@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}
}


@Article{Gertz2013,
author="Gertz, Jason
and Savic, Daniel
and Varley, Katherine E.
and Partridge, E. Christopher
and Safi, Alexias
and Jain, Preti
and Cooper, Gregory M.
and Reddy, Timothy E.
and Crawford, Gregory E.
and Myers, Richard M.",
title="Distinct properties of cell-type-specific and shared transcription factor binding sites.",
journal="Molecular cell",
year="2013",
month="Oct",
day="10",
volume="52",
number="1",
pages="25--36",
keywords="Amino Acid Sequence",
keywords="Animals",
keywords="Binding Sites",
keywords="Cell Line",
keywords="Conserved Sequence",
keywords="DNA Methylation",
keywords="Estradiol",
keywords="Estrogen Receptor alpha",
keywords="Estrogens",
keywords="Evolution, Molecular",
keywords="Gene Expression Regulation",
keywords="Humans",
keywords="Mice",
keywords="Models, Biological",
keywords="Promoter Regions, Genetic",
keywords="RNA Interference",
keywords="Receptors, Glucocorticoid",
keywords="Response Elements",
keywords="Thermodynamics",
keywords="Transcription Factors",
keywords="Transfection",
abstract="Most human transcription factors bind a small subset of potential genomic sites and often use different subsets in different cell types. To identify mechanisms that govern cell-type-specific transcription factor binding, we used an integrative approach to study estrogen receptor $\alpha$ (ER). We found that ER exhibits two distinct modes of binding. Shared sites, bound in multiple cell types, are characterized by high-affinity estrogen response elements (EREs), inaccessible chromatin, and a lack of DNA methylation, while cell-specific sites are characterized by a lack of EREs, co-occurrence with other transcription factors, and cell-type-specific chromatin accessibility and DNA methylation. These observations enabled accurate quantitative models of ER binding that suggest tethering of ER to one-third of cell-specific sites. The distinct properties of cell-specific binding were also observed with glucocorticoid receptor and for ER in primary mouse tissues, representing an elegant genomic encoding scheme for generating cell-type-specific gene regulation.",
issn="1097-4164",
doi="10.1016/j.molcel.2013.08.037",
url="http://www.ncbi.nlm.nih.gov/pubmed/24076218",
language="eng"
}


@Article{Ashburner2000,
author="Ashburner, M.
and Ball, C. A.
and Blake, J. A.
and Botstein, D.
and Butler, H.
and Cherry, J. M.
and Davis, A. P.
and Dolinski, K.
and Dwight, S. S.
and Eppig, J. T.
and Harris, M. A.
and Hill, D. P.
and Issel-Tarver, L.
and Kasarskis, A.
and Lewis, S.
and Matese, J. C.
and Richardson, J. E.
and Ringwald, M.
and Rubin, G. M.
and Sherlock, G.",
title="Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.",
journal="Nature genetics",
year="2000",
month="May",
volume="25",
number="1",
pages="25--29",
keywords="Animals",
keywords="Computer Communication Networks",
keywords="Databases, Factual",
keywords="Eukaryotic Cells",
keywords="Genes",
keywords="Humans",
keywords="Metaphysics",
keywords="Mice",
keywords="Molecular Biology",
keywords="Sequence Analysis, DNA",
keywords="Terminology as Topic",
issn="1061-4036",
doi="10.1038/75556",
url="http://www.ncbi.nlm.nih.gov/pubmed/10802651",
language="eng"
}


@article{Wingender2018,
abstract = {TFClass is a resource that classifies eukaryotic transcription factors (TFs) according to their DNA-binding domains (DBDs), available online at http://tfclass.bioinf.med.uni-goettingen.de. The classification scheme of TFClass was originally derived for human TFs and is expanded here to the whole taxonomic class of mammalia. Combining information from different resources, checking manually the retrieved mammalian TFs sequences and applying extensive phylogenetic analyses, {\textgreater}39 000 TFs from up to 41 mammalian species were assigned to the Superclasses, Classes, Families and Subfamilies of TFClass. As a result, TFClass now provides the corresponding sequence collection in FASTA format, sequence logos and phylogenetic trees at different classification levels, predicted TF binding sites for human, mouse, dog and cow genomes as well as links to several external databases. In particular, all those TFs that are also documented in the TRANSFAC{\textregistered} database (FACTOR table) have been linked and can be freely accessed. TRANSFAC{\textregistered} FACTOR can also be queried through an own search interface.},
author = {Wingender, Edgar and Schoeps, Torsten and Haubrock, Martin and Krull, Mathias and D{\"{o}}nitz, J{\"{u}}rgen},
doi = {10.1093/nar/gkx987},
file = {:Users/stvjc/PDFS/gkx987.pdf:pdf},
issn = {13624962},
journal = {Nucleic Acids Research},
number = {D1},
pages = {D343--D347},
pmid = {29087517},
title = {{TFClass: Expanding the classification of human transcription factors to their mammalian orthologs}},
volume = {46},
year = {2018}
}


