TFregulomeR

v2.3.3

changed to Canada server as default (Singapore server retired) compared to v2.0.1 (updated on April 17, 2022)

New features: 1. Link to data compendium hosted in Canada; 2. Ability to download a local copy of the data compendium; 3. Link to TF motifs and DNA methylation in human and mouse; 4. Perform TF interactome analysis coupled with DNA methylation and other chromatin signals such as chromatin accessibility.

Introduction

TFregulomeR comprises of a comprehensive compendium of transcription factor binding sites (TFBSs) derived from the MethMotif and GTRD, as well as the ready-to-use functionality in R language facilitating data access, integration and analysis. The binding motifs predicted in-silico from MethMotif and GTRD describe cell specific transcription factor (TF) binding propensities, while the DNA methylation profiles from MethMotif portray a second epigenetic dimension in TF binding events. The whole toolbox allows a better understanding of the TF binding propensities in a cell-specific manner.

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Release notes

This repository is TFregulomeR stable release

Current TFregulomeR stable version: 2.3.0 (Updated on 16 April 2024).

For development release, please visit TFregulomeR-dev

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Documentation

You can check detailed package instructions in Vignettes

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Current Functionalities v2.3.3

Click here for functionality update notes

Currently, TFregulomeR links to data compendium hosted in Canada (default). Singapore server is no longer accessible. for Canada server, please use server='ca'. For example, when browsing TFregulomeR data compendium hosted in Canada, using dataBrowser(server='ca').

Note: new function is highlighted in bold font.

1) Browse the TFregulomeR data compendium (dataBrowser()) 2) Load TF peaks (loadPeaks()) 3) Search motif matrix and DNA methylation score matrix (searchMotif()) 4) Plot motif or MethMotif logo (plotLogo) 5) Export motif matrix and DNA methylation score matrix (exportMMPFM) 6) Get context-independent peaks along with DNA methylation profiles (commonPeaks() & commonPeakResult()) 7) Get context-dependent peaks along with DNA methylation profiles (exclusivePeaks() & exclusivePeakResult()) 8) Form a intersected matrix between two lists of peak sets along with DNA methylation profiles, read enrichments and users' input external signals, for interactome and co-binding partner studies (intersectPeakMatrix() & intersectPeakMatrixResult()). - NEW Feature 9) Automatically generate a PDF report for TF co-factors along with motif sequences, DNA methylation (within motif and in 200bp regions) and read enrichments (cofactorReport()). 10) Automatically produce a dynamic three-dimensional interface showing TF interactome coupled with DNA methylation and/or users’ input external signal values (interactome3D()). - NEW Function 11) Plot the TFBS distribution in a given list of peak sets (motifDistrib() & plotDistrib()). 12) Annotate peak genomic locations (genomeAnnotate()). 13) Annotate ontologies of target genes by a peak set (greatAnnotate()). 14) Convert a motif matrix to a PFMatrix class object for TFBSTools package (toTFBSTools()).

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Current TFBSs in TFregulomeR compendium

### Click here for TFregulomeR compendium update notes

TFregulomeR data compendium version: 2.3.3

| Item | Count | | :---------:|:------:| | PWM | 2333 | | Unique TF | 676 | | PWM with DNA methylation records | 679 | | Species | human (hg38) and mouse (mm10) | | Organ | brain, stemcell, bloodandlymph, connectivetissue, liver, colorectum, muscle, bone, stomach, prostate, pancreas, skin, eye, breast, intestine, kidney, lung, esophagus, heart, testis, uterus, spleen, limb, body, cervix, placenta, undefined, adrenalgland, neckandmouth, head, ovary, pleura, thymus, fallopian, vagina | | Sample type | primarycells, cellline, tissue | Cell or tissue | 721 | | Disease state | normal, tumor, SimpsonGolabiBehmelsyndrome, progeria, metaplasia, unknown, immortalized, premetastatic| | Source | GTRD, MethMotif |

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Citation

Quy Xiao Xuan Lin, Denis Thieffry, Sudhakar Jha, Touati Benoukraf. (2019) TFregulomeR reveals transcription factors’ context-specific features and functions. Nucleic Acids Res., 10.1093/nar/gkz1088. [Manuscript]

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Methods paper

Matthew Dyer, Denis Thieffry, Touati Benoukraf. (2024) Leveraging the MethMotif toolkit to characterize context-specific features and roles of methylation sensitive transcription factors. in preparation

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Case studies

The scripts of case studies used in our manuscript are available as below.

