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https://github.com/slowkow/tftargets
:dart: Human transcription factor target genes from 6 databases in convenient R format.
https://github.com/slowkow/tftargets
bioinformatics data rstats transcription-factors
Last synced: 2 months ago
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:dart: Human transcription factor target genes from 6 databases in convenient R format.
- Host: GitHub
- URL: https://github.com/slowkow/tftargets
- Owner: slowkow
- Created: 2015-03-02T16:55:17.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2018-10-04T19:55:28.000Z (about 6 years ago)
- Last Synced: 2024-05-09T10:27:07.190Z (8 months ago)
- Topics: bioinformatics, data, rstats, transcription-factors
- Language: R
- Homepage:
- Size: 25.4 MB
- Stars: 84
- Watchers: 10
- Forks: 19
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
Awesome Lists containing this project
README
# tftargets
[Transcription factors (TFs)][TF] activate and repress target genes. This R package
provides easy access to query a particular TF and find its targets in humans.
The data has been collected from multiple different databases.
[TF]: https://www.khanacademy.org/science/biology/gene-regulation/gene-regulation-in-eukaryotes/a/eukaryotic-transcription-factors
Credit: © KENNETH EWARD/BIOGRAFX/PHOTO RESEARCHERS, INC
## Citation
For now, please provide a link to this github repository:
## Usage
You may install this package with [devtools]:
[devtools]: https://github.com/hadley/devtools
```{r}
devtools::install_github("slowkow/tftargets")
library(tftargets)
length(TRED)
# [1] 133
```
Alternatively, you can download just the RData file:
```{r}
# Download the file:
# install.packages("RCurl")
library(RCurl)
download.file(
url = "https://raw.githubusercontent.com/slowkow/tftargets/master/data/tftargets.rda",
destfile = "tftargets.rda",
method = "curl"
)
# Load the file:
load("tftargets.rda")
# View the variables stored in the file:
ls()
[1] "ENCODE" "ITFP" "Marbach2016"
[4] "Neph2012" "TRED" "TRRUST"
```
## Data
This package contains the following datasets:
| Dataset | Structure | Gene Identifier | # TF's | # TF-gene assocations | Reference |
|-------------|--------------|-------------------|--------|-----------------------|-----------------------------------|
| TRED | list | ENTREZ | 133 | 7,066 | [TRED][tred] (2007) |
| ITFP | list | HGNC Symbol/Alias | 1974 | 67,154 | [ITFP][itfp] (2008) |
| ENCODE | list | ENTREZ | 157 | 20,428 | [ENCODE][encode] (2012) |
| Neph2012 | nested list* | HGNC Symbol/Alias | 536 | 16,484 | [Neph2012][neph2012] (2012) |
| TRRUST | list | HGNC Symbol/Alias | 748 | 8,215 | [TRRUST][trrust] (2015) |
| Marbach2016 | list | HGNC Symbol/Alias | 643 | 1,305,782 | [Marbach2016][marbach2016] (2016) |
[tred]: https://github.com/slowkow/tftargets#tred
[itfp]: https://github.com/slowkow/tftargets#itfp
[encode]: https://github.com/slowkow/tftargets#encode
[neph2012]: https://github.com/slowkow/tftargets#neph2012
[trrust]: https://github.com/slowkow/tftargets#trrust
[marbach2016]: https://github.com/slowkow/tftargets#marbach2016
\* Note: The `Neph2012` is organized as a nested list where the top-level keys
refer to tissue types (e.g. "fBrain-DS11872").
See [`data-raw/make_rdata.R`][make_rdata] for the script that converts the raw
data into lists of gene sets.
[make_rdata]: https://github.com/slowkow/tftargets/blob/master/data-raw/make_rdata.R
- - -
### TRED
#### Citation
> Jiang, C., Xuan, Z., Zhao, F. & Zhang, M. Q. TRED: a transcriptional
> regulatory element database, new entries and other development. Nucleic
> Acids Res. 35, D137–40 (2007).
> [PubMed](http://www.ncbi.nlm.nih.gov/pubmed/17202159)
#### Source
#### Description
Predicted and known human transcription factor targets.
Here we find that TRED claims 59 genes are targeted by [STAT3].
[STAT3]: http://www.genecards.org/cgi-bin/carddisp.pl?gene=STAT3
```{r}
# Entrez Gene IDs.
TRED[["STAT3"]]
[1] 2 332 355 595 596 598 896 943 958 1026 1051
[12] 1401 1588 1962 2194 2209 2353 3082 3162 3320 3326 3479
[23] 3559 3572 3586 3659 3718 3725 3929 4170 4582 4585 4609
[34] 4843 5008 5021 5292 5551 5967 6095 6347 6654 7076 7078
[45] 7097 7124 7200 7422 7432 8651 8996 9021 11336 23514 26229
[56] 27151 55893 117153 201254
```
#### Figures
- - -
### ITFP
#### Citation
> Zheng, G., Tu, K., Yang, Q., Xiong, Y., Wei, C., Xie, L., Zhu, Y. & Li, Y.
