liftr/inst/examples/bioc-rnaseq.bib

@article{Anders2010Differential,
  author =	 {Anders, Simon and Huber, Wolfgang},
  doi =		 {10.1186/gb-2010-11-10-r106},
  journal =	 {Genome Biology},
  number =	 10,
  pages =	 {R106+},
  pmcid =	 {PMC3218662},
  pmid =	 20979621,
  title =	 {{Differential expression analysis for sequence count data}},
  volume =	 11,
  year =	 2010
}

@article{Anders2015HTSeqa,
  author =	 {Anders, Simon and Pyl, Paul T. and Huber, Wolfgang},
  doi =		 {10.1093/bioinformatics/btu638},
  journal =	 {Bioinformatics},
  number =	 2,
  pages =	 {166--169},
  pmid =	 25260700,
  title =	 {{HTSeq -- a Python framework to work with high-throughput sequencing
                  data}},
  volume =	 31,
  year =	 2015
}

@article{Benjamini1995Controlling,
  author =	 {Benjamini, Yoav and Hochberg, Yosef},
  journal =	 {Journal of the Royal Statistical Society. Series B (Methodological)},
  number =	 1,
  pages =	 {289--300},
  title =	 {{Controlling the False Discovery Rate: A Practical and Powerful Approach
                  to Multiple Testing}},
  url =		 {http://www.jstor.org/stable/2346101},
  volume =	 57,
  year =	 1995
}

@article{Bourgon2010Independent,
  author =	 {Bourgon, R. and Gentleman, R. and Huber, W.},
  doi =		 {10.1073/pnas.0914005107},
  journal =	 {Proceedings of the National Academy of Sciences},
  number =	 21,
  pages =	 {9546--9551},
  pmcid =	 {PMC2906865},
  pmid =	 20460310,
  title =	 {{Independent filtering increases detection power for high-throughput
                  experiments}},
  volume =	 107,
  year =	 2010
}

@article{Bray2016Near,
  author =	 {Bray, Nicolas and Pimentel, Harold and Melsted, Pall and Pachter, Lior},
  journal =	 {Nature Biotechnology},
  pages =	 {525–-527},
  title =	 {Near-optimal probabilistic RNA-seq quantification},
  volume =	 34,
  url =		 {http://dx.doi.org/10.1038/nbt.3519},
  year =	 2016
}

@article{Dobin2013STAR,
  author =	 {Dobin, Alexander and Davis, Carrie A. and Schlesinger, Felix and
                  Drenkow, Jorg and Zaleski, Chris and Jha, Sonali and Batut, Philippe and
                  Chaisson, Mark and Gingeras, Thomas R.},
  doi =		 {10.1093/bioinformatics/bts635},
  journal =	 {Bioinformatics},
  number =	 1,
  pages =	 {15--21},
  pmcid =	 {PMC3530905},
  pmid =	 23104886,
  title =	 {{STAR: ultrafast universal RNA-seq aligner}},
  url =		 {http://dx.doi.org/10.1093/bioinformatics/bts635},
  volume =	 29,
  year =	 2013
}

@article{Dudoit2002Statistical,
  author =	 {Dudoit, Rine and Yang, Yee H. and Callow, Matthew J. and Speed, Terence
                  P.},
  journal =	 {Statistica Sinica},
  pages =	 {111--139},
  title =	 {{Statistical methods for identifying differentially expressed genes in
                  replicated cDNA microarray experiments}},
  year =	 2002
}

@article{Durinck2009Mapping,
  author =	 {Durinck, Steffen and Spellman, Paul T. and Birney, Ewan and Huber,
                  Wolfgang},
  doi =		 {10.1038/nprot.2009.97},
  journal =	 {Nature Protocols},
  number =	 8,
  pages =	 {1184--1191},
  pmcid =	 {PMC3159387},
  pmid =	 19617889,
  publisher =	 {Nature Publishing Group},
  title =	 {{Mapping identifiers for the integration of genomic datasets with the
                  R/Bioconductor package biomaRt.}},
  url =		 {http://dx.doi.org/10.1038/nprot.2009.97},
  volume =	 4,
  year =	 2009
}

@article{Flicek2014Ensembl,
  author =	 {Flicek, Paul and Amode, M. Ridwan and Barrell, Daniel and Beal, Kathryn
                  and Billis, Konstantinos and Brent, Simon and Carvalho-Silva, Denise and
                  Clapham, Peter and Coates, Guy and Fitzgerald, Stephen and Gil, Laurent
                  and Gir\'{o}n, Carlos G. and Gordon, Leo and Hourlier, Thibaut and Hunt,
                  Sarah and Johnson, Nathan and Juettemann, Thomas and K\"{a}h\"{a}ri,
                  Andreas K. and Keenan, Stephen and Kulesha, Eugene and Martin, Fergal
                  J. and Maurel, Thomas and McLaren, William M. and Murphy, Daniel N. and
                  Nag, Rishi and Overduin, Bert and Pignatelli, Miguel and Pritchard,
                  Bethan and Pritchard, Emily and Riat, Harpreet S. and Ruffier, Magali
                  and Sheppard, Daniel and Taylor, Kieron and Thormann, Anja and
                  Trevanion, Stephen J. and Vullo, Alessandro and Wilder, Steven P. and
                  Wilson, Mark and Zadissa, Amonida and Aken, Bronwen L. and Birney, Ewan
                  and Cunningham, Fiona and Harrow, Jennifer and Herrero, Javier and
                  Hubbard, Tim J. P. and Kinsella, Rhoda and Muffato, Matthieu and Parker,
                  Anne and Spudich, Giulietta and Yates, Andy and Zerbino, Daniel R. and
                  Searle, Stephen M. J.},
  doi =		 {10.1093/nar/gkt1196},
  issn =	 {1362-4962},
  journal =	 {Nucleic Acids Research},
  number =	 {D1},
  pages =	 {D749--D755},
  pmid =	 24316576,
  title =	 {{Ensembl 2014}},
  url =		 {http://dx.doi.org/10.1093/nar/gkt1196},
  volume =	 42,
  year =	 2014
}

