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Updated: May 10, 2026

A Web Tool for Generating High Quality Machine-readable Biological Pathways

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

MetaPathways: a modular pipeline for constructing pathway/genome databases from environmental sequence information.

Kishori M Konwar¹, Niels W Hanson, Antoine P Pagé

¹Department of Microbiology & Immunology, University of British Columbia, Vancouver, BC V6T1Z3, Canada. shallam@mail.ubc.ca

BMC Bioinformatics

|June 27, 2013

Summary

This summary is machine-generated.

MetaPathways offers a new pipeline for reconstructing microbial metabolic networks from environmental DNA sequences. This open-source tool provides an alternative to existing methods for analyzing microbial community function.

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Area of Science:

Microbial ecology
Metagenomics
Bioinformatics

Background:

Reconstructing microbial metabolic networks from sequence data is crucial for understanding ecosystem roles.
Current methods often rely on BLAST for functional annotation and mapping to KEGG pathways or SEED subsystems.

Purpose of the Study:

To present MetaPathways, an open-source pipeline for metabolic pathway inference from environmental sequence data.
To provide an alternative to KEGG pathways and SEED subsystems for metabolic reconstruction.

Main Methods:

MetaPathways utilizes the PathoLogic algorithm to map functional annotations to the MetaCyc database.
The pipeline processes assembled or unassembled nucleotide sequences, performs quality control, and predicts genes.
It also incorporates MLTreeMap for phylogenetic analysis and generates various data formats for downstream analysis and submission.

Main Results:

MetaPathways constructs environmental Pathway/Genome Databases (ePGDBs) compatible with Pathway Tools.
The pipeline generates phylogenetic trees, gene feature tables, and pathway coverage summaries.
It produces Sequin files for direct GenBank submission.

Conclusions:

MetaPathways provides a modular pipeline for predicting microbial metabolic interaction networks.
The tool is extensible to diverse genomic and transcriptomic datasets.
It generates valuable data products for microbial community structure and function analysis.