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Mantis: flexible and consensus-driven genome annotation.

Pedro Queirós1, Francesco Delogu1, Oskar Hickl2

  • 1Systems Ecology, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367 Esch-sur-Alzette, Luxembourg.

Gigascience
|June 2, 2021
PubMed
Summary
This summary is machine-generated.

Mantis is a new protein annotation tool that integrates data from multiple sources for accurate functional predictions. This flexible and reproducible tool enhances the analysis of large omics datasets, improving protein function annotation efficiency.

Keywords:
HMMbioinformaticsconsensushomologyprotein function annotation

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • The (meta-)omics fields generate vast amounts of high-resolution data, enabling functional inference for unannotated proteins.
  • Protein function annotation involves identifying protein domains and assigning biological roles, but current tools face challenges in speed, flexibility, and reproducibility.
  • Integrating knowledge from diverse data sources is crucial in the big data era to overcome limitations of single-reference analyses.

Purpose of the Study:

  • To develop a novel protein function annotation tool, Mantis, that addresses existing challenges in speed, flexibility, and reproducibility.
  • To create a consensus-driven annotation output by integrating knowledge from multiple reference data sources.
  • To enhance the accuracy and efficiency of protein function annotation for single genomes and large metagenomes.

Main Methods:

  • Implementation of Mantis, a protein annotation tool utilizing database identifier intersection and text mining.
  • Integration of knowledge from multiple reference data sources into a single, consensus-driven output.
  • Application of a depth-first search algorithm for domain-specific annotation and parallelized processing for improved runtime.

Main Results:

  • Mantis provides flexible and reproducible protein function annotations, customizable for various research needs.
  • Domain-specific annotation using a depth-first search algorithm significantly improved annotation performance.
  • The parallelized implementation of Mantis ensures short runtimes while delivering high-coverage, high-quality protein function annotations.

Conclusions:

  • Mantis is a user-friendly protein function annotation tool producing high-quality, consensus-driven results.
  • The tool is scalable, suitable for analyzing data from single genomes to large metagenomes.
  • Mantis is available under the MIT license, promoting accessibility and further development.