MALBoost

MALBoost performs gene regulatory network (GRN) inference and analysis for Plasmodium falciparum to identify transcriptional regulatory relationships and contextualize them with genomic datasets.

Key Features:

• Arboreto integration: Implements the Arboreto library and suite algorithms for GRN inference.
• Asynchronous processing: Uses Celery workers with Redis for queued job execution and computational task management.
• Input data constraints: Accepts regulator lists of 5 to 50 regulators alongside expression sets of up to 5,200 genes.
• Pre-compiled reference network: Provides access to a pre-compiled network for contextualizing inferred interactions.
• Cross-referencing with genomic data: Enables comparison of inferred networks with ChIP-seq and transcriptome datasets.
• Sensitivity in bulk data: Detects low-level signatures within bulk RNA datasets.

Scientific Applications:

• GRN construction in Plasmodium falciparum: Enables reconstruction of transcriptional regulatory networks to study parasite gene regulation.
• Transcription factor target validation (AP2-G, AP2-I): Supported validation use cases include AP2-G and AP2-I with cross-referencing to ChIP-seq and transcriptome data, reporting enrichment in 5 ChIP-seq targets and additional strong evidence for seven more targets.

Methodology:

Applies Arboreto GRN inference algorithms, performs queued execution via Celery workers and Redis, processes datasets with 5–50 regulators and up to 5,200 genes, and cross-references inferred interactions with ChIP-seq and transcriptome datasets to resolve signatures in bulk RNA data.