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The Transcription Factor Encyclopedia Yusuf, Dimas; Butland, Stefanie L; Swanson, Magdalena I; Bolotin, Eugene; Ticoll, Amy; Cheung, Warren A; Cindy Zhang, Xiao Y; Dickman, Christopher T; Fulton, Debra L; Lim, Jonathan S; Schnabl, Jake M; Ramos, Oscar H; Vasseur-Cognet, Mireille; de Leeuw, Charles N; Simpson, Elizabeth M; Ryffel, Gerhart U; Lam, Eric W; Kist, Ralf; Wilson, Miranda S; Marco-Ferreres, Raquel; Brosens, Jan J; Beccari, Leonardo L; Bovolenta, Paola; Benayoun, Bérénice A; Monteiro, Lara J; Schwenen, Helma D; Grontved, Lars; Wederell, Elizabeth; Mandrup, Susanne; Veitia, Reiner A; Chakravarthy, Harini; Hoodless, Pamela A; Mancarelli, M M; Torbett, Bruce E; Banham, Alison H; Reddy, Sekhar P; Cullum, Rebecca L; Liedtke, Michaela; Tschan, Mario P; Vaz, Michelle; Rizzino, Angie; Zannini, Mariastella; Frietze, Seth; Farnham, Peggy J; Eijkelenboom, Astrid; Brown, Philip J; Laperrière, David; Leprince, Dominique; de Cristofaro, Tiziana; Prince, Kelly L; Putker, Marrit; del Peso, Luis; Camenisch, Gieri; Wenger, Roland H; Mikula, Michal; Rozendaal, Marieke; Mader, Sylvie; Ostrowski, Jerzy; Rhodes, Simon J; Van Rechem, Capucine; Boulay, Gaylor; Olechnowicz, Sam W; Breslin, Mary B; Lan, Michael S; Nanan, Kyster K; Wegner, Michael; Hou, Juan; Mullen, Rachel D; Colvin, Stephanie C; Noy, Peter J; Webb, Carol F; Witek, Matthew E; Ferrell, Scott; Daniel, Juliet M; Park, Jason; Waldman, Scott A; Peet, Daniel J; Taggart, Michael; Jayaraman, Padma-Sheela; Karrich, Julien J; Blom, Bianca; Vesuna, Farhad; O’Geen, Henriette; Sun, Yunfu; Gronostajski, Richard M; Woodcroft, Mark W; Hough, Margaret R; Chen, Edwin; Europe-Finner, G N; Karolczak-Bayatti, Magdalena; Bailey, Jarrod; Hankinson, Oliver; Raman, Venu; LeBrun, David P; Biswal, Shyam; Harvey, Christopher J; DeBruyne, Jason P; Hogenesch, John B; Hevner, Robert F; Héligon, Christophe; Luo, Xin M; Blank, Marissa C; Millen, Kathleen J; Sharlin, David S; Forrest, Douglas; Dahlman-Wright, Karin; Zhao, Chunyan; Mishima, Yuriko; Sinha, Satrajit; Chakrabarti, Rumela; Portales-Casamar, Elodie; Sladek, Frances M; Bradley, Philip H; Wasserman, Wyeth W Mar 29, 2012

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SOFTWARE Open AccessThe Transcription Factor EncyclopediaDimas Yusuf1, Stefanie L Butland1, Magdalena I Swanson2, Eugene Bolotin3, Amy Ticoll4, Warren A Cheung5,Xiao Yu Cindy Zhang1, Christopher TD Dickman6, Debra L Fulton7, Jonathan S Lim1, Jake M Schnabl8,Oscar HP Ramos9, Mireille Vasseur-Cognet10, Charles N de Leeuw1, Elizabeth M Simpson1, Gerhart U Ryffel11,Eric W-F Lam12, Ralf Kist13, Miranda SC Wilson12, Raquel Marco-Ferreres14, Jan J Brosens15, Leonardo L Beccari16,Paola Bovolenta14, Bérénice A Benayoun17, Lara J Monteiro12, Helma DC Schwenen12, Lars Grontved18,Elizabeth Wederell19, Susanne Mandrup18, Reiner A Veitia20, Harini Chakravarthy21, Pamela A Hoodless19,M Michela Mancarelli22, Bruce E Torbett23, Alison H Banham24, Sekhar P Reddy25, Rebecca L Cullum19,Michaela Liedtke26, Mario P Tschan27, Michelle Vaz28, Angie Rizzino29, Mariastella Zannini30, Seth Frietze31,Peggy J Farnham31, Astrid Eijkelenboom32, Philip J Brown33, David Laperrière34, Dominique Leprince35,Tiziana de Cristofaro30, Kelly L Prince36, Marrit Putker37, Luis del Peso38, Gieri Camenisch39, Roland H Wenger39,Michal Mikula40, Marieke Rozendaal41, Sylvie Mader42, Jerzy Ostrowski40, Simon J Rhodes43,Capucine Van Rechem44, Gaylor Boulay35, Sam WZ Olechnowicz45, Mary B Breslin46, Michael S Lan47,Kyster K Nanan48, Michael Wegner49, Juan Hou19, Rachel D Mullen50, Stephanie C Colvin36, Peter John Noy51,Carol F Webb52, Matthew E Witek53, Scott Ferrell54, Juliet M Daniel55, Jason Park56, Scott A Waldman57,Daniel J Peet58, Michael Taggart59, Padma-Sheela Jayaraman60, Julien J Karrich61, Bianca Blom61, Farhad Vesuna62,Henriette O’Geen63, Yunfu Sun64, Richard M Gronostajski65, Mark W Woodcroft66, Margaret R Hough67,Edwin Chen68, G Nicholas Europe-Finner59, Magdalena Karolczak-Bayatti69, Jarrod Bailey70, Oliver Hankinson71,Venu Raman72, David P LeBrun48, Shyam Biswal73, Christopher J Harvey73, Jason P DeBruyne74,John B Hogenesch75, Robert F Hevner76, Christophe Héligon77, Xin M Luo78, Marissa Cathleen Blank79,Kathleen Joyce Millen80, David S Sharlin81, Douglas Forrest81, Karin Dahlman-Wright82, Chunyan Zhao82,Yuriko Mishima80, Satrajit Sinha83, Rumela Chakrabarti83, Elodie Portales-Casamar1, Frances M Sladek8,Philip H Bradley4 and Wyeth W Wasserman1*AbstractHere we present the Transcription Factor Encyclopedia (TFe), a new web-based compendium of mini reviewarticles on transcription factors (TFs) that is founded on the principles of open access and collaboration. Ourconsortium of over 100 researchers has collectively contributed over 130 mini review articles on pertinent human,mouse and rat TFs. Notable features of the TFe website include a high-quality PDF generator and web API forprogrammatic data retrieval. TFe aims to rapidly educate scientists about the TFs they encounter through thedelivery of succinct summaries written and vetted by experts in the field. TFe is available at http://www.cisreg.ca/tfe.* Correspondence: wyeth@cmmt.ubc.ca1Department of Medical Genetics, Faculty of Medicine, Centre for MolecularMedicine and Therapeutics, Child and Family Research Institute, University ofBritish Columbia, 950 West 28th Avenue, Vancouver, British Columbia V5Z4H4, CanadaFull list of author information is available at the end of the articleYusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24© 2012 Yusuf et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly cited.BackgroundAs modulators of gene expression, transcription factors(TFs) act on all eukaryotic biochemical systems, driving‘networks’ or ‘regulatory programs’ that define the devel-opmental stages of life and maintain cells in dynamicallychanging microenvironments. From regulating muscledifferentiation in embryonic development (MYOD) [1]to helping the kidneys reclaim water at times of dehy-dration (NR3C2) [2] and even instigate oncogenesis(MYC) [3], the pervasive roles of TFs are becomingincreasingly appreciated and experimentally character-ized. TFs are amongst the most highly studied class ofproteins. Even though TFs comprise fewer than 5% ofhuman protein-encoding genes [4,5], over 16% of gene-related papers address members of this critical class(Figure 1).Increasingly, TFs are the focus of research aimed atdeciphering the complex regulatory programs that allowa single genome to specify hundreds of phenotypicallydistinct cell types. The study of stem cell differentiationis dominated by efforts to understand how the activationof individual TFs can direct the progression to specificlineages. Perhaps the most important of these advancesin recent years is the realization that, by introducingspecific ‘sets’ of TFs into terminally differentiated cells,one can induce these cells to return to a pluripotentcapacity [6,7]. A complete understanding of TFs and theprocesses that alter their activity is a fundamental goalof modern life science research.Rapidly advancing knowledge in TFs is nearly impossibleto track, with over 8,000 TF-related papers published in2009 alone (Figure 1). In this light, the authors of thiswork believe that non-TF researchers are sometimes con-fronted with the need to understand the properties of cer-tain TFs that they come across within their research, as apotential participant in some differentiation, signaling orregulatory pathway they are studying. In this scenario, anaccessible, high quality synopsis of the TF can catalyzerapid progress in the study, allowing researchers to chartan efficient approach. Such synopses have traditionally15.47% 15.75% 16.05% 16.31% 16.81% 14.5% 15.0% 15.5% 16.0% 16.5% 17.0% 2005 2006 2007 2008 2009 Percentage of new articles in Pubmed  associated with human or mouse TFs Year 5,267 of 34,039 5,775 of 36,666 7,085 of 44,138 8,317 of 51,007 8,499 of 50,571 Figure 1 New journal articles associated with human or mouse TFs. Over the past five years, 216,421 journal articles associated with humanor mouse genes have been published and indexed in NCBI PubMed. This amount represents 5.59% of all articles published and indexed duringthe same time frame (3,871,190 articles). Out of the 216,421 articles associated with human or mouse genes, at least 34,943 are associated withhuman or mouse TFs, or 16.15%. This is astounding when considering that known TFs represent only 5% of the genome. The proportion ofjournal articles associated with TFs has also been rising steadily over the past five years - from 15.47% in 2005 to 16.81% in 2009. These figureswere determined with a conservative set of approximately 3,200 human and mouse TF genes derived from the works of Fulton et al. [4] andVaquerizas et al. [5] and the publicly available ‘gene2pubmed’ annotation from NCBI.Yusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 2 of 25been obtained from published review articles, but the needfor timely information about the growing pool of activelystudied genes has increasingly led researchers to onlineinformation sources.In the Internet Age, gene-specific resources haveemerged that present information gathered from highlyspecialized biomedical databases. Examples of suchresources include Entrez Gene [8] and GeneCards [9].While automated content can be useful, many research-ers seek summary descriptions of the proteins. The clas-sic UniProt/Swiss-Prot [10,11] model for curated contentis often viewed as a gold standard, while automated sys-tems have emerged to extract key sentences from theresearch literature, such as iHOP [12] and WikiGenes[13]. The community participation model for maintainingcurrent information exemplified by Wikipedia has argu-ably not been proven successful for small communitieswith specialized interests and need for peer-reviewedcontent, perhaps reflecting the limited time availablefrom the small cadre of qualified experts. The Gene Wikiproject within Wikipedia has been the most advancedeffort, providing automated stub articles for many geneswithin the confines of Wikipedia [14]. However, theabsence of a rigorous and enforced peer review processand the lack of oversight in monitoring contributor quali-fications makes the model less than ideal for scientistswho seek bona fide information in the digital realm.TFs are proteins with special abilities and attributes notfound in other classes of proteins. For example, they oftenwork in pairs or networks to modulate specific regulatorypathways. They directly or indirectly bind to DNA. Somealso interact with ligands or hormones. In short, theunique properties of TFs place special demands on - andpresents opportunities for innovation with regards to - thekind of information TF-specific biomedical resources canoffer, and how this information can be displayed to userssuch that it is intuitive, sensible, and helpful. There aremany different kinds of TF-specific useful data that can becaptured. Sequence-specific DNA binding TFs act on tar-get genes, interact with other TFs to achieve specificity inaction, and have structural characteristics that are predic-tive of DNA interaction mechanisms. A well-characterizedTF will be represented by a binding profile that defines thetarget sequences to which it can bind. These class-specificproperties have spurred the development of key databases,such as JASPAR [15], PAZAR [16] and TRANSFAC® [17].These efforts, however, are constrained by limited capacityto identify and curate data from the scientific literature.Based on the importance of TFs, the rapid accumulationof research advances in the scientific literature, and theneed to provide class-specific information, we have createda new web-based platform called the Transcription FactorEncyclopedia (TFe). TFe’s mission is to facilitate the cura-tion, evaluation, and dissemination of TF data. TFeespouses the principles of open access and promotes colla-boration within the TF research community. It rewardsscientists for contributing their data, and aims to optimizecontent quality ensuring expert editorship and multiplelevels of peer review, both internal and external. TFe iscurated and managed by the TFe consortium, a collabora-tion of over 100 TF researchers from throughout theworld (see Figure 2 for the list of completed mini reviewarticles that they contributed, and Figure 3 for their distri-bution by country). The objective of the TFe consortiumis to produce concise mini review articles on pertinenthuman and mouse TFs, and to accelerate the curation ofTF-specific data.To date, the TFe consortium has prepared over 800 TFmini review articles, 136 of which are sufficiently com-plete to be presented here in the inaugural paper. Overall,the TFe database contains 184 original tables and dia-grams, 221 TF binding site profiles, 3,083 non-redundantbinding sequences, 2,334 genomic targets, 212 three-dimensional structural predictions, 6,308 protein-proteinand protein-ligand interactions, 42,500 TF-to-diseasepredictions based on Medical Subject Headings (MeSH),and more.The long-term goal of TFe is to create an online ency-clopedic collection about well-studied TFs, combining amixture of both expert-curated and automatically popu-lated content.Resource contentIn this paper we present a collection of 136 mini reviewarticles about human and mouse TFs. These articles areavailable on the TFe website [18]. Two versions of everyarticle are available. A definitive version can be viewedonline, while an abridged version can be downloaded inPortable Document Format (PDF) from the website. Asample PDF article is enclosed in Additional file 1, whileAdditional file 2 contains the raw data files.The completed articles represent 15% of all TF articlesthat have been pre-populated with automated content inTFe. As for the remaining articles, most are awaiting anexpert volunteer author or remain at a preliminary state ofdevelopment. An ongoing effort aims to recruit appropri-ate authors to curate these ‘orphaned’ articles. In total,TFe currently hosts 803 TF articles, 216 of which arehuman, 585 of which are mouse, and 2 of which are rat.While TFs that bind directly to DNA are considered forinclusion in TFe at this time, a few contributed articleshave addressed other TFs. Recent research has suggestedthat there are well over 1,300 TFs in the human genome[4,5]. With the increasing availability of data, our goal is toeventually characterize all TFs in the human and mousegenomes. See Additional file 3 for an inventory of all TFarticles currently available in TFe alongside their classifica-tion, which is discussed in further detail below.Yusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 3 