Mind the Gaps: Validating species presence under sparse genomic recovery in clinical metagenomics
Abstract
1Clinical metagenomic sequencing is increasingly used to detect pathogens directly from clinical samples, but sparse and uneven genome recovery caused by low microbial biomass and stochastic fragment loss makes species-level validation difficult. Under these conditions, the same observed coverage may arise either from genuine genome-wide representation or from reads confined to conserved loci shared among related taxa. Current heuristic thresholds based on read counts, breadth or related alignment summaries target the realised coverage rather than the generating process. We show that, under hidden fragment loss, coverage is the wrong inferential object for uniformly valid species-level inference. What remains informative is the spatial dispersion of surviving reads across genomic coordinates. This change of inferential object allows us to ask how much of the candidate genome must be represented in any source capable of reproducing the observed evidence. This yields the Metagenomic Alignment Validation Index (MAVI), a conservative lower bound on the fraction of the candidate genome required to explain the observed read dispersion. MAVI therefore provides an error-controlled, interpretable measure of genomic confidence: the minimum genomic content a competing source would need to share with the candidate to mimic the observed evidence. Evaluated across 643 long-read metagenomic samples, including 280 clinical samples, MAVI reproduced independently supported organism calls, resolved sparse low-coverage ambiguities and exposed workflow-level collapse of stochastic dispersion, without relying on heuristic coverage thresholds.
Related
- Reporting and interpretation of respiratory metagenomic results in clinical practice: a systematic review and consensus guidelines from the UK NHS Respiratory Metagenomics Network
- Designing and validating a clinical competency logbook for workplace-based assessment in undergraduate medical education: A foundational psychometric analysis
- Clinical outcomes and recovery impact of implementing enhanced recovery after surgery (ERAS) pathway in elderly patients with gynecological malignant tumors
- Mechanisms of Exosome-Supplemented WJ-MSC Therapy in Degenerative Disc Disease: Integrating Proteomic Profiling with Clinical Functional Recovery
- COVID-19-associated pulmonary aspergillosis: clinical characteristics, species distribution, environmental fungal contamination, and antifungal susceptibility
BibTeX
@misc{behruznia2026mind,
title = {{Mind the Gaps: Validating species presence under sparse genomic recovery in clinical metagenomics}},
author = {M. Behruznia and F. Silva-Toro and D. A. Marquez},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.23.26363603},
url = {https://www.medrxiv.org/content/10.64898/2026.09.23.26363603v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Mind the Gaps: Validating species presence under sparse genomic recovery in clinical metagenomics
AU - M. Behruznia
AU - F. Silva-Toro
AU - D. A. Marquez
PY - 2026
DA - 2026/09/23
PB - medRxiv
DO - 10.64898/2026.09.23.26363603
UR - https://www.medrxiv.org/content/10.64898/2026.09.23.26363603v1
AB - 1Clinical metagenomic sequencing is increasingly used to detect pathogens directly from clinical samples, but sparse and uneven genome recovery caused by low microbial biomass and stochastic fragment loss makes species-level validation difficult. Under these conditions, the same observed coverage may arise either from genuine genome-wide representation or from reads confined to conserved loci shared among related taxa. Current heuristic thresholds based on read counts, breadth or related alignment summaries target the realised coverage rather than the generating process. We show that, under hidden fragment loss, coverage is the wrong inferential object for uniformly valid species-level inference. What remains informative is the spatial dispersion of surviving reads across genomic coordinates. This change of inferential object allows us to ask how much of the candidate genome must be represented in any source capable of reproducing the observed evidence. This yields the Metagenomic Alignment Validation Index (MAVI), a conservative lower bound on the fraction of the candidate genome required to explain the observed read dispersion. MAVI therefore provides an error-controlled, interpretable measure of genomic confidence: the minimum genomic content a competing source would need to share with the candidate to mimic the observed evidence. Evaluated across 643 long-read metagenomic samples, including 280 clinical samples, MAVI reproduced independently supported organism calls, resolved sparse low-coverage ambiguities and exposed workflow-level collapse of stochastic dispersion, without relying on heuristic coverage thresholds.
ER -