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Taxonomic assignment for large-scale metagenomic data on high-perfomance systems

Vinh Van Le, Hoai Van Tran, Hieu Ngoc Duong, Giang Xuan Bui, Lang Van Tran


Metagenomics is a powerful approach to study environment samples which do not require the isolation and cultivation of individual organisms. One of the essential tasks in a metagenomic project is to identify the origin of reads, referred to as taxonomic assignment. Due to the fact that each metagenomic project has to analyze large-scale datasets, the metatenomic assignment is very much computation intensive. This study proposes a parallel algorithm for the taxonomic assignment problem, called SeMetaPL, which aims to deal with the computational challenge. The proposed algorithm is evaluated with both simulated and real datasets on a high performance computing system. Experimental results demonstrate that the algorithm is able to achieve good performance and utilize resources of the system efficiently. The software implementing the algorithm and all test datasets can be downloaded at


DNA sequences, homology search, metagenomics, parallel algorithm, taxonomic assignment

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