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DRPPM

DNA RNA Proteome Post-translational Modifications

DRPPM is a library collection of scripts for analyzing DNA/RNA/Proteome/Post-translational Modifications. The program is capable of performing data wrangling of genomics, transcriptomics, and proteomics data. While the program collection includes some personalized pipelines, it also contains standardized RNAseq analysis pipelines like WRAP for splicing analysis. In addition to basic data processing, the DRPPM also contains the ability to generate publication-ready figures and interactive plots of gene/protein networks. See https://github.com/gatechatl/DRPPM_Example_Input_Output for the examples.

Details are documented in the Wiki page

  • Access additional details about the program wiki

Git Checkout

git clone git@github.com:gatechatl/DRPPM.git # Clone the repo

Prerequisites

Required R script libraries (possibly an incomplete list)

Packages version
limma
edgeR
DESeq2
dplyr
magrittr
readr
Rtsne
ggpubr
sva
ggplot2
pheatmap
pvclust
Peptides
lavaan
gridExtra
BioNet
VennDiagram
RUVSeq
RColorBrewer
reshape2
Seurat
Matrix
monocle
rsnps
RPMM

Current downloadable release

Usage


DRPPM is a toolbox for processing integrated datasets.

optional arguments:
  -h, --help   show this help message and exit

### Search for a program
type drppm -Find [keyword]

### Inputs and Outputs
The inputs for DRPPM are defined. To see the list of inputs, type drppm -[program]

### Outputs
The outputs for DRPPM are also defined in the parameter. To see the list of outputs in drppm -[program]

Reference

Timothy I. Shaw. DRPPM: an integrated software for processing omics data. 2020.

Example Publications

KH Lee, P Zhang, HJ Kim et al. C9orf72 Dipeptide Repeats Impair the Assembly, Dynamics, and Function of Membrane-Less Organelles. Cell 167 (3), 774-788. e17

KM Joo JH, Wang B et al. The Noncanonical Role of ULK/ATG1 in ER-to-Golgi Trafficking Is Essential for Cellular Homeostasis. Mol Cell 62 (4), 491-506.

Tan H, Yang K, Li Y, Shaw T et al. Integrative Proteomics and Phosphoproteomics Profiling Reveals Dynamic Signaling Networks and Bioenergetics Pathways Underlying T Cell Activation. Immunity. 2017 Mar 21;46(3):488-503. doi: 10.1016/j.immuni.2017.02.010.

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Integrated software for processing omics data.

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