HPC-CLUST: distributed hierarchical clustering for large sets of nucleotide sequences

Nucleotide sequence data are being produced at an ever increasing rate. Clustering such sequences by similarity is often an essential first step in their analysis-intended to reduce redundancy, define gene families or suggest taxonomic units. Exact clustering algorithms, such as hierarchical cluster...

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Bibliographic Details
Published in:Bioinformatics 2014-01, Vol.30 (2), p.287-288
Main Authors: Matias Rodrigues, João F, von Mering, Christian
Format: Article
Language:English
Subjects:
Algorithms
Applications Notes
Cluster Analysis
RNA, Bacterial - genetics
RNA, Ribosomal, 16S - genetics
Sequence Analysis, DNA - methods
Software
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Summary:Nucleotide sequence data are being produced at an ever increasing rate. Clustering such sequences by similarity is often an essential first step in their analysis-intended to reduce redundancy, define gene families or suggest taxonomic units. Exact clustering algorithms, such as hierarchical clustering, scale relatively poorly in terms of run time and memory usage, yet they are desirable because heuristic shortcuts taken during clustering might have unintended consequences in later analysis steps. Here we present HPC-CLUST, a highly optimized software pipeline that can cluster large numbers of pre-aligned DNA sequences by running on distributed computing hardware. It allocates both memory and computing resources efficiently, and can process more than a million sequences in a few hours on a small cluster. Source code and binaries are freely available at http://meringlab.org/software/hpc-clust/; the pipeline is implemented in Cþþ and uses the Message Passing Interface (MPI) standard for distributed computing.
ISSN:1367-4803
1367-4811
1460-2059
DOI:10.1093/bioinformatics/btt657