@article{Nowicka2016,
author = {Nowicka, M and Robinson, M D},
doi = {10.12688/f1000research.8900.2},
journal = {F1000Research},
number = {1356},
title = {{DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics [version 2; referees: 2 approved]}},
url = {https://f1000research.com/articles/5-1356/v2},
volume = {5},
year = {2016}
}

@article{Brooks2011,
abstract = {Alternative splicing is generally controlled by proteins that bind directly to regulatory sequence elements and either activate or repress splicing of adjacent splice sites in a target pre-mRNA. Here, we have combined RNAi and mRNA-seq to identify exons that are regulated by Pasilla (PS), the Drosophila melanogaster ortholog of mammalian NOVA1 and NOVA2. We identified 405 splicing events in 323 genes that are significantly affected upon depletion of ps, many of which were annotated as being constitutively spliced. The sequence regions upstream and within PS-repressed exons and downstream from PS-activated exons are enriched for YCAY repeats, and these are consistent with the location of these motifs near NOVA-regulated exons in mammals. Thus, the RNA regulatory map of PS and NOVA1/2 is highly conserved between insects and mammals despite the fact that the target gene orthologs regulated by PS and NOVA1/2 are almost entirely nonoverlapping. This observation suggests that the regulatory codes of individual RNA binding proteins may be nearly immutable, yet the regulatory modules controlled by these proteins are highly evolvable.},
author = {Brooks, Angela N and Yang, Li and Duff, Michael O and Hansen, Kasper D and Park, Jung W and Dudoit, Sandrine and Brenner, Steven E and Graveley, Brenton R},
file = {:Users/gosia/Documents/Mendeley Desktop/Brooks et al/Brooks et al. - 2011 - Conservation of an RNA regulatory map between Drosophila and mammals.pdf:pdf},
institution = {Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA.},
journal = {Genome research},
keywords = {Pasilla},
mendeley-groups = {Data},
mendeley-tags = {Pasilla},
number = {2},
pages = {193--202},
title = {{Conservation of an RNA regulatory map between Drosophila and mammals.}},
volume = {21},
year = {2011}
}

@article{Lappalainen2013,
abstract = {Genome sequencing projects are discovering millions of genetic variants in humans, and interpretation of their functional effects is essential for understanding the genetic basis of variation in human traits. Here we report sequencing and deep analysis of messenger RNA and microRNA from lymphoblastoid cell lines of 462 individuals from the 1000 Genomes Project--the first uniformly processed high-throughput RNA-sequencing data from multiple human populations with high-quality genome sequences. We discover extremely widespread genetic variation affecting the regulation of most genes, with transcript structure and expression level variation being equally common but genetically largely independent. Our characterization of causal regulatory variation sheds light on the cellular mechanisms of regulatory and loss-of-function variation, and allows us to infer putative causal variants for dozens of disease-associated loci. Altogether, this study provides a deep understanding of the cellular mechanisms of transcriptome variation and of the landscape of functional variants in the human genome.},
author = {Lappalainen, Tuuli and Sammeth, Michael and Friedl{\"{a}}nder, Marc R and {'t Hoen}, Peter A C and Monlong, Jean and Rivas, Manuel A and Gonz{\`{a}}lez-Porta, Mar and Kurbatova, Natalja and Griebel, Thasso and Ferreira, Pedro G and Barann, Matthias and Wieland, Thomas and Greger, Liliana and van Iterson, Maarten and Alml{\"{o}}f, Jonas and Ribeca, Paolo and Pulyakhina, Irina and Esser, Daniela and Giger, Thomas and Tikhonov, Andrew and Sultan, Marc and Bertier, Gabrielle and MacArthur, Daniel G and Lek, Monkol and Lizano, Esther and Buermans, Henk P J and Padioleau, Ismael and Schwarzmayr, Thomas and Karlberg, Olof and Ongen, Halit and Kilpinen, Helena and Beltran, Sergi and Gut, Marta and Kahlem, Katja and Amstislavskiy, Vyacheslav and Stegle, Oliver and Pirinen, Matti and Montgomery, Stephen B and Donnelly, Peter and McCarthy, Mark I and Flicek, Paul and Strom, Tim M and Lehrach, Hans and Schreiber, Stefan and Sudbrak, Ralf and Carracedo, Angel and Antonarakis, Stylianos E and H{\"{a}}sler, Robert and Syv{\"{a}}nen, Ann-Christine and van Ommen, Gert-Jan and Brazma, Alvis and Meitinger, Thomas and Rosenstiel, Philip and Guig{\'{o}}, Roderic and Gut, Ivo G and Estivill, Xavier and Dermitzakis, Emmanouil T},
file = {:Users/gosia/Documents/Mendeley Desktop/Lappalainen et al/Lappalainen et al. - 2013 - Transcriptome and genome sequencing uncovers functional variation in humans(2).pdf:pdf;:Users/gosia/Documents/Mendeley Desktop/Lappalainen et al/Lappalainen et al. - 2013 - Transcriptome and genome sequencing uncovers functional variation in humans.pdf:pdf},
issn = {1476-4687},
journal = {Nature},
keywords = {Alleles,Cell Line,Exons,Exons: genetics,GEUVADIS,Gene Expression Profiling,Genetic Variation,Genetic Variation: genetics,Genome,High-Throughput Nucleotide Sequencing,Human,Human: genetics,Humans,Messenger,Messenger: analysis,Messenger: genetics,Polymorphism,Quantitative Trait Loci,Quantitative Trait Loci: genetics,RNA,Sequence Analysis,Single Nucleotide,Single Nucleotide: genetics,Transcriptome,Transcriptome: genetics,Transformed},
mendeley-groups = {sQTL analysis/Geuvadis,.PhD report CM2,sQTL analysis/Data},
mendeley-tags = {GEUVADIS},
number = {7468},
pages = {506--11},
pmid = {24037378},
title = {{Transcriptome and genome sequencing uncovers functional variation in humans.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3918453{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {501},
year = {2013}
}



