{"id":17223370,"url":"https://github.com/slowkow/tftargets","last_synced_at":"2025-04-14T00:22:13.706Z","repository":{"id":28054912,"uuid":"31551250","full_name":"slowkow/tftargets","owner":"slowkow","description":":dart: Human transcription factor target genes from 6 databases in convenient R format.","archived":false,"fork":false,"pushed_at":"2018-10-04T19:55:28.000Z","size":26609,"stargazers_count":86,"open_issues_count":2,"forks_count":19,"subscribers_count":9,"default_branch":"master","last_synced_at":"2025-03-27T14:21:52.777Z","etag":null,"topics":["bioinformatics","data","rstats","transcription-factors"],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/slowkow.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2015-03-02T16:55:17.000Z","updated_at":"2025-01-01T20:35:49.000Z","dependencies_parsed_at":"2022-09-04T10:00:35.557Z","dependency_job_id":null,"html_url":"https://github.com/slowkow/tftargets","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/slowkow%2Ftftargets","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/slowkow%2Ftftargets/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/slowkow%2Ftftargets/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/slowkow%2Ftftargets/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/slowkow","download_url":"https://codeload.github.com/slowkow/tftargets/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248800048,"owners_count":21163404,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bioinformatics","data","rstats","transcription-factors"],"created_at":"2024-10-15T04:08:08.724Z","updated_at":"2025-04-14T00:22:13.675Z","avatar_url":"https://github.com/slowkow.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# tftargets\n\n[Transcription factors (TFs)][TF] activate and repress target genes. This R package\nprovides easy access to query a particular TF and find its targets in humans.\nThe data has been collected from multiple different databases.\n\n[TF]: https://www.khanacademy.org/science/biology/gene-regulation/gene-regulation-in-eukaryotes/a/eukaryotic-transcription-factors\n\n\u003cimg src=\"https://github.com/slowkow/tftargets/raw/master/figures/transcription_factor_image.jpg\"\u003e\n\nCredit: © KENNETH EWARD/BIOGRAFX/PHOTO RESEARCHERS, INC\n\n## Citation\n\nFor now, please provide a link to this github repository:\n\n\u003chttps://github.com/slowkow/tftargets\u003e\n\n\n## Usage\n\nYou may install this package with [devtools]:\n\n[devtools]: https://github.com/hadley/devtools\n\n```{r}\ndevtools::install_github(\"slowkow/tftargets\")\n\nlibrary(tftargets)\n\nlength(TRED)\n# [1] 133\n```\n\nAlternatively, you can download just the RData file:\n\n```{r}\n# Download the file:\n\n# install.packages(\"RCurl\")\nlibrary(RCurl)\ndownload.file(\n  url = \"https://raw.githubusercontent.com/slowkow/tftargets/master/data/tftargets.rda\",\n  destfile = \"tftargets.rda\",\n  method = \"curl\"\n)\n\n# Load the file:\nload(\"tftargets.rda\")\n\n# View the variables stored in the file:\nls()\n[1] \"ENCODE\"      \"ITFP\"        \"Marbach2016\"\n[4] \"Neph2012\"    \"TRED\"        \"TRRUST\"\n```\n\n\n## Data\n\nThis package contains the following