Manually weighted taxonomy classifiers improve species-specific rumen microbiome analysis compared to unweighted or average weighted taxonomy classifiers

Posted by mhb on 2025-11-06 09:53:29 |

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Introduction

This study addresses the challenges in accurately classifying species in the rumen microbiome, a crucial area for ruminant animal nutrition and productivity. Previous methods have struggled with rumen-specific taxonomic resolution due to a lack of suitable reference data. This paper introduces a manually weighted taxonomy classifier (MWTC) designed specifically for the rumen microbiome, combining shotgun and amplicon sequencing data to improve accuracy in species-level classification.

Methods

• Taxonomy Classifiers: The study compared three classifiers: unweighted taxonomy classifier (UWTC), average weighted taxonomy classifier (AWTC), and manually weighted taxonomy classifier (MWTC).

• Databases: The classifiers were evaluated using several prominent databases, including SILVA, Greengenes, GTDB, NCBI RefSeq, and RDP, focusing on species-level classification.

• Datasets: Data from both in vivo and in vitro rumen samples of Hanwoo cattle were used to evaluate classifier performance.

Results

• Performance Evaluation: MWTC showed significant improvement in classification accuracy, with higher classification counts, fully classified ratios, and reduced error rates compared to UWTC and AWTC, especially at the species level.

• Database Comparison: NCBI RefSeq and RDP performed best with MWTC, showing up to 47% error reduction at the species level. Other databases like GG2 and SILVA showed less improvement due to inherent limitations in their classifications.

• Alpha and Beta Diversity: The MWTC improved diversity metrics, particularly at the species level, across both full-length and V3-V4 amplicon sequences.

Discussion

• The study emphasizes the importance of breed-specific classifiers for improving microbiome analysis. While databases like GG2 and SILVA struggled with accuracy, NCBI RefSeq and RDP, when paired with MWTC, offered the most reliable results for species-level classification.

• The findings suggest that integrating rumen-specific taxonomic weights, rather than relying solely on general databases, can substantially enhance microbiome studies.

Conclusion

The study concludes that MWTC, when applied to rumen microbiome analyses, significantly improves species-level classification accuracy. It recommends using NCBI RefSeq for species classification, with MWTC as the preferred method for processing rumen microbiome data. The results underscore the need for breed-specific classifiers in microbiome research.

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