Efficient Index Maintenance Under Dynamic Genome Modification

TL;DR

This paper introduces SkipPatch, a novel data structure for efficiently maintaining a k-mer-based index on dynamically changing genomes, significantly improving update and query performance over existing methods.

Contribution

The paper presents SkipPatch, a new data structure combining a hash-based k-mer index with a skip list to efficiently handle genome modifications.

Findings

SkipPatch outperforms dynamic extended suffix array in speed

SkipPatch efficiently maintains genome edits in real-time

The approach is practical for large-scale genomic analysis

Abstract

Efficient text indexing data structures have enabled large-scale genomic sequence analysis and are used to help solve problems ranging from assembly to read mapping. However, these data structures typically assume that the underlying reference text is static and will not change over the course of the queries being made. Some progress has been made in exploring how certain text indices, like the suffix array, may be updated, rather than rebuilt from scratch, when the underlying reference changes. Yet, these update operations can be complex in practice, difficult to implement, and give fairly pessimistic worst-case bounds. We present a novel data structure, SkipPatch, for maintaining a k-mer-based index over a dynamically changing genome. SkipPatch pairs a hash-based k-mer index with an indexable skip list that is used to efficiently maintain the set of edits that have been applied to the original genome. SkipPatch is practically fast, significantly outperforming the dynamic extended suffix array in terms of update and query speed.