Research Infrastructure: Robust software infrastructure for bioBakery microbial community analyses in diverse environments

Bacteria, viruses, and other microbes coexist as communities that inhabit and influence nearly every environment on Earth, including oceans, soils, agricultural systems, and organisms such as humans. Advances in DNA sequencing and computation have transformed scientists’ ability to study these microbial communities, highlighting their importance for ecosystem resilience, biotechnology, agriculture, and human and animal health. However, many widely used software tools for microbial community analysis were originally developed and tested in the context of human-associated microbial community research, limiting their effectiveness in non-human environments (NHEs). This project will expand and modernize the widely used bioBakery software platform to improve the analysis of microbial communities residing in natural and engineered NHEs. The project will create new reference databases and analytical capabilities to help researchers identify microbes, predict their metabolic functions, and link them to other molecular measurements across environmental samples. The resulting open-source software infrastructure will support scientists studying topics including soil ecology, agricultural productivity, marine biology, bioenergy production, and toxic compound remediation. By improving the accuracy, scalability, and accessibility of microbial community analysis, the project will accelerate discoveries across the environmental and life sciences while strengthening the nation’s research infrastructure. The project will additionally implement an educational and workforce-development component in which undergraduate students participate in hands-on environmental microbiology research and computational training, helping to prepare the next generation of scientists for microbial community research and related data-intensive fields. All software, training materials, and data resources developed through the project will be made freely available to the scientific community. The implementation of this project will be released as bioBakery 4, an expansion of the version 3 open-source software platform for microbial community analysis with enhanced support for NHE microbiomes. The work will also use this software to integrate large-scale genomic, metagenomic, metatranscriptomic, viromic, and metabolomic datasets to improve taxonomic, functional, and strain-level profiling of microbial communities from soils, oceans, sediments, freshwater systems, plants, and other non-human hosts. To expand coverage and understanding of microbial inhabitants across NHEs, the team will assemble and process publicly available NHE metagenomes using standardized workflows to generate and expand species-level genome bins and associated pangenomes, which will then be incorporated into reference databases and other resources for the scientific community. The project will also extend recently developed bioBakery methods for virome profiling, protein function prediction, multi-omic data integration, and strain-level association analysis. To ensure robust and reproducible performance, the project will implement expanded software engineering infrastructure across the bioBakery platform, including automated testing, standardized interfaces, cloud-compatible workflows, containerized deployment, and integration with external microbiome methods and data standards. The updated bioBakery tools and databases will be quantitatively evaluated using both synthetic benchmark datasets and real-world environmental microbiome samples. The project will additionally generate publicly available tutorials, workflows, and curated data resources designed to support broad adoption by the microbiome research community. By combining scalable bioinformatics infrastructure, artificial intelligence-enabled function prediction, and advanced multi-omic analysis, the project will provide researchers with improved capabilities to study microbial diversity and ecosystem function across previously under-char NSF Award ID: 2532725 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Eric Franzosa | Institution: Harvard University, CAMBRIDGE, MA | Award Amount: $1,732,918 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2532725 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2532725.html