@article{nott_brain_2019,
	title = {Brain cell type-specific enhancer-promoter interactome maps and disease-risk association},
	volume = {366},
	issn = {1095-9203},
	doi = {10.1126/science.aay0793},
	abstract = {Noncoding genetic variation is a major driver of phenotypic diversity, but functional interpretation is challenging. To better understand common genetic variation associated with brain diseases, we defined noncoding regulatory regions for major cell types of the human brain. Whereas psychiatric disorders were primarily associated with variants in transcriptional enhancers and promoters in neurons, sporadic Alzheimer's disease (AD) variants were largely confined to microglia enhancers. Interactome maps connecting disease-risk variants in cell-type-specific enhancers to promoters revealed an extended microglia gene network in AD. Deletion of a microglia-specific enhancer harboring AD-risk variants ablated BIN1 expression in microglia, but not in neurons or astrocytes. These findings revise and expand the list of genes likely to be influenced by noncoding variants in AD and suggest the probable cell types in which they function.},
	language = {eng},
	number = {6469},
	journal = {Science (New York, N.Y.)},
	author = {Nott, Alexi and Holtman, Inge R. and Coufal, Nicole G. and Schlachetzki, Johannes C. M. and Yu, Miao and Hu, Rong and Han, Claudia Z. and Pena, Monique and Xiao, Jiayang and Wu, Yin and Keulen, Zahara and Pasillas, Martina P. and O'Connor, Carolyn and Nickl, Christian K. and Schafer, Simon T. and Shen, Zeyang and Rissman, Robert A. and Brewer, James B. and Gosselin, David and Gonda, David D. and Levy, Michael L. and Rosenfeld, Michael G. and McVicker, Graham and Gage, Fred H. and Ren, Bing and Glass, Christopher K.},
	year = {2019},
	pmid = {31727856},
	pmcid = {PMC7028213},
	keywords = {Humans, Adaptor Proteins, Signal Transducing, Alzheimer Disease, Brain, Cells, Cultured, Chromatin, Enhancer Elements, Genetic, Gene Regulatory Networks, Genetic Variation, Genome-Wide Association Study, Microglia, Nuclear Proteins, Promoter Regions, Genetic, Sequence Deletion, Tumor Suppressor Proteins},
	pages = {1134--1139},
	file = {Accepted Version:/Users/deborah.gerard/Zotero/storage/KD67D9P6/Nott et al. - 2019 - Brain cell type-specific enhancer-promoter interac.pdf:application/pdf},
}

@article{allhoff_differential_2016,
	title = {Differential peak calling of {ChIP}-seq signals with replicates with {THOR}},
	volume = {44},
	issn = {1362-4962},
	doi = {10.1093/nar/gkw680},
	abstract = {The study of changes in protein-DNA interactions measured by ChIP-seq on dynamic systems, such as cell differentiation, response to treatments or the comparison of healthy and diseased individuals, is still an open challenge. There are few computational methods comparing changes in ChIP-seq signals with replicates. Moreover, none of these previous approaches addresses ChIP-seq specific experimental artefacts arising from studies with biological replicates. We propose THOR, a Hidden Markov Model based approach, to detect differential peaks between pairs of biological conditions with replicates. THOR provides all pre- and post-processing steps required in ChIP-seq analyses. Moreover, we propose a novel normalization approach based on housekeeping genes to deal with cases where replicates have distinct signal-to-noise ratios. To evaluate differential peak calling methods, we delineate a methodology using both biological and simulated data. This includes an evaluation procedure that associates differential peaks with changes in gene expression as well as histone modifications close to these peaks. We evaluate THOR and seven competing methods on data sets with distinct characteristics from in vitro studies with technical replicates to clinical studies of cancer patients. Our evaluation analysis comprises of 13 comparisons between pairs of biological conditions. We show that THOR performs best in all scenarios.},
	language = {eng},
	number = {20},
	journal = {Nucleic Acids Research},
	author = {Allhoff, Manuel and Seré, Kristin and F Pires, Juliana and Zenke, Martin and G Costa, Ivan},
	month = nov,
	year = {2016},
	pmid = {27484474},
	pmcid = {PMC5175345},
	keywords = {Algorithms, Cell Differentiation, Chromatin Immunoprecipitation, Computational Biology, Datasets as Topic, Dendritic Cells, Epigenesis, Genetic, High-Throughput Nucleotide Sequencing, Histones, Humans, Lymphoma, B-Cell, Markov Chains, Sequence Analysis, DNA, Workflow},
	pages = {e153},
	file = {Full Text:/Users/deborah.gerard/Zotero/storage/SSDVQ93B/Allhoff et al. - 2016 - Differential peak calling of ChIP-seq signals with.pdf:application/pdf},
}