Prama Ghosh, Amity University Kolkata
There has been a rapid development in the field of meta-omics in the past decade thereby producing an unprecedented amount of data. Methods such as Next Generation Sequencing produce a large amount of data that needs to be interpreted to understand the role of previously functionally unannotated proteins of an organism. This can be done by using protein function annotation (PFA). PFA allows us to identify a region of interest or domain in a protein sequence and the biological function associated with it. Classic PFA tools lack flexibility and also fail to work well with multi-domain proteins.
Mantis is a Python-based PFA tool designed to overcome all the previous challenges and produce accurate high-quality annotations with multi-domain proteins and multiple datasets. On implementing a depth-first search algorithm for domain-specific annotation, an average 0.038 increase was observed in precision as compared to sequence-wide annotation. Mantis can produce an average coverage of 81.4\% with an average precision of 0.892 while annotating an average genome in 25-40 minutes.
Independent of any default reference datasets, Mantis is fast, precise, flexible, and customizable. It uses parallelized execution thereby making the most efficient use of available hardware which allows it to automatically download and compile several high-quality reference sources. Being a versatile and reproducible tool, Mantis solves the problem of high-throughput consensus-driven protein annotation of many genome and metagenome sequencing projects.
Licensed under the MIT, Mantis is available at https://github.com/PedroMTQ/mantis.
Reference: Mantis: flexible and consensus-driven genome annotation,Pedro Queirós, Francesco Delogu, Oskar Hickl, Patrick May, Paul Wilmes
bioRxiv 2020.11.02.360933; DOI: https://doi.org/10.1101/2020.11.02.360933
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