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bibtex.bib
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@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.
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. [email protected].",
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.
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. [email protected]",
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:
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: [email protected]
or [email protected] 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"
}