Abstract:
BACKGROUND : Gene Regulatory Networks (GRN) produce powerful insights into transcriptional regulation in cells. The
power of GRNs has been underutilized in malaria research. The Arboreto library was incorporated into a user-friendly
web-based application for malaria researchers (http:// malbo ost. bi. up. ac. za). This application will assist researchers with
gaining an in depth understanding of transcriptomic datasets.
METHODS : The web application for MALBoost was built in Python-Flask with Redis and Celery workers for queue
submission handling, which execute the Arboreto suite algorithms. A submission of 5–50 regulators and total expression
set of 5200 genes is permitted. The program runs in a point-and-click web user interface built using Bootstrap4
templates. Post-analysis submission, users are redirected to a status page with run time estimates and ultimately a
download button upon completion. Result updates or failure updates will be emailed to the users.
RESULTS : A web-based application with an easy-to-use interface is presented with a use case validation of AP2-G and
AP2-I. The validation set incorporates cross-referencing with ChIP-seq and transcriptome datasets. For AP2-G, 5 ChIPseq
targets were significantly enriched with seven more targets presenting with strong evidence of validated targets.
CONCLUSION : The MALBoost application provides the first tool for easy interfacing and efficiently allows gene regulatory
network construction for Plasmodium. Additionally, access is provided to a pre-compiled network for use as reference
framework. Validation for sexually committed ring-stage parasite targets of AP2-G, suggests the algorithm was
effective in resolving “traditionally” low-level signatures even in bulk RNA datasets.
