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% Encoding: windows-1252

@ARTICLE{Labaj2011,
  author = {{\L}abaj, Pawe{\l} P. and Leparc, Germ{\'a}n G. and Linggi, Bryan
	E. and Markillie, Lye Meng and Wiley, H. Steven and Kreil, David
	P.},
  title = {{RNA-seq precision in quantitative expression profiling}},
  journal = {Bioinformatics},
  year = {2011},
  volume = {27},
  pages = {i383-i381},
  number = {13},
  owner = {arau},
  timestamp = {2012.07.13}
}

@ARTICLE{Anders2010,
  author = {Anders, Simon and Huber, Wolfgang},
  title = {Differential expression analysis for sequence count data},
  journal = {Genome Biology},
  year = {2010},
  volume = {11},
  pages = {1-28},
  number = {R106},
  comment = {Read count is linearly related to transcript abundance. DESeq similar
	to edgeR (Negative Binomial model), but the variance is assumed to
	be the sum of a shot noise and raw variance term, which is a smooth
	function (mean-dependent local regression) of the expected value
	of the true concentration of fragments from a particular gene (=
	pool data from genes with similar expression strength for variance
	estimation). Propose a similar method for determining the size factor
	(median of ratio of gene count to geometric mean across samples),
	similar to Oshlack and Robinson. Variance-stabilizing transformation
	for clustering.},
  owner = {Andrea_2},
  timestamp = {2010.11.03}
}

@ARTICLE{Auer2011,
  author = {Auer, Paul L. and Doerge, Rebecca W.},
  title = {{A two-stage Poisson model for testing RNA-seq data}},
  journal = {Statistical Applications in Genetics and Molecular Biology},
  year = {2011},
  volume = {10},
  pages = {1-26},
  number = {26},
  owner = {Andrea_2},
  timestamp = {2011.08.01}
}

@ARTICLE{Benjamini1995,
  author = {Benjamini, Y. and Hochberg, Y.},
  title = {Controlling the false discovery rate: a practical and powerful approach
	to multiple testing},
  journal = {Journal of the Royal Statistical Society, Series B},
  year = {1995},
  volume = {57},
  pages = {289-300},
  owner = {arau},
  timestamp = {2012.07.16}
}

@ARTICLE{Birney2004,
  author = {Birney, Ewan and Andrews, T. Daniel and Bevan, Paul and Caccamo,
	Mario and Chen, Yuan and Clarke, Laura and Coates, Guy and Cuff,
	James and Curwen, Val and Cutts, Tim and Down, Thomas and Eyras,
	Eduardo and Fernandez-Suarez, Xose M. and Gane, Paul and Gibbins,
	Brian and Gilbert, James and Hammond, Martin and Hotz, Hans-Rudolf
	and Iyer, Vivek and Jekosch, Kerstin and Kahari, Andreas and Kasprzyk,
	Arek and Keefe, Damian and Keenan, Stephen and Lehvaslaiho, Heikki
	and McVicker, Graham and Melsopp, Craig and Meidl, Patrick and Mongin,
	Emmanuel and Pettett, Roger and Potter, Simon and Proctor, Glenn
	and Rae, Mark and Searle, Steve and Slater, Guy and Smedley, Damian
	and Smith, James and Spooner, Will and Stabenau, Arne and Stalker,
	James and Storey, Roy and Ureta-Vidal, Abel and Woodwark, K. Cara
	and Cameron, Graham and Durbin, Richard and Cox, Anthony and Hubbard,
	Tim and Clamp, Michele},
  title = {{An Overview of Ensembl}},
  journal = {Genome Research},
  year = {2004},
  volume = {14},
  pages = {925-928},
  number = {5},
  owner = {arau},
  timestamp = {2012.09.06}
}

@ARTICLE{Blekhman2010,
  author = {Blekhman, R. and Marioni, J. C. and Zumbo, P. and Stephens, M. and
	Gilad, Y.},
  title = {Sex-specific and lineage-specific alternative splicing in primates},
  journal = {Genome Research},
  year = {2010},
  volume = {20},
  pages = {180-189},
  number = {2},
  owner = {arau},
  timestamp = {2012.05.29}
}