@article{Hardcastle2010BaySeq,
  abstract =	 {{BACKGROUND:High throughput sequencing has become an important
                  technology for studying expression levels in many types of genomic, and
                  particularly transcriptomic, data. One key way of analysing such data is
                  to look for elements of the data which display particular patterns of
                  differential expression in order to take these forward for further
                  analysis and validation.RESULTS:We propose a framework for defining
                  patterns of differential expression and develop a novel algorithm,
                  baySeq, which uses an empirical Bayes approach to detect these patterns
                  of differential expression within a set of sequencing samples. The
                  method assumes a negative binomial distribution for the data and derives
                  an empirically determined prior distribution from the entire dataset. We
                  examine the performance of the method on real and simulated
                  data.CONCLUSIONS:Our method performs at least as well, and often better,
                  than existing methods for analyses of pairwise differential expression
                  in both real and simulated data. When we compare methods for the
                  analysis of data from experimental designs involving multiple sample
                  groups, our method again shows substantial gains in performance. We
                  believe that this approach thus represents an important step forward for
                  the analysis of count data from sequencing experiments.}},
  author =	 {Hardcastle, Thomas and Kelly, Krystyna},
  citeulike-article-id =7610091,
  citeulike-linkout-0 ={http://dx.doi.org/10.1186/1471-2105-11-422},
  citeulike-linkout-1 ={http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2928208/},
  citeulike-linkout-2 ={http://view.ncbi.nlm.nih.gov/pubmed/20698981},
  citeulike-linkout-3 ={http://www.hubmed.org/display.cgi?uids=20698981},
  doi =		 {10.1186/1471-2105-11-422},
  issn =	 {1471-2105},
  journal =	 {BMC Bioinformatics},
  keywords =	 {bayes, deseq2, rnaseq, workflow},
  number =	 1,
  pages =	 {422+},
  pmcid =	 {PMC2928208},
  pmid =	 20698981,
  posted-at =	 {2011-04-05 09:08:06},
  priority =	 2,
  title =	 {{baySeq: Empirical Bayesian methods for identifying differential
                  expression in sequence count data}},
  url =		 {http://dx.doi.org/10.1186/1471-2105-11-422},
  volume =	 11,
  year =	 2010
}

@article{Himes2014RNASeq,
  abstract =	 {{Asthma is a chronic inflammatory respiratory disease that affects over
                  300 million people worldwide. Glucocorticoids are a mainstay therapy for
                  asthma because they exert anti-inflammatory effects in multiple lung
                  tissues, including the airway smooth muscle (ASM). However, the
                  mechanism by which glucocorticoids suppress inflammation in ASM remains
                  poorly understood. Using RNA-Seq, a high-throughput sequencing method,
                  we characterized transcriptomic changes in four primary human ASM cell
                  lines that were treated with dexamethasone--a potent synthetic
                  glucocorticoid (1 µM for 18 hours). Based on a Benjamini-Hochberg
                  corrected p-value <0.05, we identified 316 differentially expressed
                  genes, including both well known (DUSP1, KLF15, PER1, TSC22D3) and less
                  investigated (C7, CCDC69, CRISPLD2) glucocorticoid-responsive
                  genes. CRISPLD2, which encodes a secreted protein previously implicated
                  in lung development and endotoxin regulation, was found to have SNPs
                  that were moderately associated with inhaled corticosteroid resistance
                  and bronchodilator response among asthma patients in two previously
                  conducted genome-wide association studies. Quantitative RT-PCR and
                  Western blotting showed that dexamethasone treatment significantly
                  increased CRISPLD2 mRNA and protein expression in ASM cells. CRISPLD2
                  expression was also induced by the inflammatory cytokine IL1β, and small
                  interfering RNA-mediated knockdown of CRISPLD2 further increased
                  IL1β-induced expression of IL6 and IL8. Our findings offer a
                  comprehensive view of the effect of a glucocorticoid on the ASM
                  transcriptome and identify CRISPLD2 as an asthma pharmacogenetics
                  candidate gene that regulates anti-inflammatory effects of
                  glucocorticoids in the ASM.}},
  author =	 {Himes, Blanca E. and Jiang, Xiaofeng and Wagner, Peter and Hu, Ruoxi and
                  Wang, Qiyu and Klanderman, Barbara and Whitaker, Reid M. and Duan,
                  Qingling and Lasky-Su, Jessica and Nikolos, Christina and Jester,
                  William and Johnson, Martin and Panettieri, Reynold A. and Tantisira,
                  Kelan G. and Weiss, Scott T. and Lu, Quan},
  citeulike-article-id =13705379,
  citeulike-linkout-0 ={http://dx.doi.org/10.1371/journal.pone.0099625},
  citeulike-linkout-1 ={http://view.ncbi.nlm.nih.gov/pubmed/24926665},
  citeulike-linkout-2 ={http://www.hubmed.org/display.cgi?uids=24926665},
  doi =		 {10.1371/journal.pone.0099625},
  issn =	 {1932-6203},
  journal =	 {PloS one},
  keywords =	 {rnaseq, workflow},
  number =	 6,
  pmid =	 24926665,
  posted-at =	 {2015-08-18 15:02:37},
  priority =	 2,
  title =	 {{RNA-Seq transcriptome profiling identifies CRISPLD2 as a glucocorticoid
                  responsive gene that modulates cytokine function in airway smooth muscle
                  cells.}},
  url =		 {http://dx.doi.org/10.1371/journal.pone.0099625},
  volume =	 9,
  year =	 2014
}

