
@ARTICLE{Heinz2010-dz,
  title    = "Simple combinations of lineage-determining transcription factors
              prime cis-regulatory elements required for macrophage and {B}
              cell identities",
  author   = "Heinz, Sven and Benner, Christopher and Spann, Nathanael and
              Bertolino, Eric and Lin, Yin C and Laslo, Peter and Cheng, Jason
              X and Murre, Cornelis and Singh, Harinder and Glass, Christopher
              K",
  abstract = "Genome-scale studies have revealed extensive, cell type-specific
              colocalization of transcription factors, but the mechanisms
              underlying this phenomenon remain poorly understood. Here, we
              demonstrate in macrophages and B cells that collaborative
              interactions of the common factor PU.1 with small sets of
              macrophage- or B cell lineage-determining transcription factors
              establish cell-specific binding sites that are associated with
              the majority of promoter-distal H3K4me1-marked genomic regions.
              PU.1 binding initiates nucleosome remodeling, followed by H3K4
              monomethylation at large numbers of genomic regions associated
              with both broadly and specifically expressed genes. These
              locations serve as beacons for additional factors, exemplified by
              liver X receptors, which drive both cell-specific gene expression
              and signal-dependent responses. Together with analyses of
              transcription factor binding and H3K4me1 patterns in other cell
              types, these studies suggest that simple combinations of
              lineage-determining transcription factors can specify the genomic
              sites ultimately responsible for both cell identity and cell
              type-specific responses to diverse signaling inputs.",
  journal  = "Mol. Cell",
  volume   =  38,
  number   =  4,
  pages    = "576--589",
  month    =  may,
  year     =  2010,
  language = "en"
}

@ARTICLE{Bailey2015-wa,
  title    = "The {MEME} Suite",
  author   = "Bailey, Timothy L and Johnson, James and Grant, Charles E and
              Noble, William S",
  abstract = "The MEME Suite is a powerful, integrated set of web-based tools
              for studying sequence motifs in proteins, DNA and RNA. Such
              motifs encode many biological functions, and their detection and
              characterization is important in the study of molecular
              interactions in the cell, including the regulation of gene
              expression. Since the previous description of the MEME Suite in
              the 2009 Nucleic Acids Research Web Server Issue, we have added
              six new tools. Here we describe the capabilities of all the tools
              within the suite, give advice on their best use and provide
              several case studies to illustrate how to combine the results of
              various MEME Suite tools for successful motif-based analyses. The
              MEME Suite is freely available for academic use at
              http://meme-suite.org, and source code is also available for
              download and local installation.",
  journal  = "Nucleic Acids Res.",
  volume   =  43,
  number   = "W1",
  pages    = "W39--49",
  month    =  jul,
  year     =  2015,
  language = "en"
}


@ARTICLE{Bailey2012-qz,
  title    = "Inferring direct {DNA} binding from {ChIP-seq}",
  author   = "Bailey, Timothy L and Machanick, Philip",
  abstract = "Genome-wide binding data from transcription factor ChIP-seq
              experiments is the best source of information for inferring the
              relative DNA-binding affinity of these proteins in vivo. However,
              standard motif enrichment analysis and motif discovery approaches
              sometimes fail to correctly identify the binding motif for the
              ChIP-ed factor. To overcome this problem, we propose 'central
              motif enrichment analysis' (CMEA), which is based on the
              observation that the positional distribution of binding sites
              matching the direct-binding motif tends to be unimodal, well
              centered and maximal in the precise center of the ChIP-seq peak
              regions. We describe a novel visualization and statistical
              analysis tool--CentriMo--that identifies the region of maximum
              central enrichment in a set of ChIP-seq peak regions and displays
              the positional distributions of predicted sites. Using CentriMo
              for motif enrichment analysis, we provide evidence that one
              transcription factor (Nanog) has different binding affinity in
              vivo than in vitro, that another binds DNA cooperatively (E2f1),
              and confirm the in vivo affinity of NFIC, rescuing a difficult
              ChIP-seq data set. In another data set, CentriMo strongly
              suggests that there is no evidence of direct DNA binding by the
              ChIP-ed factor (Smad1). CentriMo is now part of the MEME Suite
              software package available at http://meme.nbcr.net. All data and
              output files presented here are available at:
              http://research.imb.uq.edu.au/t.bailey/sd/Bailey2011a.",
  journal  = "Nucleic Acids Res.",
  volume   =  40,
  number   =  17,
  pages    = "e128",
  month    =  sep,
  year     =  2012,
  language = "en"
}

