Efficient taxa identification using a pangenome index

Tools that classify sequencing reads against a database of reference sequences require efficient index data structures. Ther-index is a compressed full-text index that answers substring presence/absence, count and locate queries in space proportional to the amount of distinct sequence in the database:O(r)space whereris the number of Burrows-Wheeler runs. To date, ther-index has lacked the ability to quickly classify matches according to which reference sequences (or sequence groupings, i.e.~taxa) a match overlaps. We present new algorithms and methods for solving this problem. Specifically, given a collectionDofddocumentsD = {T_1, T_2, ..., T_d }over an alphabet of size sigma, we extend ther-index withO(rd)additional words to support document listing queries for a patternS[1..m]that occurs inndocdocuments inDinO(m log log_w(sigma + n/r) + ndoc)time andO(rd)space, wherewis the machine word size. Applied in a bacterial mock community experiment, our method is up to 3 times faster than a comparable method that uses the standardr-index locate queries. We show that our method classifies both simulated and real nanopore reads at the strain level with higher accuracy compared to other approaches. Finally, we present strategies for compacting this structure in applications where read lengths or match lengths can be bounded.