Novikova, Polina[University of Luxembourg > CRC > Communication department (Communication Department) > ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)]
Wilmes, Paul[University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Ecology]
May, Patrick[University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core]
[en] Protein annotation ; Functional annotation ; Text mining ; Natural language processing
[en] A common approach to genome annotation involves the use of homology-based tools for the prediction of the functional role of proteins. The quality of functional annotations is dependent on the reference data used, as such, choosing the appropriate sources is crucial. Unfortunately, no single reference data source can be universally considered the gold standard, thus using multiple references could potentially increase annotation quality and coverage. However, this comes with challenges, particularly due to the introduction of redundant and exclusive annotations. Through text mining it is possible to identify highly similar functional descriptions, thus strengthening the confidence of the final protein functional annotation and providing a redundancy-free output. Here we present UniFunc, a text mining approach that is able to detect similar functional descriptions with high precision. UniFunc was built as a small module and can be independently used or integrated into protein function annotation pipelines. By removing the need to individually analyse and compare annotation results, UniFunc streamlines the complementary use of multiple reference datasets.
Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Eco-Systems Biology (Wilmes Group)
[University of Luxembourg > CRC > Communication department (Communication Department) > ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)]
