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Updated: May 21, 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

The pathway tools pathway prediction algorithm.

Peter D Karp¹, Mario Latendresse, Ron Caspi

¹Bioinformatics Research Group, SRI International 333 Ravenswood Ave, EK207, Menlo Park, CA 94025.

Standards in Genomic Sciences

|June 8, 2012

Summary

This summary is machine-generated.

PathoLogic predicts metabolic pathways using genome data. Its two-phase algorithm, leveraging the MetaCyc database, accurately infers reactions and pathways for genomic analysis.

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Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Metabolic pathway prediction is crucial for understanding cellular functions.
Genome annotation provides enzyme information but requires tools for pathway reconstruction.

Purpose of the Study:

To detail the PathoLogic algorithm for predicting metabolic pathways.
To explain how genome data and the MetaCyc database are utilized for pathway inference.

Main Methods:

PathoLogic employs a two-phase approach: reactome inference and pathway inference.
Reactome inference identifies catalyzed reactions from annotated enzymes.
Pathway inference reconstructs metabolic pathways using inferred reactions and MetaCyc data.

Main Results:

The algorithm successfully infers metabolic pathways from genomic data.
The inclusion of taxonomic range and key reactions in MetaCyc enhances prediction accuracy.
PathoLogic demonstrates improved predictive performance due to enriched database fields.

Conclusions:

PathoLogic provides a robust method for metabolic pathway prediction in newly sequenced genomes.
The algorithm's accuracy is significantly boosted by specific MetaCyc data fields.
This tool aids in the functional annotation and analysis of microbial genomes.