ArticleFrontiers in bioinformatics2023
Real or fake? Measuring the impact of protein annotation errors on estimates of domain gain and loss events.
Article in Frontiers in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 15 citations in OpenAlex.
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- Evolution of the Metazoan Protein Domain Repertoire Revealed by a Birth-Death-Gain Model.Journal of molecular evolution · 2025Article
- Computational Approaches for Pathway-Centric Analysis of Protein Post-Translational Modifications.Proteomics · 2025Review
- NGATHA carpel development genes evolved in the common ancestor of seed plants.The Plant journal : for cell and molecular biology · 2025Article
- First insight into metal binding proteins from the de novo transcriptome of acanthocephalan parasite Dentitruncus truttae.Scientific reports · 2025Article
- Annotation matters: the effect of structural gene annotation on orthology inference.Bioinformatics (Oxford, England) · 2025Article
- Rampant Interkingdom Horizontal Gene Transfer in Pezizomycotina? An Updated Inspection of Anomalous Phylogenies.International journal of molecular sciences · 2025Article
- Clade-wide fungal proteome analysis reveals structure-function conservation in divergent Dicer proteins.Computational and structural biotechnology journal · 2025Article
- Moving beyond species: fungal function in house dust provides novel targets for potential indicators of mold growth in homes.Microbiome · 2024Article
- Artificial intelligence-based prediction of pathogen emergence and evolution in the world of synthetic biology.Microbial biotechnology · 2024Review
- Functional Consequences of Shifting Transcript Boundaries in Glucose Starvation.Molecular and cellular biology · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein annotation errors can have significant consequences in a wide range of fields, ranging from protein structure and function prediction to biomedical research, drug discovery, and biotechnology. By comparing the domains of different proteins, scientists can identify common domains, classify proteins based on their domain architecture, and highlight proteins that have evolved differently in one or more species or clades. However, genome-wide identification of different protein domain architectures involves a complex error-prone pipeline that includes genome sequencing, prediction of gene exon/intron structures, and inference of protein sequences and domain annotations. Here we developed an automated fact-checking approach to distinguish true domain loss/gain events from false events caused by errors that occur during the annotation process. Using genome-wide ortholog sets and taking advantage of the high-quality human and
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