Reference : Mantis: flexible and consensus-driven genome annotation
Scientific journals : Article
Life sciences : Environmental sciences & ecology
Life sciences : Microbiology
Life sciences : Multidisciplinary, general & others
Systems Biomedicine
http://hdl.handle.net/10993/47599
Mantis: flexible and consensus-driven genome annotation
English
Queirós, Pedro [> >]
Delogu, Francesco mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) >]
Hickl, Oskar mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core]
May, Patrick mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core]
Wilmes, Paul mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Ecology]
Jun-2021
GigaScience
10
6
Yes
International
2047-217X
[en] Genome annotation ; Function predictin ; Consensus-driven protein annotation
[en] The rapid development of the (meta-)omics fields has produced an unprecedented amount of high-resolution and high-fidelity data. Through the use of these datasets we can infer the role of previously functionally unannotated proteins from single organisms and consortia. In this context, protein function annotation can be described as the identification of regions of interest (i.e., domains) in protein sequences and the assignment of biological functions. Despite the existence of numerous tools, challenges remain in terms of speed, flexibility, and reproducibility. In the big data era, it is also increasingly important to cease limiting our findings to a single reference, coalescing knowledge from different data sources, and thus overcoming some limitations in overly relying on computationally generated data from single sources.We implemented a protein annotation tool, Mantis, which uses database identifiers intersection and text mining to integrate knowledge from multiple reference data sources into a single consensus-driven output. Mantis is flexible, allowing for the customization of reference data and execution parameters, and is reproducible across different research goals and user environments. We implemented a depth-first search algorithm for domain-specific annotation, which significantly improved annotation performance compared to sequence-wide annotation. The parallelized implementation of Mantis results in short runtimes while also outputting high coverage and high-quality protein function annotations.Mantis is a protein function annotation tool that produces high-quality consensus-driven protein annotations. It is easy to set up, customize, and use, scaling from single genomes to large metagenomes. Mantis is available under the MIT license at https://github.com/PedroMTQ/mantis.
Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Eco-Systems Biology (Wilmes Group)
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/47599
10.1093/gigascience/giab042
https://doi.org/10.1093/gigascience/giab042
giab042
FnR ; FNR11823097 > Paul Wilmes > MICROH-DTU > Microbiomes In One Health > 01/09/2018 > 28/02/2025 > 2017

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