tool for reduced
	representation bisulfite sequencing.},
  journal = {Bioinformatics},
  year = {2012},
  volume = {28},
  pages = {430--432},
  number = {3},
  month = {Feb},
  abstract = {Reduced representation bisulfite sequencing (RRBS) is a powerful yet
	cost-efficient method for studying DNA methylation on a genomic scale.
	RRBS involves restriction-enzyme digestion, bisulfite conversion
	and size selection, resulting in DNA sequencing data that require
	special bioinformatic handling. Here, we describe RRBSMAP, a short-read
	alignment tool that is designed for handling RRBS data in a user-friendly
	and scalable way. RRBSMAP uses wildcard alignment, and avoids the
	need for any preprocessing or post-processing steps. We benchmarked
	RRBSMAP against a well-validated MAQ-based pipeline for RRBS read
	alignment and observed similar accuracy but much improved runtime
	performance, easier handling and better scaling to large sample sets.
	In summary, RRBSMAP removes bioinformatic hurdles and reduces the
	computational burden of large-scale epigenome association studies
	performed with RRBS.http://rrbsmap.computational-epigenetics.org/
	http://code.google.com/p/bsmap/wl1@bcm.tmc.eduSupplementary data
	are available at Bioinformatics online.},
  doi = {10.1093/bioinformatics/btr668},
  institution = {Division of Biostatistics, Dan L Duncan Cancer Center and Department
	of Molecular and Cellular Biology, Baylor College of Medicine, Houston,
	TX 77030, USA.},
  keywords = {DNA Methylation; Embryonic Stem Cells, metabolism; Genomics, methods;
	Humans; Sequence Alignment, methods; Sequence Analysis, DNA, methods;
	Software; Sulfites; User-Computer Interface},
  language = {eng},
  medline-pst = {ppublish},
  owner = {hebestrk},
  pii = {btr668},
  pmid = {22155871},
  timestamp = {2012.11.09},
  url = {http://dx.doi.org/10.1093/bioinformatics/btr668}
}

@ARTICLE{Zackay2010,
  author = {Arie Zackay and Christine Steinhoff},
  title = {MethVisual - visualization and exploratory statistical analysis of
	DNA methylation profiles from bisulfite sequencing.},
  journal = {BMC Res Notes},
  year = {2010},
  volume = {3},
  pages = {337},
  abstract = {Exploration of DNA methylation and its impact on various regulatory
	mechanisms has become a very active field of research. Simultaneously
	there is an arising need for tools to process and analyse the data
	together with statistical investigation and visualisation.MethVisual
	is a new application that enables exploratory analysis and intuitive
	visualization of DNA methylation data as is typically generated by
	bisulfite sequencing. The package allows the import of DNA methylation
	sequences, aligns them and performs quality control comparison. It
	comprises basic analysis steps as lollipop visualization, co-occurrence
	display of methylation of neighbouring and distant CpG sites, summary
	statistics on methylation status, clustering and correspondence analysis.
	The package has been developed for methylation data but can be also
	used for other data types for which binary coding can be inferred.
	The application of the package, as well as a comparison to existing
	DNA methylation analysis tools and its workflow based on two datasets
	is presented in this paper.The R package MethVisual offers various
	analysis procedures for data that can be binarized, in particular
	for bisulfite sequenced methylation data. R/Bioconductor has become
	one of the most important environments for statistical analysis of
	various types of biological and medical data. Therefore, any data
	analysis within R that allows the integration of various data types
	as provided from different technological platforms is convenient.
	It is the first and so far the only specific package for DNA methylation
	analysis, in particular for bisulfite sequenced data available in
	R/Bioconductor enviroment. The package is available for free at http://methvisual.molgen.mpg.de/
	and from the Bioconductor Consortium http://www.bioconductor.org.},
  doi = {10.1186/1756-0500-3-337},
  institution = {Department of Computational Biology, Max Planck Institute for Molecular
	Genetics, Ihnestr 73, 14195 Berlin, Germany. christine.steinhoff@molgen.mpg.de.},
  language = {eng},
  medline-pst = {epublish},
  owner = {hebestrk},
  pii = {1756-0500-3-337},
  pmid = {21159174},
  timestamp = {2012.11.09},
  url = {http://dx.doi.org/10.1186/1756-0500-3-337}
}

@comment{jabref-meta: selector_publisher:}

@comment{jabref-meta: selector_author:}

@comment{jabref-meta: selector_journal:}

@comment{jabref-meta: selector_keywords:}

