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Cited by 7,210Genomics and Phylogenetic StudiesBioinformatics and Genomic NetworksMachine Learning in Bioinformatics

The DOI matches its Crossref record; 40 of the 41 references it cites were found in a registry or an index.

UniProt: the Universal Protein Knowledgebase in 2023

Open access
The UniProt Consortium, Bateman, Alex, Martin, Maria-Jesus, Orchard, Sandra, Magrane, Michele, Ahmad, Shadab, Alpi, Emanuele, Bowler-Barnett, Emily H, Britto, Ramona, Bye-A-Jee, Hema, Cukura, Austra, Denny, Paul, Dogan, Tunca, Ebenezer, ThankGod, Fan, Jun, Garmiri, Penelope, da Costa Gonzales, Leonardo Jose, Hatton-Ellis, Emma, Hussein, Abdulrahman, Ignatchenko, Alexandr, Insana, Giuseppe, Ishtiaq, Rizwan, Joshi, Vishal, Jyothi, Dushyanth, Kandasaamy, Swaathi, Lock, Antonia, Luciani, Aurelien, Lugaric, Marija, Luo, Jie, Lussi, Yvonne, MacDougall, Alistair, Madeira, Fabio, Mahmoudy, Mahdi, Mishra, Alok, Moulang, Katie, Nightingale, Andrew, Pundir, Sangya, Qi, Guoying, Raj, Shriya, Raposo, Pedro, Rice, Daniel L, Saidi, Rabie, Santos, Rafael, Speretta, Elena, Stephenson, James, Totoo, Prabhat, Turner, Edward, Tyagi, Nidhi, Vasudev, Preethi, Warner, Kate, Watkins, Xavier, Zaru, Rossana, Zellner, Hermann, Bridge, Alan J, Aimo, Lucila, Argoud-Puy, Ghislaine, Auchincloss, Andrea H, Axelsen, Kristian B, Bansal, Parit, Baratin, Delphine, Batista Neto, Teresa M, Blatter, Marie-Claude, Bolleman, Jerven T, Boutet, Emmanuel, Breuza, Lionel, Gil, Blanca Cabrera, Casals-Casas, Cristina, Echioukh, Kamal Chikh, Coudert, Elisabeth, Cuche, Beatrice, de Castro, Edouard, Estreicher, Anne, Famiglietti, Maria L, Feuermann, Marc, Gasteiger, Elisabeth, Gaudet, Pascale, Gehant, Sebastien, Gerritsen, Vivienne, Gos, Arnaud, Gruaz, Nadine, Hulo, Chantal, Hyka-Nouspikel, Nevila, Jungo, Florence, Kerhornou, Arnaud, Le Mercier, Philippe, Lieberherr, Damien, Masson, Patrick, Morgat, Anne, Muthukrishnan, Venkatesh, Paesano, Salvo, Pedruzzi, Ivo, Pilbout, Sandrine, Pourcel, Lucille, Poux, Sylvain, Pozzato, Monica, Pruess, Manuela, Redaschi, Nicole, Rivoire, Catherine, Sigrist, Christian J A, Sonesson, Karin, Sundaram, Shyamala, Wu, Cathy H, Arighi, Cecilia N, Arminski, Leslie, Chen, Chuming, Chen, Yongxing, Huang, Hongzhan, Laiho, Kati, McGarvey, Peter, Natale, Darren A, Ross, Karen, Vinayaka, C R, Wang, Qinghua, Wang, Yuqi, Zhang, Jian·2023·Nucleic Acids Research
DOI 10.1093/nar/gkac1052PMC PMC9825514
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Abstract

The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this publication we describe enhancements made to our data processing pipeline and to our website to adapt to an ever-increasing information content. The number of sequences in UniProtKB has risen to over 227 million and we are working towards including a reference proteome for each taxonomic group. We continue to extract detailed annotations from the literature to update or create reviewed entries, while unreviewed entries are supplemented with annotations provided by automated systems using a variety of machine-learning techniques. In addition, the scientific community continues their contributions of publications and annotations to UniProt entries of their interest. Finally, we describe our new website (https://www.uniprot.org/), designed to enhance our users' experience and make our data easily accessible to the research community. This interface includes access to AlphaFold structures for more than 85% of all entries as well as improved visualisations for subcellular localisation of proteins.

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Crossref · 10.1093/nar/gkac1052