@article{Huber2015Orchestrating,
  abstract =	 {{Bioconductor is an open-source, open-development software project for
                  the analysis and comprehension of high-throughput data in genomics and
                  molecular biology. The project aims to enable interdisciplinary
                  research, collaboration and rapid development of scientific
                  software. Based on the statistical programming language R, Bioconductor
                  comprises 934 interoperable packages contributed by a large, diverse
                  community of scientists. Packages cover a range of bioinformatic and
                  statistical applications. They undergo formal initial review and
                  continuous automated testing. We present an overview for prospective
                  users and contributors.}},
  author =	 {Huber, Wolfgang and Carey, Vincent J. and Gentleman, Robert and Anders,
                  Simon and Carlson, Marc and Carvalho, Benilton S. and Bravo, Hector
                  Corrada C. and Davis, Sean and Gatto, Laurent and Girke, Thomas and
                  Gottardo, Raphael and Hahne, Florian and Hansen, Kasper D. and Irizarry,
                  Rafael A. and Lawrence, Michael and Love, Michael I. and MacDonald,
                  James and Obenchain, Valerie and Ole\'{s}, Andrzej K. and Pag\`{e}s,
                  Herv\'{e} and Reyes, Alejandro and Shannon, Paul and Smyth, Gordon
                  K. and Tenenbaum, Dan and Waldron, Levi and Morgan, Martin},
  citeulike-article-id =13504287,
  citeulike-linkout-0 ={http://dx.doi.org/10.1038/nmeth.3252},
  citeulike-linkout-1 ={http://dx.doi.org/10.1038/nmeth.3252},
  citeulike-linkout-2 ={http://view.ncbi.nlm.nih.gov/pubmed/25633503},
  citeulike-linkout-3 ={http://www.hubmed.org/display.cgi?uids=25633503},
  day =		 29,
  doi =		 {10.1038/nmeth.3252},
  issn =	 {1548-7105},
  journal =	 {Nature methods},
  keywords =	 {mine, workflow},
  month =	 feb,
  number =	 2,
  pages =	 {115--121},
  pmid =	 25633503,
  posted-at =	 {2015-05-29 16:53:20},
  priority =	 2,
  publisher =	 {Nature Publishing Group},
  title =	 {{Orchestrating high-throughput genomic analysis with Bioconductor.}},
  url =		 {http://dx.doi.org/10.1038/nmeth.3252},
  volume =	 12,
  year =	 2015
}

@article{Huntley2013ReportingTools,
  abstract =	 {{Summary: It is common for computational analyses to generate large
                  amounts of complex data that are difficult to process and share with
                  collaborators. Standard methods are needed to transform such data into a
                  more useful and intuitive format. We present ReportingTools, a
                  Bioconductor package, that automatically recognizes and transforms the
                  output of many common Bioconductor packages into rich, interactive,
                  HTML-based reports. Reports are not generic, but have been individually
                  designed to reflect content specific to the result type
                  detected. Tabular output included in reports is sortable, filterable and
                  searchable and contains context-relevant hyperlinks to external
                  databases. Additionally, in-line graphics have been developed for
                  specific analysis types and are embedded by default within table rows,
                  providing a useful visual summary of underlying raw data. ReportingTools
                  is highly flexible and reports can be easily customized for specific
                  applications using the well-defined API.}},
  author =	 {Huntley, Melanie A. and Larson, Jessica L. and Chaivorapol, Christina
                  and Becker, Gabriel and Lawrence, Michael and Hackney, Jason A. and
                  Kaminker, Joshua S.},
  citeulike-article-id =12728071,
  citeulike-linkout-0 ={http://dx.doi.org/10.1093/bioinformatics/btt551},
  citeulike-linkout-1
                  ={http://bioinformatics.oxfordjournals.org/content/29/24/3220.abstract},
  citeulike-linkout-2
                  ={http://bioinformatics.oxfordjournals.org/content/29/24/3220.full.pdf},
  citeulike-linkout-3
                  ={http://bioinformatics.oxfordjournals.org/cgi/content/abstract/29/24/3220},
  citeulike-linkout-4 ={http://view.ncbi.nlm.nih.gov/pubmed/24078713},
  citeulike-linkout-5 ={http://www.hubmed.org/display.cgi?uids=24078713},
  day =		 15,
  doi =		 {10.1093/bioinformatics/btt551},
  issn =	 {1460-2059},
  journal =	 {Bioinformatics},
  keywords =	 {workflow},
  month =	 dec,
  number =	 24,
  pages =	 {3220--3221},
  pmid =	 24078713,
  posted-at =	 {2015-08-18 15:13:59},
  priority =	 2,
  publisher =	 {Oxford University Press},
  title =	 {{ReportingTools: an automated result processing and presentation toolkit
                  for high-throughput genomic analyses}},
  url =		 {http://dx.doi.org/10.1093/bioinformatics/btt551},
  volume =	 29,
  year =	 2013
}

@article{Kent2002Human,
  abstract =	 {{As vertebrate genome sequences near completion and research refocuses
                  to their analysis, the issue of effective genome annotation display
                  becomes critical. A mature web tool for rapid and reliable display of
                  any requested portion of the genome at any scale, together with several
                  dozen aligned annotation tracks, is provided at
                  http://genome.ucsc.edu. This browser displays assembly contigs and gaps,
                  mRNA and expressed sequence tag alignments, multiple gene predictions,
                  cross-species homologies, single nucleotide polymorphisms,
                  sequence-tagged sites, radiation hybrid data, transposon repeats, and
                  more as a stack of coregistered tracks. Text and sequence-based searches
                  provide quick and precise access to any region of specific
                  interest. Secondary links from individual features lead to sequence
                  details and supplementary off-site databases. One-half of the annotation
                  tracks are computed at the University of California, Santa Cruz from
                  publicly available sequence data; collaborators worldwide provide the
                  rest. Users can stably add their own custom tracks to the browser for
                  educational or research purposes. The conceptual and technical framework
                  of the browser, its underlying MYSQL database, and overall use are
                  described. The web site currently serves over 50,000 pages per day to
                  over 3000 different users.}},
  author =	 {Kent, W. James and Sugnet, Charles W. and Furey, Terrence S. and Roskin,
                  Krishna M. and Pringle, Tom H. and Zahler, Alan M. and Haussler, David},
  citeulike-article-id =2009259,
  citeulike-linkout-0 ={http://dx.doi.org/10.1101/gr.229102},
  citeulike-linkout-1
                  ={http://dx.doi.org/10.1101/gr.229102.\%20article\%20published\%20online\%20before\%20print\%20in\%20may\%202002},
  citeulike-linkout-2 ={http://genome.cshlp.org/content/12/6/996.full.abstract},
  citeulike-linkout-3 ={http://genome.cshlp.org/content/12/6/996.full.full.pdf},
  citeulike-linkout-4 ={http://www.genome.org/cgi/content/abstract/12/6/996},
  citeulike-linkout-5 ={http://www.ncbi.nlm.nih.gov/pmc/articles/PMC186604/},
  citeulike-linkout-6 ={http://view.ncbi.nlm.nih.gov/pubmed/12045153},
  citeulike-linkout-7 ={http://www.hubmed.org/display.cgi?uids=12045153},
  day =		 1,
  doi =		 {10.1101/gr.229102},
  issn =	 {1088-9051},
  journal =	 {Genome research},
  keywords =	 {ctsca, workflow},
  month =	 jun,
  number =	 6,
  pages =	 {996--1006},
  pmcid =	 {PMC186604},
  pmid =	 12045153,
  posted-at =	 {2012-07-26 16:04:05},
  priority =	 2,
  publisher =	 {Cold Spring Harbor Laboratory Press},
  title =	 {{The human genome browser at UCSC.}},
  url =		 {http://dx.doi.org/10.1101/gr.229102},
  volume =	 12,
  year =	 2002
}

