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, which misattributes reads among closely related species sharing similar ITS sequences and fragments abundance across redundant database entries. 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 [OBrien et al., 2026] 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. On 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), 7.48% (EMITS), and 6.71% (EMU). Together with ITSxRust for upstream ITS extraction, EMITS provides a complete pipeline tuned for long-read fungal amplicon profiling.