A calibrated novelty flag for fungal ITS metabarcoding: choosing the error rate at which sequences are declared new

Environmental fungal surveys routinely recover internal transcribed spacer (ITS) sequences that cannot be assigned at fine taxonomic ranks, the so-called fungal “dark matter.” Such sequences are set aside by thresholding a similarity or confidence score at a conventional value. Those conventions do abstain, but the error rate a threshold implies is neither stated nor selectable, and a threshold defined for one kind of score does not transfer to another.

We present a conformal novelty flag that supplies what is missing: each query receives a p -value with a distribution-free guarantee that the rate of falsely declaring a known sequence novel is bounded by a user-chosen α . We evaluate it on a leave-one-genus-out benchmark built from UNITE, on alignment identity, a k -mer bootstrap consensus and two neural classifiers’ output probabilities, and on a soil fungal dataset.

The flag holds its nominal rate across two orders of magnitude in α , so an operating point can be chosen rather than inherited: at α = 0.05 it fires on 4.3% of known-genus queries and recovers 19.2% of genuinely novel genera. The cutoff holding a 5% error rate here is 64.8% identity, nowhere near the customary 97%, showing how little a threshold carries its error rate between datasets. Coverage transferred across eleven settings spanning those four scores, two amplicon regions and a sevenfold change in reference size, all within 1.1 percentage points of nominal, while detection ranged from 5.2% to 53.3%: the guarantee is on the error rate and not on power, and two of our settings are valid but uninformative. Applied to soil data the flag identifies 20.7% of amplicon sequence variants as novel at a controlled 5% error rate, 16.3% under an abundance filter. Half of those recur near-identically among GlobalFungi’s unnamed environmental variants while fewer than one in ten matches a named species hypothesis, a sixfold skew towards the uncatalogued against 1.9-fold for sequences the flag passes.

The flag turns an arbitrary cutoff into a decision with a stated error rate, and in doing so converts dark matter from a residue into a set of prioritizable targets for formal description.