DREM 2.0.4 / mirDREM – Dynamic Regulatory Events Miner

DREM 2.0.4/ mirDREM

:: DESCRIPTION

The DREM (Dynamic Regulatory Events Miner) allows one to model, analyze, and visualize transcriptional gene regulation dynamics. The method of DREM takes as input time series gene expression data and static or dynamic transcription factor-gene interaction data (e.g. ChIP-chip data), and produces as output a dynamic regulatory map. The dynamic regulatory map highlights major bifurcation events in the time series expression data and transcription factors potentially responsible for them.

mirDREM supports the use of microRNAs in DREM

::DEVELOPER

Systems Biology Group – Carnegie Mellon University

:: SCREENSHOTS

:: REQUIREMENTS

Linux / MacOsX / Windows
Java

:: DOWNLOAD

DREM , mirDREM

:: MORE INFORMATION

Citation

DREM 2.0: Improved reconstruction of dynamic regulatory networks from time-series expression data.
Schulz MH, Devanny WE, Gitter A, Zhong S, Ernst J, Bar-Joseph Z.
BMC Syst Biol. 2012 Aug 16;6(1):104.

MH Schulz, KV Pandit, CLL Cardenas, A Namasivayam, N Kaminsky and Z. Bar-Joseph.
Reconstructing dynamic miRNA regulated interaction networks
PNAS, August 28, 2013, doi: 10.1073/pnas.1303236110

ARGminer – Antibiotic Resistance Gene Miner database

ARGminer

:: DESCRIPTION

ARGminer is an online resource for the inspection and curation of ARGs based on crowdsourcing as well as a platform to promote interaction and collaboration for the ARG scientific community.

::DEVELOPER

Professor Zhang Liqing’s Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Arango-Argoty GA, Guron GKP, Garner E, Riquelme MV, Heath LS, Pruden A, Vikesland PJ, Zhang L.
ARGminer: a web platform for the crowdsourcing-based curation of antibiotic resistance genes.
Bioinformatics. 2020 May 1;36(9):2966-2973. doi: 10.1093/bioinformatics/btaa095. PMID: 32058567.

SDREM 1.2 – Signaling and Dynamic Regulatory Events Miner

SDREM 1.2

:: DESCRIPTION

SDREM is a model which integrates static and time series data to link proteins and the pathways they regulate in these networks. SDREM uses prior information about proteins’ likelihood of involvement in a disease (e.g. from screens) to improve the quality of the predicted signaling pathways.

::DEVELOPER

Systems Biology Group – Carnegie Mellon University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / MacOsX / Windows
Java

:: DOWNLOAD

SDREM

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Jul 1;29(13):i227-36. doi: 10.1093/bioinformatics/btt241.
Identifying proteins controlling key disease signaling pathways.
Gitter A1, Bar-Joseph Z.

STEM 1.3.8 – Short Time-series Expression Miner

STEM 1.3.8

:: DESCRIPTION

STEM is a Java program for clustering, comparing, and visualizing short time series gene expression data (eight time points or less). STEM allows researchers to identify significant temporal expression profiles and the genes associated with these profiles and to compare the behavior of these genes across multiple conditions. STEM is fully integrated with the Gene Ontology (GO) database and supports GO category gene enrichment analyses for sets of genes having the same temporal expression pattern. STEM also supports the ability to easily determine and visualize the behavior of genes belonging to a given GO category, identifying which temporal expression profiles were enriched for these genes.

::DEVELOPER

Systems Biology Group – Carnegie Mellon University

:: SCREENSHOTS

:: REQUIREMENTS

Linux / MacOsX / Windows
Java

:: DOWNLOAD

STEM

:: MORE INFORMATION

Citation

J. Ernst, Z. Bar-Joseph.
STEM: a tool for the analysis of short time series gene expression data.
BMC Bioinformatics, 7:191, 2006.