1. Case study of CEBPB
2. Case study of MAFF
3. Case study of ATF3

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Installation

Prerequisite pakcages

1) Required packages: the packages below are the basic prerequisite packages for TFregulomeR functionalities

- [jsonlite](https://cran.r-project.org/web/packages/jsonlite/index.html) (>= 1.5)
- [RSQLite](https://cran.r-project.org/web/packages/RSQLite/index.html) (>= 2.3.1)
- [ggplot2](https://cran.r-project.org/web/packages/ggplot2/index.html) (>= 3.0.0)
- [ggseqlogo](https://cran.r-project.org/web/packages/ggseqlogo/index.html) (>= 0.1)
- [gridExtra](https://cran.r-project.org/web/packages/gridExtra/index.html) (>= 2.3)
- [GenomicRanges](https://bioconductor.org/packages/release/bioc/html/GenomicRanges.html) (>= 1.32.7)
- [curl](https://cran.r-project.org/web/packages/curl/index.html) (>= 3.2)
- [gplots](https://cran.r-project.org/web/packages/gplots/index.html) (>= 3.0.1.1)

2) Optional packages: the packages below are optional since they are required only in some functions or some options in a function

- [rGREAT](https://bioconductor.org/packages/release/bioc/html/rGREAT.html) (>= 1.16.1): only requried in `greatAnnotate()`
- [plotly](https://cran.r-project.org/web/packages/plotly/index.html) (>= 4.10.4): only required when users opt to export an intuitive HTML report in `greatAnnotate()`
- [crosstalk](https://cran.r-project.org/web/packages/crosstalk/index.html) (>= 1.2.1): only required when users opt to export an intuitive HTML report in `greatAnnotate()`
- [DT](https://cran.r-project.org/web/packages/DT/index.html) (>= 0.33): only required when users opt to export an intuitive HTML report in `greatAnnotate()`
- [TxDb.Hsapiens.UCSC.hg38.knownGene](https://bioconductor.org/packages/release/data/annotation/html/TxDb.Hsapiens.UCSC.hg38.knownGene.html) (>= 3.4.0): only required when users opt to annotate hg38 peak locations in `genomeAnnotate()`
- [TxDb.Hsapiens.UCSC.hg19.knownGene](https://bioconductor.org/packages/release/data/annotation/html/TxDb.Hsapiens.UCSC.hg19.knownGene.html) (>= 3.2.2): only required when users opt to annotate hg19 peak locations in `genomeAnnotate()`
- [TxDb.Mmusculus.UCSC.mm10.knownGene](https://bioconductor.org/packages/release/data/annotation/html/TxDb.Mmusculus.UCSC.mm10.knownGene.html) (>= 3.4.4): only required when users opt to annotate mm10 peak locations in `genomeAnnotate()`
- [TxDb.Mmusculus.UCSC.mm9.knownGene](http://bioconductor.org/packages/release/data/annotation/html/TxDb.Mmusculus.UCSC.mm9.knownGene.html) (>= 3.2.2): only required when users opt to annotate mm9 peak locations in `genomeAnnotate()`
- [TFBSTools](http://bioconductor.org/packages/release/bioc/html/TFBSTools.html) (>= 1.20.0): only required in `toTFBSTools()`

Install

In R console,

```r

if you have not installed "devtools" package

install.packages("devtools") devtools::install_github("benoukraflab/TFregulomeR") `` The step above will automatically install the required packages. However, you still need to install optional packages if you opt to use the functions such asgreatAnnotate(),genomeAnnotate()andtoTFBSTools()`.

If you would like to query a local version of the TFregulomeR database to facilitate faster execution speeds download and unzip the file found here.

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License

GNU General Public License v3.0