SyFi: generating and using sequence fingerprints to distinguish SynCom isolates

Abstract

The plant root microbiome is a complex community shaped by interactions among bacteria, the plant host, and the environment. Synthetic community (SynCom) experiments help disentangle these interactions by inoculating host plants with a representative set of culturable microbial isolates from the natural root microbiome. Studying these simplified communities provides valuable insights into microbiome assembly and function. However, as SynComs become increasingly complex to better represent natural communities, bioinformatics challenges arise. Specifically, accurately identifying, and quantifying SynCom members based on, for example, 16S rRNA amplicon sequencing becomes more difficult due to the high similarity of the target amplicon, limiting downstream interpretations. Here, we present SynCom Fingerprinting (SyFi), a bioinformatics workflow designed to improve the resolution and accuracy of SynCom member identification. SyFi consists of three modules: the first module constructs a genomic fingerprint for each SynCom member based on its genome sequence, accounting for both copy number and sequence variation in the target gene. The second module then extracts a specific region from this genomic fingerprint to create a secondary fingerprint focused on the target amplicon. The third module uses these fingerprints as a reference to perform pseudoalignment-based quantification of SynCom member abundance from amplicon sequencing reads. We demonstrate that SyFi outperforms standard amplicon analysis by leveraging natural intragenomic variation, enabling more precise differentiation of closely related SynCom members. As a result, SyFi enhances the reliability of microbiome experiments using complex SynComs, which more accurately reflect natural communities. This improved resolution is essential for advancing our understanding of the root microbiome and its impact on plant health and productivity in agricultural and ecological settings. SyFi is available at https://github.com/adriangeerre/SyFi. Impact statementSyFi represents a significant advancement in microbiome research by enhancing the accuracy and resolution of synthetic community (SynCom) member identification. By leveraging natural intragenomic variation, SyFi improves the differentiation of closely related microbial strains, addressing a key challenge in amplicon-based sequencing analysis. This increased precision allows researchers to more reliably track microbial dynamics in complex SynCom experiments, leading to deeper insights into microbiome assembly, function, and host-microbe interactions. As a result, SyFi strengthens the interpretability of microbiome studies, ultimately contributing to a better understanding of plant health and productivity in both agricultural and ecological contexts. Data SummaryThe data reported in this article have been deposited in the National Center for Biotechnology Information Short Read Archive BioProject database. SyFi fingerprint generation was run on a collection of 737 human gut-derived bacterial genomes from Forster et al. (2019) (Genomic read data deposited in the ENA under project numbers ERP105624 and ERP012217) and 447 Arabidopsis-derived bacterial genomes (Selten et al., 2024b) (NCBI Project numbers PRJNA1138681, PRJNA1139421 (Genomes), and PRJNA1131834 (Genomic reads)). A list of the closed genomes used for SyFi validation can be found at https://github.com/adriangeerre/SyFi. Subsequently, SyFi was validated on a complex SynCom dataset by pseudoaligning 16S rRNA V3-V4 and V5-V7 amplicon reads (PRJNA1191388) to SyFi-generated fingerprints and comparing this to shotgun metagenomics-sequenced dataset of the same samples in Selten et al. (2024a) (PRJNA1131994). This complex SynCom dataset included the inoculation of the 447 bacterial isolates on Arabidopsis, Barley, and Lotus roots.