Multiseed Lossless Filtration

TL;DR

This paper introduces a multiseed lossless filtration method for approximate string matching, leveraging multiple spaced seeds to improve bioinformatics applications, with algorithms for seed family parameters and large-scale oligonucleotide selection.

Contribution

It presents novel algorithms and techniques for constructing efficient seed families using multiple spaced seeds, enhancing bioinformatics sequence matching.

Findings

Effective seed family parameters computed

Improved seed family construction techniques

Successful large-scale oligonucleotide selection application

Abstract

We study a method of seed-based lossless filtration for approximate string matching and related bioinformatics applications. The method is based on a simultaneous use of several spaced seeds rather than a single seed as studied by Burkhardt and K\"arkk\"ainen [1]. We present algorithms to compute several important parameters of seed families, study their combinatorial properties, and describe several techniques to construct efficient families. We also report a large-scale application of the proposed technique to the problem of oligonucleotide selection for an EST sequence database.