METACLUSTER

METACLUSTER performs probabilistic, context-specific analysis of expression in metabolic gene clusters to characterize biosynthetic pathway activity in plants and microbes.

Key Features:

• R package implementation: Implemented as an R package for analysis of metabolic gene cluster expression data.
• Probabilistic framework: Employs probabilistic modeling to interpret complex gene expression data from metabolic gene clusters.
• Context- and tissue-specific profiling: Analyzes condition- and tissue-specific expression patterns to capture expression dynamics of biosynthetic genes.
• Cluster-focused characterization: Targets metabolic gene clusters formed by enzymes collocated on chromosomes in plants and microbes to assess cluster activity.
• Inference of regulatory and functional roles: Enables inference of potential regulatory mechanisms and functional roles of clusters from expression patterns.

Scientific Applications:

• Plant and microbial biosynthesis research: Elucidates condition- and tissue-specific expression patterns of metabolic gene clusters in plants and microbes.
• Natural product discovery: Identifies biosynthetic pathway activity relevant to natural product biosynthesis.
• Synthetic biology: Informs pathway engineering by revealing context-specific expression of biosynthetic genes.
• Agricultural biotechnology: Supports studies of compound production related to adaptation and agronomic traits in crops.

Methodology:

Integrates context-specific gene expression data and applies probabilistic modeling in R to probabilistically characterize metabolic gene clusters and infer potential regulatory mechanisms.