Melange

A Snakemake workflow that streamlines structural and functional annotation of prokaryote genomes

Genome annotation involves identifying open reading frames (ORFs) and comparing them with information from curated databases for functional annotation. However, there are challenges with the current methods used for genome annotation, such as limited customization and automation options in web portals, and requiring technical know-how for command-line interfaces (CLIs). To address these challenges, our solution is Melange, a user-friendly, customizable, and scalable CLI tool for genome annotation. Melange combines five databases, including Pfam, COG, KEGG, CAZyme, and MEROPS, to provide a more comprehensive and reliable functional annotation of proteins in a genome. The tool is suitable for small to large-scale genome annotation efforts and can be used for various annotations needs.

Citing Melange

At the moment, Melange does not have a publication describing its features (we are working on it). Please use a link to Melange Github when referring to this tool.

Melange Contributions

• Sandra Godinho Silva ^1,2 - MicroEcoEvo - iBB, IST.
• Tina Keller-Costa ^1,2 - MicroEcoEvo - iBB, IST.
• Ulisses Nunes da Rocha ³ - Microbial Systems Data Science - UFZ, Leipzig.
• Rodrigo Costa ^1,2 - MicroEcoEvo - iBB, IST.

¹ Institute for Bioengineering and Biosciences, Department of Bioengineering, Instituto Superior Técnico da Universidade de Lisboa, Lisbon, Portugal
² Associate Laboratory, Institute for Health and Bioeconomy, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
³ Department of Environmental Microbiology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany

Funding

This work was supported by the Portuguese Foundation for Science and Technology (FCT) through the research project PTDC/MAR-BIO/1547/2014 and by ‘Direção-Geral de Política do Mar’, Ministry of the Sea through the “Fundo the Azul” funding program of (grant number FA_05_2017_032). SGS is the recipient of a PhD scholarship conceded by FCT (PD/BD/143029/2018) and was supported by a FEMS-GO-2019-511 research and training grant conceded by the Federation of European Microbiological Societies (FEMS). Further support was provided from national funds through FCT in the scope of the projects UIDB/04565/2020 and UIDP/04565/2020 of the Research Unit Institute for Bioengineering and Biosciences - iBB and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy - i4HB. UNR was funded by the Helmholtz Young Investigator grant VH-NG-1248 Micro “Big Data”.