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Skiver: Reference-free quality control of metagenomic sequencing datasets using (k,v)-mer sketches
bioRxiv · · Preprint · Open access
Gu, Sharma + 2 more
Abstract ↗AI summary
The abstract is read at the publisher; the summary is JClub's.
Skiver, a reference-free metagenomic quality control tool using (k,v)-mer sketches, accurately estimates overall error rates, error-type proportions, and quality-score calibration across multiple sequencing platforms.
- Why it matters: Existing quality control methods often rely on inefficient reference mapping or quality scores that may be uncalibrated, leaving uncertainty about error patterns in metagenomic data.
- What they did: Skiver derives consensus from sketched (k,v)-mers and estimates survival and hazard rates to model the positional distribution of errors, using an iterative outlier-filtering strategy.
- The result: Across simulated and real datasets, skiver’s estimates match observed error characteristics and enable robust QC even in complex samples with multiple strains, alleles, and repetitive regions.
The findingWhy it mattersWhat they didThe result
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