EMITS : Expectation–maximization abundance estimation for fungal ITS communities from long‐read sequencing

Abstract As long‐read amplicon sequencing becomes routine for fungal metabarcoding, species‐level abundance estimation from ITS amplicons remains limited by naive best‐hit classification. Assigning each read to its single highest‐scoring reference misattributes reads among closely related species that share similar ITS sequences, and divides the reads of one species across the several redundant entries that represent it in the reference database, so that no single entry reflects its true abundance. Expectation–maximization (EM) approaches developed for full‐length 16S rRNA, notably EMU (Curry et al., 2022), have recently been benchmarked for fungal ITS metabarcoding (Graetz et al., 2025), but applying EMU to ITS requires custom reference database construction and uses parameters originally tuned for 16S. Here we present EMITS, a Rust‐based tool that applies EM to iteratively resolve ambiguous read‐to‐reference mappings from minimap2 alignments against the UNITE database, producing probabilistic species‐level abundance estimates. EMITS provides UNITE‐native header parsing with automatic accession aggregation, empirically tuned platform presets for current Oxford Nanopore (R10.4.1, R9.4.1, Duplex) and PacBio HiFi chemistries, and integration with ITSxRust (O'Brien et al., 2026b) for upstream ITS region extraction. We validated EMITS using three complementary approaches and benchmarked it against both naive best‐hit counting and EMU (with a UNITE‐formatted reference database). In controlled simulations, EM reduced L1 error by 80%–92% compared to naive counting under realistic noise conditions. For the ATCC fungal ITS mock community, EMITS provided superior within‐genus species resolution in taxonomically challenging genera: it correctly identified Trichophyton mentagrophytes (2.21%) where EMU misattributed substantial abundance to T. tonsurans (1.54%); it suppressed Penicillium rubens false positives (0.002% vs. EMU 0.58%); and it more accurately consolidated Nakaseomyces glabratus abundance across UNITE accessions (12.40% vs. EMU 9.95%). On a 21‐species synthetic UNITE community lacking substantial within‐genus difficulty, all three methods detected expected species at 100% sensitivity, with aggregate L1 errors of 8.64% (naive), 7.30% (EMITS), and 6.71% (EMU). Together with ITSxRust for upstream ITS extraction, EMITS provides an extraction‐to‐abundance workflow tuned for long‐read fungal amplicon profiling. The tool's UNITE‐native handling and ITS‐specific parameter presets address concrete adoption barriers for ecologists working with long‐read sequencing, while its demonstrated advantage in within‐genus species resolution targets the taxonomically challenging fungal genera of greatest clinical, agricultural and ecological importance.