datasets:\n\n| Dataset     | Structure    | Gene Identifier   | # TF's | # TF-gene assocations | Reference                         |\n|-------------|--------------|-------------------|--------|-----------------------|-----------------------------------|\n| TRED        | list         | ENTREZ            | 133    | 7,066                 | [TRED][tred] (2007)               |\n| ITFP        | list         | HGNC Symbol/Alias | 1974   | 67,154                | [ITFP][itfp] (2008)               |\n| ENCODE      | list         | ENTREZ            | 157    | 20,428                | [ENCODE][encode] (2012)           |\n| Neph2012    | nested list* | HGNC Symbol/Alias | 536    | 16,484                | [Neph2012][neph2012] (2012)       |\n| TRRUST      | list         | HGNC Symbol/Alias | 748    | 8,215                 | [TRRUST][trrust] (2015)           |\n| Marbach2016 | list         | HGNC Symbol/Alias | 643    | 1,305,782             | [Marbach2016][marbach2016] (2016) |\n\n[tred]: https://github.com/slowkow/tftargets#tred\n[itfp]: https://github.com/slowkow/tftargets#itfp\n[encode]: https://github.com/slowkow/tftargets#encode\n[neph2012]: https://github.com/slowkow/tftargets#neph2012\n[trrust]: https://github.com/slowkow/tftargets#trrust\n[marbach2016]: https://github.com/slowkow/tftargets#marbach2016\n\n\\* Note: The `Neph2012` is organized as a nested list where the top-level keys\nrefer to tissue types (e.g. \"fBrain-DS11872\").\n\nSee [`data-raw/make_rdata.R`][make_rdata] for the script that converts the raw\ndata into lists of gene sets.\n\n[make_rdata]: https://github.com/slowkow/tftargets/blob/master/data-raw/make_rdata.R\n\n- - -\n\n### TRED\n\n#### Citation\n\n\u003e Jiang, C., Xuan, Z., Zhao, F. \u0026 Zhang, M. Q. TRED: a transcriptional\n\u003e regulatory element database, new entries and other development. Nucleic\n\u003e Acids Res. 35, D137–40 (2007).\n\u003e [PubMed](http://www.ncbi.nlm.nih.gov/pubmed/17202159)\n\n#### Source\n\n\u003chttps://cb.utdallas.edu/cgi-bin/TRED/tred.cgi?process=home\u003e\n\n#### Description\n\nPredicted and known human transcription factor targets.\n\nHere we find that TRED claims 59 genes are targeted by [STAT3].\n\n[STAT3]: http://www.genecards.org/cgi-bin/carddisp.pl?gene=STAT3\n\n```{r}\n# Entrez Gene IDs.\nTRED[[\"STAT3\"]]\n [1]      2    332    355    595    596    598    896    943    958   1026   1051\n[12]   1401   1588   1962   2194   2209   2353   3082   3162   3320   3326   3479\n[23]   3559   3572   3586   3659   3718   3725   3929   4170   4582   4585   4609\n[34]   4843   5008   5021   5292   5551   5967   6095   6347   6654   7076   7078\n[45]   7097   7124   7200   7422   7432   8651   8996   9021  11336  23514  26229\n[56]  27151  55893 117153 201254\n```\n\n#### Figures\n\n\u003cimg src=\"https://github.com/slowkow/tftargets/raw/master/figures/TRED_histogram.png\"\u003e\n\n- - -\n\n### ITFP\n\n#### Citation\n\n\u003e Zheng, G., Tu, K., Yang, Q., Xiong, Y., Wei, C., Xie, L., Zhu, Y. \u0026 Li, Y.\n\u003e ITFP: an integrated platform of mammalian transcription factors.\n\u003e Bioinformatics 24, 2416–2417 (2008).