of 25Article structureTo ensure uniformity, all TF articles in TFe are writtenin a standardized format that was established inresponse to input and feedback from consortiummembers. The style emphasizes relatively short articles -accompanied by a few figures and up to 75 references.These articles are concise, informative, and cater to abroad audience of life science researchers.humanARID3aFerrell et almouse Arid3aFerrell et alhumanARNTHankinsonhumanARNTLDeBruyne et almouse ArntlDeBruyne et alhumanATF2Karolczak-Bayatti et almouse Atf2Karolczak-Bayatti et alhumanCDX2Witek et alhumanCLOCKDeBruyne et almouse ClockDeBruyne et almouse Elf5Sinha et alhumanEOMESHevnermouse EomesHevnerhumanEPAS1Olechnowicz et almouse Epas1Olechnowicz et alhumanESR1Zhao et almouse Esr1Zhao et alhumanESR2Zhao et almouse Esr2Zhao et alhumanFOSL1Vaz et almouse Fosl1Vaz et alhumanFOXA1Cullum et almouse Foxa1Cullum et alhumanFOXA2Cullum et almouse Foxa2Cullum et alrat Foxa2Cullum et alhumanFOXA3Cullum et almouse Foxa3Cullum et alhumanFOXH1Hou et almouse Foxh1Hou et alhumanFOXL2Benayoun et almouse Foxl2Benayoun et alhumanFOXM1Schwenen et almouse Foxm1Monteiro et alhumanFOXO1Wilson et almouse Foxo1Wilson et alhumanFOXO3Wilson et almouse Foxo3Wilson et alhumanFOXO4Eijkelenboom et almouse Foxo4Eijkelenboom et alhumanFOXP1Brown et almouse Foxp1Brown et alhumanHHEXNoy et almouse HhexNoy et alhumanHIC1Van Rechem et alhumanHIF1ACamenisch et almouse Hif1aCamenisch et alhumanHNF1BRyffelmouse Hnf1bRyffelhumanHNF4ABolotin et almouse Hnf4aBolotin et alhumanHNF4GRyffelhumanhnRNPKMikula et almouse HnrnpkMikula et alhumanINSM1Breslin et alhumanIRF1Luomouse Irf1LuohumanISL1Sunmouse Isl1SunhumanLHX3Prince et almouse Lhx3Prince et alhumanLHX4Prince et almouse Lhx4Prince et alhumanLMX1AMishima et alhumanLMX1BMishima et almouse Lmx1bMishima et alhumanMLLLiedtkehumanNFE2L2Biswal et almouse Nfe2l2Biswal et alrat Nfe2l2Harvey et alhumanNFIAGronostajskimouse NfiaGronostajskihumanNFIBGronostajskimouse NfibGronostajskihumanNFICGronostajskimouse NficGronostajskihumanNFIXGronostajskimouse NfixGronostajskihumanNR2E1de Leeuw et almouse Nr2e1de Leeuw et alhumanNR2F2Ramos et almouse Nr2f2Ramos et alhumanPAX8de Cristofaro et almouse Pax8de Cristofaro et alhumanPBX1Woodcroft et almouse Pbx1Woodcroft et alhumanPPARAHéligonmouse PparaHéligonhumanPPARGGrontved et almouse PpargGrontved et alhumanRARALaperrière et almouse RaraLaperrière et alhumanRARBLaperrière et almouse RarbLaperrière et alhumanRARGLaperrière et almouse RargLaperrière et alhumanSIX3Beccari et almouse Six3Beccari et alhumanSIX6Marco-Ferreres et almouse Six6Marco-Ferreres et almouse Snai2Chaudhuri et alhumanSOX10Wegnermouse Sox10Wegnermouse Sox2Chakravarthy et alhumanSOX8Wegnermouse Sox8WegnerhumanSOX9Kistmouse Sox9KisthumanSPI1Mancarelli et alhumanSPIBKarrich et almouse SpibKarrich et alhumanTBR1Hevnermouse Tbr1HevnerhumanTBX21Hevnermouse Tbx21Hevnermouse Tcfe2aWoodcroftmouse ThrbSharlin et alhumanTLX1Hough et almouse Tlx1Hough et alhumanTRIM28O'Geen et almouse Trim28O'Geen et alhumanTWIST1Vesuna et almouse Twist1Vesuna et alhumanZBTB16Frietze et almouse Zbtb16Frietze et almouse Zbtb33Nanan et alhumanZIC1Blank et almouse Zic1Blank et alhumanZIC2Blank et almouse Zic2Blank et alhumanZIC3Blank et almouse Zic3Blank et alhumanZIC4Blank et almouse Zic4Blank et alhumanZIC5Blank et almouse Zic5Blank et alFigure 2 Released mini review articles. These mini review articles - listed in alphabetical order - are those that have been sufficientlycompleted and released by their respective authors. These articles can be accessed at [43].Yusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 4 of 25The article page (shown in Figure 4b) is the corner-stone of the TFe website, as it is where articles areaccessed. Articles in TFe are organized into ten tabbedsections titled ‘Summary’, ‘Structure’, ‘TFBS’ (TF bindingsite), ‘Targets’, ‘Protein’, ‘Interactions’, ‘Genetics’,‘Expression’, ‘Ontologies’, and ‘Papers’ (that is, refer-ences) (Figure 5). Above the tabs lies a standard headerthat displays pertinent information regarding the TF,including the TF symbol, species, classification, the dateof the most recent revision, and an article completionscore bar (Figure 5). Sections generally contain a mix-ture of author-curated and automatically populated con-tent, typically in the form of an expert-written overviewtext - the author-curated portion - followed by severaladditional headings filled with a mixture of author-curated and automatically populated content. See Figure6 for a comprehensive list of all automatically populatedand manually curated content available in the articlepage. The automatically populated content representsdata that we have incorporated into TFe from secondand third party resources, including: BioGRID [19],Ensembl [20], Entrez Gene [8], Gene Ontology [21],MeSH [22], the Mouse Genome Database [23], OnlineMendelian Inheritance in Man (OMIM) [24], PAZAR[25], RCSB Protein Data Bank [26], the UCSC GenomeBrowser [27] and the Allen Brain Atlas [28]. Moredetails on the software tools and data repositories uti-lized in the generation of automatically populated con-tent found in each tab are presented in Table 1.Each section - with the exceptions of the ‘Ontologies’and ‘Papers’ sections - begins with a brief, expert-writtensummary statement from the authors followed by rele-vant figures, lists, and tables. For instance, the ‘Sum-mary’ section is designed to begin with a 500-word(maximum) overview followed by one or two captionedfigures. The ‘Targets’ section contains a 200-word over-view focusing on the TF’s regulatory role, followed by atable of genomic targets populated by the author andadditional data automatically extracted from PAZAR.The expert-written summaries in TFe are meant to pro-vide the reader with some perspective, highlight keypoints, and reveal tacit knowledge. For a complete list offeatures available in each section, please see Additionalfile 4.Here we discuss each of the ten tabbed sections -‘Summary’, ‘Structure’, ‘TFBS’, ‘Targets’, ‘Protein’, ‘Inter-actions’, ‘Genetics’, ‘Expression’, ‘Ontologies’, and‘Papers’ - in greater detail.Summary tabThe ‘Summary’ tab presents insightful overview textwritten by expert authors, one or more figures as sup-plied by them, and a list of relevant references. Authorsalso have the option to post noteworthy links - forinstance, to a Wikipedia entry for the TF.Like every other tab, the ‘Summary’ tab user interfaceis a content viewer and editor combined into one.When expert authors wish to implement changes totheir articles, they may ‘sign in’ to TFe using their per-sonalized user accounts. After this is done, they are ableto see the normally hidden editing interface that allowsthem to upload text, figures, figure captions, references,external links, and data, depending on the tab. TheFigure 3 Worldwide distribution of authors by country. The TFe consortium comprises 114 authors from 13 countries. The exact distributionis as follows: Australia, 2; Canada, 25; Denmark, 2; France, 6; Germany, 2; Italy, 2; Poland, 2; Spain, 4; Sweden, 2; Switzerland, 4; the Netherlands, 4;the United Kingdom, 14; and the United States, 45.Yusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 5 of 25editing interface supports the widely used wiki syntax toallow basic text formatting, such as bolding, italicizing,underlining, and the creation of bulleted and numberedlists. All text entered in wiki syntax is converted toHTML by a local installation of the MediaWiki software.Authors also have the option to add PubMed referencesFigure 4 TFe user interface. Shown in this figure are three screenshots from the TFe web-based user interface. Built around HTML, JavaScriptand CSS standards, the TFe user interface is a quick and powerful method of viewing, downloading, and editing TFe data. Pages visualized inthis figure are: (a) the home page; (b) the article page; and (c) the classification page.Yusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 6 of 25anywhere in their text by using special tags that looklike ‘(pmid:16371163)’ - without the quotes. These tagsare automatically converted to a proper citation (Van-couver style) by the TFe software. Figures can beuploaded in many different image formats, while figurecaptions are submitted as text. PubMed citations arealso supported in figure caption text.Structure tabThe ‘Structure’ tab contains author-provided overview textregarding the structural properties of the TF, followed by -if available - the predicted three-dimensional structure ofthe TF’s DNA binding domain. These ‘structural predic-tions’, which were created by the consortium using a cus-tom-made pipeline, are available for download as bothhigh-resolution Portable Network Graphics (PNG) imagesand Protein Data Bank (PDB) formatted files. The materi-als and methods used in their construction are discussedin the Materials and methods section of this paper.TFBS tabA key property of TFs is the DNA sequences to whichthey bind. In the world of TF research, such DNAsequences are often called ‘transcription factor bindingsites’, or ‘TFBS’ for short. Knowledge of TFBS patternsis key to identifying putative binding sites in genomicsequences and to the identification of sets of genes regu-lated by the TF in promoter analysis.In light of this, disseminating TFBS data is a crucialpart of TFe’s mission. The ‘TFBS’ tab contains a sum-mary of the DNA binding characteristics of the TF,alongside one or more DNA binding target site data,when sufficient data are available. A graphical depictionof the target site pattern is displayed in the form of asequence logo, along with a brief summary text from theauthor. This information is extracted from the PAZARregulatory sequence database.TFe authors are able to create new binding models byinputting a list of binding sites, experimental evidence,and references in the ‘TFBS’ tab through a submissioninterface that is visible to authors only. It is possible forauthors to submit target sequences that exist in a gen-ome, or artificial sites, such as those generated in aSELEX (Systematic Evolution of Ligands by ExponentialEnrichment) experiment. When we receive a submissionthrough this TFBS form system, we forward the suppliedFigure 5 Tour of the user interface. (A) The project logo links back to the homepage. (B) The ‘quick search’ and ‘sign in’ widgets areconveniently placed near the top of the page. (C) The vertical site navigation bar offers fast access to all available pages in the site. (D) Theofficial symbol, name, and authors are prominently placed to immediately grab the user’s attention. Beneath the authors’ names is the date ofthe most recent revision. (E) When available, a thumbnail of the structural prediction rendering is displayed in the header area. (F) Two drop-down menus provide easy access to the top ten most recently visited and updated articles. (G) Vital information on the TF, such as itsclassification, homologs, genomic links, and synonyms, occupy the top right corner of each page. (H) An article completion score bar providesimmediate feedback to the author on the progress of their articles. (I) Articles in TFe are organized into ten tabs. Immediately underneath, thetabs are links to data downloads in PDF and Excel file formats. A ‘view content, comments’ toggle allows the user to view comments that havebeen attached to the article. By default, comments are hidden from sight. (J) Most tabbed sections start with an author-contributed ‘summary’paragraph that ranges in length from 150 to 500 words.Yusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 7 of 25information to a team of curators who review the infor-mation for errors and, if appropriate, deposit the annota-tion into the PAZAR database. Because PAZAR and TFeare programmatically linked, the annotation deposited inPAZAR will also appear in TFe.Targets tabRelated to the ‘TFBS’ tab, the ‘Targets’ tab presentsusers with an introductory text followed by a list ofgenes directly regulated by the subject TF sourced fromthe PAZAR database. At a minimum, the ‘Targets’ listrecapitulates the information in the ‘TFBS’ tab, butoftentimes, expert authors provide additional genesknown to be regulated by the TF but for which the spe-cific DNA target sequence is unknown. Authors can addadditional targets by using a specialized editing interfacethat is accessible upon sign in.Protein tabThe ‘Protein’ tab presents information about the func-tional consequences of protein modifications or distinc-tions between protein isoforms. Authors summarizeFigure 6 Content available in TFe. This diagram demonstrates the diverse range of TF-related content available in TFe. Articles in TFe areorganized into ten tabs. In this diagram, the ten tabs are represented by the ten horizontal columns labeled ‘Summary’, ‘Structure’, ‘TFBS’, and soforth. Under each tab in the article, there exist one or more relevant subheadings. In this diagram, these subheadings are represented by beigeor grey boxes, which contain partial screenshots of the actual content - whether they are text, figures, or tables. Beige boxes represent contentthat has been composed by TFe authors, while grey boxes represent content that has been largely automatically populated. Below eachscreenshot box is the name of the subheading and a brief description of the subheading. Below the description are a series of blue, red, green,and yellow icons labeled ‘WEB’, ‘PDF’, ‘XLS’, and ‘API’. As the names suggest, these icons indicate whether the content of that particularsubheading is available in various formats. All subheadings are available in web format - on the TFe website. Thus, we consider the TFe websiteformat as the most comprehensive format available. Select content is available in redacted form in the PDF format. Content that is in the formof ‘data’ can be downloaded as an Excel spreadsheet (’XLS’) or retrieved using the TFe web API (’API’) from the TFe website.Yusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 8 of 25such information in free text entries. As a late additionto the system identified as a need during the beta-test-ing process, the section has yet to be populated formany entries.Interactions tabInteractions between TFs and ligands or proteins arereported in this tab. While automated content from theBioGRID database is included, authors may also provideinformation about additional interactions not reportedin the external system through a specialized submissioninterface. Authors have a limited set of interaction types(Table 2) from which to pick labels. If the gene encod-ing the TF is subject to transcriptional regulation in aselective manner, the regulating TFs are reported in