Nathaniel K. Brown

The paper introduces ms2, a suite of algorithms for efficiently computing matching statistics and SMEMs in compressed pangenome indexes, achieving significant speedups over existing methods.

The paper introduces algorithms for constructing RLBWT-based compressed full text indexes in compressed space, reducing memory usage for large-scale pangenome analysis.

Movi 2 improves pangenome indexing by reducing memory footprint and enabling space-time tradeoffs for efficient querying.

The paper improves pangenomic classification accuracy by introducing chain statistics that capture coarse-grained co-linearity without requiring chaining algorithms.

Movi is a fast pangenome index that enables efficient querying of nanopore sequencing reads using move structures, achieving faster speeds than existing methods.

This paper introduces an instrument-free method for single-cell transcriptional profiling using kinetic confinement and bifunctional indexing reagents, enabling rapid and scalable analysis.

The paper introduces MEMO, a pangenome indexing method that efficiently handles k-mer queries and conservation analysis across pangenomes.