@article{Law2014Voom,
  abstract =	 {{Normal linear modeling methods are developed for analyzing read counts
                  from RNA-seq experiments. The voom method estimates the mean-variance
                  relationship of the log-counts, generates a precision weight for each
                  observation, and then enters these into a limma empirical Bayes analysis
                  pipeline. This opens access for RNA-seq analysts to a large body of
                  methodology developed for microarrays. Simulation studies show that voom
                  performs as well or better than count-based RNA-seq methods even when
                  the data are generated according to the assumptions of the earlier
                  methods. Two case studies illustrate the use of linear modeling and gene
                  set testing methods.}},
  author =	 {Law, Charity W. and Chen, Yunshun and Shi, Wei and Smyth, Gordon K.},
  citeulike-article-id =12965503,
  citeulike-linkout-0 ={http://dx.doi.org/10.1186/gb-2014-15-2-r29},
  citeulike-linkout-1 ={http://view.ncbi.nlm.nih.gov/pubmed/24485249},
  citeulike-linkout-2 ={http://www.hubmed.org/display.cgi?uids=24485249},
  day =		 03,
  doi =		 {10.1186/gb-2014-15-2-r29},
  issn =	 {1465-6906},
  journal =	 {Genome Biology},
  keywords =	 {deseq2, rnaguide, workflow},
  month =	 feb,
  number =	 2,
  pages =	 {R29+},
  pmid =	 24485249,
  posted-at =	 {2014-02-13 20:56:00},
  priority =	 2,
  publisher =	 {BioMed Central Ltd},
  title =	 {{Voom: precision weights unlock linear model analysis tools for RNA-seq
                  read counts}},
  url =		 {http://dx.doi.org/10.1186/gb-2014-15-2-r29},
  volume =	 15,
  year =	 2014
}

@article{Lawrence2013Software,
  abstract =	 {{We describe Bioconductor infrastructure for representing and computing
                  on annotated genomic ranges and integrating genomic data with the
                  statistical computing features of R and its extensions. At the core of
                  the infrastructure are three packages: IRanges, GenomicRanges, and
                  GenomicFeatures. These packages provide scalable data structures for
                  representing annotated ranges on the genome, with special support for
                  transcript structures, read alignments and coverage
                  vectors. Computational facilities include efficient algorithms for
                  overlap and nearest neighbor detection, coverage calculation and other
                  range operations. This infrastructure directly supports more than 80
                  other Bioconductor packages, including those for sequence analysis,
                  differential expression analysis and visualization.}},
  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.},
  citeulike-article-id =12548311,
  citeulike-linkout-0 ={http://dx.doi.org/10.1371/journal.pcbi.1003118},
  citeulike-linkout-1 ={http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3738458/},
  citeulike-linkout-2 ={http://view.ncbi.nlm.nih.gov/pubmed/23950696},
  citeulike-linkout-3 ={http://www.hubmed.org/display.cgi?uids=23950696},
  day =		 8,
  doi =		 {10.1371/journal.pcbi.1003118},
  editor =	 {Prlic, Andreas},
  issn =	 {1553-7358},
  journal =	 {PLoS Computational Biology},
  keywords =	 {deseq2, workflow},
  month =	 aug,
  number =	 8,
  pages =	 {e1003118+},
  pmcid =	 {PMC3738458},
  pmid =	 23950696,
  posted-at =	 {2014-02-14 00:17:30},
  priority =	 2,
  publisher =	 {Public Library of Science},
  title =	 {{Software for Computing and Annotating Genomic Ranges}},
  url =		 {http://dx.doi.org/10.1371/journal.pcbi.1003118},
  volume =	 9,
  year =	 2013
}

@article{Leek2014Svaseq,
  abstract =	 {{It is now known that unwanted noise and unmodeled artifacts such as
                  batch effects can dramatically reduce the accuracy of statistical
                  inference in genomic experiments. These sources of noise must be modeled
                  and removed to accurately measure biological variability and to obtain
                  correct statistical inference when performing high-throughput genomic
                  analysis. We introduced surrogate variable analysis (sva) for estimating
                  these artifacts by (i) identifying the part of the genomic data only
                  affected by artifacts and (ii) estimating the artifacts with principal
                  components or singular vectors of the subset of the data matrix. The
                  resulting estimates of artifacts can be used in subsequent analyses as
                  adjustment factors to correct analyses. Here I describe a version of the
                  sva approach specifically created for count data or FPKMs from
                  sequencing experiments based on appropriate data transformation. I also
                  describe the addition of supervised sva (ssva) for using control probes
                  to identify the part of the genomic data only affected by artifacts. I
                  present a comparison between these versions of sva and other methods for
                  batch effect estimation on simulated data, real count-based data and
                  FPKM-based data. These updates are available through the sva
                  Bioconductor package and I have made fully reproducible analysis using
                  these methods available from:
                  https://github.com/jtleek/svaseq. {\copyright} The Author(s)
                  2014. Published by Oxford University Press on behalf of Nucleic Acids
                  Research.}},
  author =	 {Leek, Jeffrey T.},
  citeulike-article-id =13385083,
  citeulike-linkout-0 ={http://dx.doi.org/10.1093/nar/gku864},
  citeulike-linkout-1
                  ={http://nar.oxfordjournals.org/content/early/2014/10/07/nar.gku864.abstract},
  citeulike-linkout-2
                  ={http://nar.oxfordjournals.org/content/early/2014/10/07/nar.gku864.full.pdf},
  citeulike-linkout-3 ={http://view.ncbi.nlm.nih.gov/pubmed/25294822},
  citeulike-linkout-4 ={http://www.hubmed.org/display.cgi?uids=25294822},
  day =		 1,
  doi =		 {10.1093/nar/gku864},
  issn =	 {1362-4962},
  journal =	 {Nucleic acids research},
  keywords =	 {workflow},
  month =	 dec,
  number =	 21,
  pages =	 000,
  pmid =	 25294822,
  posted-at =	 {2015-08-18 15:16:02},
  priority =	 2,
  publisher =	 {Oxford University Press},
  title =	 {{svaseq: removing batch effects and other unwanted noise from sequencing
                  data.}},
  url =		 {http://dx.doi.org/10.1093/nar/gku864},
  volume =	 42,
  year =	 2014
}

