SyFi: generating and using sequence fingerprints to distinguish SynCom isolates

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, <i>16S rRNA</i> 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 and, when available, raw genomic reads, accounting for both copy number and sequence variation in the target gene. The second module extracts the target region from this genomic fingerprint to create a secondary fingerprint linked to the relevant amplicon sequence. 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.