@article{Bray2016,
abstract = {We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in {\textless}10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.},
author = {Bray, Nicolas L and Pimentel, Harold and Melsted, Pall and Pachter, Lior},
issn = {1546-1696},
journal = {Nat Biotech},
keywords = {kallisto},
mendeley-groups = {.DRIMSeq paper,RNA-seq quantification/Transcript quantification},
mendeley-tags = {kallisto},
month = {apr},
publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
title = {{Near-optimal probabilistic RNA-seq quantification}},
url = {http://dx.doi.org/10.1038/nbt.3519 http://10.0.4.14/nbt.3519 http://www.nature.com/nbt/journal/vaop/ncurrent/abs/nbt.3519.html{\#}supplementary-information},
volume = {advance on},
year = {2016}
}



@article{VandenBerge2017,
abstract = {Background: Reductions in sequencing cost and innovations in expression quantification have prompted an emergence of RNA-seq studies with complex designs and data analysis at transcript resolution. These applications involve multiple hypotheses per gene, leading to challenging multiple testing problems. Conventional approaches provide separate top-lists for every contrast and false discovery rate (FDR) control at individual hypothesis level. Hence, they fail to establish proper gene-level error control, which compromises downstream validation experiments. Tests that aggregate individual hypotheses are more powerful and provide gene-level FDR control, but in the RNA-seq literature no methods are available for post-hoc analysis of individual hypotheses. Results: We introduce a two-stage procedure that leverages the increased power of aggregated hypothesis tests while maintaining high biological resolution by post-hoc analysis of genes passing the screening hypothesis. Our method is evaluated on simulated and real RNA-seq experiments. It provides gene-level FDR control in studies with complex designs while boosting power for interaction effects without compromising the discovery of main effects. In a differential transcript usage/expression context, stage-wise testing gains power by aggregating hypotheses at the gene level, while providing transcript-level assessment of genes passing the screening stage. Finally, a prostate cancer case study highlights the relevance of combining gene with transcript level results. Conclusion: Stage-wise testing is a general paradigm that can be adopted whenever individual hypotheses can be aggregated. In our context, it achieves an optimal middle ground between biological resolution and statistical power while providing gene-level FDR control, which is beneficial for downstream biological interpretation and validation.},
author = {{Van den Berge}, Koen and Soneson, Charlotte and Robinson, Mark D and Clement, Lieven},
file = {:Users/gosia/Mendeley Desktop/Van den Berge et al/Van den Berge et al. - 2017 - A general and powerful stage-wise testing procedure for differential expression and differential transcrip.pdf:pdf},
journal = {bioRxiv},
mendeley-groups = {RNA-seq DE,.DRIMSeq paper},
month = {feb},
title = {{A general and powerful stage-wise testing procedure for differential expression and differential transcript usage}},
url = {http://biorxiv.org/content/early/2017/02/16/109082.abstract},
year = {2017}
}