thissection.Genetics tabTFs perform powerful genetic roles in the developmentand physiology of organisms. Therefore, the geneticproperties of TFs can have powerful consequences uponthe phenotype of an organism. The ‘Genetics’ tab pre-sents two sets of data linking TFs to phenotype, in addi-tion to the prerequisite expert-written summary. Thefirst is a ‘cloud’ of TF-to-disease associations composedwith MeSH terms. The second set of data linking TFs tophenotype is a list of Mouse Genome Database mamma-lian phenotype terms associated with the mouse homo-log of the TF protein.Expression tabThe ‘Expression’ tab reports expression data from theGNF Expression Atlas, sourced from the UCSC GenomeBrowser, and observed regional expression in the brainaccording to the Allen Brain Atlas. Authors are encour-aged to provide a text description of known expressionproperties of the TF gene.Ontologies tabAnnotated characteristics of the TF are reported in the‘Ontologies’ tab. Gene Ontology terms linked to thegene are extracted from Entrez Gene for display. TheTable 1 Sources of automatically populated contentTab Section Sources Use of sourcesStructure Structures RSCB PDB, Pfam Structural predictions are made with the help of experimentally verified proteinstructures downloaded from the RSCB PDB. In the process of creating thestructural predictions, we use the HMM database from Protein Families (Pfam) tohelp us identify domains found in protein sequences in the RSCB PDB database(which we use as templates) as well as the protein sequences of putativestructures we want to predictTFBS TFBS logos PAZAR The logo in this section is generated with the Perl module MEME and itsdependencies, using binding site data from PAZARBinding site profiles PAZAR The logo and position frequency matrix in this section are generated with thePerl module MEME and its dependencies, using binding site data from PAZARTargets Targets (author curated) Gene Ontology (NCBI) While the author provides the gene ID, TF complex, effect, and reference,biological process GO terms associated with each target gene in this section areimported from gene-to-GO annotations from NCBITargets (automaticallypopulated)PAZAR, GeneOntology (NCBI)Target gene, TF complex, and reference data are imported from PAZAR. Theauthor supplies effect data. Biological process GO terms associated with eachtarget gene in this section are imported from GO annotations provided by NCBIInteractions Ligands (author curated) PubChem (NCBI) While ligand IDs, experiment types, natures of interaction and references aresupplied by the author, the ligand common name and image are provided fromPubChemInteractions (automaticallypopulated)BioGRID Interactor names, experiment types, and references are imported from BioGRID.Natures of interaction are provided by the authorTranscriptional regulators(automatically populated)PAZAR, GeneOntology (NCBI)Regulating TF complex, regulating TF, genomic links, and reference informationare provided by PAZAR. Biological process GO terms associated with each targetregulator in this section are imported from GO annotations provided by NCBIGenetics MeSH cloud (automaticallypopulated)MeSH (NCBI), EntrezGene, GeneRIFMeSH term associations and Fisher’s exact P-values are generated using datafrom NCBI MeSH, Entrez Gene, and GeneRIFExpression Expression (automaticallypopulated)UCSC GenomeBrowser, Allen BrainAtlasExpression data in this section are imported from the UCSC Genome Browserdatabase, GNF Expression Atlas 2 dataset, and the Allen Brain AtlasOntologies Gene Ontology(automatically populated)Gene Ontology (NCBI) GO terms associated with the transcription factor in this section are importedfrom GO annotations provided by NCBIMeSH cloud (automaticallypopulated)MeSH (NCBI), EntrezGene, GeneRIFMeSH term associations and Fisher’s exact P-values are generated using datafrom NCBI MeSH, Entrez Gene, and GeneRIFPapers Papers PubMed Detailed information on relevant papers such as authors, titles, journals, andpublication dates are imported from NCBI PubMed.Eight out of ten tabs in TFe articles contain one or more subheadings of automatically populated content. This table lists the direct sources of automaticallypopulated content by tab and subheading (’section’). GO, Gene Ontology; RSCB, Research Collaboratory for Structural Bioinformatics.Yusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 9 of 25automatically populated TF-to-MeSH associations for allMeSH terms outside of diseases are reported, followingthe same procedure as introduced in MeSHOP.Papers tabThe ‘Papers’ tab provides a set of recommended articlespertinent to the TF. Authors indicate the most usefulintroductory readings and other key papers with a twocircle rating system. Two full circles indicate an excel-lent paper in the author’s opinion, while no circles stillindicate a very good and noteworthy paper.System featuresIn this section of the paper, we discuss the important fea-tures of our platform. These features include: (1) our sys-tem of classifying TFs; (2) our concept of ‘contentinheritance’, or how articles of very closely related TFsmay derive content from each other when biologicallyappropriate; (3) our structural prediction system; (4) ourdata on TF binding sites; (5) our TF-to-disease associationpredictions; (6) our PDF rendering system; (7) TFe’s dataexport capabilities; and (8) the article completion score.Classification of transcription factorsThe classification of TFs into ‘groups’, ‘families’ and ‘sub-families’ is a very important feature of TFe. Over the pastfew years, there have been efforts to identify and classifyall TFs within the human and mouse genomes [4,5].While there are potentially several different strategies forclassifying TFs, one promising approach is to group thembased on DNA binding domain structures. Building uponthe work of Fulton et al. in the Transcription Factor Cat-alog (TFCat) project [4], we have organized all TFs inTFe into various groups, families and subfamilies as pre-viously mentioned (Additional file 3).Content inheritanceWhen comparing orthologous TFs, or recently evolvedparalogs within a species, it is commonly observed thathomologous TFs are well conserved structurally andfunctionally [29]. Indeed, some homologous TFs are sowell conserved that there is often no information thatdistinguishes the homologs. However, in TFe we haveopted to create separate articles for all TFs, includinghomologous TFs. For instance, we have several articlesfor the TF NFE2L2 - one each for human, mouse, andrat. In doing so, we aim to provide maximum flexibilityto our authors who may wish to discuss key subtle dif-ferences between closely related proteins.The drawback to this approach is that, in some cases,we end up with multiple articles for what could be con-sidered as functionally synonymous TFs. These TFs, dueto their extreme likeness, would inevitably share com-mon attributes such as binding site profiles, interactors,and target genes. In this situation, it becomes importantto keep all shared attributes current and synchronizedacross the different articles. To assist with this informa-tion management process, we implemented a contentinheritance system that enables authors to define smallclusters of homologous TFs for which certain data maybe automatically shared. Under this system, the articlethat is more annotated - the ‘parent article’ - donatestext, figures, and data as appropriate to the article thatis less annotated, the ‘child article’. However, authorsand editors are able to override the automatic sharing ofdata when it is not reflective of the underlying biology.Structural predictionsWe have developed a custom computational pipeline forpredicting the three-dimensional protein structures ofthe DNA binding domains of TFs. The final output ofour pipeline is a PDB formatted file of the predictedstructure, alongside a short segment of double-strandedTable 2 List of predefined interaction typesInteraction type Gene LigandActs on upstream signaling pathway •Competitive inhibition • •Genetic • •Indirect • •Multimerization • •Not specified • •Physical: deacetylation • •Physical: dephosphorylation • •Physical: desumoylation • •Physical: deubiqiutination • •Physical: enzyme modification: acetylation • •Physical: enzyme modification: methylation • •Physical: enzyme modification: phosphorylation • •Physical: enzyme modification: protein cleavage • •Physical: enzyme modification: sumoylation • •Physical: indirect altering posttranslational modifications • •Physical: sequestering • •Physical: translocation • •Physical: ubiquitination • •Physical: undefined direct interaction • •Physical: with another TF •Physical: with another TF: complex binds DNA •Physical: with co-activator affecting recruitment • •Physical: with co-repressor affecting recruitment • •Regulatory: decreases expression of this TF • •Regulatory: increases expression of this TF • •Unknown • •When a protein-to-protein or protein-to-ligand is added to a TFe article eitherby the author or automatically from BioGRID, the author of that article has theoption to define the interaction type if this information is known. To promotea standardized vocabulary for describing interaction types, a list of possibleinteraction types between proteins and ligands is provided to the author. Thislist continues to be adjusted and expanded based on need and authorfeedback.Yusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 10 of 25DNA for positional reference. The DNA molecules arestylistic and do not represent particular sequences suchas the consensus sequence for the TF. We have gener-ated standardized PNG image renderings of these PDBfiles for web and print purposes. Figure 7 contains arepresentative sample of the structural predictions, onefrom each family of TFs featured in our first release. Todate, 212 structural predictions have been generated,with the emphasis of effort focused on TFs with articlesthat are nearing completion. All structural predictionsare available for download in PDB format under the‘Structure’ section in the articles of their respective TFs.A brief summary of the materials and methods used inour protocol can be found in the Materials and methodssection below.Transcription factor binding site dataOne of the goals of TFe is to encourage experts toassist in the curation of TFBS sequences andgeneration of binding profiles. Working in partnershipwith PAZAR [25], an open source and open access TFand regulatory sequence annotation database, our con-sortium gains access to a powerful curation platformwith which it can store, annotate, and manage data, aswell as retrieve additional data from other projects inPAZAR. Our initial collection of 100 reviews collec-tively contain 3,083 unique binding site sequencesfrom the PAZAR database, of which a total of 452sequences have been donated to PAZAR by the con-sortium. From this set of binding site sequences, wehave generated 221 binding models and extracted1,436 genomic targets for 199 different TFs. In addi-tion, 898 genomic targets have been entered manuallyby our authors to supplement this genomic targetdataset. See Additional file 2 to see the binding dataof released articles and Additional file 5 for key bind-ing profiles that have been generated in the TFeproject.Figure 7 Structural predictions of TF DNA binding domains. To date, we have created 212 structural predictions of the active sites of selectTFs in TFe. We focused on TFs for which a structural prediction is most feasible and whose articles are nearing completion. These predictionswere generated with an in-house, custom-made pipeline that finds the most similar, experimentally determined protein structure for eachunsolved TF, and uses that experimentally determined ‘template’ to guide the prediction of the unknown structure.Yusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 11 of 25Disease associationsMany TFs are implicated in disease. Out of a growinglist of 1,321 human TFs we compiled from the work ofVaquerizas et al. [5] and Fulton et al. [4], 197 are cur-rently linked to one or more diseases in the OMIMMorbid Map [30]. In light of the strong connectionbetween TFs and disease, we have predicted 42,500 TF-to-disease associations. This was done by using the‘Entrez Gene to PubMed’ (’gene2pubmed’) and MeSHdatasets that are available at the National Center forBiotechnology Information (NCBI). With mainly thesetwo datasets, with additional datasets such as OMIMand GeneRIF to further strengthen our predictions, wedeveloped a protocol that makes the connectionbetween TF-encoding genes, papers that discuss thesegenes, and the MeSH terms that are tagged to thepapers. By indirectly mapping disease-oriented MeSHterms to TF-encoding gene identifiers, we are able gen-erate a list of MeSH terms that are associated with eachTF. Statistical analysis is applied to the raw connectionsto determine their strength - mainly by reflecting thefrequency of TF-term co-occurrence in light of thenumber of papers that refer to either the TF or theterm. This information can be viewed as a table or as a‘cloud’ under the ‘Genetics’ tab.PDF renderingWe have built a PDF rendering engine in TFe thattransforms articles into condensed, four-page PDF ‘minisummaries’ available for printing (Figure 8). These sum-maries can be downloaded by clicking on the ‘Downloadarticle (PDF)’ link that is prominently displayed on allarticle pages on the TFe website. We have included asample in Additional file 1.While the articles as they appear on the TFe websitepermit great flexibility in terms of length and variety ofcontent, the PDF format is more structured and com-pact. Thus, the PDF version of the articles can bedescribed as the ‘abridged’ form of the article. Whennecessary, we are keen to remind users that there isadditional content on the TFe website that cannot beincorporated into the abbreviated PDF article.In our effort to encourage authors to write morebalanced articles that fulfill the prescribed style, weration the available space for each section. For instance,one third of the last page is strictly allocated to theGenetics and Expressions paragraphs. If an authorchooses not to comment on those sections, that spacewill remain blank - to motivate authors to do somethingabout it. Conversely, if the author provides more textthan allowed, the surplus text will simply be trimmed tothe nearest sentence.The PDF feature was created to produce an articleformat that more closely resembles a ‘journal paper’,with pleasant typesetting and pagination. Indeed, manyopen source journals that publish exclusively online stillinvest significant resources to generate definitive PDFcopies for all of their articles, even when