MUGAN: multi-GPU accelerated AmpliconNoise server for rapid microbial diversity assessment
Author(s) -
Byunghan Lee,
Hyeyoung Min,
Sungroh Yoon
Publication year - 2018
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bty096
Subject(s) - bottleneck , metagenomics , computer science , pipeline (software) , data mining , visualization , biology , biochemistry , gene , embedded system , programming language
Metagenomic sequencing has become a crucial tool for obtaining a gene catalogue of operational taxonomic units (OTUs) in a microbial community. A typical metagenomic sequencing produces a large amount of data (often in the order of terabytes or more), and computational tools are indispensable for efficient processing. In particular, error correction in metagenomics is crucial for accurate and robust genetic cataloging of microbial communities. However, many existing error-correction tools take a prohibitively long time and often bottleneck the whole analysis pipeline.
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