@article{Leng2013EBSeq,
  abstract =	 {{Motivation: Messenger RNA expression is important in normal development
                  and differentiation, as well as in manifestation of disease. RNA-seq
                  experiments allow for the identification of differentially expressed
                  (DE) genes and their corresponding isoforms on a genome-wide
                  scale. However, statistical methods are required to ensure that accurate
                  identifications are made. A number of methods exist for identifying DE
                  genes, but far fewer are available for identifying DE isoforms. When
                  isoform DE is of interest, investigators often apply gene-level
                  (count-based) methods directly to estimates of isoform counts. Doing so
                  is not recommended. In short, estimating isoform expression is
                  relatively straightforward for some groups of isoforms, but more
                  challenging for others. This results in estimation uncertainty that
                  varies across isoform groups. Count-based methods were not designed to
                  accommodate this varying uncertainty, and consequently, application of
                  them for isoform inference results in reduced power for some classes of
                  isoforms and increased false discoveries for others.}},
  author =	 {Leng, N. and Dawson, J. A. and Thomson, J. A. and Ruotti, V. and
                  Rissman, A. I. and Smits, B. M. G. and Haag, J. D. and Gould, M. N. and
                  Stewart, R. M. and Kendziorski, C.},
  citeulike-article-id =12074857,
  citeulike-linkout-0 ={http://dx.doi.org/10.1093/bioinformatics/btt087},
  citeulike-linkout-1
                  ={http://bioinformatics.oxfordjournals.org/content/early/2013/02/21/bioinformatics.btt087.abstract},
  citeulike-linkout-2
                  ={http://bioinformatics.oxfordjournals.org/content/early/2013/02/21/bioinformatics.btt087.full.pdf},
  citeulike-linkout-3
                  ={http://bioinformatics.oxfordjournals.org/cgi/content/abstract/29/8/1035},
  citeulike-linkout-4 ={http://view.ncbi.nlm.nih.gov/pubmed/23428641},
  citeulike-linkout-5 ={http://www.hubmed.org/display.cgi?uids=23428641},
  day =		 15,
  doi =		 {10.1093/bioinformatics/btt087},
  issn =	 {1460-2059},
  journal =	 {Bioinformatics},
  keywords =	 {deseq2, workflow},
  month =	 feb,
  number =	 8,
  pages =	 {1035--1043},
  pmid =	 23428641,
  posted-at =	 {2014-05-13 22:33:51},
  priority =	 2,
  publisher =	 {Oxford University Press},
  title =	 {{EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq
                  experiments}},
  url =		 {http://dx.doi.org/10.1093/bioinformatics/btt087},
  volume =	 29,
  year =	 2013
}

@article{Leong2014Global,
  abstract =	 {{Non-coding RNAs (ncRNAs) are frequent and prevalent across the
                  taxa. Although individual non-coding loci have been assigned a function,
                  most are uncharacterized. Their global biological significance is
                  unproven and remains controversial. Here we investigate the role played
                  by ncRNAs in the stress response of Schizosaccharomyces pombe. We
                  integrate global proteomics and RNA sequencing data to identify a
                  systematic programme in which elevated antisense RNA arising both from
                  ncRNAs and from 3'-overlapping convergent gene pairs is directly
                  associated with substantial reductions in protein levels throughout the
                  genome. We describe an extensive array of ncRNAs with trans associations
                  that have the potential to influence multiple pathways. Deletion of one
                  such locus reduces levels of atf1, a transcription factor downstream of
                  the stress-activated mitogen-activated protein kinase (MAPK) pathway,
                  and alters sensitivity to oxidative stress. These non-coding transcripts
                  therefore regulate specific stress responses, adding unanticipated
                  information-processing capacity to the MAPK signalling system.}},
  author =	 {Leong, Hui S. and Dawson, Keren and Wirth, Chris and Li, Yaoyong and
                  Connolly, Yvonne and Smith, Duncan L. and Wilkinson, Caroline R. and
                  Miller, Crispin J.},
  citeulike-article-id =13705386,
  citeulike-linkout-0 ={http://dx.doi.org/10.1038/ncomms4947},
  citeulike-linkout-1 ={http://view.ncbi.nlm.nih.gov/pubmed/24853205},
  citeulike-linkout-2 ={http://www.hubmed.org/display.cgi?uids=24853205},
  doi =		 {10.1038/ncomms4947},
  issn =	 {2041-1723},
  journal =	 {Nature communications},
  keywords =	 {workflow},
  pmid =	 24853205,
  posted-at =	 {2015-08-18 15:16:55},
  priority =	 2,
  title =	 {{A global non-coding RNA system modulates fission yeast protein levels
                  in response to stress.}},
  url =		 {http://dx.doi.org/10.1038/ncomms4947},
  volume =	 5,
  year =	 2014
}