HTML ver-sions are adequate for practical purposes. We envisionthat for some users, once a TF has significantly piquedtheir interest for further perusal, they would be inclinedto review the web version to access the most completeand up-to-date information.Behind the scenes, our PDF rendering engine is basedon in-house code and the dompdf 0.5.1 open sourcemodule. It uses fuzzy logic to handle the modificationsnecessary to determine the best solution of text, images,captions, and data tables to make the page layouts asaesthetically pleasing as ‘machinely’ possible. Thesemodifications include changing the sizes of the figures,truncating excess text, reformatting the references, andcalculating trade-offs between having larger figures andmore data in data tables at the expense of less text, orkeeping more text at the expense of having fewer figuresand sparser data tables.Data exportOne of the goals of TFe is to make TF data easily acces-sible to all. To support this goal, we built a web-basedapplication programming interface (API) to facilitate astraightforward approach for extracting data from theTFe website. In addition to the TFe API, we have built aspreadsheet generator that allows visitors to downloadExcel (.xls) formatted files containing all of the informa-tion that is available through the web API, as a servicefor users who are not inclined to use the programmer-oriented web API. In short, virtually all forms of dataavailable in TFe, including binding sequences, genomictargets, interactors, key papers, and even ontologyterms, can be downloaded through the API, the spread-sheet generator, or PDF renderer. The TFe web-basedAPI and its accompanying documentation can be foundon the TFe website.The presence of a machine-friendly API is what setsTFe apart from most other biomedical wikis. For easyparsing, the API sends data in tab-delimited plain textformat. Since the API is web-based and communicatesthrough the ubiquitous HTTP protocol, it is compatiblewith all common scripting and programming languages,including PHP Hypertext Preprocessor (PHP), Perl, andPython. See Figure 9 for an illustration of how the dataretrieval process works when using the TFe web API.Article completion scoreAn article completion score (ACS) is automatically com-puted for every article in TFe. The ACS can range from0% to 100%. Its purpose is to reflect the depth of anno-tation present in the article - the article’s level ofYusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 12 of 25completeness. To illustrate, nearly complete articlestypically have an ACS of 90% or more. The ACS is pro-minently displayed in the header of all articles on theTFe website in the form of a ‘progress bar’ that changesfrom orange to green as the score approaches 100%(Figure 5). The ACS system is designed to help authorsdetermine whether their articles are sufficiently com-plete, and more importantly, identify article sectionsthat are in need of more attention. By clicking on the‘see what’s missing’ link to the right of the progress bar,authors can view a list of suggestions that they canundertake to increase the score of their article, such as‘please provide more information in the Overview sec-tion of the Summary tab.’The ACS evaluates the completeness of TFe articlesbased on several factors, which include, among others, theamount of text, figures, references, and data contributedfor each article. Overall, 19 attributes (Table 3) are takeninto account in the computation of the ACS.The ACS was implemented during TFe’s beta testingperiod, when we observed that authors need guidance asto the expected level of article content. Prior to theimplementation of the ACS, a large majority of ‘com-pleted articles’ were deemed substantially incomplete.The ACS metric has established a standard for authorcontribution and helps authors attain this standard byhighlighting sections of deficient articles that requirefurther attention, and notifies authors how the deficiencycan be remedied. While developed de novo, subsequentfeedback indicates that the progress tracking scores arereminiscent of content tracking scores utilized in the Lin-kedIn social networking system. The response to the ACShas been positive. Within six months of implementation,the completion scores of all articles increased from 40.6%to 60.2% (Figure 10). The scoring metrics for computingthe ACS are presented in Table 3.User authenticationThe user authentication system of TFe, which handlesthe ‘sign in’ and ‘sign out’ functions, is built upon thePerl CGI::Session module. All account passwords storedin the TFe database are encrypted to safeguard the priv-acy and security of TFe users.SoftwareIn this section, we discuss in technical detail the soft-ware that runs the TFe website, mainly its user interfaceand system architecture.Figure 8 Format of the PDF article. The PDF mini summaries are composed of four pages. The first page features basic information such asthe TF name, gene identifiers and classification, as well as author information. Also on the first page are the names and affiliations of theauthors, an overview of the TF, an image of its active site protein structure accompanied by a brief commentary, and a featured TF bindingprofile selected by the author. The second and third pages contain a mixture of figures, paragraph text, and tables of genomic targets andprotein as well as ligand interactors. The last page contains two brief paragraphs, a MeSH cloud, and selected references. These are the first twopages of a four-page PDF mini summary generated by the TFe system software. Our PDF creation tool, based on in-house code and thedompdf 0.5.1 open source module, is able to format a TFe article of any length and annotation depth as a standardized four-page PDF article. Afuzzy logic algorithm does all of the modifications necessary to make the conversion. These modifications may include changing the sizes of thefigures, truncating excess text, reformatting the references, and calculating trade-offs between having larger figures and data tables at theexpense of less text, or keeping more text at the expense of having fewer figures and smaller data tables.Yusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 13 of 25Figure 9 Using the TFe web API. Adventures in bioinformatics often involve large amounts of data retrieval and computation not amenable tomanual labor. Thus, in place of humans, software is written to automate the grunt work, which may include computing vast quantities of dataor obtaining large amounts of information from resources in the cloud, such as NCBI. To give researchers the option to retrieve data from TFe inan automated fashion, we have implemented a simple yet powerful web API. This figure provides a summary of what a data transaction maylook like when using the TFe web API. In this case, the goal of the data retrieval exercise is to obtain all MeSH disease terms associated with thetranscription factor ‘ATF3’.Yusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 14 of 25OverviewThe TFe software is a database-driven website applica-tion that runs the TFe website. For end users, the TFewebsite is an information resource where researcherscan read peer-reviewed, expert-written summaries onpertinent TFs as well as obtain a wide variety of TF-related data, including binding sequences, genomic tar-gets, and TF-to-disease associations. See Figure 6 for acomplete list of all types of information available onTFe.As previously discussed, the TFe website also featuresa password-protected user interface that allows expertauthors to create and edit TF articles, upload data,report technical problems (that is, bugs), and submitanonymous peer reviews of other articles. It also fea-tures a built-in Customer Relationship Management(CRM)-like tool to help the administrators recruit newauthors, as well as manage and communicate with therest of the consortium. In short, the TFe website is aspecialized and integrated software platform that hasTable 3 Computing the TFe Article Completion ScoreTab Scoring element Target Points WeightSummary Overview text 500 words 10 points 8.333%Summary References in overview text 3 references 5 points 4.167%Summary Figures 1 figure 10 points 8.333%Structure Overview text 200 words 5 points 4.167%TFBS Overview text 150 words 5 points 4.167%TFBS Binding site profiles 1 binding site profile 10 points 8.333%Targets Overview text 200 words 5 points 4.167%Targets Targets 10 targets in total (both author and auto) 10 points 8.333%Protein Isoforms text 200 words 5 points 4.167%Protein Covalent modifications text 200 words 5 points 4.167%Interactions Overview text 200 words 5 points 4.167%Interactions Ligands 1 ligand 1 point 0.833%Interactions Interactions 10 interactors in total (both author and auto) 10 points 8.333%Interactions Interactions All ‘nature of interaction’ fields annotated 10 points 8.333%Genetics Overview text 250 words 5 points 4.167%Expression Overview text 200 words 5 points 4.167%Papers Papers 15 papers 10 points 8.333%Papers Papers 3 papers marked as ‘recommended’ 3 points 2.500%(all) Links 1 link 1 point 0.833%120 points 100%The TFe Article Completion Score (ACS) is based on the 19 components listed here. Authors earn points for completing each component, up to the prescribedmaximum for that component (’Maximum points’). So, for example, an author would be granted one point for adding one link, but no additional points wouldbe granted if the author adds a second or third link. This prevents the author from adding 24 links to boost their ACS score by 20%. A ‘fully complete’ articlewould net 120 points, which gives a score of 100%.0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Article completion score (ACS) Authored TFe articles (post ACS implementation) Fourth quarter (Q4) of 2009 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Article completion score (ACS) Authored TFe articles (at ACS implementation) Second quarter (Q2) of 2009 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Article completion score (ACS) Authored TFe articles (prior to submission) Second quarter (Q2) of 2010 mean score 40.6% mean score 60.2% mean score 64.9% Figure 10 The completion scores of authored articles in TFe. The y-axis of this graph is the article completion score (ACS), while the bars onthe x-axis represent the 176 authored TF articles in TFe (some of which are still works-in-progress), ordered such that higher scoring articles arepositioned on the right (for clarity). In this graph, the completion scores of the 176 articles from three different periods - Q2 2009, Q4 2009, andQ2 2010 - are superimposed to demonstrate that the scores have been increasing over time. Within six months of the implementation of theACS system in Q2 2009, the completion scores of authored TFe articles have increased from 40.6% to 60.2%, thus attesting to the effectivenessof this feedback mechanism (see Q2 2009 versus Q4 2009).Yusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 15 of 25been custom-built to facilitate a community-curated TFwiki project.User interfaceThe TFe website, which can be accessed at [18], featuresa familiar and streamlined graphical user interface thatis written in Extensible Hypertext Markup Language(XHTML) 1.0 Transitional, Cascading Style Sheets(CSS), and JavaScript.On the homepage, a large ‘universal’ search box domi-nates the center of the screen (Figure 4a). This searchbox allows users to quickly access TFe’s built-in searchengine, which accepts 18 different types of queries,including gene symbols, fragments of binding sequences,and the names of researchers who are associated withparticular TFs through their publication records. Alter-natively, visitors can click on the ‘go to a random article’link to view a random article on the article page.Displayed in Figure 4b and as previously discussed, thearticle page is the centerpiece of the TFe-user interac-tion as it is where the bulk of TFe content lies. It fea-tures a compact yet informative and graphically richheader with key pieces of information about the TF, fol-lowed by the described ACS indicating the TF article’slevel of completeness or ‘depth’. Below the ACS, thecontents of the article are divided into ten tabs labeled‘Summary’, ‘Structure’, ‘TFBS’, ‘Targets’, ‘Protein’, ‘Inter-actions’, ‘Genetics’, ‘Expression’, ‘Ontologies’, and‘Papers’. A row of navigation links is placed unobtru-sively on the left side of the page. Other noteworthypages on the TFe website include the classification page(Figure 4c) and the browse page. The classification pagepresents an organized hierarchy of TFs based on theTFCat [4] and the extended TF classification system ofVaquerizas et al. [5]. The browse page allows users tobrowse for TF articles based on various attributes suchas name, classification, and level of completeness.System architectureThe TFe website software is written almost entirely in thePerl programming language, using the ‘LAMP’ (Linux,Apache, MySQL, Perl/PHP) paradigm for developing web-based applications. The Perl programming language waschosen for its robust text-manipulation capabilities andwidespread support within the bioinformatics researchcommunity. In developing the website software, we haveincorporated Perl and PHP modules and softwarepackages to handle specialized tasks - such as reading thedatabase, generating PDF files, and resizing images. SeeTable 4 for a list of Perl and PHP modules and softwarepackages incorporated into the TFe software.The TFe website software is designed to run quicklyand efficiently, yet remain relatively simple for program-mers and system administrators to maintain. Onechallenge we had to overcome during the developmentprocess was keeping the software fast and responsivedespite its size and complexity. One solution was to pur-posefully fragment the TFe website software into over 40independent components. Each component serves a sin-gle unique purpose - for instance, to generate the homepage, or to search the database, or to display articles.Each component can be summoned separately and with-out disturbing the other components. This fragmentationallows us to improve the speed and responsiveness of theTFe website, as at any given time only a fraction of theentire TFe software is being executed by the server.To reduce code repetition, we placed shared functions- such as those that generate the page header or naviga-tion links - in a shared module that can be summonedby any component as needed. We call this module the‘TFe core module’ because it forms the nucleus of theTFe website software. To further increase speed, wedivided this TFe core module into three separate com-ponents: (1) a component that