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Figures

Figure 1 (A) Growth of UniProt databases over the last 10 years and (B) Growth of Reference Proteomes and taxonomic breakdown.
Figure 1 (A) Growth of UniProt databases over the last 10 years and (B) Growth of Reference Proteomes and taxonomic breakdown.CC BY · PubMed Central
Figure 2. Statistics of UniProt crowdsourcing activity. (A) Cumulative number of submissions, unique publications and proteins covered, and number of contributors for selected releases. Release 2019_08 was the first release where community submissions appeared, and 2022_03 is the latest release at the time of this manuscript preparation. (B) Taxonomic distribution of unique protein entries that have at least one publication submitted by the community. (C) Block chart showing the relative distribution of annotations by categories.
Figure 2. Statistics of UniProt crowdsourcing activity. (A) Cumulative number of submissions, unique publications and proteins covered, and number of contributors for selected releases. Release 2019_08 was the first release where community submissions appeared, and 2022_03 is the latest release at the time of this manuscript preparation. (B) Taxonomic distribution of unique protein entries that have at least one publication submitted by the community. (C) Block chart showing the relative distribution of annotations by categories.CC BY · PubMed Central
Figure 3 (A) Card view of results following a search of the UniProt website and (B). Table view of results following the same search.
Figure 3 (A) Card view of results following a search of the UniProt website and (B). Table view of results following the same search.CC BY · PubMed Central
Figure 4. UniProtKB entry CL18A_HUMAN (UniProtKB: A5D8T8) shows an embedded SwissBioPics image: the generic animal cell (Eumetazoa) is selected based on organism taxonomy. It contains 71 interactive locations, of which the endoplasmic reticulum, Golgi apparatus, endosome and secretory space are highlighted using annotations from the UniProt entry.
Figure 4. UniProtKB entry CL18A_HUMAN (UniProtKB: A5D8T8) shows an embedded SwissBioPics image: the generic animal cell (Eumetazoa) is selected based on organism taxonomy. It contains 71 interactive locations, of which the endoplasmic reticulum, Golgi apparatus, endosome and secretory space are highlighted using annotations from the UniProt entry.CC BY · PubMed Central

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  1. Paysan-Lafosse, 2022, InterProin 2022, Nucleic Acids Res.

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Authors
The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., Bye-A-Jee, H., Cukura, A., Denny, P., Dogan, T., Ebenezer, T., Fan, J., Garmiri, P., da Costa Gonzales, L. J., Hatton-Ellis, E., Hussein, A., Ignatchenko, A., Insana, G., Ishtiaq, R., Joshi, V., Jyothi, D., Kandasaamy, S., Lock, A., Luciani, A., Lugaric, M., Luo, J., Lussi, Y., MacDougall, A., Madeira, F., Mahmoudy, M., Mishra, A., Moulang, K., Nightingale, A., Pundir, S., Qi, G., Raj, S., Raposo, P., Rice, D. L., Saidi, R., Santos, R., Speretta, E., Stephenson, J., Totoo, P., Turner, E., Tyagi, N., Vasudev, P., Warner, K., Watkins, X., Zaru, R., Zellner, H., Bridge, A. J., Aimo, L., Argoud-Puy, G., Auchincloss, A. H., Axelsen, K. B., Bansal, P., Baratin, D., Batista Neto, T. M., Blatter, M.-C., Bolleman, J. T., Boutet, E., Breuza, L., Gil, B. C., Casals-Casas, C., Echioukh, K. C., Coudert, E., Cuche, B., de Castro, E., Estreicher, A., Famiglietti, M. L., Feuermann, M., Gasteiger, E., Gaudet, P., Gehant, S., Gerritsen, V., Gos, A., Gruaz, N., Hulo, C., Hyka-Nouspikel, N., Jungo, F., Kerhornou, A., Le Mercier, P., Lieberherr, D., Masson, P., Morgat, A., Muthukrishnan, V., Paesano, S., Pedruzzi, I., Pilbout, S., Pourcel, L., Poux, S., Pozzato, M., Pruess, M., Redaschi, N., Rivoire, C., Sigrist, C. J. A., Sonesson, K., Sundaram, S., Wu, C. H., Arighi, C. N., Arminski, L., Chen, C., Chen, Y., Huang, H., Laiho, K., McGarvey, P., Natale, D. A., Ross, K., Vinayaka, C. R., Wang, Q., Wang, Y., & Zhang, J.
ORCID
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2022-11-21
Volume
51
Issue
D1
Pages
D523-D531
ISSN
  • 0305-1048 (ISSN-L)
  • 1362-4962
DOI
10.1093/nar/gkac1052
PMC
PMC9825514
OpenAlex
W4309506674
Cited by
7,210
References
41
Citations by year
2026: 1511 · 2025: 2294 · 2024: 2343 · 2023: 887 · 2022: 78
FWCI
464.54
Type
journal article
Language
en
Open access
Licence
Locations
  • Nucleic Acids Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • National Human Genome Research Institute (OD/DPCPSI/ODSS)
  • National Institute of Allergy and Infectious Diseases
  • National Institute on Aging
  • National Institute of General Medical Sciences
  • National Institute of Diabetes and Digestive and Kidney Diseases
  • National Eye Institute
  • National Cancer Institute
  • National Heart, Lung, and Blood Institute
  • National Institutes of Health (U24HG007822)
  • NHGRI NIH (HG002273)
  • Biotechnology and Biological Sciences Research Council (BB/T010541/1, BB/S01781X/1)
  • Open Targets
  • SERI
  • European Molecular Biology Laboratory
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
protein sequence annotation, reference proteomes, machine learning annotation, AlphaFold protein structure, subcellular localization, data pipelines
MeSH terms
Machine Learning, Amino Acid Sequence, Proteome, Databases, Protein, Knowledge Bases

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