Construction of edit-distance graphs for large sets of short reads through minimizer-bucketing.

MOTIVATION: Pairs of short reads with small edit distances, along with their unique molecular identifier tags, have been exploited to correct sequencing errors in both reads and tags. However, brute-force identification of these pairs is impractical for large datasets containing ten million or more reads due to its quadratic complexity. Minimizer-bucketing and locality-sensitive hashing have been used to partition read sets into buckets of similar reads, allowing edit-distance calculations only within each bucket. However, challenges like minimizing missing pairs, optimizing bucketing parameters, and exploring combination bucketing to improve pair detection remain. RESULTS: We define an edit-distance graph for a set of short reads, where nodes represent reads, and edges connect reads with small edit distances, and present a heuristic method, reads2graph, for high completeness of edge detection. Reads2graph uses three techniques: minimizer-bucketing, an improved Order-Min-Hash technique to divide large bins, and a novel graph neighbourhood multi-hop traversal within large bins to detect more edges. We then establish optimal bucketing settings to maximize ground truth edge coverage per bin. Extensive testing demonstrates that read2graph can achieve 97%-100% completeness in most cases, outperforming brute-force identification in speed while providing a superior speed-completeness balance compared to using a single bucketing method like Miniception or Order-Min-Hash. AVAILABILITY AND IMPLEMENTATION: reads2graph is publicly available at https://github.com/JappyPing/reads2graph.