contains the vast majorityof shared functions called ‘tfe.pm’; (2) a component thatcontains only those functions involved with databasereads and writes called ‘db.pm’; and (3) a componentthat deals with maintenance and update functions called‘update.pm’. See Figure 11 for a schematic representa-tion of the TFe website software.With regards to hardware architecture, the TFe web-site software is currently implemented in Linux-based(CentOS) environment using a dedicated virtual server.The TFe software stores data in both the UNIX file sys-tem (that is, for images and PDF files) and a MySQLdatabase, both of which are physically located in aTable 4 Perl and PHP modules used in TFeLanguage Module PurposePerl CGI Web browser interfacePerl CGI::Session User loginPerl Crypt::Blowfish Data encryption and random stringgenerationPerl DBI MySQL database interfacePerl GD::Image Creation of TF binding site diagramsPerl HTML::Detoxifier User input filteringPerl Image::Resize Image resizing and formattingPerl LWP::Simple Interface between TFe and web-basedAPIsPerl pazar Data retrieval from PAZARPerl pazar::gene Data retrieval from PAZARPerl pazar::reg_seq Data retrieval from PAZARPerl TFBS::PatternGen::MEMECreation of TF position weightmatricesPHP dompdf 0.5.1 PDF generationListed on this table are the second and third party modules incorporated intoTFe, with their respective programming languages, usage in TFe, and currentweb addresses at the time of publication.Yusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 16 of 25proximal storage area network. To share computationalload and optimize service responsiveness, a dedicateddatabase server executes all complex database queries.This database server is connected to both the primaryTFe web server and storage area network via fiber optic.Development processThe TFe system was developed over a period of threeyears. Early prototypes in 2007 were subjected tointense testing and continuous refinement by the pro-gramming team during what we refer to as the ‘pre-alpha stage’. In the 2008 ‘alpha stage’, external qualitycontrol testing was initiated by inviting ten authors toprovide feedback on the software’s design, features, andusability. By 2009, the software had evolved to a morestable and mature form. At this ‘beta stage’, we invitedover 100 TF experts from around the world to contri-bute articles.Figure 11 Software architecture. This schematic demonstrates the conceptual structure of the TFe software. Written mainly in the Perlprogramming language, the software is essentially a collection of Perl ‘scripts’ that runs on an Apache web server, in a UNIX-compatibleenvironment. The software relies on MySQL for data storage, and a number of third party modules. Over 40 ‘front line’ scripts (shown as the redrectangle) generate individual pages such as the home page and article page. These front line scripts are backed by a cluster of three TFe Perlmodules (shown as the green circles): (1) the ‘database updater’, which is summoned pro re nata whenever the TFe database needs to bemaintained or updated with new content from external sources such as NCBI; (2) the ‘main module’, which contains shared subroutines such asthose that generate page headers; and (3) the ‘database handler’, which forms the gateway between all components of the TFe software andthe TFe database. The database (shown as the yellow cylinder) is stored on a separate database server and communicates with the rest of theTFe software via fiber optic. It contains cached copies of third party resources so that the TFe software does not have to constantly retrieve datafrom the ‘cloud’. This optimizes performance. The web API (shown as the purple rectangle) is directly connected to the ultra small and efficientdatabase handler module. In bypassing activation of the large main module and database updater, the web API is able to run faster than theweb-based interface. GO, Gene Ontology; MGI, Mouse Genome Informatics.Yusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 17 of 25Over the next six months, TF experts responded toour invitations and began producing articles. To copewith the influx of feedback, we implemented an onlinefeedback form. We upgraded our bug tracking processby adopting MantisBT, a web-based system that is avail-able at [31]. All feedback was reviewed and prioritizedfor system modification if justified. Small changes wereaddressed immediately.A rigorous backup regimen occurs on a daily andweekly basis to help us quickly and fully recover in theevent of catastrophic system failure.DiscussionThree prominent systems have been introduced that relymore heavily on the community-contributed contentwiki model. These are: (1) WikiProteins [32]; (2) Wiki-Genes [13]; and (3) Gene Wiki [14]. WikiProteins usesautomated procedures to extract information from mul-tiple resources, a text-based procedure to summarizethese data, and a wiki-based format to collect user-sup-plied information. Similarly, WikiGenes uses a text-based procedure based on the iHOP service to presentautomated content organized under categorized subjects,and users are encouraged to provide content and cor-rections to the system, with their identities displayed toacknowledge contributions. Gene Wiki, the product ofwhich resides within Wikipedia, automates the creationand maintenance of ‘stub’ articles on genes, thus creat-ing a systematic framework for gene content. Despitethe quality of these systems, examples of deep commu-nity commitment to contribute content are rare. Byvisual inspection, most entries in these systems still con-tain mostly automated content.A striking divergence from the classic model is Gen-eTests [33], in which expert authors are recruited foreach subject gene, taking intellectual ownership of anarticle of substantial importance to the clinical geneticscommunity. When contrasting GeneTests to the afore-mentioned wiki-based systems, two qualities contributeprominently to the success of the former. First, GeneT-ests addresses a niche, allowing content to be tailored tothe needs of a target audience. Second, the scientistswho write articles on GeneTests are strongly acknowl-edged, allowing them to receive recognition for theirintellectual contributions. While lasting participation in- and the continuing evolution of - GeneTests may ulti-mately derive from the intense commitment of the pro-ject’s directors, it stands out as one of the rare cases inwhich prominent genetics researchers contribute origi-nal content to a community resource.TFe represents a new direction in scientific communi-cation of gene-specific information. Combining auto-mated data presentation with expert-user reviews, thewiki-based system provides succinct reports about TFs,one of the most highly studied classes of proteins. Thehighly engaged efforts by researchers worldwide demon-strate that a wiki-based system can attract active partici-pation and meet high quality standards of scholarlycontent. With over 100 mini reviews presented in theinitial release, TFe represents one of the largest commu-nity participations in a gene-focused wiki project.While the term wiki has become loosely applied overthe years, in reality the term refers to a specific class ofsoftware that allows shared development of a document.However, in its most basic sense the term is commonlyused to reflect the philosophy that information is bestmade accessible and editable by anyone pro bono. Thewiki model has caught the attention of some scientists,who see it as a powerful tool that can hasten the paceof scientific communication. In the wake of Wikipedia’ssuccess, there emerged a high profile rallying call to cre-ate a gene-function wiki for scientists [34], and severalgroups have heeded this call by creating various scienti-fic wikis, some built from the ground up [13] and somederived from existing general purpose wiki engines[32,35].Unfortunately, as evidenced by WikiProteins [32],WikiGenes [13], and to a lesser extent Gene Wiki [14],scientific wikis have generally struggled to attract thelevel of community involvement envisioned by theirfounders. There are several contributing influences forthe observed low rate of participation. The success ofWikipedia is in part attributable to the enthusiasm of atiny fraction of the large global community of Internetusers who are willing to contribute content. The scienti-fic community with expertise on a specific topic, on theother hand, is small. Thus, even if the participation rateamong these scientists remains comparable to the parti-cipation rate of the global community of Internet userswho contribute content to Wikipedia, there would stillbe far fewer scientists contributing. To make mattersworse for proponents of scientific wikis, scientists seemgenerally less willing to participate in these sorts ofendeavors than the average user, reflecting perhaps theenormous demands on their time or the relative age ofthe experts. For many, their limited time is dedicated torewarding tasks, such as performing experiments andreporting on the results in peer-reviewed journals. Earn-ing new publications appears to be a strong motivatorfor many scientists. Few are willing to spend the sameamount of time and effort to expand a wiki article thatresides in the public domain and from which theywould not receive any substantial credit.Recognizing these constraints, a critical component ofthe success of TFe is the provision for authorship credit.Furthermore, we strive to actively identify and recruitauthors, as opposed to waiting for contributors to con-tact us. Without addressing these two aspects, we doubtYusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 18 of 25that we would be able to attain the same level of com-munity involvement. Ultimately, the support of a journalwilling to publish the resulting mini reviews in the formof this article (subject to passing a peer-review process)was a key motivator for many authors to participate inthe project.The retention of peer-review within the wiki-basedarticle development process is scientifically critical.Readers of the system must hold high confidence in thequality of the reports. To meet this standard, all partici-pating authors were encouraged to provide anonymouspeer review reports for a set of articles. Approximately40% of TFe authors participated in this voluntary peerreview program as peer reviewers of other TFe articles.Author identification was a challenge. We initiallysought participation from existing collaborators andsubsequently from peer referrals. During this early partof the project, we were able to recruit a core team ofabout ten authors who also became our de facto ’alphatesters’, thus allowing us to incorporate user feedbackduring the application development process. Theseauthors - and eventually other authors as well - had sig-nificant input into the TFe system throughout itsformation.Given the large number of characterized TFs, we ulti-mately needed a larger-scale approach. To this end, weidentified researchers who frequently appeared as thesenior author in publications that discuss a specifichuman or mouse TF (using an automated analysis ofarticles in PubMed). Overall, 251 authors were individu-ally contacted via email. About 59% (149 authors)agreed to participate, in addition to 10 authors whowere directly invited at the outset of the project, and 2authors who expressed interest and joined without invi-tation. About 65% of the participants developed articlessufficiently for inclusion in this report.Moving forward, TFe can be expanded, advancing theeffort to the ultimate goal of a high-quality article forevery human TF. For the future, we plan to adopt amore targeted approach by working with communitiesof authors who represent specific structural groups ofTFs (for example, nuclear receptors) or TFs that func-tion within a specific biological context (for example,diabetes). Such efforts can be partnered with sponsoringjournals that agree to reward the community effortswith a citable publication.Citing the resourceTo cite TFe as a concept or software tool, cite thispaper. To cite specific mini review articles found on theTFe website, please use the following format whenpossible:Author(s) last name followed by initials: < TF symbolin bold and proper capitalization >. In Yusuf D et al.:The Transcription Factor Encyclopedia. Genome Biol-ogy 2012, 13: < this article’s number as assigned by Gen-ome Biology >.Example:Bolotin E, Schnabl JM, Sladek FM: HNF4a. In YusufD et al.: The Transcription Factor Encyclopedia. Gen-ome Biology 2012, 13:000.where ‘000’ refers to the number assigned to thispaper by Genome Biology.ConclusionsTFe is a new web-based platform for facilitating the col-lection, evaluation, and dissemination of TF data. It isorganized and curated by a consortium of TF expertsfrom around the world whose goal is to develop concisemini review articles on pertinent human and mouseTFs. TFe contains a wealth of TF information consistingof both automatically populated and manually curatedcontent. Over 100 released articles are currently avail-able, with more to come. By offering multiple dataexport options that include the web API, the PDF gen-erator, and spreadsheet generator, TFe strives to be aconvenient and accessible resource. The TFe is availableat http://www.cisreg.ca/tfe.Materials and methodsTFe is an amalgamation of several different and highlyinvolved projects. For the sake of brevity, here we pre-sent only the most important key points regarding thematerials and methods we employed in creating TFe.Thus, we selectively describe the materials and methodsused in creating: (1) our TF classification system; (2)our TF binding profiles; (3) our TF protein structurepredictions; and (4) our TF-to-disease associations. Wedescribe the latter two in greater detail.Transcription factor classification systemWith few exceptions, all TF genes and classificationinformation in TFe were sourced from TFCat, a largecollection of predicted and confirmed mouse TF genes[4]. This collection is based on Entrez Gene identifiers.However, not all TF genes described in TFCat wereadded to TFe, as TFe is focused on those TFs that binddirectly to DNA in a sequence-specific fashion. Thus,with few exceptions, only TFs tagged with the function-based taxonomy of ‘DNA-Binding: sequence specific’ inTFCat were added. Ultimately, out of about 1,764mouse TF genes catalogued in TFCat, 585 were suitableenough to be imported from TFCat to TFe.TFs in TFe are organized into ‘groups’ and ‘families’based on their DNA binding evidence and transcrip-tional activation functions. This method of TF classifica-tion is inherited from TFCat. ‘Groups’ of TFs representthe highest level of