| Literature DB >> 31398336 |
Simon H Ye1, Katherine J Siddle2, Daniel J Park3, Pardis C Sabeti4.
Abstract
Metagenomic sequencing is revolutionizing the detection and characterization of microbial species, and a wide variety of software tools are available to perform taxonomic classification of these data. The fast pace of development of these tools and the complexity of metagenomic data make it important that researchers are able to benchmark their performance. Here, we review current approaches for metagenomic analysis and evaluate the performance of 20 metagenomic classifiers using simulated and experimental datasets. We describe the key metrics used to assess performance, offer a framework for the comparison of additional classifiers, and discuss the future of metagenomic data analysis.Entities:
Mesh:
Year: 2019 PMID: 31398336 PMCID: PMC6716367 DOI: 10.1016/j.cell.2019.07.010
Source DB: PubMed Journal: Cell ISSN: 0092-8674 Impact factor: 41.582