@article{McCarthy2012,
abstract = {A flexible statistical framework is developed for the analysis of read counts from RNA-Seq gene expression studies. It provides the ability to analyse complex experiments involving multiple treatment conditions and blocking variables while still taking full account of biological variation. Biological variation between RNA samples is estimated separately from the technical variation associated with sequencing technologies. Novel empirical Bayes methods allow each gene to have its own specific variability, even when there are relatively few biological replicates from which to estimate such variability. The pipeline is implemented in the edgeR package of the Bioconductor project. A case study analysis of carcinoma data demonstrates the ability of generalized linear model methods (GLMs) to detect differential expression in a paired design, and even to detect tumour-specific expression changes. The case study demonstrates the need to allow for gene-specific variability, rather than assuming a common dispersion across genes or a fixed relationship between abundance and variability. Genewise dispersions de-prioritize genes with inconsistent results and allow the main analysis to focus on changes that are consistent between biological replicates. Parallel computational approaches are developed to make non-linear model fitting faster and more reliable, making the application of GLMs to genomic data more convenient and practical. Simulations demonstrate the ability of adjusted profile likelihood estimators to return accurate estimators of biological variability in complex situations. When variation is gene-specific, empirical Bayes estimators provide an advantageous compromise between the extremes of assuming common dispersion or separate genewise dispersion. The methods developed here can also be applied to count data arising from DNA-Seq applications, including ChIP-Seq for epigenetic marks and DNA methylation analyses.},
author = {McCarthy, Davis J. and Chen, Yunshun and Smyth, Gordon K.},
file = {:Users/gosia/Mendeley Desktop/McCarthy, Chen, Smyth/McCarthy, Chen, Smyth - 2012 - Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation.pdf:pdf},
journal = {Nucleic Acids Research},
keywords = {edgeR},
mendeley-groups = {.CM2 PhD report,.DRIMSeq paper,RNA-seq DE/Gene DE/edgeR,Project DM/edgeR},
mendeley-tags = {edgeR},
number = {10},
pages = {4288--4297},
pmid = {22287627},
title = {{Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation}},
volume = {40},
year = {2012}
}

@article{Danaher1988,
abstract = {We develop estimates for the parameters of the Dirichlet-multinomial distribution (DMD) when there is insufficient data to obtain maximum likelihood or method of moment estimates known in the literature. We do, however, have supplemetary beta-binomial data pertaining to the marginals of the DMD, and use these data when estimating the DMD parameters. A real situation and data set are given where our estimates are applicable.},
annote = {doi: 10.1080/03610928808829713},
author = {Danaher, Peter J},
doi = {10.1080/03610928808829713},
file = {:Users/gosia/Mendeley Desktop/Danaher/Danaher - 1988 - Parameter estimation for the dirichlet-multinomial distribution using supplementary beta-binomial data(2).pdf:pdf},
issn = {0361-0926},
journal = {Communications in Statistics - Theory and Methods},
mendeley-groups = {Project DM/Beta-Binomial},
month = {jan},
number = {6},
pages = {1777--1788},
publisher = {Taylor {\&} Francis},
title = {{Parameter estimation for the dirichlet-multinomial distribution using supplementary beta-binomial data}},
url = {http://dx.doi.org/10.1080/03610928808829713},
volume = {17},
year = {1988}
}
