Estimating Sequence Similarity from Read Sets for Clustering Sequencing Data
| Autor: | Filip Železný, Petr Ryšavý |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Sequence Computer science business.industry Sequence assembly Pattern recognition 02 engineering and technology computer.software_genre Substring Hierarchical clustering Set (abstract data type) 03 medical and health sciences 030104 developmental biology Similarity (network science) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Edit distance Data mining Artificial intelligence Cluster analysis business computer |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319463483 IDA |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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