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Related Experiment Video

Updated: Sep 8, 2025

Screening Foodstuffs for Class 1 Integrons and Gene Cassettes
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SyFi: generating and using sequence fingerprints to distinguish SynCom isolates.

Gijs Selten1, Adrián Gómez-Repollés2, Florian Lamouche2,3

  • 1Department of Biology, Science for Life, Plant-Microbe Interactions, Utrecht University, Netherlands, 3584CH Utrecht.

Microbial Genomics
|September 4, 2025
PubMed
Summary
This summary is machine-generated.

We developed SynCom Fingerprinting (SyFi), a bioinformatics tool to accurately identify and quantify synthetic microbial communities (SynComs). SyFi improves root microbiome analysis by leveraging genomic variation for precise member identification.

Keywords:
amplicon sequencingcopy number variationmarker sequencemicrobiomesynthetic community

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Area of Science:

  • Microbial Ecology
  • Bioinformatics
  • Plant Science

Background:

  • Plant root microbiomes are intricate communities influenced by bacteria, host plants, and environmental factors.
  • Synthetic community (SynCom) experiments simplify these interactions, offering insights into microbiome assembly and function.
  • Increasing SynCom complexity for greater natural representation poses bioinformatics challenges, particularly in accurately identifying and quantifying members via 16S rRNA amplicon sequencing due to high amplicon similarity.

Purpose of the Study:

  • To introduce SynCom Fingerprinting (SyFi), a novel bioinformatics workflow.
  • To enhance the resolution and accuracy of identifying and quantifying members within synthetic microbial communities (SynComs).
  • To overcome limitations in standard amplicon sequencing analysis for complex SynComs.

Main Methods:

  • SyFi constructs a genomic fingerprint for each SynCom member using genome sequences and/or raw reads, considering target gene copy number and sequence variation.
  • A secondary fingerprint linked to the amplicon sequence is created by extracting the target region from the genomic fingerprint.
  • Pseudoalignment-based quantification of SynCom member abundance is performed using these fingerprints as a reference against amplicon sequencing reads.

Main Results:

  • SyFi demonstrates superior performance compared to standard amplicon analysis methods.
  • The workflow effectively utilizes natural intragenomic variation for precise differentiation of closely related SynCom members.
  • SyFi significantly improves the reliability of analyzing complex SynComs that more closely mimic natural root microbiomes.

Conclusions:

  • SyFi enhances the accuracy of identifying and quantifying members in complex synthetic microbial communities.
  • This improved resolution is crucial for advancing the understanding of root microbiome dynamics and their impact on plant health.
  • The SyFi workflow supports more reliable microbiome research in agricultural and ecological contexts.