> ITFP: an integrated platform of mammalian transcription factors.
> Bioinformatics 24, 2416–2417 (2008).
> [PubMed](http://www.ncbi.nlm.nih.gov/pubmed/18713790)
#### Source
#### Description
Predicted human transcription factor targets.
```{r}
# Gene symbols used on the ITFP website.
ITFP[["STAT3"]]
[1] "FIGNL1" "NCOR1" "SUV420H1"
```
#### Figures
- - -
### ENCODE
#### Citation
> ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the
> human genome. Nature 489, 57–74 (2012).
> [PubMed](http://www.ncbi.nlm.nih.gov/pubmed/22955616)
#### Source
#### Description
Putative human transcription factor targets based on [ChIP-seq] data from
the Encyclopedia of DNA Elements (ENCODE) Project.
[ChIP-seq]: https://en.wikipedia.org/wiki/ChIP-sequencing
```{r}
# Entrez Gene IDs.
head(ENCODE[["STAT3"]], 100)
[1] 23 31 35 40 81 90 93 98 100 104 105 111 114 118 119 135 147 150 159
[20] 160 161 174 178 210 224 238 257 259 267 272 273 286 287 307 313 320 321 323
[39] 328 333 351 368 369 378 402 408 412 419 421 432 444 463 467 472 473 482 491
[58] 495 529 534 550 571 577 581 586 593 596 597 598 602 622 627 631 636 637 640
[77] 651 658 667 669 687 694 695 714 740 752 753 770 773 779 780 781 783 788 800
[96] 805 811 814 817 821
```
#### Figures
- - -
### Neph2012
#### Citation
> Neph, S., Stergachis, A. B., Reynolds, A., Sandstrom, R., Borenstein, E.
> & Stamatoyannopoulos, J. A. Circuitry and dynamics of human transcription
> factor regulatory networks. Cell 150, 1274–1286 (2012).
> [PubMed](http://www.ncbi.nlm.nih.gov/pubmed/22959076)
#### Source
#### Description
Transcription factor targets discovered by DNaseI footprinting and TF
recognition sequences. Targets include only transcription factors and not
other genes.
```{r}
# Entrez Gene IDs.
Neph2012[["AG10803-DS12374"]][["STAT3"]]
[1] 466 1386 467 468 22809 22926 11016 1385 9586 1390 10664
[12] 1958 1959 1960 1961 2735 2736 2737 148979 2969 8462 9314
[23] 4149 4150 4609 4800 4801 4802 2494 5076 5080 5453 5454
[34] 6667 6668 6670 6671 6774 7020 7021 7022 29842 7490 7494
[45] 51043 7707 10127
```
#### Raw Data
```bash
zcat data-raw/Neph2012/human_2013-09-16/AG10803-DS12374/genes.regulate.genes.bz2 | head
AHR BHLHE41
AHR CNOT3
AHR CREB1
AHR CREB5
AHR CTCF
AHR EGR1
AHR EGR2
AHR EGR3
AHR EGR4
AHR EPAS1
```
#### Figures
- - -
### TRRUST
#### Citation
> Han, H., Shim, H., Shin, D., Shim, J. E., Ko, Y., Shin, J., Kim, H., Cho,
> A., Kim, E., Lee, T., Kim, H., Kim, K., Yang, S., Bae, D., Yun, A., Kim, S.,
> Kim, C. Y., Cho, H. J., Kang, B., Shin, S. & Lee, I. TRRUST: a reference
> database of human transcriptional regulatory interactions. Sci. Rep. 5,
> 11432 (2015).
> [PubMed](http://www.ncbi.nlm.nih.gov/pubmed/26066708)
#### Source
#### Description
> TRRUST is a manually curated database of human transcriptional regulatory
> network.
>
> Current version of TRRUST contains 8,015 transcriptional regulatory
> relationships between 748 human transcription factors (TFs) and 1,975 non-TF
> genes, derived from 6,175 pubmed articles, which describe small-scale
> experimental studies of transcriptional regulations. To efficiently search
> for regulatory relationships from over 20 million pubmed articles, we used
> sentence-based text mining approach.
>
> TRRUST database also provide information of mode of regulation (activation
> or repression). Currently 4,861 (60.6%) regulatory relationships are known
> for mode of regulation.
