AbstractBackgroundNext-generation sequencing (NGS) technologies have enabled affordable sequencing of billions of short DNA fragments at high throughput, paving the way for population-scale genomics. Genomics data analytics at this scale requires overcoming performance bottlenecks, such as searching for short DNA sequences over long reference sequences.ResultsIn this paper, we introduce LISA (Learned Indexes for Sequence Analysis), a novel learning-based approach to DNA sequence search. We focus on accelerating two of the most essential flavors of DNA sequence search—exact search and super-maximal exact match (SMEM) search. LISA builds on and extends FM-index, which is the state-of-the-art technique widely deployed in genomics tools. Experiments with human, animal, and plant genome datasets indicate that LISA achieves up to 2.2× and 13.3× speedups over the state-of-the-art FM-index based implementations for exact search and super-maximal exact match (SMEM) search, respectively.
Stretch Profile: A pruning technique to accelerate DNA sequence search
