EC2KEGG: a command line tool for comparison of metabolic pathways

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Next-generation sequencing generates numerous genomes requiring annotation.
• Comparative analysis of metabolic strategies is crucial for newly sequenced organisms.
• Existing tools for high-throughput enzyme analysis and visualization are limited.

Purpose of the Study:

• To develop a command-line toolkit for automated comparative analysis of enzyme sets.
• To facilitate high-throughput annotation of newly sequenced genomes.
• To enable comparison of metabolic strategies against reference organisms.

Main Methods:

• Utilized Perl scripts for automated data retrieval from the KEGG database via its REST API.
• Employed the two-tailed Fisher exact test with Benjamini and Hochberg correction for pathway enrichment analysis.

Main Results:

• The toolkit maps enzymes to metabolic pathways, identifies shared/unique enzymes, and links to KEGG Pathway visualizations.
• It computes pathway enrichment and lists non-mapped enzymes.
• Comparative analysis includes mapping, shared/unique enzyme identification, and pathway enrichment.

Conclusions:

• EC2KEGG is a platform-independent toolkit for automated enzyme set comparison against reference genomes.
• It supports both manual and high-throughput annotation pipelines.
• The tool is publicly available for automated metabolic pathway analysis.