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Efficient large-scale sequence comparison by locality-sensitive hashing
Abstract
Motivation: Comparison of multimegabase genomic DNA sequences is a popular technique for finding and annotating conserved genome features. Performing such comparisons entails finding many short local alignments between sequences up to tens of megabases in length. To process such long sequences efficiently, existing algorithms find alignments by expanding around short runs of matching bases with no substitutions or other differences. Unfortunately, exact matches that are short enough to occur often in significant alignments also occur frequently by chance in the background sequence. Thus, these algorithms must trade off between efficiency and sensitivity to features without long exact matches. Results: We introduce a new algorithm, lsh-all-pairs, to find ungapped local alignments in genomic sequence with up to a specified fraction of substitutions. The length and substitution rate of these alignments can be chosen so that they appear frequently in significant similarities yet still remain rare in the background sequence. The algorithm finds ungapped alignments efficiently using a randomized search technique, locality-sensitive hashing. We have found lsh-all-pairsto be both efficient and sensitive for finding local similarities with as little as 63% identity in mammalian genomic sequences up to tens of megabases in length Availability: Contact the author at the address below. Contact: jbuhler@cs.washington.edu Supplementary information: The sequences and local alignment data described in this work are available at http://bio.cs.washington.edu/jbuhler-bioinformatics-2001/.
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Cites
Approximate nearest neighbors: towards removing the curse of dimensionality
Indyk, P., & Motwani, R. (1998) · Proceedings of the thirtieth annual ACM symposium on Theory of computing - STOC '98 · doi:10.1145/276698.276876 · Cited by 4,188
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Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Mapping short DNA sequencing reads and calling variants using mapping quality scores
Li, H., Ruan, J., & Durbin, R. (2008) · Genome Research · doi:10.1101/gr.078212.108 · Cited by 2,638
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Andoni, A., & Indyk, P. (2008) · Communications of the ACM · doi:10.1145/1327452.1327494 · Cited by 1,437
Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions
Andoni, A., & Indyk, P. (2006) · 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06) · doi:10.1109/focs.2006.49 · Cited by 1,067
Metadata
- Authors
- Buhler, J.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2001-05-01
- Volume
- 17
- Issue
- 5
- Pages
- 419-428
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2109034006
- Cited by
- 259
- References
- 1
- FWCI
- 14.27
- Type
- journal article
- Language
- en
- Open access
- closed
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