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Unlocking plant bioactive pathways: omics data harnessing and machine learning assisting.

Mickael Durand¹, Sébastien Besseau¹, Nicolas Papon²

¹Biomolécules et Biotechnologies Végétales, EA2106, Université de Tours, 37200 Tours, France.

Current Opinion in Biotechnology

|May 10, 2024

Summary

This summary is machine-generated.

Harnessing omics data and machine learning advances our understanding of plant specialized metabolic pathways. This enables novel strategies for discovering plant natural products and their biosynthetic routes.

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Area of Science:

Plant biochemistry and molecular biology
Metabolomics and transcriptomics
Bioinformatics and computational biology

Background:

Plant bioactives are crucial for medicine and food industries.
High-quality omics data (metabolomics, transcriptomics) are vital for studying plant metabolic pathways.
Previous methods successfully identified plant natural product (PNP) biosynthetic pathways using omics data.

Purpose of the Study:

To review recent advancements in deciphering plant specialized biosynthetic pathways.
To highlight the impact of omics data integration and machine learning on this field.
To explore new opportunities for pathway discovery and disruption.

Main Methods:

Review of current literature on omics data applications in plant science.
Analysis of machine learning techniques applied to metabolomics and transcriptomics data.
Discussion of integrated data approaches for pathway elucidation.

Main Results:

Omics data, when harnessed effectively, aids in unveiling PNP biosynthetic pathways.
Machine learning democratizes biological data analysis, offering new avenues for pathway exploration.
Recent breakthroughs demonstrate the potential of these techniques in disrupting specialized biosynthetic pathways.

Conclusions:

The synergy between omics data and machine learning significantly enhances the exploration of plant specialized metabolic pathways.
These integrated approaches are key to unlocking the full potential of plant bioactives.
Future research will likely focus on further refining these computational strategies for broader applications.