@Article{Grant2011,
author="Grant, Charles E.
and Bailey, Timothy L.
and Noble, William Stafford",
title="FIMO: scanning for occurrences of a given motif.",
journal="Bioinformatics (Oxford, England)",
year="2011",
month="Apr",
day="01",
volume="27",
number="7",
pages="1017--1018",
keywords="Amino Acid Motifs",
keywords="Base Sequence",
keywords="Binding Sites",
keywords="CCCTC-Binding Factor",
keywords="Conserved Sequence",
keywords="DNA",
keywords="Databases, Genetic",
keywords="Genome, Human",
keywords="Humans",
keywords="Position-Specific Scoring Matrices",
keywords="Repressor Proteins",
keywords="Sequence Analysis, DNA",
keywords="Sequence Analysis, Protein",
keywords="Software",
abstract="A motif is a short DNA or protein sequence that contributes to the biological function of the sequence in which it resides. Over the past several decades, many computational methods have been described for identifying, characterizing and searching with sequence motifs. Critical to nearly any motif-based sequence analysis pipeline is the ability to scan a sequence database for occurrences of a given motif described by a position-specific frequency matrix.",
issn="1367-4811",
doi="10.1093/bioinformatics/btr064",
url="http://www.ncbi.nlm.nih.gov/pubmed/21330290",
language="eng"
}


@article{Lambert2018,
abstract = {Transcription factors (TFs) recognize specific DNA sequences to control chromatin and transcription, forming a complex system that guides expression of the genome. Despite keen interest in understanding how TFs control gene expression, it remains challenging to determine how the precise genomic binding sites of TFs are specified and how TF binding ultimately relates to regulation of transcription. This review considers how TFs are identified and functionally characterized, principally through the lens of a catalog of over 1,600 likely human TFs and binding motifs for two-thirds of them. Major classes of human TFs differ markedly in their evolutionary trajectories and expression patterns, underscoring distinct functions. TFs likewise underlie many different aspects of human physiology, disease, and variation, highlighting the importance of continued effort to understand TF-mediated gene regulation. Knowing how and where transcription factors bind to the genome is crucial for understanding how they control gene expression. This Review looks at how human TFs are identified and the ways they interact with DNA sequences.},
author = {Lambert, Samuel A. and Jolma, Arttu and Campitelli, Laura F. and Das, Pratyush K. and Yin, Yimeng and Albu, Mihai and Chen, Xiaoting and Taipale, Jussi and Hughes, Timothy R. and Weirauch, Matthew T.},
doi = {10.1016/j.cell.2018.01.029},
file = {:Users/stvjc/PDFS/weirauchTheHumanTF.pdf:pdf},
isbn = {1097-4172 (Electronic)
0092-8674 (Linking)},
issn = {10974172},
journal = {Cell},
number = {4},
pages = {650--665},
pmid = {29425488},
publisher = {Elsevier Inc.},
title = {{The Human Transcription Factors}},
url = {https://doi.org/10.1016/j.cell.2018.01.029},
volume = {172},
year = {2018}
}

@article{Sonawane2017,
abstract = {Although all human tissues carry out common processes, tissues are distinguished by gene expression patterns, implying that distinct regulatory programs control tissue specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that the regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes. Understanding gene regulation is important for many fields in biology and medicine. Sonawane et al. reconstruct and investigate regulatory networks for 38 human tissues. They find that regulation of tissue-specific function is largely independent of transcription factor expression and that tissue specificity appears to be mediated by tissue-specific regulatory network paths.},
author = {Sonawane, Abhijeet Rajendra and Platig, John and Fagny, Maud and Chen, Cho Yi and Paulson, Joseph Nathaniel and Lopes-Ramos, Camila Miranda and DeMeo, Dawn Lisa and Quackenbush, John and Glass, Kimberly and Kuijjer, Marieke Lydia},
doi = {10.1016/j.celrep.2017.10.001},
file = {:Users/stvjc/PDFS/kimbieTFTissueSpec.pdf:pdf},
issn = {22111247},
journal = {Cell Reports},
keywords = {GTEx,gene expression,gene regulation,network biology,network medicine,regulatory networks,tissue specificity,transcription factors,transcriptional regulation,transcriptome},
number = {4},
pages = {1077--1088},
pmid = {29069589},
publisher = {ElsevierCompany.},
title = {{Understanding Tissue-Specific Gene Regulation}},
url = {https://doi.org/10.1016/j.celrep.2017.10.001},
volume = {21},
year = {2017}
}
