SCiMS: Sex Calling in Metagenomic Sequences
Abstract
Background: Host sex is a critical determinant of microbial community structure across many host species, influenced by hormonal profiles, physiology, and sex-stratified behaviors. Despite its importance, sex metadata is frequently missing in microbiome studies, including animal-associated samples. Host chromosomal sex can be inferred from the host-derived reads present in metagenomic data, but existing genomic sex prediction tools rely on fixed coverage thresholds calibrated for human XY chromosomes and require relatively high numbers of host reads, limiting their use on low host-biomass samples such as stool and on organisms with other sex-determination systems.
Results: Here, we present SCiMS (Sex Calling in Metagenomic Sequences), a bioinformatic tool that leverages host-derived DNA within shotgun metagenomic data to predict host chromosomal sex, even at low host coverage. SCiMS uses a multinomial likelihood computed from observed read counts under each sex and reports chromosomal sex calls. Because the expected read distribution is derived directly from chromosome lengths and ploidy under each candidate karyotype, SCiMS applies to any organism with a heterogametic sex-determination system. We benchmarked SCiMS against existing tools on simulated metagenomic data, human metagenomic samples spanning multiple body sites, and metagenomic samples from seven animal species. SCiMS matched or outperformed existing tools, with a noticeable advantage at low host read conditions.
Conclusions: SCiMS provides an accurate, scalable, and generalizable solution for host chromosomal sex classification across host species, even when host DNA is minimal. By enabling recovery of missing sex metadata, it serves as a quality-control tool for analyses in microbiome research. SCiMS is freely available at https://github.com/davenport-lab/SCiMS .
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BibTeX
@article{tran2026scims,
title = {{SCiMS: Sex Calling in Metagenomic Sequences}},
author = {Hanh N. Tran and Kobie J. Kirven and Emily R. Davenport},
journal = {Microbiome},
year = {2026},
month = oct,
doi = {10.1186/s40168-026-02547-x},
url = {https://link.springer.com/10.1186/s40168-026-02547-x},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - SCiMS: Sex Calling in Metagenomic Sequences
AU - Hanh N. Tran
AU - Kobie J. Kirven
AU - Emily R. Davenport
PY - 2026
DA - 2026/10/05
JO - Microbiome
DO - 10.1186/s40168-026-02547-x
UR - https://link.springer.com/10.1186/s40168-026-02547-x
AB - Background: Host sex is a critical determinant of microbial community structure across many host species, influenced by hormonal profiles, physiology, and sex-stratified behaviors. Despite its importance, sex metadata is frequently missing in microbiome studies, including animal-associated samples. Host chromosomal sex can be inferred from the host-derived reads present in metagenomic data, but existing genomic sex prediction tools rely on fixed coverage thresholds calibrated for human XY chromosomes and require relatively high numbers of host reads, limiting their use on low host-biomass samples such as stool and on organisms with other sex-determination systems. Results: Here, we present SCiMS (Sex Calling in Metagenomic Sequences), a bioinformatic tool that leverages host-derived DNA within shotgun metagenomic data to predict host chromosomal sex, even at low host coverage. SCiMS uses a multinomial likelihood computed from observed read counts under each sex and reports chromosomal sex calls. Because the expected read distribution is derived directly from chromosome lengths and ploidy under each candidate karyotype, SCiMS applies to any organism with a heterogametic sex-determination system. We benchmarked SCiMS against existing tools on simulated metagenomic data, human metagenomic samples spanning multiple body sites, and metagenomic samples from seven animal species. SCiMS matched or outperformed existing tools, with a noticeable advantage at low host read conditions. Conclusions: SCiMS provides an accurate, scalable, and generalizable solution for host chromosomal sex classification across host species, even when host DNA is minimal. By enabling recovery of missing sex metadata, it serves as a quality-control tool for analyses in microbiome research. SCiMS is freely available at https://github.com/davenport-lab/SCiMS .
SN - 2049-2618
ER -