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Fast embedding methods for clustering tens of thousands of sequences

Paper ID Volume ID Publish Year Pages File Format Full-Text
15354 1406 2008 5 PDF Available
Title
Fast embedding methods for clustering tens of thousands of sequences
Abstract

Most sequence clustering methods require a full distance matrix to be computed between all pairs of sequences. This requires computer memory and time proportional to N2 for N sequences. For small N or say up to 10 000 or so, this can be accomplished in reasonable times for sequences of moderate length. For very large N, however, this becomes increasingly prohibitive. In this paper, we have tested variations on a class of published embedding methods that have been designed for clustering large numbers of complex objects where the individual distance calculations are expensive. These methods involve embedding the sequences in a space where the similarities within a set of sequences can be closely approximated without having to compute all pair-wise distances. We show how this approach greatly reduces computation time and memory requirements for clustering large numbers of sequences and demonstrate the quality of the clusterings by benchmarking them as guide trees for multiple alignments. Source code is available on request from the authors.

Keywords
Clustering; Edit distance; Multiple sequence alignment; Data embedding
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Fast embedding methods for clustering tens of thousands of sequences
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 32, Issue 4, August 2008, Pages 282–286
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
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