FunOrder

FunOrder identifies essential biosynthetic genes within biosynthetic gene clusters (BGCs) by detecting co-evolutionary relationships among encoded proteins through phylogenetic tree comparisons.

Key Features:

• Semi-automated workflow: Implements a semi-automated computational workflow for co-evolution analysis of genes within BGCs.
• Protein sequence BLAST: Performs BLAST searches of BGC-encoded protein sequences against a suitable proteome database.
• Phylogenetic tree construction: Builds phylogenetic trees for each protein to represent evolutionary relationships.
• treeKO tree comparisons: Uses treeKO to compare phylogenetic trees and detect co-evolutionary patterns among proteins.
• Essential vs gap gene inference: Infers likely essential biosynthetic genes by identifying co-evolving gene sets within BGCs.
• Visualization outputs: Produces visualization-friendly output formats for interpretation of tree comparisons and co-evolution signals.

Scientific Applications:

• Distinguishing essential biosynthetic genes: Differentiates essential enzymes from non-essential "gap" genes within BGCs based on co-evolutionary links.
• Genome mining for secondary metabolites: Aids genome-mining efforts to characterize secondary metabolite (SM) biosynthetic pathways.
• Prioritizing genes for heterologous expression: Supports selection of candidate genes for heterologous expression by identifying co-evolving pathway components.
• Studying BGCs in native hosts: Facilitates analysis of biosynthetic pathway organization and evolution in native host genomes.
• Discovery of novel SMs: Contributes to the discovery pipeline for novel secondary metabolites by prioritizing biosynthetic genes within BGCs.

Methodology:

Protein sequences from a BGC are subjected to BLAST searches against a proteome database; phylogenetic trees are constructed for each protein; trees are compared using treeKO to detect co-evolutionary patterns; results are exported in visualization-friendly formats.