@Article{Tremblay2024,
    title = {universalmotif: An R package for biological motif analysis},
    author = {Benjamin JM Tremblay},
    journal = {Journal of Open Source Software},
    volume = {9},
    year = {2024},
    pages = {7012},
    url = {https://joss.theoj.org/papers/10.21105/joss.07012},
    doi = {10.21105/joss.07012},
  }


@ARTICLE{Kulakovskiy2013-jt,
  title    = "{HOCOMOCO}: a comprehensive collection of human transcription
              factor binding sites models",
  author   = "Kulakovskiy, Ivan V and Medvedeva, Yulia A and Schaefer, Ulf and
              Kasianov, Artem S and Vorontsov, Ilya E and Bajic, Vladimir B and
              Makeev, Vsevolod J",
  abstract = "Transcription factor (TF) binding site (TFBS) models are crucial
              for computational reconstruction of transcription regulatory
              networks. In existing repositories, a TF often has several models
              (also called binding profiles or motifs), obtained from different
              experimental data. Having a single TFBS model for a TF is more
              pragmatic for practical applications. We show that integration of
              TFBS data from various types of experiments into a single model
              typically results in the improved model quality probably due to
              partial correction of source specific technique bias. We present
              the Homo sapiens comprehensive model collection (HOCOMOCO,
              http://autosome.ru/HOCOMOCO/, http://cbrc.kaust.edu.sa/hocomoco/)
              containing carefully hand-curated TFBS models constructed by
              integration of binding sequences obtained by both low- and
              high-throughput methods. To construct position weight matrices to
              represent these TFBS models, we used ChIPMunk software in four
              computational modes, including newly developed periodic
              positional prior mode associated with DNA helix pitch. We
              selected only one TFBS model per TF, unless there was a clear
              experimental evidence for two rather distinct TFBS models. We
              assigned a quality rating to each model. HOCOMOCO contains 426
              systematically curated TFBS models for 401 human TFs, where 172
              models are based on more than one data source.",
  journal  = "Nucleic Acids Res.",
  volume   =  41,
  number   = "Database issue",
  pages    = "D195--202",
  month    =  jan,
  year     =  2013,
  language = "en"
}


@ARTICLE{Sandelin2004-mv,
  title    = "{JASPAR}: an open-access database for eukaryotic transcription
              factor binding profiles",
  author   = "Sandelin, Albin and Alkema, Wynand and Engstr{\"o}m, P{\"a}r and
              Wasserman, Wyeth W and Lenhard, Boris",
  abstract = "The analysis of regulatory regions in genome sequences is
              strongly based on the detection of potential transcription factor
              binding sites. The preferred models for representation of
              transcription factor binding specificity have been termed
              position-specific scoring matrices. JASPAR is an open-access
              database of annotated, high-quality, matrix-based transcription
              factor binding site profiles for multicellular eukaryotes. The
              profiles were derived exclusively from sets of nucleotide
              sequences experimentally demonstrated to bind transcription
              factors. The database is complemented by a web interface for
              browsing, searching and subset selection, an online sequence
              analysis utility and a suite of programming tools for genome-wide
              and comparative genomic analysis of regulatory regions. JASPAR is
              available at http://jaspar. cgb.ki.se.",
  journal  = "Nucleic Acids Res.",
  volume   =  32,
  number   = "Database issue",
  pages    = "D91--4",
  month    =  jan,
  year     =  2004,
  language = "en"
}

@Article{ou2018,
    title = {motifStack for the analysis of transcription factor binding site evolution},
    author = {Jianhong Ou and Scot A Wolfe and Michael H Brodsky and Lihua Julie Zhu},
    journal = {Nature Methods},
    volume = {15},
    year = {2018},
    pages = {8-9},
    url = {http://dx.doi.org/10.1038/nmeth.4555},
    doi = {10.1038/nmeth.4555},
  }


