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Updated: Oct 22, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
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MicFunPred: A conserved approach to predict functional profiles from 16S rRNA gene sequence data.

Dattatray S Mongad1, Nikeeta S Chavan2, Nitin P Narwade3

  • 1National Centre for Cell Science, Savitribai Phule Pune University Campus, Ganeshkhind, Pune, Maharashtra 411007, India.

Genomics
|August 27, 2021
PubMed
Summary

MicFunPred accurately predicts microbial functions from 16S rRNA data using core genes, minimizing errors. This computational tool offers a faster, more reliable alternative for functional profiling in microbiome research.

Keywords:
16S rRNA geneAmplicon sequencingImputed metagenomesMicFunPredMicrobiome

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • 16S rRNA gene amplicon sequencing is widely used for microbial taxonomic profiling but lacks functional insights.
  • Existing tools for predicting microbial function from 16S data have limitations, including potential overestimation due to inaccurate taxonomic resolution.
  • The need for accurate and efficient functional prediction from 16S rRNA data is critical for microbiome studies.

Purpose of the Study:

  • To develop a novel computational tool, MicFunPred, for predicting microbial functional profiles from 16S rRNA gene sequence data.
  • To improve the accuracy of functional predictions by minimizing false positives using a core gene-based imputation approach.
  • To evaluate the performance of MicFunPred against existing tools in terms of accuracy, speed, and computational requirements.

Main Methods:

  • MicFunPred was developed using a novel approach that imputes metagenomes based on a curated set of core genes.
  • The performance of MicFunPred was assessed using simulated datasets and seven real-world microbiome datasets.
  • Key metrics for evaluation included False Positive Rate (FPR) and Spearman's correlation to assess prediction accuracy.

Main Results:

  • MicFunPred demonstrated the lowest False Positive Rate on simulated datasets, with a mean Spearman's correlation of 0.89.
  • On real datasets, MicFunPred achieved a mean Spearman's correlation of 0.75, outperforming or matching other available tools.
  • The tool exhibited faster processing times and lower computational demands compared to existing methods.

Conclusions:

  • MicFunPred provides a more accurate and reliable method for predicting microbial functional profiles from 16S rRNA data.
  • The core gene-based imputation strategy effectively minimizes false-positive predictions, enhancing functional profile reliability.
  • MicFunPred offers a computationally efficient and high-performing solution for microbiome functional analysis.