UNCROSS2: identification of cross-talk in 16S rRNA OTU tables
Abstract
Next-generation amplicon sequencing is widely used for surveying biological diversity in applications such as microbial metagenomics, immune system repertoire analysis and targeted tumor sequencing of cancer-associated genes. In such studies, assignment of reads to incorrect samples (cross-talk) is a well-documented problem that is rarely considered in practice. Here, I describe UNCROSS2, an algorithm designed to detect and filter cross-talk in OTU tables generated by next-generation sequencing of the 16S ribosomal RNA gene. On eight published datasets, cross-talk rates are estimated to range from 0.4% to 1.5% mis-assigned reads. On a mock community test, UNCROSS2 identifies spurious counts due to cross-talk with sensitivity [~]80% to 90% and error rate from [~]1% to [~]20%, but it is not clear whether the accuracy of the algorithm is sufficient to decisively improve diversity rates in practice.
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Abstract summary
Claude Opus 5.5 · xhigh effort · 18 Oct 2026 · from the abstract
An algorithm that flags counts from reads landing in the wrong sample in 16S rRNA OTU tables, catching ~80% to 90% of spurious counts on a mock community.
Reads assigned to the wrong sample are a documented problem in amplicon sequencing that is rarely dealt with in practice, introducing spurious counts into OTU tables. UNCROSS2 offers a detection and filtering step for 16S rRNA tables, together with the author's own doubt about whether its accuracy decisively improves diversity rates.
Key results
ShownMis-assignment is present but small in published data: between 0.4% and 1.5% of reads were estimated to come from the wrong sample.
Estimated across eight published datasets; the datasets are not named in the abstract and no per-dataset values are given. · Abstract
ShownOn a mock community the algorithm found most spurious counts, but how often it is wrong varies by more than an order of magnitude.
Sensitivity for cross-talk counts was ~80% to 90% with an error rate from ~1% to ~20%; the conditions spanning that range are not described. · Abstract
ClaimedThe author stops short of claiming the method improves diversity estimates.
The abstract states it is unclear whether the accuracy suffices in practice; no before-and-after diversity comparison is reported. · Abstract
Methods, data and reproducibility
UNCROSS2 is an algorithm for detecting and filtering counts produced by reads assigned to the wrong sample in OTU tables from next-generation sequencing of the 16S ribosomal RNA gene. It was run on eight published datasets to estimate how often this happens, and on a mock community to measure sensitivity and error rate.
- Published 16S rRNA amplicon OTU tables used to estimate cross-talk rates · n = eight published datasets
- Mock community test used to measure sensitivity and error rate
- Methods & tools
- UNCROSS2
Limitations (3)
- The author states that it is not clear whether the algorithm is accurate enough to decisively improve diversity estimates in practice. · stated by the authors
- The reported error rate covers a very wide band (~1% to ~20%), and the abstract neither defines how it is computed nor says what pushes it to the high end. · noted in this summary
- Accuracy is assessed on a single mock community; the eight published datasets only supply rate estimates, not an independent check of filtering accuracy. · noted in this summary
Who should read:Read this if you build OTU tables from 16S rRNA amplicon sequencing and need to detect or filter spurious counts caused by sample-to-sample cross-talk.
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