SyFi: generating and using sequence fingerprints to distinguish SynCom isolates

Gijs Selten; Adrián Gómez-Repollés; Florian Lamouche; Simona Radutoiu; Ronnie de Jonge

doi:10.1099/mgen.0.001461

SyFi: generating and using sequence fingerprints to distinguish SynCom isolates

Gijs Selten
, Adrián Gómez-Repollés
, Florian Lamouche
, Simona Radutoiu
, Ronnie de Jonge^*

^*Corresponding author for this work

Department of Molecular Biology and Genetics - Plant Molecular Biology

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

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 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 leverag-ing 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 commu-nities. 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.

Original language	English
Article number	001461
Journal	Microbial Genomics
Volume	11
Issue	9
DOIs	https://doi.org/10.1099/mgen.0.001461
Publication status	Published - 2025

Keywords

amplicon sequencing
copy number variation
marker sequence
microbiome
synthetic community

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title = "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 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 leverag-ing 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 commu-nities. 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.",

keywords = "amplicon sequencing, copy number variation, marker sequence, microbiome, synthetic community",

author = "Gijs Selten and Adri{\'a}n G{\'o}mez-Repoll{\'e}s and Florian Lamouche and Simona Radutoiu and \{de Jonge\}, Ronnie",

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doi = "10.1099/mgen.0.001461",

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TY - JOUR

T1 - SyFi

T2 - generating and using sequence fingerprints to distinguish SynCom isolates

AU - Selten, Gijs

AU - Gómez-Repollés, Adrián

AU - Lamouche, Florian

AU - Radutoiu, Simona

AU - de Jonge, Ronnie

PY - 2025

Y1 - 2025

N2 - 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 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 leverag-ing 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 commu-nities. 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.

AB - 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 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 leverag-ing 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 commu-nities. 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.

KW - amplicon sequencing

KW - copy number variation

KW - marker sequence

KW - microbiome

KW - synthetic community

UR - https://www.scopus.com/pages/publications/105015496352

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DO - 10.1099/mgen.0.001461

M3 - Journal article

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AN - SCOPUS:105015496352

SN - 2057-5858

VL - 11

JO - Microbial Genomics

JF - Microbial Genomics

IS - 9

M1 - 001461

ER -

SyFi: generating and using sequence fingerprints to distinguish SynCom isolates

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