@ARTICLE{Bottomly2011,
  author = {Bottomly, D. and Walter, N. A. and Hunter, J. E. and Darakjian, P.
	and Kawane, S. and Buck, K. J. and Searles, R. P. and Mooney, M.
	and McWeeney, S. K. and Hitzemann, R.},
  title = {{Evaluating gene expression in C57BL/GJ and DBA/2J mouse striatum
	using RNA-seq and microarrays}},
  journal = {PLoS One},
  year = {2011},
  volume = {6},
  pages = {e17820},
  number = {3},
  owner = {arau},
  timestamp = {2012.05.29}
}

@ARTICLE{Bourgon2010,
  author = {Bourgon, Richard and Gentleman, Robert and Huber, Wolfgang},
  title = {Independent filtering increases detection power for high-throughput
	experiments},
  journal = {PNAS},
  year = {2010},
  volume = {107},
  pages = {9546-9551},
  number = {21},
  owner = {arau},
  timestamp = {2012.07.12}
}

@ARTICLE{Brooks2011,
  author = {Brooks, A. N. and Yang, L. and Duff, M. O. and Hansen, K. D. and
	Park, J. W. and Dudoit, S. and Brenner, S. E. and Graveley, B. R.},
  title = {{Conservation of an RNA regulatory map between Drosophila and mammals}},
  journal = {Genome Research},
  year = {2011},
  volume = {21},
  pages = {193-202},
  number = {2},
  owner = {arau},
  timestamp = {2012.10.04}
}

@ARTICLE{Cleveland1979,
  author = {Cleveland, William S.},
  title = {Robust locally weighted regression and smoothing scatterplots},
  journal = {Journal of the American Statistical Association},
  year = {1979},
  volume = {74},
  pages = {829-836},
  number = {368},
  owner = {arau},
  timestamp = {2012.09.06}
}

@ARTICLE{Canovas2010,
  author = {C{\'a}novas, Angela and Rincon, Gonzalo and Islas-Trejo, Alma and
	Wickramasinghe, Saumya and Medrano, Juan F.},
  title = {{SNP discovery in the bovine milk transcriptome using RNA-seq technology}},
  journal = {Mammalian Genome},
  year = {2010},
  volume = {21},
  pages = {592-598},
  owner = {arau},
  timestamp = {2012.07.13}
}

@ARTICLE{Dice1945,
  author = {Dice, L. R.},
  title = {Measures of the amount of ecologic association between species},
  journal = {Ecology},
  year = {1945},
  volume = {26},
  pages = {297-302},
  owner = {arau},
  timestamp = {2012.05.29}
}

@Article{Dillies2012,
  author    = {Dillies, Marie-Agn{\`e}s and Rau, Andrea and Aubert, Julie and Hennequet-Antier, Christelle and Jeanmougin, Marine and Servant, Nicolas and Keime, C{\'e}line and Marot, Guillemette and Castel, David and Estelle, Jordi and Guernec, Gregory and Jagla, Bernd and Jouneau, Luc and Lalo{\"e}, Denis and Le Gall, Caroline and Scha{\"e}ffer, Brigitte and Le Crom, St{\'e}phane and Jaffr{\'e}zic, Florence},
  title     = {{A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis}},
  journal   = {Briefings in Bioinformatics},
  year      = {2013},
  volume    = {14},
  number    = {6},
  pages     = {671-83},
  owner     = {arau},
  timestamp = {2012.07.16},
}

@ARTICLE{Frazee2011,
  author = {Frazee, A. C. and Langmead, B. and Leek, J. T.},
  title = {{ReCount: a multi-experiment resource of analysis-ready RNA-seq gene
	count datasets}},
  journal = {BMC Bioinformatics},
  year = {2011},
  volume = {12},
  number = {449},
  owner = {arau},
  timestamp = {2012.05.29}
}

