# Estimating Sequence Similarity from Read Sets for Clustering Next-Generation Sequencing data

## TL;DR

This paper introduces a novel method to estimate sequence similarity directly from read sets in next-generation sequencing data, bypassing the need for sequence assembly and improving clustering accuracy especially at low coverage.

## Contribution

It adapts the Monge-Elkan similarity for sequence read sets, providing a more effective and computationally feasible similarity measure for clustering without assembly.

## Key findings

- Better approximation of true sequence similarities at low coverage
- Improved clustering results compared to assembly-based methods
- Avoids NP-hard sequence assembly problem

## Abstract

To cluster sequences given only their read-set representations, one may try to reconstruct each one from the corresponding read set, and then employ conventional (dis)similarity measures such as the edit distance on the assembled sequences. This approach is however problematic and we propose instead to estimate the similarities directly from the read sets. Our approach is based on an adaptation of the Monge-Elkan similarity known from the field of databases. It avoids the NP-hard problem of sequence assembly. For low coverage data it results in a better approximation of the true sequence similarities and consequently in better clustering, in comparison to the first-assemble-then-cluster approach.