\n\u003e [PubMed](http://www.ncbi.nlm.nih.gov/pubmed/18713790)\n\n#### Source\n\n\u003chttp://itfp.biosino.org/itfp/\u003e\n\n#### Description\n\nPredicted human transcription factor targets.\n\n```{r}\n# Gene symbols used on the ITFP website.\nITFP[[\"STAT3\"]]\n [1] \"FIGNL1\"   \"NCOR1\"    \"SUV420H1\"\n```\n\n#### Figures\n\n\u003cimg src=\"https://github.com/slowkow/tftargets/raw/master/figures/ITFP_histogram.png\"\u003e\n\n- - -\n\n### ENCODE\n\n#### Citation\n\n\u003e ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the\n\u003e human genome. Nature 489, 57–74 (2012).\n\u003e [PubMed](http://www.ncbi.nlm.nih.gov/pubmed/22955616)\n\n#### Source\n\n\u003chttp://hgdownload.cse.ucsc.edu/goldenpath/hg19/encodeDCC/wgEncodeRegTfbsClustered/\u003e\n\n#### Description\n\nPutative human transcription factor targets based on [ChIP-seq] data from\nthe Encyclopedia of DNA Elements (ENCODE) Project.\n\n[ChIP-seq]: https://en.wikipedia.org/wiki/ChIP-sequencing\n\n```{r}\n# Entrez Gene IDs.\nhead(ENCODE[[\"STAT3\"]], 100)\n  [1]  23  31  35  40  81  90  93  98 100 104 105 111 114 118 119 135 147 150 159\n [20] 160 161 174 178 210 224 238 257 259 267 272 273 286 287 307 313 320 321 323\n [39] 328 333 351 368 369 378 402 408 412 419 421 432 444 463 467 472 473 482 491\n [58] 495 529 534 550 571 577 581 586 593 596 597 598 602 622 627 631 636 637 640\n [77] 651 658 667 669 687 694 695 714 740 752 753 770 773 779 780 781 783 788 800\n [96] 805 811 814 817 821\n```\n\n#### Figures\n\n\u003cimg src=\"https://github.com/slowkow/tftargets/raw/master/figures/ENCODE_histogram.png\"\u003e\n\n- - -\n\n### Neph2012\n\n#### Citation\n\n\u003e Neph, S., Stergachis, A. B., Reynolds, A., Sandstrom, R., Borenstein, E.\n\u003e \u0026 Stamatoyannopoulos, J. A. Circuitry and dynamics of human transcription\n\u003e factor regulatory networks. Cell 150, 1274–1286 (2012).\n\u003e [PubMed](http://www.ncbi.nlm.nih.gov/pubmed/22959076)\n\n#### Source\n\n\u003chttp://www.regulatorynetworks.org/\u003e\n\n#### Description\n\nTranscription factor targets discovered by DNaseI footprinting and TF\nrecognition sequences. Targets include only transcription factors and not\nother genes.\n\n```{r}\n# Entrez Gene IDs.\nNeph2012[[\"AG10803-DS12374\"]][[\"STAT3\"]]\n [1]    466   1386    467    468  22809  22926  11016   1385   9586   1390  10664\n[12]   1958   1959   1960   1961   2735   2736   2737 148979   2969   8462   9314\n[23]   4149   4150   4609   4800   4801   4802   2494   5076   5080   5453   5454\n[34]   6667   6668   6670   6671   6774   7020   7021   7022  29842   7490   7494\n[45]  51043   7707  10127\n```\n\n#### Raw Data\n\n```bash\nzcat data-raw/Neph2012/human_2013-09-16/AG10803-DS12374/genes.regulate.genes.bz2 | head\nAHR     BHLHE41\nAHR     CNOT3\nAHR     CREB1\nAHR     CREB5\nAHR     CTCF\nAHR     EGR1\nAHR     EGR2\nAHR     EGR3\nAHR     EGR4\nAHR     EPAS1\n```\n\n#### Figures\n\n\u003cimg src=\"https://github.com/slowkow/tftargets/raw/master/figures/Neph2012_1_histogram.png\"\u003e\n\n- - -\n\n### TRRUST\n\n#### Citation\n\n\u003e Han, H., Shim, H., Shin, D., Shim, J. E., Ko, Y., Shin, J., Kim, H., Cho,\n\u003e A., Kim, E., Lee, T., Kim, H., Kim, K., Yang, S., Bae, D., Yun, A., Kim, S.,\n\u003e Kim, C. Y., Cho, H. J., Kang, B., Shin, S. \u0026 Lee, I. TRRUST: a reference\n\u003e database of human transcriptional regulatory interactions. Sci. Rep. 5,\n\u003e 11432 (2015).