metashot prok-quality

metashot prok-quality assesses the quality of prokaryotic genomes reconstructed from metagenomic contigs and bins by producing MIMAG-compliant genome quality reports.


Key Features:

  • Genome quality assessment: Evaluates the quality of genomes reconstructed from metagenomic contigs and bins originating from bacterial and archaeal species.
  • MIMAG compliance: Generates genome quality reports that adhere to the Minimum Information about a Metagenome-Assembled Genome (MIMAG) standard.
  • Nextflow workflow: Implemented as a Nextflow pipeline to organize and execute analytical steps.
  • Container support: Executes within Docker or Singularity containers to ensure reproducibility and portability across computing clusters and cloud infrastructures.
  • Automated end-to-end processing: Provides an automated workflow for systematic quality assessment of prokaryotic draft genomes.
  • Integration with metashot collection: Operates as part of the metashot suite of analysis pipelines for metagenomic processing.

Scientific Applications:

  • Metagenomic genome quality evaluation: Assessment of draft prokaryotic genomes derived from metagenomic sequencing and binning of complex microbial communities.
  • Standardized reporting: Production of MIMAG-compliant quality reports to support standardized reporting of genome quality in research outputs.
  • Reproducible analyses: Enabling consistent quality assessment across computing clusters and cloud-based batch infrastructures.

Methodology:

Implemented as a Nextflow workflow executed within Docker or Singularity containers that takes metagenomic contigs and bins as input and outputs MIMAG-compliant genome quality reports.

Topics

Details

License:
GPL-3.0
Cost:
Free of charge
Tool Type:
command-line tool
Operating Systems:
Mac, Linux, Windows
Programming Languages:
Python, Other
Added:
1/13/2022
Last Updated:
1/13/2022

Operations

Publications

Albanese D, Donati C. Large-scale quality assessment of prokaryotic genomes with metashot/prok-quality. F1000Research. 2021;10:822. doi:10.12688/f1000research.54418.1.

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