bibtex.bib

@ARTICLE{Kim2013-vg,
  title       = {{TopHat2}: accurate alignment of transcriptomes in the
                 presence of insertions, deletions and gene fusions},
  author      = "Kim, Daehwan and Pertea, Geo and Trapnell, Cole and Pimentel,
                 Harold and Kelley, Ryan and Salzberg, Steven L",
  affiliation = "Center for Bioinformatics and Computational Biology,
                 University of Maryland, College Park, MD, 20742, USA.
                 infphilo@umiacs.umd.edu.",
  journal     = "Genome Biol.",
  volume      =  14,
  number      =  4,
  pages       = "R36",
  month       =  "25~" # apr,
  year        =  2013,
  url         = "http://dx.doi.org/10.1186/gb-2013-14-4-r36",
  issn        = "1465-6906",
  pmid        = "23618408",
  doi         = "10.1186/gb-2013-14-4-r36"
}
@ARTICLE{Langmead2012-bs,
  title     = {Fast gapped-read alignment with Bowtie 2},
  author    = "Langmead, Ben and Salzberg, Steven L",
  journal   = "Nat. Methods",
  publisher = "Nature Publishing Group",
  volume    =  9,
  number    =  4,
  pages     = "357--359",
  month     =  apr,
  year      =  2012,
  url       = "http://dx.doi.org/10.1038/nmeth.1923",
  issn      = "1548-7091",
  pmid      = "22388286",
  doi       = "10.1038/nmeth.1923"
}
@ARTICLE{Howard2013-fq,
  title       = {High-throughput {RNA} sequencing of pseudomonas-infected
                 Arabidopsis reveals hidden transcriptome complexity and novel
                 splice variants},
  author      = "Howard, Brian E and Hu, Qiwen and Babaoglu, Ahmet Can and
                 Chandra, Manan and Borghi, Monica and Tan, Xiaoping and He,
                 Luyan and Winter-Sederoff, Heike and Gassmann, Walter and
                 Veronese, Paola and Heber, Steffen",
  affiliation = "Department of Computer Science, North Carolina State
                 University, Raleigh, North Carolina, United States of America.",
  journal     = "PLoS One",
  volume      =  8,
  number      =  10,
  pages       = "e74183",
  month       =  "1~" # oct,
  year        =  2013,
  url         = "http://dx.doi.org/10.1371/journal.pone.0074183",
  issn        = "1932-6203",
  pmid        = "24098335",
  doi         = "10.1371/journal.pone.0074183",
  pmc         = "PMC3788074"
}
@ARTICLE{Li2009-oc,
  title   = {Fast and accurate short read alignment with {Burrows-Wheeler}
             transform},
  author  = "Li, H and Durbin, R",
  journal = "Bioinformatics",
  volume  =  25,
  number  =  14,
  pages   = "1754--1760",
  month   =  jul,
  year    =  2009,
  url     = "http://dx.doi.org/10.1093/bioinformatics/btp324",
  issn    = "1367-4803",
  pmid    = "19451168",
  doi     = "10.1093/bioinformatics/btp324"
}
@ARTICLE{Li2013-oy,
  title         = {Aligning sequence reads, clone sequences and assembly
                   contigs with {BWA-MEM}},
  author        = "Li, Heng",
  journal       = "arXiv [q-bio.GN]",
  month         =  03,
  year          =  2013,
  url           = "http://arxiv.org/abs/1303.3997",
  archivePrefix = "arXiv",
  eprint        = "1303.3997",
  primaryClass  = "q-bio.GN",
  arxivid       = "1303.3997"
}
@ARTICLE{Liao2013-bn,
  title       = {The Subread aligner: fast, accurate and scalable read mapping
                 by seed-and-vote},
  author      = "Liao, Yang and Smyth, Gordon K and Shi, Wei",
  affiliation = "Division of Bioinformatics, The Walter and Eliza Hall
                 Institute of Medical Research, 1G Royal Parade, Parkville,
                 Victoria 3052, Australia.",
  journal     = "Nucleic Acids Res.",
  volume      =  41,
  number      =  10,
  pages       = "e108",
  month       =  "4~" # apr,
  year        =  2013,
  url         = "http://dx.doi.org/10.1093/nar/gkt214",
  issn        = "0305-1048",
  pmid        = "23558742",
  doi         = "10.1093/nar/gkt214",
  pmc         = "PMC3664803"
}
@ARTICLE{H_Backman2016-bt,
  title       = "{systemPipeR: NGS workflow and report generation environment}",
  author      = "H Backman, Tyler W and Girke, Thomas",
  affiliation = "Institute for Integrative Genome Biology, University of
                 California, Riverside, 1207F Genomics Building, 3401 Watkins