organization in this classificationYusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 19 of 25system. Within each group exist different ‘families’ ofTFs. For nuclear receptors, this classification system isfurther extended with a ‘subfamily’ category. Placementof nuclear receptors within the subfamily category isguided by recommendations from the Nuclear ReceptorsNomenclature Committee [36]. For a comprehensive listof the groups, families, and subfamilies that are repre-sented in TFe, refer to Additional file 3.Transcription factor binding profilesMost of the profiles in TFe are generated through man-ual curation. Binding site data from our authors are sub-mitted via a web-based form. Submissions wereprocessed by the curatorial staff of the PAZAR databasewho confirm the quality of the submitted informationand enter the data into the TFe division of the PAZARdatabase. Authors may submit either genomic coordi-nates or TF binding motifs, such as those generated inselection and amplification experiments.Protein structure predictionsIn summary, DNA binding transcription factors havebeen extensively studied and can be grouped accordingto a structural classification system [4]. For each of thesmall set of structural domains known to facilitatesequence-specific protein-DNA interactions, solved pro-tein structures have been reported. Thus, it is feasible toproduce homology-based models for many DNA-bind-ing domains of proteins represented in TFe by usingthese solved protein structures as templates.We generated a set of 202 predicted protein struc-tures–homology-based predictions of the DNA bindingdomains of TFs. To do this, we developed a custompipeline, written in Python, that incorporates two toolswell-known in the realm of protein studies: HMMER[37] and Modeller [38]. Our protocol is based on thework of Morozov and Siggia [39], in which templatesare selected to optimize similarity of DNA-binding resi-dues. This method has been shown to increase modelingaccuracy at the DNA-binding interface.There are three main steps in generating the struc-tural predictions: (1) building the template library; (2)finding a suitable template for each unsolved structurewe would like to model; and (3) creating the structuralprediction using the template as a guide.Building the template libraryWe downloaded the entire RCSB PDB database [40] andthe Protein Families (Pfam) Pfam-A HMMs database[41]. Using a custom Python script, we identified andextracted records from the PDB database that appear tocontain a DNA binding domain and depict a protein-DNA binding interface (see Additional file 6 for a list ofPDB records extracted). Each record is fragmented intoone or more files, such that each file contains only onechain and the DNA residue. Using HMMER and thePfam-A HMMs database, we analyzed each fragmentedPDB record to catalogue all Pfam domains contained inthe protein sequence. The result of this exercise is a listof relationships between Pfam domains and PDBrecords (Additional file 7). This constitutes our templatelibrary.Finding a suitable template for each unsolved structureFor each unsolved TF protein structure, we looked forPfam domains in the protein sequence by reviewing pro-tein domain annotations provided by Entrez Gene. Sincewe are focused on modeling just the DNA bindingdomain of the TF protein, we removed the rest of theprotein sequence. We then looked for templates in ourPDB set that contain the same Pfam binding domains.We take these matching templates and compare eachindividually with our unsolved protein structure untilthe most suitable template is found. Our comparisons,which are done by an alignment tool, are scored basedon similarity of the DNA-binding domain residues. ForTFs known to form homodimers, a homodimeric tem-plate is selected.Creating the structural predictionAfter the most appropriate template is found, we inputthe unsolved protein sequence and the chosen templateto Modeller 9v2, which constructs the predicted struc-ture. After the structure is complete, we transfer theDNA residue from the template to the model by super-imposing the two protein structures in three-dimen-sional space to find the most optimal superimposition,copying the DNA residue from the template to themodel, and transposing the DNA residue per the super-imposition coordinates. As mentioned earlier, the DNAmolecules are stylistic and do not represent particularsequences - for example, the consensus sequence for theTF. The final predicted structure is rendered using iMolfor presentation on the website.Transcription factor to disease associationsTFs are a class of proteins that are highly implicated indisease. Thus, we have made disease annotations animportant component of TFe. Under the ‘Genetics’ sec-tion of all TFe articles, we have implemented a ‘cloud’report of associated MeSH disease terms, along withtheir respective P-values. These annotations were gener-ated in-house using a novel pipeline. Conceptually, thepipeline works as follows.In PubMed, most - if not all - articles are tagged witha list of MeSH terms by NCBI curators. Some of theseterms refer to diseases such as ‘Diabetes Mellitus, Type2’ or ‘Aniridia’. In addition, articles are often taggedwith the identifiers of genes that are featured promi-nently in the report. Ultimately, an article is tagged witha list of MeSH terms, and also a list of genes. In TFe,Yusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 20 of 25we have leveraged these annotations to infer associationsbetween TFs and certain diseases. For instance, muta-tions in the TF PAX6 are causal for the genetic disorderaniridia [42]. An automated analysis of all PAX6-refer-ring articles identifies the term ‘Aniridia’ as appearingfar more often than expected by chance (Fisher’s exactP-value 3.2 × 10-184).In the end, we generated 58,807 predicted TF-to-dis-ease associations for the TFs in TFe (mean of 74.2 asso-ciations per TF) with a scoring threshold of 0.05. Theseassociations can be viewed under the ‘Genetics’ tab onthe TFe website. An overview of our approach is asfollows.Creating the associationsWe derived these associations utilizing data fromPubMed and Entrez Gene. In the PubMed database, thepublications indexed in PubMed are associated withMeSH terms. For instance, in PubMed, a publicationabout the well-characterized gene TP53 may be asso-ciated with the MeSH terms ‘Cell Line, Tumor’, ‘Onco-gene Proteins, Fusion’, ‘Tumor Suppressor Protein p53’,and the like. We refer to this set of data as ‘mesh2-pubmed’ as it links MeSH terms to PubMed references.In the Entrez Gene database, there similarly exists the‘gene2pubmed’ dataset that associates PubMed refer-ences with genes. Given these resources, it is possible tocreate a link between genes and MeSH terms throughPubMed references. The end result is a set of ‘many-to-many’ associations between MeSH terms and genes,such that each MeSH term is associated with numerousgenes, and vice versa. As MeSH is a hierarchical con-trolled vocabulary maintained by curators and only themost specific relevant terms are ultimately associatedwith each PubMed article, each MeSH term is expandedto include all of its more generic parent terms. Forinstance, the MeSH term ‘Diabetes Mellitus, Type 2’would be expanded to include ‘Diabetes Mellitus’, ‘Glu-cose Metabolism Disorders’, ‘Metabolic Diseases’, and‘Nutritional and Metabolic Diseases’ (the latter being thebroadest and most generic term).Following this exercise we are left with millions ofgene to MeSH associations, and - in particular - 662,163associations between TF-encoding genes and MeSHterms. Yet, not all associations are informative. Forinstance, the MeSH term ‘Humans’ is associated withmany genes and - in practice - offers little annotationvalue. On the other hand, the association - or multipleassociations - of a relatively rare term such as ‘Leuke-mia, Erythroblastic, Acute’ with a TF-encoding genemay offer greater insight into the function of that gene.To evaluate the quality of these associations, we com-puted Fisher’s exact test P-value scores (Equation 1) foreach TF to MeSH term association. In this equation, nis the number of articles associated with the gene via‘gene2pubmed’; k is the number of n articles associatedwith the gene annotated with the MeSH term; N is thenumber of articles in PubMed; and m is the number ofarticles in PubMed annotated with the MeSH term. Forthe background set, we compute the average rate ofoccurrence for all possible gene-to-MeSH term associa-tions, taking into account not only whether an associa-tion exists, but also how often the same associationoccurs in each gene. In short, relatively rare MeSHterms that are associated multiple times with the samegene will yield a low (significant) P-value, while rela-tively common MeSH terms that are associated a fewtimes with the same gene will yield a high (insignificant)P-value. We have not applied corrections for multiplehypothesis testing in our P-values, although we plan toimplement this option in the future.Pr(K ≤ k) =k∑i=0(mi)(N − mn − i)(Nn) (1)On the TFe website, these data are displayed to theuser through the use of MeSH term clouds, where click-ing on a term in the cloud launches a PubMed searchdisplaying the relevant articles.This procedure resulted in 662,163 TF to MeSH termassociations, 333,909 of which return a P-value of ≤0.05. About 58,807 of these associations are of TFs inthe TFe database. Overall, TFs are significantly linked to2,121 out of over 4,400 disease terms described in theMeSH vocabulary.Additional materialAdditional file 1: PDF of released article. The PDF version of thehuman FOXL2 article. Other PDF versions of released TFe articles can beaccessed on the TFe website.Additional file 2: Data of released articles. Additional data related tothe released mini review articles to supplement the four-page PDFversions, arranged in alphabetical order by TF name.Additional file 3: Classification of transcription factors in TFe. Thereare 803 human, mouse, and rat articles in TFe, most of which areorganized into groups, families and subfamilies of TFs. The classificationscheme utilized in TFe is derived from the work of Fulton et al. [5] inTFCat. There are 8 large groups, which are further subclassified into 34families. Several TFs, namely nuclear receptors, are even furthersubclassified into subfamilies.Additional file 4: The TFe article structure. Articles in TFe areorganized into ten tabs labeled ‘Summary’, ‘Structure’, ‘TFBS’, ‘Targets’,‘Protein’, ‘Interactions’, ‘Genetics’, ‘Expression’, ‘Ontologies’, and ‘Papers’.Each tab, with the exception of the Ontologies and Papers tabs, typicallybegins with a brief overview written by the authors, followed by amixture of tables and figures that features data from both the authorsand second (that is, PAZAR) or third party (that is, BioGRID) sources.Additional file 5: Binding models produced in the TFe project.Images of the binding models produced in TFe that are sufficientlycharacterized to be used in a study.Yusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 21 of 25Additional file 6: PDB records depicting protein-DNA bindinginterface. A list of PDB records that depict a protein-DNA bindinginterface.Additional file 7: Relationship Between Pfam domains and PDBrecords. A list of Pfam binding domains followed by PDB records inwhich the domains can be found.AbbreviationsACS: article completion score; API: application programming interface; CIHR:Canadian Institutes of Health Research; MeSH: Medical Subject Headings;MSFHR: Michael Smith Foundation for Health Research; NCBI: NationalCenter for Biotechnology Information; OMIM: Online Mendelian Inheritancein Man; PDB: Protein Data Bank; PDF: Portable Document Format; PHP: PHPHypertext Preprocessor; RSCB: Research Collaboratory for StructuralBioinformatics; TF: transcription factor; TFBS: transcription factor binding site;TFCat: Transcription Factor Catalog; TFe: Transcription Factor Encyclopedia.AcknowledgementsWe thank all authors who have made this project possible, especially ouralpha and beta testers who have worked closely with us to refine thecontents and user interface of this resource. We thank David J Arenillas, andMiroslav Hatas for providing programming assistance and systemsadministration support. We highlight author Frances M Sladek (University ofCalifornia, Riverside) for extensive suggestions about the system interfaceand contents. We thank Frederick Pio (Simon Fraser University) for earlyassistance with TF structural modeling. TFe was supported by the PleiadesPromoter project, which is funded by Genome Canada, Genome BritishColumbia, GlaxoSmithKline R&D Limited, the British Columbia Mental Healthand Addiction Services, Child and Family Research Institute, the University ofBritish Columbia Institute of Mental Health, and the University of BritishColumbia Office of the Vice President Research. It is now supported byfunding from the National Institutes of Health (grant no. R01GM084875 toWWW). Computer hardware resources utilized in this project were supportedby the Gene Regulation Bioinformatics Laboratory funded by the CanadaFoundation for Innovation (CFI). We sincerely thank the multitude of fundingagencies throughout the world that have turned this project into reality.Here are our funding acknowledgments. DY is supported by funding fromthe University of British Columbia Work Study Program as well as the Facultyof Medicine of the University of British Columbia. EB is funded by T32training grant. WAC is funded by the National Sciences and EngineeringResearch Council of Canada, the Michael Smith Foundation for HealthResearch (MSFHR) and the Canadian Institutes of Health Research/MSFHRStrategic Program in Bioinformatics. DLF is supported by a CanadianInstitutes of Health Research (CIHR) Frederick Banting and Charles BestCanada Graduate Scholarship Doctoral Award and MSFHR Graduate DoctoralScholarship award. OHPR is funded by CHAARM in Europe. MVC is fundedby INSERM, EFSD, and ANR. CNdL is funded by a CIHR CGS-M GraduateAward and a MSFHR Junior Graduate Scholarship. EMS is currently fundedby Canadian Institutes of Health Research, Sharon Stewart Trust, andGenome British Columbia. GUR’s work is supported by funding fromDeutsche Forschungsgemeinschaft. EWFL’s work is supported by CancerResearch UK and Breast Cancer Campaign. RK was funded by a researchfellowship of the Faculty of Medical Sciences, Newcastle University. MSW isfunded by a CASE Studentship from the BBSRC in partnership with Novartis.RMF’s work is supported by funding from Ministerio de Economía yCompetitividad (MEyC) in Spain, CAM, and private foundations Fundaluce,ONCE, and Retina España. JJB is funded by the