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Title

Functionathon: a manual data mining workflow to generate functional hypotheses for uncharacterized human proteins and its application by undergraduate students

Authors
Zahn-Zabal, Monique
Published in Database. 2021, vol. 2021
Abstract About 10% of human proteins have no annotated function in protein knowledge bases. A workflow to generate hypotheses for the function of these uncharacterized proteins has been developed, based on predicted and experimental information on protein properties, interactions, tissular expression, subcellular localization, conservation in other organisms, as well as phenotypic data in mutant model organisms. This workflow has been applied to seven uncharacterized human proteins (C6orf118, C7orf25, CXorf58, RSRP1, SMLR1, TMEM53 and TMEM232) in the frame of a course-based undergraduate research experience named Functionathon organized at the University of Geneva to teach undergraduate students how to use biological databases and bioinformatics tools and interpret the results. C6orf118, CXorf58 and TMEM232 were proposed to be involved in cilia-related functions; TMEM53 and SMLR1 were proposed to be involved in lipid metabolism and C7orf25 and RSRP1 were proposed to be involved in RNA metabolism and gene expression. Experimental strategies to test these hypotheses were also discussed. The results of this manual data mining study may contribute to the project recently launched by the Human Proteome Organization (HUPO) Human Proteome Project aiming to fill gaps in the functional annotation of human proteins. Database URL: http://www.nextprot.org.
Identifiers
PMID: 34318869
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Research group Calipho (80)
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DUEK, Paula Debora et al. Functionathon: a manual data mining workflow to generate functional hypotheses for uncharacterized human proteins and its application by undergraduate students. In: Database, 2021, vol. 2021. doi: 10.1093/database/baab046 https://archive-ouverte.unige.ch/unige:154204

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Deposited on : 2021-08-27

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