dREG Gateway

Find the location of transcriptional regulatory elements and transcription factoring binding using genomic data.

The gateway status and updates are here!


dREG Service

The dREG model in the gateway predicts the location of enhancers and promoters using PRO-seq, GRO-seq, or ChRO-seq data. The server takes as input bigWig files provided by the user, which represent PRO-seq signal on the plus and minus strand. The gateway uses a pre-trained dREG model to identify divergent transcript start sites and impute the predicted DNase-I hypersensitivity signal across the genome. The current dREG model works in any mammalian organism.

Registered users need only upload experimental data in the required format and push the start button. Once the job is finished, the user will be notified by e-mail. Results can be downloaded to the user’s local machine, or viewed in the Genome Browser via the handy trackhub link.

Use the Danko lab's mapping pipeline (here) to prepare bigWig files from fastq files or convert BAM files of mapped reads to bigWig (here).

See our documentation, FAQ, GitHub, dREG paper, or dREG protocol for additional questions.

dREG model

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dTOX Service

The dTOX models in the gateway predict the binding status of transcription factor binding sites using PRO-seq, ATAC-seq, or DNase-I-seq data. The server takes as input bigWig files provided by the user, which represent the PRO-seq, ATAC-seq, or DNase-1-seq signal on the plus and minus strand. The gateway uses two pre-trained dTOX models to identify transcription factor binding patterns genome-wide. The current dTOX models work in any mammalian organism and on any motif that has an associated position-weight matrix. To run the dTOX models on genomes other than hg19 and mm10, download the R package (here).

The web operations are same as the dREG model. Users need to login -> upload data -> run data. Results can be downloaded or viewed in the WashU Genome browser.

Use the Danko lab's pipeline to convert BAM files of mapped reads to bigWig (here for PRO-seq), (here for DNase-I-seq), and (here for ATAC-seq).

See our documentation, FAQ for additional questions.

dTOX model

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tfTarget Service

Transcription factors (TFs) regulate complex programs of gene transcription by binding to short DNA sequence motifs within transcription regulatory elements (TRE). tfTarge is a unified framework that identifies the "TF -> TRE -> target gene" networks that are differential regulated between two conditions, e.g. experimental vs. control, using PRO-seq/GRO-seq/ChRO-seq data as the input. The online service provies a convenient method for users without assuming knowledge with R environment, users can directly run the bigWig data on the dREG gateway.

Registered users need only upload experimental data in the required format and push the start button. Once the job is finished, the user will be notified by e-mail. Results can be downloaded to the user's local machine.

Use the Danko lab's mapping pipeline (here) to prepare bigWig files from fastq files or convert BAM files of mapped reads to bigWig (here).

See our documentation, FAQ, GitHub, NG's paper, or dREG protocol for additional questions.

tftarget model

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BayesPrism Service

BayesPrism is a fully Bayesian inference of tumor microenvironment composition and gene expression. It consists of the deconvolution module and the embedding learning module. The deconvolution module leverages cell-type-specific expression profiles from scRNA-seq and implements a fully Bayesian inference to jointly estimate the posterior distribution of cell-type composition and cell type-specific gene expression from bulk RNA-seq expression of tumor samples. The embedding learning module uses Expectation-maximization (EM) to approximate the tumor expression using a linear combination of tumor pathways while conditional on the inferred expression and fraction of non-tumor cells estimated by the deconvolution module. Only the deconvolution module has been implemented as an online service due to the limitation of running time on the dREG gateway.

The web operations are the same as the dREG model. Users need to login -> upload data -> run data. Results can be downloaded and further analyzed in R or Python.

See our documentation, FAQ, GitHub, or paper for additional questions.

BayesPrism model

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Gateway Introduction

The dREG gateway is a cloud platform developed by the Danko lab at the Baker Institute, Cornell University and supported by the SciGap (Science Gateway Platform as a Service) and XSEDE (Extreme Science and Engineering Discovery Environment).

Currently, this gateway hosts four bioinformatics services for functional analysis of sequencing data, dREG peak calling, dTOX, tfTarget, and BayesPrism on XSEDE computing nodes. The architecture and details are here.

Publications

dREG model

Chu, T. & Danko, C. G. (2020). Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions. bioRxiv.

dREG model

Wang, Z., Chu, T., Choate, L. A., & Danko, C. G. (2019). Identification of regulatory elements from nascent transcription using dREG. Genome research, 29(2), 293-303.

tfTarget model

Chu, T., Edward, J. R., Gregory, T. B., ... & Danko, C. G.(2018). Chromatin run-on and sequencing maps the transcriptional regulatory landscape of glioblastoma multiforme. Nature genetics, 50, 1553-1564.

dREG model

Danko, C. G., Hyland, S. L., Core, L. J., Martins, A. L., Waters, C. T., Lee, H. W., ... & Siepel, A. (2015). Identification of active transcriptional regulatory elements from GRO-seq data. Nature methods, 12(5), 433-438.