SMeta, a binning tool using single-cell sequences to aid in reconstructing species from metagenome accurately
Yuhao Zhang,
Mingyue Cheng,
Kang Ning
Abstract:Because of the large volume and complex structure of metagenomic data, traditional binning methods are often hard to classify microbial metagenomes effectively. To deal with these challenges, introducing longer and more accurate single-cell sequencing data is a possible solution. Inspired by the existing MetaBAT2 tool, this study develops a new vector-based binning algorithm, SMeta, which uses both metagenomic and single-cell sequencing data. SMeta is specifically designed for eukaryotic microbial metagenomes,… Show more
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