                 Drive, Riverside, 92521, CA, USA. Institute for Integrative
                 Genome Biology, University of California, Riverside, 1207F
                 Genomics Building, 3401 Watkins Drive, Riverside, 92521, CA,
                 USA. thomas.girke@ucr.edu.",
  journal     = "BMC Bioinformatics",
  volume      =  17,
  number      =  1,
  pages       = "388",
  month       =  "20~" # sep,
  year        =  2016,
  url         = "http://dx.doi.org/10.1186/s12859-016-1241-0",
  keywords    = "Analysis workflow; ChIP-Seq; Next Generation Sequencing (NGS);
                 RNA-Seq; Ribo-Seq; VAR-Seq",
  language    = "en",
  issn        = "1471-2105",
  pmid        = "27650223",
  doi         = "10.1186/s12859-016-1241-0",
  pmc         = "PMC5029110"
}
@ARTICLE{Lawrence2013-kt,
  title       = {Software for computing and annotating genomic ranges},
  author      = "Lawrence, Michael and Huber, Wolfgang and Pag\`{e}s, Herv\'{e}
                 and Aboyoun, Patrick and Carlson, Marc and Gentleman, Robert
                 and Morgan, Martin T and Carey, Vincent J",
  affiliation = "Bioinformatics and Computational Biology, Genentech, Inc.,
                 South San Francisco, California, United States of America.
                 michafla@gene.com",
  journal     = "PLoS Comput. Biol.",
  volume      =  9,
  number      =  8,
  pages       = "e1003118",
  month       =  "8~" # aug,
  year        =  2013,
  url         = "http://dx.doi.org/10.1371/journal.pcbi.1003118",
  issn        = "1553-734X",
  pmid        = "23950696",
  doi         = "10.1371/journal.pcbi.1003118",
  pmc         = "PMC3738458"
}
@ARTICLE{Robinson2010-uk,
  title   = {edgeR: a Bioconductor package for differential expression analysis
             of digital gene expression data},
  author  = "Robinson, M D and McCarthy, D J and Smyth, G K",
  journal = "Bioinformatics",
  volume  =  26,
  number  =  1,
  pages   = "139--140",
  month   =  jan,
  year    =  2010,
  url     = "http://dx.doi.org/10.1093/bioinformatics/btp616",
  issn    = "1367-4803",
  pmid    = "19910308",
  doi     = "10.1093/bioinformatics/btp616"
}
@ARTICLE{Love2014-sh,
  title   = {Moderated estimation of fold change and dispersion for {RNA-seq}
             data with {DESeq2}},
  author  = "Love, Michael and Huber, Wolfgang and Anders, Simon",
  journal = "Genome Biol.",
  volume  =  15,
  number  =  12,
  pages   = "550",
  year    =  2014,
  url     = "http://genomebiology.com/2014/15/12/550",
  issn    = "1465-6906",
  doi     = "10.1186/s13059-014-0550-8"
}
@ARTICLE{McKenna2010-ql,
  title       = {The Genome Analysis Toolkit: a {MapReduce} framework for
                 analyzing next-generation {DNA} sequencing data},
  author      = "McKenna, Aaron and Hanna, Matthew and Banks, Eric and
                 Sivachenko, Andrey and Cibulskis, Kristian and Kernytsky,
                 Andrew and Garimella, Kiran and Altshuler, David and Gabriel,
                 Stacey and Daly, Mark and DePristo, Mark A",
  affiliation = "Program in Medical and Population Genetics, The Broad
                 Institute of Harvard and MIT, Cambridge, Massachusetts 02142,
                 USA.",
  journal     = "Genome Res.",
  volume      =  20,
  number      =  9,
  pages       = "1297--1303",
  month       =  "19~" # jul,
  year        =  2010,
  url         = "http://dx.doi.org/10.1101/gr.107524.110",
  issn        = "1088-9051",
  pmid        = "20644199",
  doi         = "10.1101/gr.107524.110",
  pmc         = "PMC2928508"
}
@ARTICLE{Li2011-ll,
  title   = {A statistical framework for {SNP} calling, mutation discovery,
             association mapping and population genetical parameter estimation
             from sequencing data},
  author  = "Li, Heng",
  journal = "Bioinformatics",
  volume  =  27,
  number  =  21,
  pages   = "2987--2993",
  month   =  "1~" # nov,
  year    =  2011,
  url     = "http://bioinformatics.oxfordjournals.org/content/27/21/2987.abstract",
  issn    = "1367-4803",
  doi     = "10.1093/bioinformatics/btr509"
}
@ARTICLE{Wu2010-iq,
  title   = {Fast and {SNP-tolerant} detection of complex variants and splicing
             in short reads},
  author  = "Wu, T D and Nacu, S",