@article{Li2009Sequence,
  abstract =	 {{The Sequence Alignment/Map (SAM) format is a generic alignment format
                  for storing read alignments against reference sequences, supporting
                  short and long reads (up to 128 Mbp) produced by different sequencing
                  platforms. It is flexible in style, compact in size, efficient in random
                  access and is the format in which alignments from the 1000 Genomes
                  Project are released. SAMtools implements various utilities for
                  post-processing alignments in the SAM format, such as indexing, variant
                  caller and alignment viewer, and thus provides universal tools for
                  processing read alignments. http://samtools.sourceforge.net.}},
  author =	 {Li, Heng and Handsaker, Bob and Wysoker, Alec and Fennell, Tim and Ruan,
                  Jue and Homer, Nils and Marth, Gabor and Abecasis, Goncalo and Durbin,
                  Richard and {1000 Genome Project Data Processing Subgroup}},
  citeulike-article-id =4778506,
  citeulike-linkout-0 ={http://dx.doi.org/10.1093/bioinformatics/btp352},
  citeulike-linkout-1
                  ={http://bioinformatics.oxfordjournals.org/content/25/16/2078.abstract},
  citeulike-linkout-2
                  ={http://bioinformatics.oxfordjournals.org/content/25/16/2078.full.pdf},
  citeulike-linkout-3
                  ={http://bioinformatics.oxfordjournals.org/cgi/content/abstract/25/16/2078},
  citeulike-linkout-4 ={http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2723002/},
  citeulike-linkout-5 ={http://view.ncbi.nlm.nih.gov/pubmed/19505943},
  citeulike-linkout-6 ={http://www.hubmed.org/display.cgi?uids=19505943},
  day =		 15,
  doi =		 {10.1093/bioinformatics/btp352},
  issn =	 {1367-4811},
  journal =	 {Bioinformatics (Oxford, England)},
  keywords =	 {workflow},
  month =	 aug,
  number =	 16,
  pages =	 {2078--2079},
  pmcid =	 {PMC2723002},
  pmid =	 19505943,
  posted-at =	 {2015-08-18 15:05:40},
  priority =	 2,
  publisher =	 {Oxford University Press},
  title =	 {{The Sequence Alignment/Map format and SAMtools.}},
  url =		 {http://dx.doi.org/10.1093/bioinformatics/btp352},
  volume =	 25,
  year =	 2009
}

@article{Li2011RSEM,
  author =	 {Li, Bo and Dewey, Colin N.},
  doi =		 {10.1186/1471-2105-12-3231},
  journal =	 {BMC Bioinformatics},
  pages =	 {323+},
  title =	 {{RSEM: accurate transcript quantification from RNA-Seq data with or
                  without a reference genome.}},
  url =		 {http://dx.doi.org/10.1186/1471-2105-12-323},
  volume =	 12,
  year =	 2011
}

@article{Liao2014FeatureCounts,
  abstract =	 {{ Next-generation sequencing technologies generate millions of short
                  sequence reads, which are usually aligned to a reference genome. In many
                  applications, the key information required for downstream analysis is
                  the number of reads mapping to each genomic feature, for example to each
                  exon or each gene. The process of counting reads is called read
                  summarization. Read summarization is required for a great variety of
                  genomic analyses but has so far received relatively little attention in
                  the literature.   We present featureCounts, a read summarization program
                  suitable for counting reads generated from either RNA or genomic DNA
                  sequencing experiments. featureCounts implements highly efficient
                  chromosome hashing and feature blocking techniques. It is considerably
                  faster than existing methods (by an order of magnitude for gene-level
                  summarization) and requires far less computer memory. It works with
                  either single or paired-end reads and provides a wide range of options
                  appropriate for different sequencing applications.Availability and
                  implementation: featureCounts is available under GNU General Public
                  License as part of the Subread (http://subread.sourceforge.net) or
                  Rsubread (http://www.bioconductor.org) software packages.
                   [email protected].}},
  author =	 {Liao, Y. and Smyth, G. K. and Shi, W.},
  citeulike-article-id =12796380,
  citeulike-linkout-0 ={http://dx.doi.org/10.1093/bioinformatics/btt656},
  citeulike-linkout-1
                  ={http://bioinformatics.oxfordjournals.org/content/early/2013/11/13/bioinformatics.btt656.abstract},
  citeulike-linkout-2
                  ={http://bioinformatics.oxfordjournals.org/content/early/2013/11/13/bioinformatics.btt656.full.pdf},
  citeulike-linkout-3
                  ={http://bioinformatics.oxfordjournals.org/cgi/content/abstract/30/7/923},
  citeulike-linkout-4 ={http://view.ncbi.nlm.nih.gov/pubmed/24227677},
  citeulike-linkout-5 ={http://www.hubmed.org/display.cgi?uids=24227677},
  day =		 13,
  doi =		 {10.1093/bioinformatics/btt656},
  issn =	 {1460-2059},
  journal =	 {Bioinformatics},
  keywords =	 {deseq2, workflow},
  month =	 apr,
  number =	 7,
  pages =	 {923--930},
  pmid =	 24227677,
  posted-at =	 {2014-02-18 20:28:26},
  priority =	 2,
  publisher =	 {Oxford University Press},
  title =	 {{featureCounts: an efficient general purpose program for assigning
                  sequence reads to genomic features}},
  url =		 {http://dx.doi.org/10.1093/bioinformatics/btt656},
  volume =	 30,
  year =	 2014
}

@article{Love2014Moderated,
  abstract =	 {{In comparative high-throughput sequencing assays, a fundamental task is
                  the analysis of count data, such as read counts per gene in RNA-seq, for
                  evidence of systematic changes across experimental conditions. Small
                  replicate numbers, discreteness, large dynamic range and the presence of
                  outliers require a suitable statistical approach. We present DESeq2, a
                  method for differential analysis of count data, using shrinkage
                  estimation for dispersions and fold changes to improve stability and
                  interpretability of estimates. This enables a more quantitative analysis
                  focused on the strength rather than the mere presence of differential
                  expression. The DESeq2 package is available at
                  http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html.}},
  author =	 {Love, Michael I. and Huber, Wolfgang and Anders, Simon},
  citeulike-article-id =13505832,
  citeulike-linkout-0 ={http://dx.doi.org/10.1186/s13059-014-0550-8},
  citeulike-linkout-1 ={http://view.ncbi.nlm.nih.gov/pubmed/25516281},
  citeulike-linkout-2 ={http://www.hubmed.org/display.cgi?uids=25516281},
  day =		 05,
  doi =		 {10.1186/s13059-014-0550-8},
  issn =	 {1465-6906},
  journal =	 {Genome Biology},
  keywords =	 {mine, workflow},
  month =	 dec,
  number =	 12,
  pages =	 {550+},
  pmid =	 25516281,
  posted-at =	 {2015-08-18 15:29:41},
  priority =	 2,
  publisher =	 {BioMed Central Ltd},
  title =	 {{Moderated estimation of fold change and dispersion for RNA-seq data
                  with DESeq2}},
  url =		 {http://dx.doi.org/10.1186/s13059-014-0550-8},
  volume =	 15,
  year =	 2014
}