@ARTICLE{Machlab2022-rg,
  title    = "monaLisa: an {R/Bioconductor} package for identifying regulatory
              motifs",
  author   = "Machlab, Dania and Burger, Lukas and Soneson, Charlotte and
              Rijli, Filippo M and Sch{\"u}beler, Dirk and Stadler, Michael B",
  abstract = "SUMMARY: Proteins binding to specific nucleotide sequences, such
              as transcription factors, play key roles in the regulation of
              gene expression. Their binding can be indirectly observed via
              associated changes in transcription, chromatin accessibility, DNA
              methylation and histone modifications. Identifying candidate
              factors that are responsible for these observed experimental
              changes is critical to understand the underlying biological
              processes. Here, we present monaLisa, an R/Bioconductor package
              that implements approaches to identify relevant transcription
              factors from experimental data. The package can be easily
              integrated with other Bioconductor packages and enables seamless
              motif analyses without any software dependencies outside of R.
              AVAILABILITY AND IMPLEMENTATION: monaLisa is implemented in R and
              available on Bioconductor at
              https://bioconductor.org/packages/monaLisa with the development
              version hosted on GitHub at
              https://github.com/fmicompbio/monaLisa. SUPPLEMENTARY
              INFORMATION: Supplementary data are available at Bioinformatics
              online.",
  journal  = "Bioinformatics",
  volume   =  38,
  number   =  9,
  pages    = "2624--2625",
  month    =  apr,
  year     =  2022,
  language = "en"
}


@ARTICLE{Tan2016-lj,
  title    = "{TFBSTools}: an R/bioconductor package for transcription factor
              binding site analysis",
  author   = "Tan, Ge and Lenhard, Boris",
  abstract = "UNLABELLED: : The ability to efficiently investigate
              transcription factor binding sites (TFBSs) genome-wide is central
              to computational studies of gene regulation. TFBSTools is an
              R/Bioconductor package for the analysis and manipulation of TFBSs
              and their associated transcription factor profile matrices.
              TFBStools provides a toolkit for handling TFBS profile matrices,
              scanning sequences and alignments including whole genomes, and
              querying the JASPAR database. The functionality of the package
              can be easily extended to include advanced statistical analysis,
              data visualization and data integration. AVAILABILITY AND
              IMPLEMENTATION: The package is implemented in R and available
              under GPL-2 license from the Bioconductor website
              (http://bioconductor.org/packages/TFBSTools/). CONTACT:
              ge.tan09@imperial.ac.uk SUPPLEMENTARY INFORMATION: Supplementary
              data are available at Bioinformatics online.",
  journal  = "Bioinformatics",
  volume   =  32,
  number   =  10,
  pages    = "1555--1556",
  month    =  may,
  year     =  2016,
  language = "en"
}


@ARTICLE{Hickey2021-mz,
  title    = "The androgen receptor is a tumor suppressor in estrogen
              receptor-positive breast cancer",
  author   = "Hickey, Theresa E and Selth, Luke A and Chia, Kee Ming and
              Laven-Law, Geraldine and Milioli, Heloisa H and Roden, Daniel and
              Jindal, Shalini and Hui, Mun and Finlay-Schultz, Jessica and
              Ebrahimie, Esmaeil and Birrell, Stephen N and Stelloo, Suzan and
              Iggo, Richard and Alexandrou, Sarah and Caldon, C Elizabeth and
              Abdel-Fatah, Tarek M and Ellis, Ian O and Zwart, Wilbert and
              Palmieri, Carlo and Sartorius, Carol A and Swarbrick, Alex and
              Lim, Elgene and Carroll, Jason S and Tilley, Wayne D",
  abstract = "The role of the androgen receptor (AR) in estrogen receptor
              (ER)-$\alpha$-positive breast cancer is controversial,
              constraining implementation of AR-directed therapies. Using a
              diverse, clinically relevant panel of cell-line and
              patient-derived models, we demonstrate that AR activation, not
              suppression, exerts potent antitumor activity in multiple disease
              contexts, including resistance to standard-of-care ER and CDK4/6
              inhibitors. Notably, AR agonists combined with standard-of-care
              agents enhanced therapeutic responses. Mechanistically, agonist
              activation of AR altered the genomic distribution of ER and
              essential co-activators (p300, SRC-3), resulting in repression of
              ER-regulated cell cycle genes and upregulation of AR target
              genes, including known tumor suppressors. A gene signature of AR
              activity positively predicted disease survival in multiple
              clinical ER-positive breast cancer cohorts. These findings
              provide unambiguous evidence that AR has a tumor suppressor role
              in ER-positive breast cancer and support AR agonism as the
              optimal AR-directed treatment strategy, revealing a rational
              therapeutic opportunity.",
  journal  = "Nat. Med.",
  volume   =  27,
  number   =  2,
  pages    = "310--320",
  month    =  feb,
  year     =  2021,
  language = "en"
}