  journal = "Bioinformatics",
  volume  =  26,
  number  =  7,
  pages   = "873--881",
  month   =  apr,
  year    =  2010,
  url     = "http://dx.doi.org/10.1093/bioinformatics/btq057",
  issn    = "1367-4803",
  pmid    = "20147302",
  doi     = "10.1093/bioinformatics/btq057"
}
@ARTICLE{Zhang2008-pc,
  title   = {Model-based analysis of {ChIP-Seq} ({MACS})},
  author  = "Zhang, Y and Liu, T and Meyer, C A and Eeckhoute, J and Johnson, D
             S and Bernstein, B E and Nussbaum, C and Myers, R M and Brown, M
             and Li, W and Liu, X S",
  journal = "Genome Biol.",
  volume  =  9,
  number  =  9,
  year    =  2008,
  url     = "http://dx.doi.org/10.1186/gb-2008-9-9-r137",
  issn    = "1465-6906",
  pmid    = "18798982",
  doi     = "10.1186/gb-2008-9-9-r137"
}

@ARTICLE{Yu2015-xu,
  title       = {{ChIPseeker}: an {R/Bioconductor} package for {ChIP} peak
                 annotation, comparison and visualization},
  author      = "Yu, Guangchuang and Wang, Li-Gen and He, Qing-Yu",
  affiliation = "Key Laboratory of Functional Protein Research of Guangdong
                 Higher Education Institutes, College of Life Science and
                 Technology, Jinan University, Guangzhou 510632, China, State
                 Key Laboratory of Emerging Infectious Diseases, School of
                 Public Health, The University of Hong Kong, Hong Kong SAR,
                 China and. Guangdong Information Center, Guangzhou 510031,
                 China. Key Laboratory of Functional Protein Research of
                 Guangdong Higher Education Institutes, College of Life Science
                 and Technology, Jinan University, Guangzhou 510632, China.",
  journal     = "Bioinformatics",
  volume      =  31,
  number      =  14,
  pages       = "2382--2383",
  month       =  "15~" # jul,
  year        =  2015,
  url         = "http://dx.doi.org/10.1093/bioinformatics/btv145",
  issn        = "1367-4803, 1367-4811",
  pmid        = "25765347",
  doi         = "10.1093/bioinformatics/btv145"
}

@ARTICLE{Zhu2010-zo,
  title       = {{ChIPpeakAnno}: a Bioconductor package to annotate {ChIP-seq}
                 and {ChIP-chip} data},
  author      = "Zhu, Lihua J and Gazin, Claude and Lawson, Nathan D and
                 Pag\`{e}s, Herv\'{e} and Lin, Simon M and Lapointe, David S
                 and Green, Michael R",
  affiliation = "Program in Gene Function and Expression, University of
                 Massachusetts Medical School, Worcester, Massachusetts 01605,
                 USA. julie.zhu@umassmed.edu",
  journal     = "BMC Bioinformatics",
  volume      =  11,
  pages       = "237",
  month       =  "11~" # may,
  year        =  2010,
  url         = "http://dx.doi.org/10.1186/1471-2105-11-237",
  issn        = "1471-2105",
  pmid        = "20459804",
  doi         = "10.1186/1471-2105-11-237",
  pmc         = "PMC3098059"
}

@ARTICLE{Juntawong2014-ny,
  title       = {Translational dynamics revealed by genome-wide profiling of
                 ribosome footprints in Arabidopsis},
  author      = "Juntawong, Piyada and Girke, Thomas and Bazin, J\'{e}r\'{e}mie
                 and Bailey-Serres, Julia",
  affiliation = "Center for Plant Cell Biology and Department of Botany and
                 Plant Sciences, University of California, Riverside, CA 92521.",
  journal     = "Proc. Natl. Acad. Sci. U. S. A.",
  volume      =  111,
  number      =  1,
  pages       = "E203--12",
  month       =  "7~" # jan,
  year        =  2014,
  url         = "http://dx.doi.org/10.1073/pnas.1317811111",
  annote      = "PMID: 24367078",
  keywords    = "alternative splicing; long intergenic noncoding RNA; ribosome
                 profiling; translational efficiency; uORF",
  issn        = "0027-8424, 1091-6490",
  pmid        = "24367078",
  doi         = "10.1073/pnas.1317811111",
  pmc         = "PMC3890782"
}
@ARTICLE{Ingolia2009-cb,
  title   = {Genome-wide analysis in vivo of translation with nucleotide
             resolution using ribosome profiling},
  author  = "Ingolia, N T and Ghaemmaghami, S and Newman, J R and Weissman, J S",
  journal = "Science",
  volume  =  324,
  number  =  5924,
