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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 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, \(\alpha =4\), \(l=40\).
References
Bao, E., Jiang, T., Kaloshian, I., Girke, T.: Seed: efficient clustering of next-generation sequences. Bioinformatics 27(18), 2502–2509 (2011)
Fowlkes, E.B., Mallows, C.L.: A method for comparing two hierarchical clusterings. J. Am. Stat. Assoc. 78(383), 553–569 (1983)
Hernandez, D., et al.: De novo bacterial genome sequencing: millions of very short reads assembled on a desktop computer. Genome Res. 18(5), 802–809 (2008)
Jalovec, K., Železný, F.: Binary classification of metagenomic samples using discriminative dna superstrings. In: 8th International Workshop on Machine Learning in Systems Biology, MLSB 2014 (2014)
Lander, E.: Initial impact of the sequencing of the human genome. Nature 470(7333), 187–197 (2011)
Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. Sov. Phys. Dokl. 10(8), 707–710 (1966)
Malhotra, R., Elleder, D., Bao, L., Hunter, D.R., Acharya, R., Poss, M.: Clustering pipeline for determining consensus sequences in targeted next-generation sequencing. arXiv (Conrell University Library) arXiv:1410.1608 (2016)
Monge, A.E., Elkan, C.P.: The webfind tool for finding scientific papers over the worldwide web. In: Proceedings of the 3rd International Congress on Computer Science Research (1996)
Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48(3), 443–453 (1970)
Saitou, N., Nei, M.: The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4(4), 406–425 (1987)
Simpson, J.T., et al.: ABySS: a parallel assembler for short read sequence data. Genome Res. 9(6), 1117–1123 (2009)
Sokal, R.R., Michener, C.D.: A statistical method for evaluating systematic relationships. Univ. Kansas Sci. Bull. 38, 1409–1438 (1958)
Železný, F., Jalovec, K., Tolar, J.: Learning meets sequencing: a generality framework for read-sets. In: 24th International Conference on Inductive Logic Programming, Late-Breaking Papers, ILP 2014 (2014)
Wagner, R.A., Fischer, M.J.: The string-to-string correction problem. J. ACM 21(1), 168–173 (1974). http://doi.acm.org/10.1145/321796.321811
Warren, R.L., et al.: Assembling millions of short DNA sequences using SSAKE. Bioinformatics 23(4), 500–501 (2007)
Weitschek, E., Santoni, D., Fiscon, G., Cola, M.C.D., Bertolazzi, P., Felici, G.: Next generation sequencing reads comparison with an alignment-free distance. BMC Res. Notes 7(1), 869 (2014)
Acknowledgment
This work was supported by Czech Science Foundation project 14-21421S.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-46349-0_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46348-3
Online ISBN: 978-3-319-46349-0
eBook Packages: Computer ScienceComputer Science (R0)