Locality sensitive hashing for the edit distance

Abstract

AbstractMotivationSequence alignment is a central operation in bioinformatics pipeline and, despite many improvements, remains a computationally challenging problem. Locality Sensitive Hashing (LSH) is one method used to estimate the likelihood of two sequences to have a proper alignment. Using an LSH, it is possible to separate, with high probability and relatively low computation, the pairs of sequences that do not have an alignment from those that may have an alignment. Therefore, an LSH reduces in the overall computational requirement while not introducing many false negatives (i.e., omitting to report a valid alignment). However, current LSH methods treat sequences as a bag ofk-mers and do not take into account the relative ordering ofk-mers in sequences. And due to the lack of a practical LSH method for edit distance, in practice, LSH methods for Jaccard similarity or Hamming distance are used as a proxy.ResultsWe present an LSH method, called Order Min Hash (OMH), for the edit distance. This method is a refinement of the minHash LSH used to approximate the Jaccard similarity, in that OMH is not only sensitive to thek-mer contents of the sequences but also to the relative order of thek-mers in the sequences. We present theoretical guarantees of the OMH as a gapped LSH.Contactgmarcais@cs.cmu.edu,carlk@cs.cmu.edu