  pages   = "218--223",
  month   =  apr,
  year    =  2009,
  url     = "http://dx.doi.org/10.1016/j.ymeth.2009.03.016",
  issn    = "0036-8075",
  pmid    = "19213877",
  doi     = "10.1016/j.ymeth.2009.03.016"
}
@ARTICLE{Aspden2014-uu,
  title       = {Extensive translation of small Open Reading Frames revealed by
                 {Poly-Ribo-Seq}},
  author      = "Aspden, Julie L and Eyre-Walker, Ying Chen and Phillips, Rose
                 J and Amin, Unum and Mumtaz, Muhammad Ali S and Brocard,
                 Michele and Couso, Juan-Pablo",
  affiliation = "School of Life Sciences, University of Sussex, Brighton,
                 United Kingdom. School of Life Sciences, University of Sussex,
                 Brighton, United Kingdom. School of Life Sciences, University
                 of Sussex, Brighton, United Kingdom. School of Life Sciences,
                 University of Sussex, Brighton, United Kingdom. School of Life
                 Sciences, University of Sussex, Brighton, United Kingdom.
                 School of Life Sciences, University of Sussex, Brighton,
                 United Kingdom. School of Life Sciences, University of Sussex,
                 Brighton, United Kingdom.",
  journal     = "Elife",
  volume      =  3,
  pages       = "e03528",
  month       =  "21~" # aug,
  year        =  2014,
  url         = "http://dx.doi.org/10.7554/eLife.03528",
  keywords    = "biochemistry; d. melanogaster; evolutionary biology; genomics;
                 non-coding RNAs; small open reading Frames; transmembrane
                 peptides",
  issn        = "2050-084X",
  pmid        = "25144939",
  doi         = "10.7554/eLife.03528",
  pmc         = "PMC4359375"
}
@ARTICLE{Ingolia2011-fc,
  title   = {Ribosome profiling of mouse embryonic stem cells reveals the
             complexity and dynamics of mammalian proteomes},
  author  = "Ingolia, N T and Lareau, L F and Weissman, J S",
  journal = "Cell",
  volume  =  147,
  number  =  4,
  pages   = "789--802",
  month   =  "11~" # nov,
  year    =  2011,
  url     = "http://www.ncbi.nlm.nih.gov/pubmed/22056041",
  issn    = "0092-8674, 1097-4172;0092-8674",
  pmid    = "22056041",
  doi     = "10.1016/j.cell.2011.10.002"
}
@ARTICLE{Juntawong2015-ru,
  title       = {Ribosome profiling: a tool for quantitative evaluation of
                 dynamics in {mRNA} translation},
  author      = "Juntawong, Piyada and Hummel, Maureen and Bazin, Jeremie and
                 Bailey-Serres, Julia",
  affiliation = "Center for Plant Cell Biology and Department of Botany and
                 Plant Sciences, University of California, Riverside, CA,
                 92521, USA.",
  journal     = "Methods Mol. Biol.",
  volume      =  1284,
  pages       = "139--173",
  year        =  2015,
  url         = "http://dx.doi.org/10.1007/978-1-4939-2444-8_7",
  issn        = "1064-3745, 1940-6029",
  pmid        = "25757771",
  doi         = "10.1007/978-1-4939-2444-8\_7"
}
@MISC{Girke2014-oy,
  title       = {{systemPipeR}: {NGS} workflow and report generation
                 environment},
  author      = "Girke, Thomas",
  institution = "UC Riverside",
  month       =  "28~" # jun,
  year        =  2014,
  url         = "https://github.com/tgirke/systemPipeR"
}

@ARTICLE{Ganguly2017-ur,
  title    = "The Arabidopsis {DNA} Methylome Is Stable under Transgenerational
              Drought Stress",
  author   = "Ganguly, Diep R and Crisp, Peter A and Eichten, Steven R and
              Pogson, Barry J",
  abstract = "Improving the responsiveness, acclimation, and memory of plants
              to abiotic stress holds substantive potential for improving
              agriculture. An unresolved question is the involvement of
              chromatin marks in the memory of agriculturally relevant
              stresses. Such potential has spurred numerous investigations
              yielding both promising and conflicting results. Consequently, it
              remains unclear to what extent robust stress-induced DNA
              methylation variation can underpin stress memory. Using a
              slow-onset water deprivation treatment in Arabidopsis