@article{Patro2014Sailfish,
  author =	 {Patro, Rob and Mount, Stephen M. and Kingsford, Carl},
  journal =	 {Nature Biotechnology},
  pages =	 {462--464},
  title =	 {{Sailfish enables alignment-free isoform quantification from RNA-seq
                  reads using lightweight algorithms}},
  doi =		 {10.1038/nbt.2862},
  url =		 {http://dx.doi.org/10.1038/nbt.2862},
  volume =	 32,
  year =	 2014
}

@article{Patro2016Salmon,
  author =	 {Patro, Rob and Duggal, Geet and Love, Michael I. and Irizarry, Rafael
                  A. and Kingsford, Carl},
  journal =	 {bioRxiv},
  title =	 {Salmon provides accurate, fast, and bias-aware transcript expression
                  estimates using dual-phase inference},
  url =		 {http://biorxiv.org/content/early/2016/08/30/021592},
  year =	 2016
}

@article{Risso2014Normalization,
  author =	 {Risso, Davide and Ngai, John and Speed, Terence P. and Dudoit, Sandrine},
  citeulike-article-id =13336814,
  citeulike-linkout-0 ={http://dx.doi.org/10.1038/nbt.2931},
  citeulike-linkout-1 ={http://dx.doi.org/10.1038/nbt.2931},
  day =		 24,
  doi =		 {10.1038/nbt.2931},
  issn =	 {1087-0156},
  journal =	 {Nature Biotechnology},
  keywords =	 {rnaguide, workflow},
  month =	 aug,
  number =	 9,
  pages =	 {896--902},
  posted-at =	 {2014-09-11 20:51:49},
  priority =	 2,
  publisher =	 {Nature Publishing Group},
  title =	 {{Normalization of RNA-seq data using factor analysis of control genes or
                  samples}},
  url =		 {http://dx.doi.org/10.1038/nbt.2931},
  volume =	 32,
  year =	 2014
}

@article{Robert2015Errors,
  author =	 {Robert, Christelle and Watson, Mick},
  doi =		 {10.1186/s13059-015-0734-x},
  journal =	 {Genome Biology},
  title =	 {{Errors in RNA-Seq quantification affect genes of relevance to human
                  disease}},
  url =		 {http://dx.doi.org/10.1186/s13059-015-0734-x},
  year =	 2015
}

@article{Robinson2009EdgeR,
  abstract =	 {{It is expected that emerging digital gene expression (DGE) technologies
                  will overtake microarray technologies in the near future for many
                  functional genomics applications. One of the fundamental data analysis
                  tasks, especially for gene expression studies, involves determining
                  whether there is evidence that counts for a transcript or exon are
                  significantly different across experimental conditions. edgeR is a
                  Bioconductor software package for examining differential expression of
                  replicated count data. An overdispersed Poisson model is used to account
                  for both biological and technical variability. Empirical Bayes methods
                  are used to moderate the degree of overdispersion across transcripts,
                  improving the reliability of inference. The methodology can be used even
                  with the most minimal levels of replication, provided at least one
                  phenotype or experimental condition is replicated. The software may have
                  other applications beyond sequencing data, such as proteome peptide
                  count data.  The package is freely available under the LGPL licence from
                  the Bioconductor web site (http://bioconductor.org).}},
  author =	 {Robinson, M. D. and McCarthy, D. J. and Smyth, G. K.},
  citeulike-article-id =6109634,
  citeulike-linkout-0 ={http://dx.doi.org/10.1093/bioinformatics/btp616},
  citeulike-linkout-1
                  ={http://bioinformatics.oxfordjournals.org/content/btp616v1/.abstract},
  citeulike-linkout-2
                  ={http://bioinformatics.oxfordjournals.org/content/btp616v1/.full.pdf},
  citeulike-linkout-3
                  ={http://bioinformatics.oxfordjournals.org/cgi/content/abstract/26/1/139},
  citeulike-linkout-4 ={http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2796818/},
  citeulike-linkout-5 ={http://view.ncbi.nlm.nih.gov/pubmed/19910308},
  citeulike-linkout-6 ={http://www.hubmed.org/display.cgi?uids=19910308},
  day =		 11,
  doi =		 {10.1093/bioinformatics/btp616},
  issn =	 {1460-2059},
  journal =	 {Bioinformatics},
  keywords =	 {cnv, deseq2, overdispersion, rnaseq, workflow},
  month =	 nov,
  number =	 1,
  pages =	 {139--140},
  pmcid =	 {PMC2796818},
  pmid =	 19910308,
  posted-at =	 {2011-06-25 18:43:51},
  priority =	 2,
  publisher =	 {Oxford University Press},
  title =	 {{edgeR: a Bioconductor package for differential expression analysis of
                  digital gene expression data}},
  url =		 {http://dx.doi.org/10.1093/bioinformatics/btp616},
  volume =	 26,
  year =	 2009
}

@article{Schurch2016How,
  author =	 {Schurch, Nicholas J. and Schofield, Pieta and Gierlinski, Marek and
                  Cole, Christian and Sherstnev, Alexander and Singh, Vijender and Wrobel,
                  Nicola and Gharbi, Karim and Simpson, Gordon G. and Owen-Hughes, Tom and
                  Blaxter, Mark and Barton, Geoffrey J.},
  title =	 {How many biological replicates are needed in an RNA-seq experiment and
                  which differential expression tool should you use?},
  volume =	 22,
  number =	 6,
  pages =	 {839-851},
  year =	 2016,
  doi =		 {10.1261/rna.053959.115},
  url =		 {http://dx.doi.org/10.1261/rna.053959.115}
}

@article{Soneson2015Differential,
  url =		 {http://dx.doi.org/10.12688/f1000research.7563.1},
  doi =		 {10.12688/f1000research.7563.1},
  author =	 {Soneson, Charlotte and Love, Michael I. and Robinson, Mark},
  title =	 {{Differential analyses for RNA-seq: transcript-level estimates improve
                  gene-level inferences}},
  journal =	 {F1000Research},
  year =	 2015,
  Volume =	 4,
  Issue =	 1521
}