```{r}
head(TRRUST[["STAT3"]], 100)
[1] "A2M" "AKAP12" "AKT1" "BCL2" "BCL2" "BCL2L1" "BCL2L1" "BCL6" "BIRC5" "BST2" "CCL11" "CCL20"
[13] "CCND1" "CCND1" "CCND2" "CCND3" "CD46" "CDH1" "CDK4" "CDKN1A" "CDKN1B" "CFB" "CFLAR" "CHI3L1"
[25] "CISH" "COPS5" "CRP" "CSRP1" "CTGF" "CXCL8" "CYP19A1" "CYR61" "DDIT3" "DNMT1" "EGFR" "ESR2"
[37] "ETV6" "F2R" "FAAH" "FAS" "FAS" "FGF1" "FGF2" "FGG" "FGL1" "FLT3" "FOS" "GAST"
[49] "GFAP" "HAMP" "HGF" "HIF1A" "HMOX1" "HP" "HSPA4" "HSPB1" "ICAM1" "IFNAR1" "IFNG" "IKBKE"
[61] "IL10" "IL11" "IL1RN" "IL2" "IL21" "IL2RA" "IL6" "IL6" "IRF1" "JAK2" "JAK3" "JUNB"
[73] "KLF11" "KRT17" "LCAT" "LEP" "LGALS3BP" "LTBP1" "MCL1" "MCL1" "MDC1" "MICA" "MMP1" "MMP14"
[85] "MMP2" "MMP2" "MMP3" "MMP7" "MMP7" "MMP9" "MMP9" "MUC1" "MUC4" "MYC" "MYC" "NANOG"
[97] "NDUFA13" "NME1" "NOSTRIN" "NOX5"
```
#### Raw Data
```bash
zcat data-raw/TRRUST/trrust_rawdata.txt.gz | head | column -t
AATF BAK1 Unknown 22983126
AATF BAX Repression 22909821
AATF BBC3 Unknown 22983126
AATF CDKN1A Unknown 17157788
AATF MYC Activation 20549547
AATF TP53 Unknown 17157788
ABL1 BAX Activation 11753601
ABL1 BCL2 Repression 11753601
ABL1 BCL6 Repression 15509806
ABL1 CCND2 Activation 15509806
```
#### Figures
- - -
### Marbach2016
#### Citation
> Marbach, D., Lamparter, D., Quon, G., Kellis, M., Kutalik, Z. & Bergmann, S.
> Tissue-specific regulatory circuits reveal variable modular perturbations
> across complex diseases. Nat. Methods 13, 366–370 (2016).
> [PubMed](http://www.ncbi.nlm.nih.gov/pubmed/26950747)
#### Source
#### Description
> We developed a comprehensive resource of close to 400 cell type- and
> tissue-specific gene regulatory networks for human. Our study shows that
> disease-associated genetic variants often perturb regulatory modules in cell
> types or tissues that are highly specific to that disease.
```{r}
head(Marbach2016[["STAT3"]], 100)
[1] "SURF1" "ZNF230" "EIF5" "ATG4C" "LYSMD4" "ZWILCH" "TFB1M" "SLC12A7" "DNAL1" "PPP1R8" "SEPT9" "SDCCAG8"
[13] "CMTR1" "GSAP" "PPIA" "CLCN6" "ZFP69" "ZFP64" "RNPC3" "BRPF1" "ZKSCAN5" "ZNF410" "ASF1B" "PES1"
[25] "TMEM41B" "F2RL1" "DARS" "ZNF24" "RPL4" "SYF2" "AGTPBP1" "NANOS1" "ZNF140" "SEC14L1" "CHAC1" "CDC42SE2"
[37] "LIPG" "PROS1" "MIIP" "DENND1A" "ADAMTSL2" "TBC1D22B" "PHACTR4" "TNFAIP2" "SLC35C1" "ZNF284" "NCCRP1" "ZFYVE16"
[49] "TBL1XR1" "UNC45A" "TIMM50" "PRRT1" "RNF215" "PAF1" "SPINT1" "RABL2B" "DMWD" "RIN3" "PAK2" "NOTCH4"
[61] "INPP5F" "PSMA8" "MX2" "TBC1D7" "CCDC135" "ATP2B4" "HLA-DQA2" "IPO8" "EID2B" "OGDH" "ZFYVE21" "DDB1"
[73] "SEC31A" "SURF6" "EXD2" "KIF3A" "RPUSD3" "SYMPK" "ASB13" "CASC5" "RLF" "LIN54" "TNXB" "TRABD"
[85] "PHTF2" "COPS4" "FAM32A" "PDLIM4" "CPSF7" "ZNF720" "RBFOX2" "COA4" "ATP10A" "MTMR1" "TNRC6C" "TMED4"
[97] "BUD31" "GADD45B" "MTMR3" "CDC42EP4"
```
#### Raw Data
Columns:
1. Transcription factor.
2. Target gene.
3. Edge weight.
```bash
zcat data-raw/regulatorycircuits/FANTOM5_individual_networks/394_individual_networks/synoviocyte.txt.gz | head
RAX PPP2R2A 1.79016453E-3
MYCN RHOA 1.81311653E-2
TFAP2 RRM1 7.13096624E-3
PRDM4 KPNA2 1.61069158E-2
FOXB1 SCARF2 1.78696733E-3
ATF4 NDUFA11 1.53625527E-3
SPIC C9orf69 8.60099271E-4
FLI1 CENPU 6.72504942E-3
HNF4A LHFPL2 1.47391413E-2
STAT3 SURF1 3.14614561E-3
```
#### Figures
