A parallel computational framework for ultra-large-scale sequence clustering analysis.

ABSTRACT:

Motivation

The rapid development of sequencing technology has led to an explosive accumulation of genomic data. Clustering is often the first step to be performed in sequence analysis. However, existing methods scale poorly with respect to the unprecedented growth of input data size. As high-performance computing systems are becoming widely accessible, it is highly desired that a clustering method can easily scale to handle large-scale sequence datasets by leveraging the power of parallel computing.

Results

In this paper, we introduce SLAD (Separation via Landmark-based Active Divisive clustering), a generic computational framework that can be used to parallelize various de novo operational taxonomic unit (OTU) picking methods and comes with theoretical guarantees on both ac

SUBMITTER: Zheng W 

PROVIDER: S-EPMC6931356 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature