Spectral Jaccard Similarity: A New Approach to Estimating Pairwise Sequence Alignments.
PATTERNS 2020;
1:100081. [PMID:
33205128 PMCID:
PMC7660437 DOI:
10.1016/j.patter.2020.100081]
[Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 06/09/2020] [Accepted: 07/03/2020] [Indexed: 01/02/2023]
Abstract
Pairwise sequence alignment is often a computational bottleneck in genomic analysis pipelines, particularly in the context of third-generation sequencing technologies. To speed up this process, the pairwise k-mer Jaccard similarity is sometimes used as a proxy for alignment size in order to filter pairs of reads, and min-hashes are employed to efficiently estimate these similarities. However, when the k-mer distribution of a dataset is significantly non-uniform (e.g., due to GC biases and repeats), Jaccard similarity is no longer a good proxy for alignment size. In this work, we introduce a min-hash-based approach for estimating alignment sizes called Spectral Jaccard Similarity, which naturally accounts for uneven k-mer distributions. The Spectral Jaccard Similarity is computed by performing a singular value decomposition on a min-hash collision matrix. We empirically show that this new metric provides significantly better estimates for alignment sizes, and we provide a computationally efficient estimator for these spectral similarity scores.
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