Efficient taxa identification using a pangenome index

Abstract

Tools that classify sequencing reads against a database of reference sequences require efficient index data-structures. The r-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, where r is the number of Burrows–Wheeler runs. To date, the r-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 collection D of d documents, D = {T1, T2, . . ., Td} over an alphabet of size s, we extend the r-index with O(rd) additional words to support document listing queries for a pattern S[1..m] that occurs in ndoc documents in D in O(m log logw(s + n/ r) + ndoc) time and O(rd) space, where w is the machine word size. Applied in a bacterial mock community experiment, our method is up to three times faster than a comparable method that uses the standard r-index locate queries. We show that our method classifies both simulated and real nanopore reads at the strain level with higher accuracy compared with other approaches. Finally, we present strategies for compacting this structure in applications in which read lengths or match lengths can be bounded.