Abstract
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.
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Notes
- 1.
Should the right hand side be non-integer, we neglect its fractional part.
- 2.
Here we alter the Monge-Elkan similarity into a distance measure. The standard way of using Monge-Elkan is as a similarity measure with \(\min \) replaced by \(\max \) and distance calculation by similarity calculation.
- 3.
Strictly speaking, this reasoning is incorrect if read a is drawn from a place close to A’s margins, more precisely, if it starts in fewer than t (\(t+l\), respectively) symbols from A’s left (right) margin, as then not all of the 2t shifts are possible. This is however negligible due to Ineq. (2).
- 4.
- 5.
Implementation is available on https://github.com/petrrysavy/readsIDA2016.
- 6.
AF389115, AF389119, AY260942, AY260945, AY260949, AY260955, CY011131, CY011135, CY011143, HE584750, J02147, K00423 and outgroup AM050555. The genomes are available at http://www.ebi.ac.uk/ena/data/view/accession.
- 7.
AB073912, AB236320, AM050555, D13784, EU376394, FJ560719, GU076451, JN680353, JN998607, M14707, U06714, U46935, U66304, U81989, X05817, Y13051 and outgroup AY884005.
- 8.
CY011119, CY011127, CY011140, FJ966081, AF144300, AF144300, J02057, AJ437618, FR717138, FJ869909, L00163, KJ938716, KP202150, D00664, HM590588, KM874295, \(\alpha =4\), \(l=40\).
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Acknowledgment
This work was supported by Czech Science Foundation project 14-21421S.
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Ryšavý, P., Železný, F. (2016). Estimating Sequence Similarity from Read Sets for Clustering Sequencing Data. In: Boström, H., Knobbe, A., Soares, C., Papapetrou, P. (eds) Advances in Intelligent Data Analysis XV. IDA 2016. Lecture Notes in Computer Science(), vol 9897. Springer, Cham. https://doi.org/10.1007/978-3-319-46349-0_18
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