The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
In silico identification of archaeal DNA-binding proteins
Open accessPublished version of a preprint
Abstract
Motivation: The rapid advancement of next-generation sequencing technologies has generated an immense volume of genetic data. However, these data are unevenly distributed, with well-studied organisms being disproportionately represented, while other organisms, such as from archaea, remain significantly underexplored. The study of archaea is particularly challenging due to the extreme environments they inhabit and the difficulties associated with culturing them in the laboratory. Despite these challenges, archaea likely represent a crucial evolutionary link between eukaryotic and prokaryotic organisms, and their investigation could shed light on the early stages of life on Earth. Yet, a significant portion of archaeal proteins are annotated with limited or inaccurate information. Among the various classes of archaeal proteins, DNA-binding proteins are of particular importance. While they represent a large portion of every known proteome, their identification in archaea is complicated by the substantial evolutionary divergence between archaeal and the other better studied organisms. Results: To address the challenges of identifying DNA-binding proteins in archaea, we developed Xenusia, a neural network-based tool capable of screening entire archaeal proteomes to identify DNA-binding proteins. Xenusia has proven effective across diverse datasets, including metagenomics data, successfully identifying novel DNA-binding proteins, with experimental validation of its predictions. Availability and implementation: Xenusia is available as a PyPI package, with source code accessible at https://github.com/grogdrinker/xenusia, and as a Google Colab web server application at xenusia.ipynb.
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Cites
Highly accurate protein structure prediction with AlphaFold
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Complex archaea that bridge the gap between prokaryotes and eukaryotes
Spang, A., Saw, J. H., Jørgensen, S. L., Zaremba-Niedzwiedzka, K., Martijn, J., Lind, A. E., van Eijk, R., Schleper, C., Guy, L., & Ettema, T. J. G. (2015) · Nature · doi:10.1038/nature14447 · Cited by 1,324
Asgard archaea illuminate the origin of eukaryotic cellular complexity
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Metadata
- Authors
- Donvil, L., Housmans, J. A. J., Peeters, E., Vranken, W., & Orlando, G.
- Affiliations
- Vrije Universiteit Brussel (BE)
- Ghent University (BE)
- Université Libre de Bruxelles (BE)
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Orlando, Gabriele
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2025-05
- Volume
- 41
- Issue
- 5
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC12065626
- OpenAlex
- W4410028085
- Cited by
- 3
- References
- 3
- Citations by year
- 2026: 3
- FWCI
- 0.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- VUBIR (Vrije Universiteit Brussel) (submittedVersion)
- Funders
- Vrije Universiteit Brussel
- University of Montpellier and by an ANR (ANR-23-CPJ1-0061)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- protein function prediction, neural network classifier, archaeal proteome, metagenomics, protein annotation, early evolution of life, archaea
- MeSH terms
- Archaea, Computer Simulation, DNA-Binding Proteins, Software, Neural Networks, Computer, Computational Biology, Archaeal Proteins, Proteome
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