@Manual{Gentleman,
  title     = {{genefilter: methods for filtering genes from microarray experiments}},
  author    = {R. Gentleman and V. Carey and W. Huber and F. Hahne},
  year      = {2020},
  note      = {R package version 1.38.0},
  owner     = {Andrea},
  timestamp = {2012.10.17},
}

@ARTICLE{Gentleman2004,
  author = {Gentleman, R. C. and Carey, V. J. and Bates, D. M. and Bolstad, B.
	and Dettling, M. and Dudoit, S. and Ellis, B. and Gautier, L. and
	Ge, Y. and Gentry, J. and Hornik, K. and Hothorn, T. and Huber, W.
	and Iacus, S. and Irizarry, R. and Leisch, F. and Li, C. and Maechler,
	M. and Rossini, A. J. and Sawitzki, G. and Smith, C. and Smyth, G.
	and Tierney, L. and Yang, J. Y. H. and Zhang, J.},
  title = {{Bioconductor: Open software development for comptuational biology
	and bioinformatics.}},
  journal = {Genome Biology},
  year = {2004},
  volume = {5},
  number = {R80},
  owner = {arau},
  timestamp = {2012.05.29}
}

@ARTICLE{Hammer2010,
  author = {Hammer, P. and Banck, M. S. and Amberg, R. and Wang, C. and Petznick,
	G. and Luo, S. and Khrebtukova, I. and Schroth, G. P. and Beyerlein,
	P. and Beutler, A. S.},
  title = {{mRNA-seq with agnostic splice site discovery for nervous system
	transcriptomics tested in chronic pain}},
  journal = {Genome Research},
  year = {2010},
  volume = {20},
  pages = {847-860},
  number = {6},
  owner = {arau},
  timestamp = {2012.10.04}
}

@Article{Hansen2012,
  author      = {Hansen, Kasper D. and Irizarry, Rafael A. and Wu, Zhijin},
  title       = {{Removing technical variability in RNA-seq data using conditional quantile normalization}},
  journal     = {Biostatistics},
  year        = {2012},
  volume      = {13},
  number      = {2},
  pages       = {204-16},
  comment     = {GC content has SAMPLE-SPECIFIC effect on expression measurements.
	Propose a robust generalized regression + quartile normalization
	(R package cqn), returns a gene-specific normalization offset effect.
	Used three currently available RNA-seq datasets (human) with large
	number of samples + verification with technical replicates from MAQC
	Consortium. "Clearly a scaling normalization, that is, a shift in
	log scale expression, is not sufficient to normalized counts between
	samples."},
  institution = {Johns Hopkins University, Department of Biostatistics},
  owner       = {Andrea_2},
  timestamp   = {2011.09.05},
}

@MANUAL{Huber,
  title = {{pasilla: Data package with per-exon and per-gene read counts of
	RNA-seq samples of Pasilla knock-down by Brooks et al., Genome Research
	2011}},
  author = {W. Huber and A. Reyes},
  owner = {Andrea},
  timestamp = {2012.10.17}
}

@MISC{Huber2012,
  author = {Huber, W. and Reyes, A.},
  title = {{pasilla: Data package with per-exon and per-gene read counts of
	RNA-seq samples of Pasilla knock-down by Brooks et al., Genome Research
	2011}},
  howpublished = {R package version 0.2.13},
  year = {2012},
  journal = {Genome Research},
  owner = {arau},
  timestamp = {2012.10.04}
}

@ARTICLE{Jaccard1901,
  author = {Jaccard, Paul},
  title = {{{\'E}tude comparative de la distribution florale dans une portion
	des Alpes et des Jura}},
  journal = {{Bulletin de la Soci{\'e}t{\'e} Vaudoise des Sciences Naturelles}},
  year = {1901},
  volume = {37},
  pages = {547-549},
  owner = {arau},
  timestamp = {2012.05.29}
}

@ARTICLE{Kasprzyk2004,
  author = {Kasprzyk, Arek and Keefe, Damian and Smedley, Damian and London,
	Darin and Spooner, William and Melsopp, Craig and Hammond, Martin
	and Rocca-Serra, Philippe and Cox, Tony and Birney, Ewan},
  title = {{EnsMart: A generic system for fast and flexible access to biological
	data}},
  journal = {Genome Research},
  year = {2004},
  volume = {14},
  pages = {160-169},
  number = {1},
  owner = {arau},
  timestamp = {2012.09.06}
}