Biomedical Research Unit inReproductive Health, University Hospitals Coventry and Warwickshire NHSTrust. LLB’s work is supported by funding from Ministerio de Economía yCompetitividad (MEyC) in Spain, CAM, and private foundations Fundaluce,ONCE, and Retina España. PB’s work is supported by funding from Ministeriode Economía y Competitividad (MEyC) in Spain, CAM, and privatefoundations Fundaluce, ONCE, and Retina España. BAB was supported by anAMN and Université Paris Diderot-Paris 7 PhD fellowship. LG is funded bythe Danish Natural Science Research Council. SuM is funded by the DanishNatural Science Research Council. RAV’s laboratory is funded by the Centrenational de la recherche scientifique (CNRS), Université Paris-Diderot,Association pour la recherche contre le cancer (ARC), Fondation pour larecherche médicale (FRM), and Institut Universitaire de France (IUF). PAH issupported by funding from the MSFHR, Genome Canada, Genome BritishColumbia and Canadian Institutes for Health Research. BET is funded bygrants number HL091219 and R24 EY017540. AHB is funded by Leukaemiaand Lymphoma Research and Cancer Research UK. SPR is funded by theNational Institutes of Health (NIH) grants ES11863, ES018998 and HL66109.RLC is funded by CIHR and the Heart and Stroke Foundation of Canada. MLis funded by the National Institutes of Health (NIH) grant K08 CA120349. MVis funded by the National Institutes of Health (NIH) grants ES11893 and HL66109. AR is funded by the National Institute for General Medical Sciences,Nebraska Department of Health and the National Cancer Institute. MZ isfunded by the Italian Association for Cancer Research (AIRC) and the ItalianMinistry of Education, University and Research (MIUR-PRIN). SF is funded bythe National Institutes of Health (grants CA45250, 1U54HG004558,GM007377). PJF is funded by the National Institutes of Health (grantsCA45250, 1U54HG004558, and U01 ES017154). PJB is funded by Leukaemiaand Lymphoma Research. KLP is funded by the National Institutes of Health(NIH) grant F32HD068113, and SJR is funded by NIH grant HD42024. LdP isfunded by the Spanish Ministry of Science and Innovation, as well as the 7thFramework research Program of the European Union. RHW is funded by theEU FP7 and the Swiss National Science Foundation (grant 31003A_129962/1). MM is supported by the Ministry of Science and Higher Education (grantIP 2010 026770). MR was supported by studentships from the Faculté desÉtudes Supérieures at Université de Montréal and from the Montreal Centerfor Experimental Therapeutics in Cancer. SyM is funded by the CanadianInstitutes for Health Research (grant MOP 13-147) and holds the CIBC BreastCancer Research Chair at Université de Montréal. JO is supported by theMinistry of Science and Higher Education (grant N N401 071439) SJR isfunded by the National Institutes of Health grant R01 HD42024. MBB isfunded by the Research Institute for Children, Children’s Hospital NewOrleans, LA. MBB performed this work in collaboration with the Diana HelisHenry Medical Research Foundation. MSL is funded by the Research Instituteat Children’s Hospital, New Orleans, and the National Institutes of Health(NIH) grant R01DK061436. KKN and JMD are funded by Canadian Institutesof Health Research (grant MOP-84320). MW is funded by DeutscheForschungsgemeinschaft and Fonds der Chemischen Industrie. JH is fundedby CIHR (grant MOP-89806). PN is funded by the Department of Medicaland Dental Sciences, University of Birmingham. CFW is funded by theNational Institutes of Health (grant 044215) and the Oklahoma Center forAdult Stem Cell Research. JMD is supported by funding from the CIHR(grant MOP-84320) and the Natural Sciences and Engineering ResearchCouncil of Canada (NSERC grant RGPIN/238700-2010). SW is funded by theNational Institutes of Health grants CA75123, CA95026, and CA146033. DJP isfunded by Australian Research Council and the National Health and MedicalResearch Council of Australia PSJ is funded by the Welcome Trust and BreastCancer Campaign. JJK is funded by the Dutch Organization for MedicalResearch (ZonMW). BB is funded by the Netherlands organization for healthresearch and development ZonMW (grant VIDI-917-66-310), the Landsteinerfoundation (grant 0608), and the National Institutes of Health (grant NIH/NIAID R01-AI080564-01). RMG is supported by funding from the NationalHeart, Lung and Blood Institute (NHLBI) at the National Institutes of Health(NIH), and New York State Stem Cell Science (NYSTEM). MWW is a recipientof a Doctoral Award from the Canadian Institutes of Health Research. MRH isfunded by the Canadian Institutes of Health Research and the CancerResearch Society. EC is funded by the Dina Gordon-Malkin Ontario GraduateScholarship in Science and Technology. GNEF have been supported byfunding from the Medical Research Council (MRC) in the United Kingdom,the British Heart Foundation, the Wellcome Trust, and Action MedicalResearch. MKB is funded by the Medical Research Council (MRC) in theUnited Kingdom, grant G0800202. OH is funded by the National Institutes ofHealth (NIH) grant R01CA28868. VR is funded by the NIH grant 5RO1CA097226-03. DPL is funded by the Canadian Institutes of Health Research,Cancer Research Society, and the Leukemia and Lymphoma Society ofCanada. SB is funded by the National Institutes of Health grants GM079239and HL081205, National Heart, Lung, and Blood Institute Specialized Centersof Clinically Oriented Research grant P50HL084945, Center for ChildhoodAsthma in the Urban Environment grant P50ES015903, National Institute onEnvironmental Health Sciences Center grant P30 ES003819, and a ClinicalInnovator Award from the Flight Attendant Medical Research Institute. CJH isfunded by NIEHS grant ES07141. RFH is funded by the National Institutes ofHealth (grant R01 MH058869). MCB was funded by National Institutes ofYusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 22 of 25Health (NIH) training grant T32GM007183 and also by the NIH grant R01NS050386 (through KJM). KJM is funded by the National Institutes of Health(grants R01NS050386, R01NS044262). KD is funded by the Swedish MedicalResearch Council, the Swedish Cancer Foundation, and the Novo NordiskFoundation. CZ’s work was supported by grants from the Swedish CancerSociety (Cancerfonden), grant CAN 2007/1113. SS is funded by the NationalInstitutes of Health (grant R01GM069417). FMS is funded by the NationalInstitutes of Health (NIH) grants R01 DK053892 and R21 MH087397. PHB isfunded by a new development funding from the Fred Hutchinson CancerResearch Center (FHCRC) and the National Institutes of Health (grant no. R01GM088277-01).Author details1Department of Medical Genetics, Faculty of Medicine, Centre for MolecularMedicine and Therapeutics, Child and Family Research Institute, University ofBritish Columbia, 950 West 28th Avenue, Vancouver, British Columbia V5Z4H4, Canada. 2Evaluation and Research Services, Fraser Health Authority, 300- 10334 152A Street, Surrey, British Columbia V3R 7P8, Canada. 3Children’sHospital Oakland Research Institute, 5700 Martin Luther King Junior Way,Oakland, CA 94609-1809, USA. 4Computational Biology Program, PublicHealth Sciences Division, Fred Hutchinson Cancer Research Center, 1100Fairview Avenue North, Seattle, WA 98109, USA. 5Department ofBioinformatics, Centre for Molecular Medicine and Therapeutics, Child andFamily Research Institute, University of British Columbia, 950 West 28thAvenue, Vancouver, British Columbia V5Z 4H4, Canada. 6Department ofBiology, University of Western Ontario, 1151 Richmond Street, London,Ontario N6A5B7, Canada. 7Genetics Program, Centre for Molecular Medicineand Therapeutics, Child and Family Research Institute, University of BritishColumbia, 950 West 28th Avenue, Vancouver, British Columbia V5Z 4H4,Canada. 8Cell Biology and Neuroscience, Institute of Integrated GenomeBiology, University of California at Riverside, 2115 Biological SciencesBuilding, Riverside, CA 92521, USA. 9SIMOPRO, Laboratory of Life Sciences(Laboratoire de Sciences du Vivant), CEA (Commissariat à l’ÉnergieAtomique), Gif-sur-Yvette, Saclay, Île-de-France 91191, France. 10DepartmentEndocrinology, Metabolism and Cancer, INSERM (Unité 1016), Institut Cochin,24 Rue du Faubourg Saint Jacques, Paris, Île-de-France 75014, France.11Institut für Zellbiologie, Universitätsklinikum Essen, Universität Duisburg-Essen, Hufelandstrasse 55, Essen, Nordrhein-Westfalen 45122, Germany.12Department of Surgery and Cancer, Division of Cancer, Imperial CollegeLondon, Du Cane Road, London, London W12 0NN, UK. 13Centre for OralHealth Research, School of Dental Sciences, Newcastle University, MedicalSchool, Framlington Place, Newcastle upon Tyne, Tyne and Wear NE2 4BW,UK. 14Department of Development and Differentiation, Centro de BiologiaMolecular Severo Ochoa (CBMSO), Consejo Superior de InvestigacionesCientíficas (CSIC) and CIBER de Enfermedades Raras (CIBERER), NicolasCabrera 1, Cantoblanco, Madrid, Madrid 28049, Spain. 15Division ofReproductive Health, Warwick Medical School, University of Warwick, CliffordBridge Road, Coventry, West Midlands CV2 2DX, UK. 16NeurobiologiaMolecular Celular y del desarrollo, Centro de Biologia Molecular SeveroOchoa (CBMSO), Centro de Biologia Molecular Severo Ochoa and CIBER deEnfermedades Raras (CIBERER), Nicolas Cabrera 1, Cantoblanco, Madrid,Madrid 28049, Spain. 17Department of Molecular and Cellular Pathology,Institut Jacques Monod, Université Paris Diderot (Paris 7), 15 rue HélèneBrion, Paris, Île-de-France 75013, France. 18Department of Biochemistry andMolecular Biology, University of Southern Denmark, Campusvej 55, Odense,Region Syddanmark 5230, Denmark. 19Terry Fox Laboratory, BC CancerAgency, Provincial Health Services Authority, 675 West 10th Avenue,Vancouver, British Columbia V5Z 1L3, Canada. 20Molecular and CellularPathology Program, Institut Jacques Monod, Université Paris Diderot (Paris 7),15 rue Hélène Brion, Paris, Île-de-France 75013, France. 21Eppley Institute forResearch in Cancer and Allied Diseases, University of Nebraska MedicalCenter, University of Nebraska, 985950 Nebraska Medical Center, Omaha, NE68198-5950, USA. 22Department of Molecular Experimental Medicine, ScrippsResearch Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA.23Departments of Molecular and Experimental Medicine and Immunologyand Microbial Sciences (MEM 131), Scripps Research Institute, 10550 NorthTorrey Pines Road, La Jolla, CA 92037, USA. 24Nuffield Department of ClinicalLaboratory Sciences, John Radcliffe Hospital, Oxford NIHR BiomedicalResearch Centre, University of Oxford, Level 4 Academic Block, John RadcliffeHospital, Headington, Oxford, Oxfordshire OX3 9DU, UK. 25Department ofPediatrics, College of Medicine, University of Illinois at Chicago, 840 SouthWood Street (M/C 856), Chicago, IL 60612, USA. 26Department of Medicine/Hematology, Stanford University School of Medicine, Stanford University, 875Blake Wilbur Drive, Stanford, CA 94305, USA. 27Department of Medicine,University of Bern, Hochschulstrasse 4, Bern, Bern-Mittelland CH-3012,Switzerland. 28Department of Oncology, Sidney Kimmel ComprehensiveCancer Center at Johns Hopkins, Johns Hopkins University School ofMedicine, 1650 Orleans Street Room 530, Baltimore, MD 21237, USA.29Eppley Institute for Research in Cancer and Allied Diseases, University ofNebraska Medical Center, University of Nebraska, 986805 Nebraska MedicalCenter, Omaha, NE 68198-6805, USA. 30Institute of ExperimentalEndocrinology and Oncology (IEOS), CNR - National Research Council, viaPansini 5, Naples, Naples 80131, Italy. 31Department of Biochemistry andMolecular Biology, Norris Comprehensive Cancer Center, University ofSouthern California, 1450 Biggy Street, Los Angeles, CA 90089, USA.32Department of Molecular Cancer Research, University Medical CenterUtrecht, Utrecht University, Universiteitsweg 100, Utrecht, Utrecht 3584 CG,The Netherlands. 33Nuffield Department of Clinical Laboratory Sciences,Medical Sciences Division, University of Oxford, Level 4 Academic Block,John Radcliffe Hospital, Headington, Oxford, Oxfordshire OX3 9DU, UK.34Molecular Targeting in Breast Cancer research unit, Institute for Research inImmunology and Cancer, Université de Montréal, 2950 Chemin dePolytechnique, Montréal, Québec H3T 1J4, Canada. 35Institut de Biologie deLille, Institut Pasteur de Lille, Centre National de la Recherche Scientifique(CNRS) UMR 8161, 1 Rue du Pr Calmette, Lille, Nord-Pas-de-Calais 59021,France. 36Department of Cellular and Integrative Physiology, IndianaUniversity School of Medicine, Indiana University-Purdue UniversityIndianapolis, 635 Barnhill Drive, Indianapolis, IN 46202, USA. 37Department ofPhysiological Chemistry, University Medical Centre Utrecht, UtrechtUniversity, Universiteitsweg 100, Utrecht, Utrecht 3584 CG, The Netherlands.38Department of Biochemistry, School of Medicine, Universidad Autonomade Madrid, Arzobispo Morcillo, 4, Madrid, Madrid 28029, Spain. 39Institute ofPhysiology, Zurich Center for Integrative Human Physiology, University ofZurich, Winterthurerstrasse 190, Zurich, Zurich CH-8057, Switzerland.40Department of Oncological Genetics, Medical Center of PostgraduateEducation, Maria Sklodowska-Curie Memorial Cancer Center and Institute ofOncology, Roentgena 5, Warsaw, Mazovia 02-781, Poland. 41Department ofBiochemistry, Institute for Research in Immunology and Cancer, Universitéde Montréal, PO Box 6128, Station Centre-Ville, Montréal, Québec H3C 3J7,Canada. 42Department of Biochemistry, Institute for Research in Immunologyand Cancer, Université de Montréal, 2950 Chemin de Polytechnique,Montréal, Québec H3T 1J4, Canada. 43Department of Biology, School ofScience, Indiana University-Purdue University Indianapolis, LD222, 402 NorthBlackford Street, Indianapolis, IN 46202, USA. 44Department of Medicine,Cancer Center, Massachusetts General Hospital, Harvard Medical School, 13thStreet, Building 149, Room 7.103, Charlestown, MA 02129, USA.45Department of Biochemistry, School of Molecular and Biomedical Science,University of Adelaide, North Terrace, Adelaide, South Australia 5005,Australia. 46Department of Pediatrics and Biochemistry and MolecularBiology, Research Institute for Children, Children’s Hospital at New Orleans,Louisiana State University Health Sciences Center, 200 Henry Clay Avenue,New Orleans, LA 70118, USA. 47Departments of Pediatrics and Genetics,Research Institute for Children, Children’s Hospital at New Orleans, LouisianaState University Health Sciences Center, 200 Henry Clay Avenue, NewOrleans, LA 70118, USA. 48Department of Pathology and Molecular Medicine,Queen’s Cancer Research Institute, Queen’s University, 18 Stuart Street,Botterell Hall, Kingston, Ontario K7L 3N6, Canada. 