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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 MORPH-R web server and software tool for predicting missing genes in biological pathways.

David Amar¹, Itziar Frades², Tim Diels^3,4

¹Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.

Physiologia Plantarum

|January 28, 2015

Summary

This summary is machine-generated.

Discovering missing genes in biological pathways is challenging. A new tool, MORPH-R, aids researchers by identifying these essential pathway genes in plants like tomato, Arabidopsis, rice, and potato.

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Area of Science:

Bioinformatics
Systems Biology
Genomics

Background:

Biological pathways are crucial for understanding cellular processes.
Identifying all genes within a pathway is a persistent challenge in biological research.
Existing methods may not fully capture the complexity of gene interactions within pathways.

Purpose of the Study:

To introduce MORPH-R, a user-friendly tool for discovering missing genes in biological pathways.
To demonstrate the capabilities of the MORPH algorithm across different plant species.
To provide both web server and standalone software options for broad accessibility.

Main Methods:

Implementation of the MOdule-guided Ranking of candidate PatHway genes (MORPH) algorithm.
Development of MORPH-R as an accessible web server and standalone software.
Application of the tool to analyze biological pathways in tomato, Arabidopsis, rice, and potato.

Main Results:

MORPH-R successfully identifies potential missing genes in plant biological pathways.
The tool supports analysis across multiple key plant species, including newly sequenced genomes.
Both web and standalone versions are functional, offering flexibility for diverse user needs.

Conclusions:

MORPH-R is an effective and accessible tool for advancing biological pathway research.
The algorithm aids in completing pathway annotations, crucial for understanding gene function.
This tool has significant implications for plant genomics and systems biology research.