\n\u003e [PubMed](http://www.ncbi.nlm.nih.gov/pubmed/26066708)\n\n#### Source\n\n\u003chttp://www.grnpedia.org/trrust/\u003e\n\n#### Description\n\n\u003e TRRUST is a manually curated database of human transcriptional regulatory\n\u003e network.\n\u003e \n\u003e Current version of TRRUST contains 8,015 transcriptional regulatory\n\u003e relationships between 748 human transcription factors (TFs) and 1,975 non-TF\n\u003e genes, derived from 6,175 pubmed articles, which describe small-scale\n\u003e experimental studies of transcriptional regulations. To efficiently search\n\u003e for regulatory relationships from over 20 million pubmed articles, we used\n\u003e sentence-based text mining approach.\n\u003e \n\u003e TRRUST database also provide information of mode of regulation (activation\n\u003e or repression). Currently 4,861 (60.6%) regulatory relationships are known\n\u003e for mode of regulation.\n\n```{r}\nhead(TRRUST[[\"STAT3\"]], 100)\n  [1] \"A2M\"      \"AKAP12\"   \"AKT1\"     \"BCL2\"     \"BCL2\"     \"BCL2L1\"   \"BCL2L1\"   \"BCL6\"     \"BIRC5\"    \"BST2\"     \"CCL11\"    \"CCL20\"   \n [13] \"CCND1\"    \"CCND1\"    \"CCND2\"    \"CCND3\"    \"CD46\"     \"CDH1\"     \"CDK4\"     \"CDKN1A\"   \"CDKN1B\"   \"CFB\"      \"CFLAR\"    \"CHI3L1\"  \n [25] \"CISH\"     \"COPS5\"    \"CRP\"      \"CSRP1\"    \"CTGF\"     \"CXCL8\"    \"CYP19A1\"  \"CYR61\"    \"DDIT3\"    \"DNMT1\"    \"EGFR\"     \"ESR2\"    \n [37] \"ETV6\"     \"F2R\"      \"FAAH\"     \"FAS\"      \"FAS\"      \"FGF1\"     \"FGF2\"     \"FGG\"      \"FGL1\"     \"FLT3\"     \"FOS\"      \"GAST\"    \n [49] \"GFAP\"     \"HAMP\"     \"HGF\"      \"HIF1A\"    \"HMOX1\"    \"HP\"       \"HSPA4\"    \"HSPB1\"    \"ICAM1\"    \"IFNAR1\"   \"IFNG\"     \"IKBKE\"   \n [61] \"IL10\"     \"IL11\"     \"IL1RN\"    \"IL2\"      \"IL21\"     \"IL2RA\"    \"IL6\"      \"IL6\"      \"IRF1\"     \"JAK2\"     \"JAK3\"     \"JUNB\"    \n [73] \"KLF11\"    \"KRT17\"    \"LCAT\"     \"LEP\"      \"LGALS3BP\" \"LTBP1\"    \"MCL1\"     \"MCL1\"     \"MDC1\"     \"MICA\"     \"MMP1\"     \"MMP14\"   \n [85] \"MMP2\"     \"MMP2\"     \"MMP3\"     \"MMP7\"     \"MMP7\"     \"MMP9\"     \"MMP9\"     \"MUC1\"     \"MUC4\"     \"MYC\"      \"MYC\"      \"NANOG\"   \n [97] \"NDUFA13\"  \"NME1\"     \"NOSTRIN\"  \"NOX5\"  \n```\n\n#### Raw Data\n\n```bash\nzcat data-raw/TRRUST/trrust_rawdata.txt.gz | head | column -t\nAATF  BAK1    Unknown     22983126\nAATF  BAX     Repression  22909821\nAATF  BBC3    Unknown     22983126\nAATF  CDKN1A  Unknown     17157788\nAATF  MYC     Activation  20549547\nAATF  TP53    Unknown     17157788\nABL1  BAX     Activation  11753601\nABL1  BCL2    Repression  11753601\nABL1  BCL6    Repression  15509806\nABL1  CCND2   Activation  15509806\n```\n\n#### Figures\n\n\u003cimg src=\"https://github.com/slowkow/tftargets/raw/master/figures/TRRUST_histogram.png\"\u003e\n\n- - -\n\n### Marbach2016\n\n#### Citation\n\n\u003e Marbach, D., Lamparter, D., Quon, G., Kellis, M., Kutalik, Z. \u0026 Bergmann, S.