Abstract Algorithmic solutions to index and search biological databases are a fundamental part of bioinformatics, providing underlying components to many end-user tools. Inexpensive next generation sequencing has filled publicly available databases such as the Sequence Read Archive beyond the capacity of traditional indexing methods. Recently, the Sequence Bloom Tree (SBT) and its derivatives were proposed as a way to efficiently index such data for queries about transcript presence. We build on the SBT framework to construct the HowDe-SBT data structure, which uses a novel partitioning of information to reduce the construction and query time as well as the size of the index. We evaluate HowDe-SBT by both proving theoretical bounds on its performance and using real RNA-seq data. Compared to previous SBT methods, HowDe-SBT can construct the index in less than 36% the time, and with 39% less space, and can answer small-batch queries at least five times faster. HowDe-SBT is available as a free open source program on https://github.com/medvedevgroup/HowDeSBT .