@article{Tonner2016,
  author =	 {Tonner, Peter D and Darnell, Cynthia L and Engelhardt, Barbara E and
                  Schmid, Amy K},
  doi =		 {10.1101/gr.210286.116},
  pages =	 {320--333},
  title =	 {{Detecting differential growth of microbial populations with Gaussian
                  process regression}},
  year =	 2017,
  volume =	 27,
  journal =	 {Genome Research}
}

@article{Trapnell2013Differential,
  author =	 {Trapnell, Cole and Hendrickson, David G and Sauvageau, Martin and Goff,
                  Loyal and Rinn, John L and Pachter, Lior},
  doi =		 {10.1038/nbt.2450},
  journal =	 {Nature Biotechnology},
  title =	 {{Differential analysis of gene regulation at transcript resolution with
                  RNA-seq}},
  url =		 {http://dx.doi.org/10.1038/nbt.2450},
  year =	 2013
}

@book{Wickham2009Ggplot2,
  address =	 {New York, NY},
  author =	 {Wickham, Hadley},
  booktitle =	 {ggplot2},
  citeulike-article-id =10715717,
  citeulike-linkout-0 ={http://dx.doi.org/10.1007/978-0-387-98141-3},
  citeulike-linkout-1 ={http://www.springerlink.com/content/978-0-387-98140-6},
  doi =		 {10.1007/978-0-387-98141-3},
  isbn =	 {978-0-387-98140-6},
  keywords =	 {workflow},
  posted-at =	 {2015-08-18 15:12:19},
  priority =	 2,
  publisher =	 {Springer New York},
  title =	 {{ggplot2}},
  url =		 {http://dx.doi.org/10.1007/978-0-387-98141-3},
  year =	 2009
}

@article{Witten2011Classification,
  abstract =	 {{In recent years, advances in high throughput sequencing technology have
                  led to a need for specialized methods for the analysis of digital gene
                  expression data. While gene expression data measured on a microarray
                  take on continuous values and can be modeled using the normal
                  distribution, RNA sequencing data involve nonnegative counts and are
                  more appropriately modeled using a discrete count distribution, such as
                  the Poisson or the negative binomial. Consequently, analytic tools that
                  assume a Gaussian distribution (such as classification methods based on
                  linear discriminant analysis and clustering methods that use Euclidean
                  distance) may not perform as well for sequencing data as methods that
                  are based upon a more appropriate distribution. Here, we propose new
                  approaches for performing classification and clustering of observations
                  on the basis of sequencing data. Using a Poisson log linear model, we
                  develop an analog of diagonal linear discriminant analysis that is
                  appropriate for sequencing data. We also propose an approach for
                  clustering sequencing data using a new dissimilarity measure that is
                  based upon the Poisson model. We demonstrate the performances of these
                  approaches in a simulation study, on three publicly available RNA
                  sequencing data sets, and on a publicly available chromatin
                  immunoprecipitation sequencing data set.}},
  author =	 {Witten, Daniela M.},
  citeulike-article-id =13172798,
  citeulike-linkout-0 ={http://dx.doi.org/10.1214/11-AOAS493},
  day =		 28,
  doi =		 {10.1214/11-AOAS493},
  issn =	 {1932-6157},
  journal =	 {The Annals of Applied Statistics},
  keywords =	 {chipseq, ctsca, deseq2, rnaseq, workflow},
  month =	 dec,
  number =	 4,
  pages =	 {2493--2518},
  posted-at =	 {2014-05-16 17:18:08},
  priority =	 2,
  title =	 {{Classification and clustering of sequencing data using a Poisson
                  model}},
  url =		 {http://dx.doi.org/10.1214/11-AOAS493},
  volume =	 5,
  year =	 2011
}

@article{Wu2013New,
  abstract =	 {{Recent developments in RNA-sequencing (RNA-seq) technology have led to
                  a rapid increase in gene expression data in the form of counts. RNA-seq
                  can be used for a variety of applications, however, identifying
                  differential expression (DE) remains a key task in functional
                  genomics. There have been a number of statistical methods for DE
                  detection for RNA-seq data. One common feature of several leading
                  methods is the use of the negative binomial (Gamma–Poisson mixture)
                  model. That is, the unobserved gene expression is modeled by a gamma
                  random variable and, given the expression, the sequencing read counts
                  are modeled as Poisson. The distinct feature in various methods is how
                  the variance, or dispersion, in the Gamma distribution is modeled and
                  estimated. We evaluate several large public RNA-seq datasets and find
                  that the estimated dispersion in existing methods does not adequately
                  capture the heterogeneity of biological variance among samples. We
                  present a new empirical Bayes shrinkage estimate of the dispersion
                  parameters and demonstrate improved DE detection.}},
  author =	 {Wu, Hao and Wang, Chi and Wu, Zhijin},
  citeulike-article-id =11345725,
  citeulike-linkout-0 ={http://dx.doi.org/10.1093/biostatistics/kxs033},
  citeulike-linkout-1
                  ={http://biostatistics.oxfordjournals.org/content/early/2012/09/22/biostatistics.kxs033.abstract},
  citeulike-linkout-2
                  ={http://biostatistics.oxfordjournals.org/content/early/2012/09/22/biostatistics.kxs033.full.pdf},
  citeulike-linkout-3 ={http://view.ncbi.nlm.nih.gov/pubmed/23001152},
  citeulike-linkout-4 ={http://www.hubmed.org/display.cgi?uids=23001152},
  day =		 01,
  doi =		 {10.1093/biostatistics/kxs033},
  issn =	 {1468-4357},
  journal =	 {Biostatistics},
  keywords =	 {deseq2, rnaseq, workflow},
  month =	 apr,
  number =	 2,
  pages =	 {232--243},
  pmid =	 23001152,
  posted-at =	 {2013-02-26 17:09:19},
  priority =	 2,
  publisher =	 {Oxford University Press},
  title =	 {{A new shrinkage estimator for dispersion improves differential
                  expression detection in RNA-seq data}},
  url =		 {http://dx.doi.org/10.1093/biostatistics/kxs033},
  volume =	 14,
  year =	 2013
}