\n\u003e Tissue-specific regulatory circuits reveal variable modular perturbations\n\u003e across complex diseases. Nat. Methods 13, 366–370 (2016).\n\u003e [PubMed](http://www.ncbi.nlm.nih.gov/pubmed/26950747)\n\n#### Source\n\n\u003chttp://regulatorycircuits.org\u003e\n\n#### Description\n\n\u003e We developed a comprehensive resource of close to 400 cell type- and\n\u003e tissue-specific gene regulatory networks for human. Our study shows that\n\u003e disease-associated genetic variants often perturb regulatory modules in cell\n\u003e types or tissues that are highly specific to that disease.\n\n```{r}\nhead(Marbach2016[[\"STAT3\"]], 100)\n  [1] \"SURF1\"    \"ZNF230\"   \"EIF5\"     \"ATG4C\"    \"LYSMD4\"   \"ZWILCH\"   \"TFB1M\"    \"SLC12A7\"  \"DNAL1\"    \"PPP1R8\"   \"SEPT9\"    \"SDCCAG8\" \n [13] \"CMTR1\"    \"GSAP\"     \"PPIA\"     \"CLCN6\"    \"ZFP69\"    \"ZFP64\"    \"RNPC3\"    \"BRPF1\"    \"ZKSCAN5\"  \"ZNF410\"   \"ASF1B\"    \"PES1\"    \n [25] \"TMEM41B\"  \"F2RL1\"    \"DARS\"     \"ZNF24\"    \"RPL4\"     \"SYF2\"     \"AGTPBP1\"  \"NANOS1\"   \"ZNF140\"   \"SEC14L1\"  \"CHAC1\"    \"CDC42SE2\"\n [37] \"LIPG\"     \"PROS1\"    \"MIIP\"     \"DENND1A\"  \"ADAMTSL2\" \"TBC1D22B\" \"PHACTR4\"  \"TNFAIP2\"  \"SLC35C1\"  \"ZNF284\"   \"NCCRP1\"   \"ZFYVE16\" \n [49] \"TBL1XR1\"  \"UNC45A\"   \"TIMM50\"   \"PRRT1\"    \"RNF215\"   \"PAF1\"     \"SPINT1\"   \"RABL2B\"   \"DMWD\"     \"RIN3\"     \"PAK2\"     \"NOTCH4\"  \n [61] \"INPP5F\"   \"PSMA8\"    \"MX2\"      \"TBC1D7\"   \"CCDC135\"  \"ATP2B4\"   \"HLA-DQA2\" \"IPO8\"     \"EID2B\"    \"OGDH\"     \"ZFYVE21\"  \"DDB1\"    \n [73] \"SEC31A\"   \"SURF6\"    \"EXD2\"     \"KIF3A\"    \"RPUSD3\"   \"SYMPK\"    \"ASB13\"    \"CASC5\"    \"RLF\"      \"LIN54\"    \"TNXB\"     \"TRABD\"   \n [85] \"PHTF2\"    \"COPS4\"    \"FAM32A\"   \"PDLIM4\"   \"CPSF7\"    \"ZNF720\"   \"RBFOX2\"   \"COA4\"     \"ATP10A\"   \"MTMR1\"    \"TNRC6C\"   \"TMED4\"   \n [97] \"BUD31\"    \"GADD45B\"  \"MTMR3\"    \"CDC42EP4\"\n```\n\n#### Raw Data\n\nColumns:\n\n1. Transcription factor.\n2. Target gene.\n3. Edge weight.\n\n```bash\nzcat data-raw/regulatorycircuits/FANTOM5_individual_networks/394_individual_networks/synoviocyte.txt.gz | head\nRAX    PPP2R2A  1.79016453E-3\nMYCN   RHOA     1.81311653E-2\nTFAP2  RRM1     7.13096624E-3\nPRDM4  KPNA2    1.61069158E-2\nFOXB1  SCARF2   1.78696733E-3\nATF4   NDUFA11  1.53625527E-3\nSPIC   C9orf69  8.60099271E-4\nFLI1   CENPU    6.72504942E-3\nHNF4A  LHFPL2   1.47391413E-2\nSTAT3  SURF1    3.14614561E-3\n```\n\n#### Figures\n\n\u003cimg src=\"https://github.com/slowkow/tftargets/raw/master/figures/Marbach2016_histogram.png\"\u003e\n\n\u003cimg src=\"https://github.com/slowkow/tftargets/raw/master/figures/Marbach2016_weights_histogram.png\"\u003e\n\n\u003cimg src=\"https://github.com/slowkow/tftargets/raw/master/figures/Marbach2016_targets_vs_weight.png\"\u003e\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fslowkow%2Ftftargets","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fslowkow%2Ftftargets","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fslowkow%2Ftftargets/lists"}