Mar 31, 2016

Evaluating performance of metagenomic characterization algorithms using in silico datasets generated with FASTQSim

BioRxiv : the Preprint Server for Biology
Darrell O. RickeNelson Chiu

Abstract

Background In silico bacterial, viral, and human truth datasets were generated to evaluate available metagenomics algorithms. Sequenced datasets include background organisms, creating ambiguity in the true source organism for each read. Bacterial and viral datasets were created with even and staggered coverage to evaluate organism identification, read mapping, and gene identification capabilities of available algorithms. These truth datasets are provided as a resource for the development and refinement of metagenomic algorithms. Algorithm performance on these truth datasets can inform decision makers on strengths and weaknesses of available algorithms and how the results may be best leveraged for bacterial and viral organism identification and characterization. Source organisms were selected to mirror communities described in the Human Microbiome Project as well as the emerging pathogens listed by the National Institute of Allergy and Infectious Diseases. The six in silico datasets were used to evaluate the performance of six leading metagenomics algorithms: MetaScope, Kraken, LMAT, MetaPhlAn, MetaCV, and MetaPhyler. Results Algorithms were evaluated on runtime, true positive organisms identified to the genus and species levels...Continue Reading

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Mentioned in this Paper

Cerebellar Decompression Injury
Virus
Genes
Leukocyte Migration Agarose Test
Evaluation
Reading Frames (Nucleotide Sequence)
Organism
Microbial
Genus
Asthenia

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