SynCom101: A web-based platform for the standardized design of functionally tailored synthetic microbial communities

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract Background Synthetic microbial communities (SynComs) are essential tools for dissecting the causal mechanisms in host-microbiota interactions. To date, however, SynCom design suffers from a lack of standardization, typically oscillating between arbitrary strain selection and computational pipelines that misalign with experimental design. As microbiome research transitions toward functionally defined community systems with reproducible experimental outcomes, there is a strong need for a user-friendly platform that integrates multi-dimensional genomic and/or biological data into a standardized and tailored SynComs design. Results Here, we present SynCom101, a web-based platform that democratizes the design of reproducible, hypothesis-driven SynComs. SynCom101 accommodates diverse input formats including genomic annotations and laboratory-obtained phenotypic traits, allowing users to customize their design criteria with high flexibility. The platform utilizes a parsimony algorithm to ensure computational scalability for large datasets, complemented by an optional correlation-aware mode to account for microbial compatibility and co-occurrence patterns when ecological interactions among strains are available. A core innovation of SynCom101 is its suite of trait-weighting modules, which empowers researchers to strategically guide the selection algorithm toward maximal functional trait coverage, the emulation of natural community architectures, or the enrichment of positively correlated microbial assemblages to enhance community stability. We showcase the functionalities of the platform by in silico design of communities from different datasets, demonstrating its capacity to generate concise, functionally prioritized SynComs aligned with targeted design objectives. Conclusion By providing a transparent, parameter-documented workflow, SynCom101 ensures that community design is no longer a “black box” but a reproducible scientific record. This platform establishes a necessary standard for in silico community assembly, facilitating the transition from descriptive microbiome studies toward high-throughput, predictive functional screening and cross-study comparability. Availability SynCom101 can be accessed via the web interface ( https://syncom101.bioinformatics.nl/ ). The datasets used for case studies are available on Zenodo ( https://doi.org/10.5281/zenodo.18310451 ). The source code is available at Git ( https://git.wur.nl/jiayi.jing/syncom101 ).

Authors 7

1. Jiayi Jing

   Netherlands Institute of Ecology · Wageningen University & Research

   Affiliation as printed

   Bioinformatics Group, Wageningen University and Research, Wageningen, The Netherlands

   Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands

2. Siemen Rockx

   Wageningen University & Research

   Affiliation as printed

   Bioinformatics Group, Wageningen University and Research, Wageningen, The Netherlands

3. Aster Liu

   Wageningen University & Research

   Affiliation as printed

   Bioinformatics Group, Wageningen University and Research, Wageningen, The Netherlands

4. Chrats Melkonian

   Utrecht University · Wageningen University & Research

   Affiliation as printed

   Bioinformatics Group, Wageningen University and Research, Wageningen, The Netherlands

   Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Utrecht, The Netherlands

5. Jos M. Raaijmakers Aachen Institute of Biology, Leiden University

   Leiden University · Netherlands Institute of Ecology

   Affiliation as printed

   Institute of Biology, Leiden University, Leiden, The Netherlands

   Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands

6. Paolina Garbeva

   Maastricht University · Netherlands Institute of Ecology

   Affiliation as printed

   Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands

   System Earth Science, Faculty of Science and Engineering, Maastricht University, Venlo, The Netherlands

7. Marnix H. Medema corresponding Aachen Institute of Biology, Leiden University

   Leiden University · Wageningen University & Research

   Affiliation as printed

   Bioinformatics Group, Wageningen University and Research, Wageningen, The Netherlands

   Institute of Biology, Leiden University, Leiden, The Netherlands