Mantis: flexible and consensus-driven genome annotation

Abstract

Background: The past decades have seen a rapid development of the (meta-)omics fields, producing an unprecedented amount of data. Through the use of well-characterized datasets we can infer the role of previously functionally unannotated proteins from single organisms and consortia. In this context, protein function annotation allows the identification of regions of interest (i.e. domains) in protein sequences and the assignment of biological functions. Despite the existence of numerous tools, some challenges remain, specifically in terms of speed, flexibility, and reproducibility. In the era of big data it also becomes increasingly important to cease limiting our findings to a single reference, coalescing knowledge from different data sources, thus overcoming some limitations in overly relying on computationally generated data. Results: We implemented a protein annotation tool - Mantis, which uses text mining to integrate knowledge from multiple reference data sources into a single consensus-driven output. Mantis is flexible, allowing for total customization of the reference data used, adaptable, and reproducible across different research goals and user environments. We implemented a depth-first search algorithm for domain-specific annotation, which led to an average 0.038 increase in precision when compared to sequence-wide annotation. Mantis is fast, annotating an average genome in 25-40 minutes, whilst also outputting high-quality annotations (average coverage 81.4\%, average precision 0.892). Conclusions: 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 available at https://github.com/PedroMTQ/mantis

Challenges, Strategies, and Perspectives for Reference-Independent Longitudinal Multi-Omic Microbiome Studies

“…Microbiomes may be studied from a gene-centric perspective ( Roume et al, 2015 ), which requires read or contig-level taxonomic classification ( Segata et al, 2012 ; Wood and Salzberg, 2014 ), ORF prediction ( Hyatt et al, 2010 ; Rho et al, 2010 ), and gene annotation ( Seemann, 2014 ; Buchfink et al, 2015 ; Franzosa et al, 2018 ; Queirós et al, 2020 ). Metagenome assembled genomes (MAGs) provide genomic context and can be obtained through binning ( Chen et al, 2020 ; Yue et al, 2020 ) followed by taxonomic classification ( Bremges et al, 2020 ; Chaumeil et al, 2020 ) and functional annotation.…”

Mantis: flexible and consensus-driven genome annotation

“…It also dynamically configures its execution depending on the resources available. A conda environment and extensive documentation [ 54 ] are available.…”

“…The MANTIS.config file allows for configuration of the reference data and its respective weights and enables the compilation of specific eggNOG TSHMMs. For more details, see the documentation [ 54 ]. Owing to issues with Python’s multiprocessing in MacOS, and the fact that HMMER is not available on Windows, Mantis is only available on Linux-based systems.…”

“…The data and code supporting the results of this article are available at https://git-r3lab.uni.lu/pedro.queiros/mantis_supplements . An archival copy of the code and supporting data is available via the GigaScience repository, GigaDB [ 77 ].…”

Unification of functional annotation descriptions using text mining

“…As a result, a collection of 1681 non-redundant high-quality archaeal genomes was composed and used to create archaea-specific HMMs. Protein-coding genes were predicted with Prodigal (Hyatt et al 2010), and functionally annotated with Mantis (Queirós et al 2020). MMseqs2 (Steinegger and Söding 2017) was used to cluster proteins by sequence similarity.…”