@ARTICLE{Li2011,
  author = {Li, Jun and Witten, Daniela M. and Johnstone, Iain M. and Tibshirani,
	Robert},
  title = {{Normalization, testing, and false discovery rate estimation for
	RNA-sequencing data}},
  journal = {Biostatistics},
  year = {2012},
  volume = {13},
  pages = {523-538},
  number = {3},
  abstract = {We discuss the identification of genes that are associated with an
	outcome in RNA sequencing and other sequence-based comparative genomic
	experiments. RNA-sequencing data take the form of counts, so models
	based on the Gaussian distribution are unsuitable. Moreover, normalization
	is challenging because different sequencing experiments may generate
	quite different total numbers of reads. To overcome these difficulties,
	we use a log-linear model with a new approach to normalization. We
	derive a novel procedure to estimate the false discovery rate (FDR).
	Our method can be applied to data with quantitative, two-class, or
	multiple-class outcomes, and the computation is fast even for large
	data sets. We study the accuracy of our approaches for significance
	calculation and FDR estimation, and we demonstrate that our method
	has potential advantages over existing methods that are based on
	a Poisson or negative binomial model. In summary, this work provides
	a pipeline for the significance analysis of sequencing data.},
  comment = {TC normalization calculated using only non-differentially expressed
	genes (iterative procedure to identify NDE genes), proposed a different
	strategy to estimate FDR -- instead of using the "theoretical p-values",
	they propose a permuatation test to iteratively determine null distribution
	using only a set of genes with small calculated score from score
	test. Also propose using a power transformation to make the data
	more closely follow a Poisson distribution.},
  owner = {arau},
  timestamp = {2011.12.16}
}

@ARTICLE{Mortazavi2008,
  author = {Mortazavi, Ali and Williams, Brian and McCue, Kenneth and Schaeffer,
	Lorian and Wold, Barbara},
  title = {{Mapping and quantifying mammalian transcriptomes by RNA-Seq}},
  journal = {Nature Methods},
  year = {2008},
  volume = {5},
  pages = {621-628},
  number = {7},
  comment = {Introduces RPKM (reads per kilbase per million) to facilitate comparisons
	within and between samples, and idea that expression can span 5 orders
	of magnitude. Uses mouse (brain, liver, and skeletal muscle tissue
	samples, 2 technical replicates of each). 25-bp sequences (unique
	reads + spliced reads + multireads) mapped using ELAND and ERANGE
	after performing RNA fragmentation before copying. Challenges: how
	uniform are sequences from transcripts? How much sequence is required
	for low abundance classes? Hot to quantify data? Problem with throwing
	away multireads: undercount or fail to report expression for genes
	with closely related paralogs. Briefly RNA-seq background noise.},
  owner = {Andrea_2},
  timestamp = {2010.10.13}
}

@ARTICLE{Oshlack2010,
  author = {Oshlack, Alici and Robinson, Mark D. and Young, Matthew D.},
  title = {{From RNA-seq reads to differential expression results}},
  journal = {Genome Biology},
  year = {2010},
  volume = {11},
  number = {220},
  owner = {arau},
  timestamp = {2012.09.06}
}

@MANUAL{Team2009,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Development Core Team}},
  organization = {{R Foundation for Statistical Computing}},
  address = {Vienna, Austria},
  year = {2009},
  note = {{ISBN} 3-900051-07-0},
  owner = {arau},
  timestamp = {2009.11.10},
  url = {http://www.R-project.org}
}

@ARTICLE{Ramskold2009,
  author = {Ramsk{\"o}ld, D. and Wang, E. T. and Burge, C. B. and Sandberg, R.},
  title = {{An abundance of ubiquitously expressed genes revealed by tissue
	transcriptome sequence data}},
  journal = {PLoS Computational Biology},
  year = {2009},
  volume = {5},
  pages = {e1000598},
  number = {12},
  owner = {arau},
  timestamp = {2012.05.29}
}

