Themed collection Big Data and the Changing Landscape of Natural Products Research

Big data is changing how we do natural products research and creating exciting new possibilities. Continued attention to enhancing data quality, increasing access, and preserving foundational skills is needed.

This Highlight examines recently developed machine learning models to predict biological effects of natural products with focus on molecular featurization.

The continual growth of publicly available genomic databases offers researchers unprecedented bioinformatic opportunities. This review examines different approaches to mining genomic data for the targeted discovery of bioactive natural products.

Strategies to enhance the rate of natural product discovery from bacteria provide guidance for designing discovery initiatives.

This review covers the current and potential use of mass spectrometry-based metabolomics data mining in natural products. Public data, metadata, databases and data analysis tools are critical. The value and success of data mining rely on community participation.

Here we provide a comprehensive guide for studying natural product biosynthesis using genomics, metabolomics, and their integrated datasets. We emphasize integrated strategies and provide a critical outlook on remaining challenges in the field.

The development and application of evolutionary genome mining tools and databases has propelled natural products research into the modern age of Big Data.

This review highlights the key computational tools and emerging strategies for metabolite annotation, and discusses how these advances will enable integrated large-scale analysis to accelerate natural product discovery.

Shotgun metagenomic approaches to uncover new enzymes are underdeveloped relative to PCR- or activity-based functional metagenomics. Here we review computational and experimental strategies to discover biosynthetic enzymes from metagenomes.

Online databases are becoming key to natural product research, as publication of data is increasingly digitized. Here, we review databases of chemical structures, gene clusters and analytical data, and discuss key challenges and opportunities.