              (Arabidopsis thaliana), we investigated the malleability of the
              DNA methylome to drought stress within a generation and under
              repeated drought stress over five successive generations. While
              drought-associated epi-alleles in the methylome were detected
              within a generation, they did not correlate with
              drought-responsive gene expression. Six traits were analyzed for
              transgenerational stress memory, and the descendants of
              drought-stressed lineages showed one case of memory in the form
              of increased seed dormancy, and that persisted one generation
              removed from stress. With respect to transgenerational drought
              stress, there were negligible conserved differentially methylated
              regions in drought-exposed lineages compared with unstressed
              lineages. Instead, the majority of observed variation was tied to
              stochastic or preexisting differences in the epigenome occurring
              at repetitive regions of the Arabidopsis genome. Furthermore, the
              experience of repeated drought stress was not observed to
              influence transgenerational epi-allele accumulation. Our findings
              demonstrate that, while transgenerational memory is observed in
              one of six traits examined, they are not associated with
              causative changes in the DNA methylome, which appears relatively
              impervious to drought stress.",
  journal  = "Plant Physiol.",
  volume   =  175,
  number   =  4,
  pages    = "1893--1912",
  month    =  dec,
  year     =  2017,
  language = "en"
}


@ARTICLE{Krueger2011-uu,
  title    = "Bismark: a flexible aligner and methylation caller for
              {Bisulfite-Seq} applications",
  author   = "Krueger, Felix and Andrews, Simon R",
  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/.",
  journal  = "Bioinformatics",
  volume   =  27,
  number   =  11,
  pages    = "1571--1572",
  month    =  jun,
  year     =  2011,
  language = "en"
}


@ARTICLE{Akalin2012-ez,
  title    = "methylKit: a comprehensive {R} package for the analysis of
              genome-wide {DNA} methylation profiles",
  author   = "Akalin, Altuna and Kormaksson, Matthias and Li, Sheng and
              Garrett-Bakelman, Francine E and Figueroa, Maria E and Melnick,
              Ari and Mason, Christopher E",
  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.",
  journal  = "Genome Biol.",
  volume   =  13,
  number   =  10,
  pages    = "R87",
  month    =  oct,
  year     =  2012,
  language = "en"
}


@ARTICLE{Li2011-qu,
  title    = "A statistical framework for {SNP} calling, mutation discovery,
              association mapping and population genetical parameter estimation
              from sequencing data",
  author   = "Li, Heng",
  abstract = "MOTIVATION: Most existing methods for DNA sequence analysis rely
              on accurate sequences or genotypes. However, in applications of
              the next-generation sequencing (NGS), accurate genotypes may not
              be easily obtained (e.g. multi-sample low-coverage sequencing or
              somatic mutation discovery). These applications press for the
              development of new methods for analyzing sequence data with
              uncertainty. RESULTS: We present a statistical framework for
              calling SNPs, discovering somatic mutations, inferring population
              genetical parameters and performing association tests directly
              based on sequencing data without explicit genotyping or
              linkage-based imputation. On real data, we demonstrate that our
              method achieves comparable accuracy to alternative methods for
              estimating site allele count, for inferring allele frequency
              spectrum and for association mapping. We also highlight the
              necessity of using symmetric datasets for finding somatic
              mutations and confirm that for discovering rare events,
              mismapping is frequently the leading source of errors.