@Article{Rau2012a,
  author    = {Rau, A. and Gallopin, M. and Celeux, G. and Jaffr{\'e}zic, F.},
  title     = {Data-based filtering for replicated high-throughput transcriptome sequencing experiments},
  journal   = {Bioinformatics},
  year      = {2013},
  volume    = {29},
  number    = {17},
  pages     = {2146-52},
  owner     = {Andrea},
  timestamp = {2012.10.17},
}

@ARTICLE{Rau2012,
  author = {Rau, A. and Marot, G. and Jaffr{\'e}zic, F.},
  title = {{Differential meta-analysis of RNA-seq data}},
  journal = {(submitted)},
  year = {2013},
  owner = {arau},
  timestamp = {2012.10.04}
}

@ARTICLE{Risso2011,
  author = {Risso, Davide and Schwartz, Katja and Sherlock, Gavin and Dudoit,
	Sandrine},
  title = {{GC-content normalization for RNA-seq data}},
  journal = {BMC Bioinformatics},
  year = {2011},
  volume = {12},
  number = {480},
  comment = {Bioconductor/R package EDASeq. Normalization to correct for sample-specific
	GC-content effects. Compare bias and MSE for expression fold-change
	estimation (based on MAQC Consortium data), Type I error comparison
	(for within condition DE analysis) and p-value distributions for
	various GC content %. Discusses difference between within-lane (length,
	GC bias, sequence composition) and between-lane normalization (global-scaling,
	full qunatile, and GLM). Consider for within-lane norm 1) regression
	normalization, 2) global-scaling, and 3) full quantile + full quantile
	between lane. PCR is likely most important cause for GC-content bias,
	tends to be more important for biological replicates than technical
	replicates.},
  institution = {U.C. Berkeley Division of Biostatistics},
  owner = {Andrea_2},
  timestamp = {2011.11.16}
}

@ARTICLE{Robinson2010a,
  author = {Robinson, M. D. and McCarthy, D. J. and Smyth, G. K.},
  title = {{edgeR: a Bioconductor package for differential expression analysis
	of digital gene expression data}},
  journal = {Bioinformatics},
  year = {2010},
  volume = {26},
  pages = {139-140},
  owner = {arau},
  timestamp = {2012.08.30}
}

@ARTICLE{Robinson2010,
  author = {Robinson, Mark D. and Oshlack, Alicia},
  title = {{A scaling normalization method for differential expression analysis
	of RNA-seq data}},
  journal = {Genome Biology},
  year = {2010},
  volume = {11},
  number = {R25},
  comment = {The number of tags expected to map to a gene is not only dependent
	on the expression level and length of the gene, but also the composition
	of the RNA population that is being sampled...we cannot always expect
	the proportions of different experiment conditions (e.g;, tissues)
	to be directly comparable! Proportion of reads attributed to a given
	gene in a library depends on the expression properties of the whold
	sample rather than just the expression level of that gene. Also can
	use estimated normalization factors directly in a statistical model
	while preserving sampling properties of the data. LOTS of informative
	code/data available on website. TMM Normalization.},
  owner = {Andrea_2},
  timestamp = {2010.11.10}
}

@ARTICLE{Robinson2007,
  author = {Robinson, Mark D. and Smyth, Gordon K.},
  title = {{Moderated statistical tests for assessing differences in tag abundance}},
  journal = {Bioinformatics},
  year = {2007},
  volume = {23},
  pages = {2881-2887},
  number = {21},
  comment = {edgeR paper},
  owner = {Andrea_2},
  timestamp = {2011.05.03}
}

@ARTICLE{Sam2011,
  author = {Sam, Lee T. and Lipson, Doron and Raz, Tal and Cao, Xuhong and Thompson,
	John and Milos, Patrice M. and Robinson, Dan and Chinnaiyan, Arul
	M. and Kumar-Sinha, Chandan and Maher, Christopher A.},
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@Comment{jabref-meta: databaseType:bibtex;}