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Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

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Published on: December 22, 2014

SCiMS: Sex Calling in Metagenomic Sequences.

Hanh N Tran^1,2, Kobie J Kirven^2,3, Emily R Davenport^1,2

¹Department of Biology, Pennsylvania State University, University Park, PA.

Biorxiv : the Preprint Server for Biology

|February 27, 2026

Summary

A new tool called SCiMS (Sex Calling in Metagenomic Sequences) accurately determines host sex from DNA in microbiome samples. This method works even with low amounts of host DNA, improving microbiome research data quality.

Keywords:

Metagenomics host sex microbiome sex inference

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Area of Science:

Microbiome research
Genomics
Bioinformatics

Background:

Host sex significantly influences microbial community structure but is often missing or mislabeled in studies.
Existing sex-calling tools struggle with low host DNA concentrations, common in samples like stool.
Accurate host sex data is crucial for understanding sex-specific biological variations.

Purpose of the Study:

To develop a robust bioinformatic tool for accurate host sex determination in metagenomic datasets.
To address the challenge of missing or unreliable sex metadata in microbiome research.
To provide a scalable and generalizable solution for host sex classification across different species.

Main Methods:

Developed SCiMS (Sex Calling in Metagenomic Sequences), a tool utilizing sex-chromosome read density ratios.
Employed a Bayesian classifier to predict host sex from host-derived DNA in metagenomic samples.
Validated SCiMS performance through simulations and application to real-world datasets (Human Microbiome Project, murine, chicken).

Main Results:

SCiMS accurately predicts host sex even with minimal host DNA (e.g., >85% accuracy with ~450 host reads in simulations).
Outperformed existing tools on Human Microbiome Project data, demonstrating superior accuracy and precision-recall balance.
Showcased cross-species generalizability with 100% accuracy in murine and strong performance in chicken datasets.

Conclusions:

SCiMS offers an accurate, scalable, and broadly applicable method for host sex classification in metagenomic studies.
Enables recovery of critical sex metadata, enhancing the integrity and reliability of microbiome analyses.
The tool serves as a vital quality control measure for microbiome research and is publicly available.