              AVAILABILITY: http://samtools.sourceforge.net. CONTACT:
              hengli@broadinstitute.org.",
  journal  = "Bioinformatics",
  volume   =  27,
  number   =  21,
  pages    = "2987--2993",
  month    =  nov,
  year     =  2011,
  language = "en"
}
 @Manual{biseq,
    title = {BiSeq: Processing and analyzing bisulfite sequencing data},
    author = {Katja Hebestreit and Hans-Ulrich Klein},
    year = {2015},
    note = {R package version 1.20.0},
  }

@ARTICLE{Park2016-hm,
  title    = "Differential methylation analysis for {BS-seq} data under general
              experimental design",
  author   = "Park, Yongseok and Wu, Hao",
  abstract = "MOTIVATION: DNA methylation is an epigenetic modification with
              important roles in many biological processes and diseases.
              Bisulfite sequencing (BS-seq) has emerged recently as the
              technology of choice to profile DNA methylation because of its
              accuracy, genome coverage and higher resolution. Current
              statistical methods to identify differential methylation mainly
              focus on comparing two treatment groups. With an increasing
              number of experiments performed under a general and
              multiple-factor design, particularly in reduced representation
              bisulfite sequencing, there is a need to develop more flexible,
              powerful and computationally efficient methods. RESULTS: We
              present a novel statistical model to detect differentially
              methylated loci from BS-seq data under general experimental
              design, based on a beta-binomial regression model with 'arcsine'
              link function. Parameter estimation is based on transformed data
              with generalized least square approach without relying on
              iterative algorithm. Simulation and real data analyses
              demonstrate that our method is accurate, powerful, robust and
              computationally efficient. AVAILABILITY AND IMPLEMENTATION: It is
              available as Bioconductor package DSS. CONTACT: yongpark@pitt.edu
              or hao.wu@emory.edu SUPPLEMENTARY INFORMATION: Supplementary data
              are available at Bioinformatics online.",
  journal  = "Bioinformatics",
  volume   =  32,
  number   =  10,
  pages    = "1446--1453",
  month    =  may,
  year     =  2016,
  language = "en"
}


@ARTICLE{Feng2014-ft,
  title    = "A Bayesian hierarchical model to detect differentially methylated
              loci from single nucleotide resolution sequencing data",
  author   = "Feng, Hao and Conneely, Karen N and Wu, Hao",
  abstract = "DNA methylation is an important epigenetic modification that has
              essential roles in cellular processes including gene regulation,
              development and disease and is widely dysregulated in most types
              of cancer. Recent advances in sequencing technology have enabled
              the measurement of DNA methylation at single nucleotide
              resolution through methods such as whole-genome bisulfite
              sequencing and reduced representation bisulfite sequencing. In
              DNA methylation studies, a key task is to identify differences
              under distinct biological contexts, for example, between tumor
              and normal tissue. A challenge in sequencing studies is that the
              number of biological replicates is often limited by the costs of
              sequencing. The small number of replicates leads to unstable
              variance estimation, which can reduce accuracy to detect
              differentially methylated loci (DML). Here we propose a novel
              statistical method to detect DML when comparing two treatment
              groups. The sequencing counts are described by a
              lognormal-beta-binomial hierarchical model, which provides a
              basis for information sharing across different CpG sites. A Wald
              test is developed for hypothesis testing at each CpG site.
              Simulation results show that the proposed method yields improved
              DML detection compared to existing methods, particularly when the
              number of replicates is low. The proposed method is implemented
              in the Bioconductor package DSS.",
  journal  = "Nucleic Acids Res.",
  volume   =  42,
  number   =  8,
  pages    = "e69",
  month    =  apr,
  year     =  2014,
  language = "en"
}