@BOOK{Smith:2012qr,
	title = {{B}ook {T}itle},
	publisher = {Publisher},
	author = {Smith, J.~M. and Jones, A.~B.},
	year = {2012},
	edition = {7th},
}

@ARTICLE{Smith:2013jd,
	author = {Jones, A.~B. and Smith, J.~M.},
	title = {{A}rticle {T}itle},
	journal = {Journal title},
	year = {2013},
	volume = {13},
	pages = {123-456},
	number = {52},
	month = {March},
	publisher = {Publisher}
}


@article{Kulakovskiy2018,
abstract = {We present a major update of the HOCOMOCO collection that consists of patterns describing DNA binding specificities for human and mouse transcription factors. In this release, we profited from a nearly doubled volume of published in vivo experiments on transcription factor (TF) binding to expand the repertoire of binding models, replace low-quality models previously based on in vitro data only and cover more than a hundred TFs with previously unknown binding specificities. This was achieved by systematic motif discovery from more than five thousand ChIP-Seq experiments uniformly processed within the BioUML framework with several ChIP-Seq peak calling tools and aggregated in the GTRD database. HOCOMOCO v11 contains binding models for 453 mouse and 680 human transcription factors and includes 1302 mononucleotide and 576 dinucleotide position weight matrices, which describe primary binding preferences of each transcription factor and reliable alternative binding specificities. An interactive interface and bulk downloads are available on the web: http://hocomoco.autosome.ru and http://www.cbrc.kaust.edu.sa/hocomoco11. In this release, we complement HOCOMOCO by MoLoTool (Motif Location Toolbox, http://molotool.autosome.ru) that applies HOCOMOCO models for visualization of binding sites in short DNA sequences.},
author = {Kulakovskiy, Ivan V. and Vorontsov, Ilya E. and Yevshin, Ivan S. and Sharipov, Ruslan N. and Fedorova, Alla D. and Rumynskiy, Eugene I. and Medvedeva, Yulia A. and Magana-Mora, Arturo and Bajic, Vladimir B. and Papatsenko, Dmitry A. and Kolpakov, Fedor A. and Makeev, Vsevolod J.},
doi = {10.1093/nar/gkx1106},
file = {:Users/stvjc/PDFS/gkx1106.pdf:pdf},
issn = {13624962},
journal = {Nucleic Acids Research},
number = {D1},
pages = {D252--D259},
pmid = {29140464},
publisher = {Oxford University Press},
title = {{HOCOMOCO: Towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis}},
volume = {46},
year = {2018}
}


@article {Subramanian15545,
	author = {Subramanian, Aravind and Tamayo, Pablo and Mootha, Vamsi K. and Mukherjee, Sayan and Ebert, Benjamin L. and Gillette, Michael A. and Paulovich, Amanda and Pomeroy, Scott L. and Golub, Todd R. and Lander, Eric S. and Mesirov, Jill P.},
	title = {Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles},
	volume = {102},
	number = {43},
	pages = {15545--15550},
	year = {2005},
	doi = {10.1073/pnas.0506580102},
	publisher = {National Academy of Sciences},
	abstract = {Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.},
	issn = {0027-8424},
	URL = {http://www.pnas.org/content/102/43/15545},
	eprint = {http://www.pnas.org/content/102/43/15545.full.pdf},
	journal = {Proceedings of the National Academy of Sciences}
}


@article{Weirauch2014,
abstract = {Transcription factor (TF) DNA sequence preferences direct their regulatory activity, but are currently known for only ∼1{\%} of eukaryotic TFs. Broadly sampling DNA-binding domain (DBD) types from multiple eukaryotic clades, we determined DNA sequence preferences for {\textgreater}1,000 TFs encompassing 54 different DBD classes from 131 diverse eukaryotes. We find that closely related DBDs almost always have very similar DNA sequence preferences, enabling inference of motifs for ∼34{\%} of the ∼170,000 known or predicted eukaryotic TFs. Sequences matching both measured and inferred motifs are enriched in chromatin immunoprecipitation sequencing (ChIP-seq) peaks and upstream of transcription start sites in diverse eukaryotic lineages. SNPs defining expression quantitative trait loci in Arabidopsis promoters are also enriched for predicted TF binding sites. Importantly, our motif "library" can be used to identify specific TFs whose binding may be altered by human disease risk alleles. These data present a powerful resource for mapping transcriptional networks across eukaryotes.},
archivePrefix = {arXiv},
arxivId = {NIHMS150003},
author = {Weirauch, Matthew T. and Yang, Ally and Albu, Mihai and Cote, Atina G. and Montenegro-Montero, Alejandro and Drewe, Philipp and Najafabadi, Hamed S. and Lambert, Samuel A. and Mann, Ishminder and Cook, Kate and Zheng, Hong and Goity, Alejandra and van Bakel, Harm and Lozano, Jean-Claude and Galli, Mary and Lewsey, Mathew G. and Huang, Eryong and Mukherjee, Tuhin and Chen, Xiaoting and Reece-Hoyes, John S. and Govindarajan, Sridhar and Shaulsky, Gad and Walhout, Albertha J.M. and Bouget, Fran{\c{c}}ois-Yves and Ratsch, Gunnar and Larrondo, Luis F. and Ecker, Joseph R. and Hughes, Timothy R.},
doi = {10.1016/j.cell.2014.08.009},
eprint = {NIHMS150003},
file = {:Users/stvjc/Research/TFutils/inst/article/TFutils/hughesCisBP.pdf:pdf},
isbn = {1097-4172 (Electronic) 0092-8674 (Linking)},
issn = {00928674},
journal = {Cell},
number = {6},
pages = {1431--1443},
pmid = {25215497},
title = {{Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0092867414010368},
volume = {158},
year = {2014}
}
