Estimating Sequence Similarity from Read Sets for Clustering Sequencing Data

Clustering biological sequences is a central task in bioinformatics. The typical result of new-generation sequencers is a set of short substrings (“reads”) of a target sequence, rather than the sequence itself. 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 and in empirical experiments 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.