ArticlePLoS computational biology2013
Biases in the experimental annotations of protein function and their effect on our understanding of protein function space.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.
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Who cites it
73 citing papers in PubMed.
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- CB-Search: a method for searching for similar protein motifs with biased compositions.BMC bioinformatics · 2026Article
- Integrative network analysis reveals organizational principles of the endocannabinoid system.Journal of cannabis research · 2026Article
- Interpretable prediction of nucleic acid-binding proteins using a protein language model.Bioinformatics advances · 2026Article
- Bioactive peptides targeting dyslipidemia and atherosclerosis: from computational discovery and data bottlenecks to precision medicine translation.Frontiers in molecular biosciences · 2026Review
- Beyond sequence similarity: toward function-based screening of nucleic acid synthesis.Frontiers in bioengineering and biotechnology · 2026Article
- Computational approaches to enzymatic reaction assignment: a review of methods, validations, and future directions.Briefings in bioinformatics · 2025Review
- Differences in GenBank and RefSeq annotations may affect genomics data interpretation formSphere · 2025Article
- PlasmoFP: leveraging deep learning to predict protein function of uncharacterized proteins across the malaria parasite genus.bioRxiv : the preprint server for biology · 2025Article
- Protein functional site annotation using local structure embeddings.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A longitudinal analysis of function annotations of the human proteome reveals consistently high biases.Database : the journal of biological databases and curation · 2025Article
- Urbanization leads to asynchronous homogenization of soil microbial communities across biomes.Environmental science and ecotechnology · 2025Article
- EvoWeaver: large-scale prediction of gene functional associations from coevolutionary signals.Nature communications · 2025Article
- Functional profiling of the sequence stockpile: a protein pair-based assessment of in silico prediction tools.Bioinformatics (Oxford, England) · 2025Article
- KEGG orthology prediction of bacterial proteins using natural language processing.BMC bioinformatics · 2024Article
- Gut microbiome-derived hydrolases-an underrated target of natural product metabolism.Frontiers in cellular and infection microbiology · 2024Review
- CELL-E 2: Translating Proteins to Pictures and Back with a Bidirectional Text-to-Image Transformer.Advances in neural information processing systems · 2023Article
- Predicting enzymatic function of protein sequences with attention.Bioinformatics (Oxford, England) · 2023Article
- An objective criterion to evaluate sequence-similarity networks helps in dividing the protein family sequence space.PLoS computational biology · 2023Article
13 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
The ongoing functional annotation of proteins relies upon the work of curators to capture experimental findings from scientific literature and apply them to protein sequence and structure data. However, with the increasing use of high-throughput experimental assays, a small number of experimental studies dominate the functional protein annotations collected in databases. Here, we investigate just how prevalent is the "few articles - many proteins" phenomenon. We examine the experimentally validated annotation of proteins provided by several groups in the GO Consortium, and show that the distribution of proteins per published study is exponential, with 0.14% of articles providing the source of annotations for 25% of the proteins in the UniProt-GOA compilation. Since each of the dominant articles describes the use of an assay that can find only one function or a small group of functions, this leads to substantial biases in what we know about the function of many proteins. Mass-spectrometry, microscopy and RNAi experiments dominate high throughput experiments. Consequently, the functional information derived from these experiments is mostly of the subcellular location of proteins, and of the participation of proteins in embryonic developmental pathways. For some organisms, the information provided by different studies overlap by a large amount. We also show that the information provided by high throughput experiments is less specific than those provided by low throughput experiments. Given the experimental techniques available, certain biases in protein function annotation due to high-throughput experiments are unavoidable. Knowing that these biases exist and understanding their characteristics and extent is important for database curators, developers of function annotation programs, and anyone who uses protein function annotation data to plan experiments.
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