docs: added dReg documentation

docs/conda/dREG.yml

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+name: dREG
+channels:
+  - conda-forge
+  - bioconda
+  - anaconda
+  - defaults
+dependencies:
+  - python ==3.8
+  - conda-forge::r-base==3.6.3
+  - bioconda::bedops
+  - bioconda::ucsc-bedgraphtobigwig
+  - anaconda::pcre
+  - conda-forge::boost
+prefix: /projects/dan1/data/Brickman/conda/envs/dReg

docs/dangpu/dReg.md

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+# 1. Running
+
+## Make dReg bigwigs
+
+If your nascent RNA-seq data is already aligned, bw suitable for use with dReg can be prepared using Danko-Lab [RunOnBamToBigWig](https://github.com/Danko-Lab/RunOnBamToBigWig)
+
+If you have fastq files from PRO-seq, GRO-seq, or CHrO-seq, run the Danko-Lab's [mapping pipeline](https://github.com/Danko-Lab/proseq2.0) using the shared `dReg_dataprep` conda environment
+
+**Example SBATCH script for mapping pipeline**
+
+```bash
+#!/bin/bash
+
+#SBATCH --job-name=pro_align
+#SBATCH -c 20
+#SBATCH --mem=30gb
+#SBATCH --time=00-24:00:00
+#SBATCH --output=01_proseq_alignment.out
+#SBATCH --mail-type=BEGIN,END
+#SBATCH --mail-user=YOUR-EMAIL
+
+module load miniconda/latest
+source activate dReg_dataprep
+
+PROSEQ=("/maps/projects/dan1/data/Brickman/proseq2.0/proseq2.0.bsh")
+GENO=("/scratch/Brickman/references/mus_musculus/ensembl/GRCm38_102/")
+RESL=("/maps/projects/dan1/data/Brickman/projects/NAME_DATE/data/external/proseq/")
+SAMPLES=("SRX14164616_SRR18010280 SRX14164617_SRR18010278")
+
+for sample in ${SAMPLES}; do
+    bash ${PROSEQ} -i ${GENO}bwa \
+    -c ${GENO}GRCm38.102.genome \
+    -PE --RNA5=R2_5prime --UMI1=6 \
+    -O ${RESL} \
+    -I ${sample} \
+    --thread=20
+done
+```
+
+## GPU check
+
+Check available GPUs and running processes before using dReg. GPU 0 is reserved for Brickman group
+
+```bash
+nvidia-smi
+```
+
+## Example dReg script
+
+```bash
+#!/bin/bash
+
+#SBATCH --job-name=dREG
+#SBATCH -c 30
+#SBATCH --mem=30gb
+#SBATCH --time=00-24:00:00
+#SBATCH --output=01-1_dREG.out
+#SBATCH --mail-type=BEGIN,END
+#SBATCH --mail-user=YOUR-EMAIL
+
+module load miniconda/latest cuda/11.8-dangpu cudnn/8.6.0-dangpu
+source activate dReg
+
+BW=("../data/assays/RNA_INITIAL_DATE/processed/bw/")
+RESL=("../results/01/dREG/")
+dREG=("/projects/dan1/data/Brickman/dREG/run_dREG.bsh")
+MODEL=("/projects/dan1/data/Brickman/dREG/resources/asvm.gdm.6.6M.20170828.rdata")
+
+
+SAMPLES=("0h_A 0h_B 2h_A 2h_B")
+
+for sample in ${SAMPLES}; do
+    bash ${dREG} ${BW}${sample}_sorted_filt_dedup_plus.bw ${BW}${sample}_sorted_filt_dedup_minus.bw \
+    ${RESL}${sample}_test ${MODEL} \
+    30 0
+done
+```
+
+# 2. Installation
+
+## Installing dReg
+
+**Note:** Python version in conda env must be 3.8, and R version < 4.0
+
+```bash
+cd /maps/projects/dan1/data/Brickman/conda/
+module load miniconda/latest
+mamba env create -p /projects/dan1/data/Brickman/conda/envs/dReg -f dREG.yml
+source activate dReg
+
+cd /maps/projects/dan1/data/Brickman/
+git clone https://github.com/Danko-Lab/dREG
+cd dREG
+make R_dependencies
+
+R
+devtools::install_github("CshlSiepelLab/RPHAST")
+devtools::install_version("MASS", version="7.3-51.5", repos="https://mirrors.dotsrc.org/cran/")
+install.packages("e1071", repos="https://mirrors.dotsrc.org/cran/")
+devtools::install_version("randomForest", version="4.6-14", repos="https://mirrors.dotsrc.org/cran/")
+quit()
+
+make dreg
+mkdir resources
+cd resources
+wget ftp://cbsuftp.tc.cornell.edu/danko/hub/dreg.models/asvm.gdm.6.6M.20170828.rdata
+```
+
+## Installing Rgtsvm
+
+Rgtsvm is required for dReg to use GPU resources
+
+```bash
+# make sure in dREG repo and that dReg environment is activated
+cd /maps/projects/dan1/data/Brickman/dREG
+source activate dReg
+
+R
+install.packages(c("bit64", "snow", "SparseM"), repos="https://mirrors.dotsrc.org/cran/")
+devtools::install_version("lattice", version="0.20-41", repos="https://mirrors.dotsrc.org/cran/")
+install.packages("Matrix", repos="https://mirrors.dotsrc.org/cran/")
+quit()
+mamba install -c conda-forge boost=1.70.0
+
+mkdir third-party
+cd third-party
+git clone https://github.com/Danko-Lab/Rgtsvm.git
+cd Rgtsvm
+
+module load cuda/11.8-dangpu
+module load cudnn/8.6.0-dangpu
+
+R CMD INSTALL --configure-args="--with-boost-home=$CONDA_PREFIX" Rgtsvm
+```