@ARTICLE{Wilson2019-ln,
  title    = "The harmonic mean \textit{p}-value for combining dependent tests",
  author   = "Wilson, Daniel J",
  abstract = "Analysis of ``big data'' frequently involves statistical
              comparison of millions of competing hypotheses to discover hidden
              processes underlying observed patterns of data, for example, in
              the search for genetic determinants of disease in genome-wide
              association studies (GWAS). Controlling the familywise error rate
              (FWER) is considered the strongest protection against false
              positives but makes it difficult to reach the multiple
              testing-corrected significance threshold. Here, I introduce the
              harmonic mean p-value (HMP), which controls the FWER while
              greatly improving statistical power by combining dependent tests
              using generalized central limit theorem. I show that the HMP
              effortlessly combines information to detect statistically
              significant signals among groups of individually nonsignificant
              hypotheses in examples of a human GWAS for neuroticism and a
              joint human-pathogen GWAS for hepatitis C viral load. The HMP
              simultaneously tests all ways to group hypotheses, allowing the
              smallest groups of hypotheses that retain significance to be
              sought. The power of the HMP to detect significant hypothesis
              groups is greater than the power of the Benjamini-Hochberg
              procedure to detect significant hypotheses, although the latter
              only controls the weaker false discovery rate (FDR). The HMP has
              broad implications for the analysis of large datasets, because it
              enhances the potential for scientific discovery.",
  journal  = "Proc. Natl. Acad. Sci. U. S. A.",
  volume   =  116,
  number   =  4,
  pages    = "1195--1200",
  month    =  jan,
  year     =  2019,
  keywords = "big data; false positives; model averaging; multiple testing;
              p-values",
  language = "en"
}

@article{gao2019,
    author = {Gao, Tianshun and Qian, Jiang},
    title = "{EnhancerAtlas 2.0: an updated resource with enhancer annotation in 586 tissue/cell types across nine species}",
    journal = {Nucleic Acids Research},
    volume = {48},
    number = {D1},
    pages = {D58-D64},
    year = {2019},
    month = {11},
    abstract = "{Enhancers are distal cis-regulatory elements that activate the transcription of their target genes. They regulate a wide range of important biological functions and processes, including embryogenesis, development, and homeostasis. As more and more large-scale technologies were developed for enhancer identification, a comprehensive database is highly desirable for enhancer annotation based on various genome-wide profiling datasets across different species. Here, we present an updated database EnhancerAtlas 2.0 (http://www.enhanceratlas.org/indexv2.php), covering 586 tissue/cell types that include a large number of normal tissues, cancer cell lines, and cells at different development stages across nine species. Overall, the database contains 13 494 603 enhancers, which were obtained from 16 055 datasets using 12 high-throughput experiment methods (e.g. H3K4me1/H3K27ac, DNase-seq/ATAC-seq, P300, POLR2A, CAGE, ChIA-PET, GRO-seq, STARR-seq and MPRA). The updated version is a huge expansion of the first version, which only contains the enhancers in human cells. In addition, we predicted enhancer–target gene relationships in human, mouse and fly. Finally, the users can search enhancers and enhancer–target gene relationships through five user-friendly, interactive modules. We believe the new annotation of enhancers in EnhancerAtlas 2.0 will facilitate users to perform useful functional analysis of enhancers in various genomes.}",
    issn = {0305-1048},
    doi = {10.1093/nar/gkz980},
    url = {https://doi.org/10.1093/nar/gkz980},
    eprint = {https://academic.oup.com/nar/article-pdf/48/D1/D58/31697342/gkz980.pdf},
}

@Book{Wickham2016,
    author = {Hadley Wickham},
    title = {ggplot2: Elegant Graphics for Data Analysis},
    publisher = {Springer-Verlag New York},
    year = {2016},
    isbn = {978-3-319-24277-4},
    url = {https://ggplot2.tidyverse.org},
  }