49School of Medicine,Institut fuer Biochemie, Emil-Fischer-Zentrum, Friedrich-AlexanderUniversitaet Erlangen-Nuernberg, Fahrstrasse 17, Erlangen, Bavaria 91096,Germany. 50Department of Molecular Biology and Biochemistry, IndianaUniversity School of Medicine, Indiana University-Purdue UniversityIndianapolis, 635 Barnhill Drive, Indianapolis, IN 46202, USA. 51Department ofImmunity and Infection, School of Medical and Dental Sciences, University ofBirmingham, Wolfson Drive, Edgbaston, Birmingham, West Midlands B15 2TT,UK. 52Immunobiology and Cancer Program, Oklahoma Medical ResearchFoundation, 825 NE 13th Street, Oklahoma City, Oklahoma 73104, USA.53Radiation Oncology, Department of Pharmacology and ExperimentalTherapeutics, Jefferson University Hospital, 1020 Locust Street, Philadelphia,PA 19107, USA. 54Department of Microbiology and Immunology, Universityof Oklahoma Health Sciences Center, University of Oklahoma, 100 NorthLindsay Avenue, Oklahoma City, OK 73104, USA. 55Department of Biology,McMaster University, LSB-331, 1280 Main Street West, Hamilton, OntarioYusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 23 of 25L8S4K1, Canada. 56School of Medicine, Johns Hopkins University, 720 RutlandAvenue, Baltimore, MD 21205, USA. 57Department of Pharmacology andExperimental Therapeutics, Jefferson Medical College, Thomas JeffersonUniversity, 132 South 10th Street, 1170 Main, Philadelphia, PA 19107, USA.58Discipline of Biochemistry, School of Molecular and Biomedical Science,University of Adelaide, North Terrace, Adelaide, South Australia 5005,Australia. 59Institute of Cellular Medicine, Faculty of Medicine, NewcastleUniversity, Medical School, Framlington Place, Newcastle upon Tyne, Tyneand Wear NE1 7RU, UK. 60Department of Immunity and Immunology, Schoolof Medical and Dental Sciences, University of Birmingham, Wolfson Drive,Edgbaston, Birmingham, West Midlands B15 2TT, UK. 61Department of CellBiology and Histology, Center for Immunology Amsterdam, AcademicMedical Center, University of Amsterdam, Meibergdreef 15, Amsterdam,Noord Holland 1105 AZ, The Netherlands. 62Division of Cancer ImagingResearch, Department of Radiology, School of Medicine, Johns HopkinsUniversity, 720 Rutland Avenue, Baltimore, MD 21205, USA. 63GenomeCenter, University of California at Davis, 1 Shields Avenue, Davis, CA 95616,USA. 64University of California at San Diego, 9500 Gilman Drive, San Diego,CA 92093, USA. 65Department of Biochemistry and Developmental GenomicsGroup, Center of Excellence in Bioinformatics and Life Sciences, StateUniversity of New York at Buffalo, 701 Ellicott Street B3-303, Buffalo, NewYork 14203, USA. 66Department of Pathology and Molecular Medicine,Queen’s Cancer Research Institute, Queen’s University, 18 Stuart Street,Botterell Hall, Kingston, Ontario K7K 4G4, Canada. 67Department of Molecularand Cellular Biology, Department of Laboratory Medicine and Pathobiology,Sunnybrook Health Sciences Centre, University of Toronto, 2075 BayviewAvenue, Toronto, Ontario M4N 3M5, Canada. 68Department of Molecular andCellular Biology, Sunnybrook Health Sciences Centre, University of Toronto,2075 Bayview Avenue, Toronto, Ontario M4N 3M5, Canada. 69Institute ofCellular Medicine, Newcastle University, Medical School, Framlington Place,Newcastle upon Tyne, Tyne and Wear NE2 4HH, UK. 70Faculty of MedicalSciences, Institute of Cellular Medicine, Newcastle University, Medical School,Framlington Place, Newcastle upon Tyne, Tyne and Wear NE2 4AA, UK.71Department of Pathology and Laboratory Medicine, David Geffen Schoolof Medicine, University of California at Los Angeles, 10833 Le Conte Avenue,Los Angeles, CA 90095-1732, USA. 72Radiology and Oncology, School ofMedicine, Johns Hopkins University, 720 Rutland Avenue, Baltimore, MD21205, USA. 73Department of Environmental Health Sciences, Johns HopkinsBloomberg School of Public Health, Johns Hopkins University, 615 NorthWolfe Street, Baltimore, MD 21205, USA. 74Department of Pharmacology andToxicology, Neuroscience Institute, Morehouse School of Medicine, 720Westview Drive Southwest, Atlanta, GA 30310, USA. 75Department ofPharmacology, Perelman School of Medicine, University of Pennsylvania, 10-124 Translational Research Center, 3400 Civic Center Boulevard Building 421,Philadelphia, PA 19104-5158, USA. 76Department of Neurological Surgery,Seattle Children’s Research Institute, University of Washington, 1900 NinthAvenue, Seattle, WA 98101, USA. 77Faculty of Biology and Medicine, Centerfor Integrated Genomics, University of Lausanne, CH-1015 Lausanne,Lausanne, Vaud CH-1015, Switzerland. 78Department of Biomedical Sciencesand Pathobiology, VA-MD Regional College of Veterinary Medicine, VirginiaPolytechnic Institute and State University, Duck Pond Drive, Blacksburg, VA24061, USA. 79Department of Molecular Genetics and Cell Biology, Universityof Illinois at Chicago, 920 East 58th Street, Chicago, IL 60637, USA. 80Centerfor Integrative Brain Research, Seattle Children’s Research Institute, Universityof Washington, 1900 Ninth Avenue, Seattle, WA 98101, USA. 81ClinicalEndocrinology Branch, National Institute of Diabetes, Digestive, and KidneyDisorders, National Institutes of Health, 10 Center Drive, Bethesda, MD20892-1772, USA. 82Department of Biosciences and Nutrition, Novum,Karolinska Institutet, Hälsovägen 7-9, Huddinge, Stockholm SE-141 83,Sweden. 83Department of Biochemistry, University of Buffalo School ofMedicine and Biomedical Sciences, State University of New York at Buffalo,701 Ellicott Street, Buffalo, NY 14203, USA.Authors’ contributionsWWW conceptualized and led the project. WWW, DY and SLB defined TFecontent. DY designed, developed, and maintained the database, interfaceand software. WWW, SLB, MS and DY invited authors, corresponded withcollaborators, and managed day-to-day operations. WWW, MS, WAC, SLB andDY devised and implemented the author recruitment and relationshipmanagement system. AT and PB envisioned and designed the structuralprediction pipeline. AT, PB, and DY produced all native structural predictions.XYCZ and CTDD annotated the binding site profiles with the assistance ofEP. WAC performed the TF to MeSH term association analysis. EP and SLBmanaged the process of importing binding site data into PAZAR foreventual use in TFe. DLF provided early suggestions on the creation of awiki-based system and provided the TF classification system. DY and WWWdrafted the manuscript with the assistance of WAC, AT, and SLB. All otherauthors contributed content and provided feedback. All authors read andapproved the final manuscript for publication.Competing interestsThe authors declare that they have no competing interests.Received: 2 February 2012 Revised: 19 March 2012Accepted: 29 March 2012 Published: 29 March 2012References1. Brand-Saberi B: Genetic and epigenetic control of skeletal muscledevelopment. Ann Anat 2005, 187:199-207.2. Balsamo A, Cicognani A, Gennari M, Sippell WG, Menabo S, Baronio F,Riepe FG: Functional characterization of naturally occurring NR3C2 genemutations in Italian patients suffering from pseudohypoaldosteronismtype 1. Eur J Endocrinol 2007, 156:249-256.3. Field JK, Spandidos DA: The role of ras and myc oncogenes in humansolid tumours and their relevance in diagnosis and prognosis (review).Anticancer Res 1990, 10:1-22.4. Fulton DL, Sundararajan S, Badis G, Hughes TR, Wasserman WW, Roach JC,Sladek R: TFCat: The Curated Catalog of Mouse and Human TranscriptionFactors. Genome Biol 2009, 10:R29.5. Vaquerizas JM, Kummerfeld SK, Teichmann SA, Luscombe NM: A census ofhuman transcription factors: function, expression and evolution. Nat RevGenet 2009, 10:252-263.6. Takahashi K, Yamanaka S: Induction of pluripotent stem cells from mouseembryonic and adult fibroblast cultures by defined factors. Cell 2006,126:663-676.7. Takahashi K, Tanabe K, Ohnuki M, Narita M, Ichisaka T, Tomoda K,Yamanaka S: Induction of pluripotent stem cells from adult humanfibroblasts by defined factors. Cell 2007, 131:861-872.8. Maglott D, Ostell J, Pruitt KD, Tatusova T: Entrez Gene: gene-centeredinformation at NCBI. Nucleic Acids Res 2007, 35:D26-31.9. Safran M, Solomon I, Shmueli O, Lapidot M, Shen-Orr S, Adato A, Ben-Dor U, Esterman N, Rosen N, Peter I, Olender T, Chalifa-Caspi V, Lancet D:GeneCards 2002: towards a complete, object-oriented, human genecompendium. Bioinformatics 2002, 18:1542-1543.10. UniProt Consortium: The Universal Protein Resource (UniProt) in 2010.Nucleic Acids Res 2010, 38:D142-148.11. Boeckmann B, Bairoch A, Apweiler R, Blatter MC, Estreicher A, Gasteiger E,Martin MJ, Michoud K, O’Donovan C, Phan I, Pilbout S, Schneider M: TheSWISS-PROT protein knowledgebase and its supplement TrEMBL in2003. Nucleic Acids Res 2003, 31:365-370.12. Fernandez JM, Hoffmann R, Valencia A: iHOP web services. Nucleic AcidsRes 2007, 35:W21-26.13. Hoffmann R: A wiki for the life sciences where authorship matters. NatGenet 2008, 40:1047-1051.14. Huss JW, Lindenbaum P, Martone M, Roberts D, Pizarro A, Valafar F,Hogenesch JB, Su AI: The Gene Wiki: community intelligence applied tohuman gene annotation. Nucleic Acids Res 2010, 38:D633-639.15. Portales-Casamar E, Thongjuea S, Kwon AT, Arenillas D, Zhao X, Valen E,Yusuf D, Lenhard B, Wasserman WW, Sandelin A: JASPAR 2010: the greatlyexpanded open-access database of transcription factor binding profiles.Nucleic Acids Res 2010, 38:D105-110.16. Portales-Casamar E, Kirov S, Lim J, Lithwick S, Swanson MI, Ticoll A,Snoddy J, Wasserman WW: PAZAR: a framework for collection anddissemination of cis-regulatory sequence annotation. Genome Biol 2007,8:R207.17. Matys V, Kel-Margoulis OV, Fricke E, Liebich I, Land S, Barre-Dirrie A, Reuter I,Chekmenev D, Krull M, Hornischer K, Voss N, Stegmaier P, Lewicki-Potapov B, Saxel H, Kel AE, Wingender E: TRANSFAC and its moduleTRANSCompel: transcriptional gene regulation in eukaryotes. NucleicAcids Res 2006, 34:D108-110.18. Transcription Factor Encyclopedia.. [http://www.cisreg.ca/tfe].Yusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 24 of 2519. Breitkreutz BJ, Stark C, Reguly T, Boucher L, Breitkreutz A, Livstone M,Oughtred R, Lackner DH, Bahler J, Wood V, Dolinski K, Tyers M: TheBioGRID Interaction Database: 2008 update. Nucleic Acids Res 2008, 36:D637-640.20. Hubbard TJ, Aken BL, Ayling S, Ballester B, Beal K, Bragin E, Brent S, Chen Y,Clapham P, Clarke L, Coates G, Fairley S, Fitzgerald S, Fernandez-Banet J,Gordon L, Graf S, Haider S, Hammond M, Holland R, Howe K, Jenkinson A,Johnson N, Kahari A, Keefe D, Keenan S, Kinsella R, Kokocinski F, Kulesha E,Lawson D, Longden I, et al: Ensembl 2009. Nucleic Acids Res 2009, 37:D690-697.21. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP,Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A,Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Geneontology: tool for the unification of biology. The Gene OntologyConsortium. Nat Genet 2000, 25:25-29.22. Lipscomb CE: Medical Subject Headings (MeSH). Bull Med Libr Assoc 2000,88:265-266.23. Bult CJ, Kadin JA, Richardson JE, Blake JA, Eppig JT: The Mouse GenomeDatabase: enhancements and updates. Nucleic Acids Res 2010, 38:D586-592.24. McKusick VA: Mendelian Inheritance in Man and its online version,OMIM. Am J Hum Genet 2007, 80:588-604.25. Portales-Casamar E, Arenillas D, Lim J, Swanson MI, Jiang S, McCallum A,Kirov S, Wasserman WW: The PAZAR database of gene regulatoryinformation coupled to the ORCA toolkit for the study of regulatorysequences. Nucleic Acids Res 2009, 37:D54-60.26. Berman H, Henrick K, Nakamura H: Announcing the worldwide ProteinData Bank. Nat Struct Biol 2003, 10:980.27. Rhead B, Karolchik D, Kuhn RM, Hinrichs AS, Zweig AS, Fujita PA,Diekhans M, Smith KE, Rosenbloom KR, Raney BJ, Pohl A, Pheasant M,Meyer LR, Learned K, Hsu F, Hillman-Jackson J, Harte RA, Giardine B,Dreszer TR, Clawson H, Barber GP, Haussler D, Kent WJ: The UCSC GenomeBrowser database: update 2010. Nucleic Acids Res 2010, 38:D613-619.28. Jones AR, Overly CC, Sunkin SM: The Allen Brain Atlas: 5 years andbeyond. Nat Rev Neurosci 2009, 10:821-828.29. Lowry JA, Atchley WR: Molecular evolution of the GATA family oftranscription factors: conservation within the DNA-binding domain. JMol Evol 2000, 50:103-115.30. Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA: OnlineMendelian Inheritance in Man (OMIM), a knowledgebase of humangenes and genetic disorders. Nucleic Acids Res 2005, 33:D514-517.31. Mantis Bug Tracker.. [http://www.mantisbt.org/].32. Mons B, Ashburner M, Chichester C, van Mulligen E, Weeber M, denDunnen J, van Ommen GJ, Musen M, Cockerill M, Hermjakob H, Mons A,Packer A, Pacheco R, Lewis S, Berkeley A, Melton W, Barris N, Wales J,Meijssen G, Moeller E, Roes PJ, Borner K, Bairoch A: Calling on a millionminds for community annotation in WikiProteins. Genome Biol 2008, 9:R89.33. Pagon RA: GeneTests: an online genetic information resource for healthcare providers. J Med Libr Assoc 2006, 94:343-348.34. Wang K: Gene-function wiki would let biologists pool worldwideresources. Nature 2006, 439:534.35. Maier H, Dohr S, Grote K, O’Keeffe S, Werner T, Hrabe de Angelis M,Schneider R: LitMiner and WikiGene: identifying problem-related keyplayers of gene regulation using publication abstracts. Nucleic Acids Res2005, 33:W779-782.36. A unified nomenclature system for the nuclear receptor superfamily..Cell 1999, 97:161-163.37. Eddy SR: A probabilistic model of local sequence alignment thatsimplifies statistical significance estimation. PLoS Comput Biol 2008, 4:e1000069.38. Eswar N, Webb B, Marti-Renom MA, Madhusudhan MS, Eramian D,Shen MY, Pieper U, Sali A: Comparative protein structure modeling usingMODELLER. Curr Protoc Protein Sci 2007, Chapter 2. Unit 2.9.39. Morozov AV, Siggia ED: Connecting protein structure with predictions ofregulatory sites. Proc Natl Acad Sci USA 2007, 104:7068-7073.40. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H,Shindyalov IN, Bourne PE: The Protein Data Bank. Nucleic Acids Res 2000,28:235-242.41. Finn RD, Tate J, Mistry J, Coggill PC, Sammut SJ, Hotz HR, Ceric G,Forslund K, Eddy SR, Sonnhammer EL, Bateman A: The Pfam proteinfamilies database. Nucleic Acids Res 2008, 36:D281-288.42. Lee H, Khan R, O’Keefe M: Aniridia: current pathology and management.Acta Ophthalmol 2008, 86:708-715.43. Released mini review articles in the TFe website.. [http://www.cisreg.ca/cgi-bin/tfe/browse.pl].doi:10.1186/gb-2012-13-3-r24Cite this article as: Yusuf et al.: The Transcription Factor Encyclopedia.Genome Biology 2012 13:R24.Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionSubmit your manuscript at www.biomedcentral.com/submitYusuf et al. Genome Biology 2012, 13:R24http://genomebiology.com/2012/13/3/R24Page 25 of 25

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