Abstract
BackgroundGene 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://malboost.bi.up.ac.za). This application will assist researchers with gaining an in depth understanding of transcriptomic datasets.MethodsThe 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.ResultsA 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 ChIP-seq targets were significantly enriched with seven more targets presenting with strong evidence of validated targets.ConclusionThe 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.
Highlights
Gene Regulatory Networks (GRN) produce powerful insights into transcriptional regulation in cells
Intended use GRNs constructed through supervised machine learning algorithms such as GENIE3 and GRNBoost2 offer a fast and reliable tools for GRN research [12, 13]
MALBoost offers malaria researchers easy access to these machine-learning tools through a user-friendly, web-based application framework. Researchers can submit their own transcriptomic data for de novo network construction or download a pre-compiled and validated network built for P. falciparum that can be used as reference framework
Summary
Gene Regulatory Networks (GRN) produce powerful insights into transcriptional regulation in cells. The Arboreto library was incorporated into a user-friendly web-based application for malaria researchers (http://malboost.bi.up.ac.za) This application will assist researchers with gaining an in depth understanding of transcriptomic datasets. In its simplest form, a GRN connects a “regulatory” gene ( referred to as candidate gene or effector) to its target gene (affected gene), capturing the effector vs. A GRN synthesized for a disease phenotype consists of a collection of regulators that make up a powerful dataset to understand molecular effectors allowing disease severity, progression, pathological markers and therapeutic efficacy. A GRN synthesized for a disease phenotype consists of a collection of regulators that make up a powerful dataset to understand molecular effectors allowing disease severity, progression, pathological markers and therapeutic efficacy Such a causal molecular map is, extraordinarily useful to define observed phenotypes. Weighted co-expression networks constructed from large DNA-microarray datasets have provided insights regarding the involvements of regulators potentially affecting parasite transmission including the Apicomplexan Apetala 2 transcription factor AP2-G, histone deacetylase 1 (HDAC1) and a putative histone deacetylase (HDA1) [6]
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