MetaShot: an accurate workflow for taxon classification of host-associated microbiome from shotgun metagenomic data

B Fosso, M Santamaria, M D'Antonio, D Lovero, G Corrado, E Vizza, N Passaro, A R Garbuglia, M R Capobianchi, M Crescenzi, G Valiente, G Pesole

Research output: Contribution to journalArticle

Abstract

Summary: Shotgun metagenomics by high-throughput sequencing may allow deep and accurate characterization of host-associated total microbiomes, including bacteria, viruses, protists and fungi. However, the analysis of such sequencing data is still extremely challenging in terms of both overall accuracy and computational efficiency, and current methodologies show substantial variability in misclassification rate and resolution at lower taxonomic ranks or are limited to specific life domains (e.g. only bacteria). We present here MetaShot, a workflow for assessing the total microbiome composition from host-associated shotgun sequence data, and show its overall optimal accuracy performance by analyzing both simulated and real datasets.

Availability and Implementation: https://github.com/bfosso/MetaShot.

Contact: graziano.pesole@uniba.it.

Supplementary information: Supplementary data are available at Bioinformatics online.

Original languageEnglish
Pages (from-to)1730-1732
Number of pages3
JournalBioinformatics
Volume33
Issue number11
DOIs
Publication statusPublished - Jun 1 2017

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Keywords

  • Algorithms
  • Bacteria/classification
  • Fungi/classification
  • High-Throughput Nucleotide Sequencing/methods
  • Humans
  • Metagenomics/methods
  • Microbiota/genetics
  • Sequence Analysis, DNA/methods
  • Software
  • Viruses/classification
  • Workflow

Cite this

Fosso, B., Santamaria, M., D'Antonio, M., Lovero, D., Corrado, G., Vizza, E., Passaro, N., Garbuglia, A. R., Capobianchi, M. R., Crescenzi, M., Valiente, G., & Pesole, G. (2017). MetaShot: an accurate workflow for taxon classification of host-associated microbiome from shotgun metagenomic data. Bioinformatics, 33(11), 1730-1732. https://doi.org/10.1093/bioinformatics/btx036