standOff. When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote shown on the same row, so the borrowing can be checked against the thing borrowed from without leaving this page. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.| second reading | alternatives | an independent re-reading of a facet, recorded whether or not it agrees |
| relation to other work | claim_relations | how this paper stands to another: agrees, contradicts, extends, qualifies |
| claim type | claim_type | the shape of the claim: existence, mechanism, method, critique, prediction, null |
| concept edge | concept_edges | an asserted relation between two concepts, with the sentence asserting it |
| concept | concepts | an idea the paper works with, quoted where the paper uses it |
| content tag | content_tags | a finding, definition, quantity, statistic or variable named in the paper |
| contribution | contribution | what the paper claims to add |
| control | controls | an experiment run to eliminate a rival explanation |
| limitation | limitations | what the paper says its evidence cannot support |
| mechanism | mechanisms | how the reported effect is said to come about |
| method | method | how the paper produced its evidence |
| object of study | object_of_study | what the paper is about — the thing it investigates |
| phenomenon | phenomenon | the effect or regularity under study |
| research question | research_question | the question the paper sets itself |
| sense | senses | a distinct use of a term, quoted where the paper fixes it |
| unrecordable relation | unhoused_relations | a relation the paper asserts that this vocabulary cannot express, with the reason |
The reference list is not part of the text we hold for this paper. Our copy was extracted from the publisher's page and the bibliography did not survive that extraction, so the article below ends before its references. Nothing has been removed by us.
Abstract. Abstract research_questionconcepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗content_tagsThe 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. alternativesIn this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. alternativesWe describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/ . concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗sensessensesINTRODUCTION content_tagsThe UniProt databases exist to support biological and biomedical research by providing a complete compendium of all known protein sequence data linked to a summary of the experimentally verified, or computationally predicted, functional information about that protein. object_of_studyclaim_typemechanismslimitationsconcepts↗concepts↗concepts↗sensessensesconcept_edgescontent_tagscontent_tagscontent_tags↗The UniProt Knowledgebase (UniProtKB) combines reviewed UniProtKB/Swiss-Prot entries, to which data have been added by our expert biocuration team, with the unreviewed UniProtKB/TrEMBL entries that are annotated by automated systems. The UniRef databases cluster sequence sets at various levels of sequence identity and sensesconcept_edgesthe UniProt Archive (UniParc) delivers a complete set of known sequences, including historical obsolete sequences. unhoused_relationsUniProt additionally integrates, interprets, and standardizes data from multiple selected resources to add biological knowledge and associated metadata to protein records and acts as a central hub from which users can link out to 180 other resources. In recognition of the quality of our data, and the service we provide, UniProt was recognised as an ELIXIR Core Data Resource in 2017 ( 1 ) and received the CoreTrustSeal certification in 2020. claim_relationsThe data resource fully supports the concept_edgesFindable, Accessible, Interoperable and Reusable (FAIR) data principles ( 2 ), for example by making data available in a number of community recognised formats, such as text, XML and RDF and via Application Programming Interfaces (API)s and File Transfer Protocol (FTP) downloads, providing stable and traceable identifiers for protein sequence and protein sequence features and by fully evidencing our data sources throughout. We have also reviewed and updated our data licencing policies. UniProt is continually evolving to meet new challenges while still working to capture all available protein sequence data and to curate the ever-increasing amount of functional data described in the scientific literature. alternativesIn our last update published in this journal in 2019 ( 3 ), we described how we are responding to the growth in microbial protein sequence records, largely derived from high-quality metagenomic assembled genomes. These will increasingly be added to by large-scale eukaryotic sequencing programs, such as the Darwin Tree of Life ( www.darwintreeoflife.org ) and Earth Biogenome ( www.earthbiogenome.org ) projects. limitationsclaim_relationscontent_tagscontent_tagsCollectively, these have already resulted in the number of entries contained in UniProtKB growing by >65 million records, an increase of >50% in just 2 years. As the volume of sequence data continues to grow, we will continue to explore different ways to ensure database sustainability and scalability whilst still providing the best possible service to our user community. concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗PROGRESS AND NEW DEVELOPMENTS Growth of sequence records in UniProt limitationscontent_tagsUniProt release 2020_04 contains over 189 million sequence records (Figure 1 ), with >292 000 proteomes, the complete set of proteins believed to be expressed by an organism, originating from completely sequenced viral, bacterial, archaeal and eukaryotic genomes available through the UniProtKB Proteomes portal ( https://www.uniprot.org/proteomes/ ). The majority of these proteomes continue to be based on the translation of genome sequence submissions to the INSDC source databases—ENA, GenBank and the DDBJ ( 4 )—supplemented by genomes sequenced and/or annotated by groups such as Ensembl ( 5 ), NCBI RefSeq ( 6 ), Vectorbase ( 7 ) and WormBase ParaSite ( 8 ). Viral proteomes are manually checked and verified and periodically added to the database. Figure 1. Growth in the number of entries in the UniProt databases over the last decade. concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗sensesUniProt proteomes provide the set of proteins currently believed to be expressed by an organism. Previously, this dataset only consisted of complete proteomes derived from fully sequenced genomes. This has now been updated to enable the user to access a wider set of proteomes with different quality and completeness metrics. contributionlimitationscontrolsconcepts↗content_tagsTo enable researchers to evaluate proteome completeness and expected gene content, we have concept_edgesadopted the BUSCO (Benchmarking Universal Single-Copy Orthologs) scoring method for vertebrate, arthropod, fungal, and prokaryotic organisms on the Proteomes portal, in addition to providing details of species and the protein count. BUSCO v3 ( 9 ) identifies complete, duplicated, fragmented, and potentially missing genes by comparison to a defined set of near-universal single copy orthologs. As a result of our adoption of the BUSCO algorithm, a number of incomplete and poor-quality proteomes were identified and removed from UniProtKB. The Proteomes webpage has been redesigned to enable users to view full details of their proteome(s) of interest in a single table view (Figure 2 ). method↗mechanismscontrolsconcepts↗sensescontent_tagscontent_tags↗content_tagsWe also display the results of the ‘Complete Proteome Detector’ (CPD), an in-house algorithm, which statistically evaluates the completeness and quality of each proteome by directly comparing it to those of a group of at least three closely taxonomically related species. The CPD classifies each proteome as either ‘standard’, ‘close to standard’ or an ‘outlier’, according to protein count vs. the standard distribution of protein count expected for completeness in comparison to a cluster of closely related organisms ( https://www.uniprot.org/help/assessing_proteomes ). We additionally provide the assessment of the genome assembly status imported from the source of the genome assembly and annotation (e.g. Ensembl or RefSeq). Users may sort by BUSCO or CPD score and/or filter by membership of the Reference Proteome set (∼7% of total proteomes) unhoused_relationsunhoused_relationssensesA Reference Proteome is one that has been selected either by the research community or by computational clustering ( 10 ) as providing the best annotated proteome in their cluster and is stably maintained as the chosen representative unless a higher quality proteome is identified in that cluster. UniProt Proteome pages now also provide a link to download a one-to-one protein set for the corresponding number of unique genes found in the genome. For each gene, a single protein sequence is algorithmically chosen from the proteome. sensesconcept_edgesThis allows users to get a gene-centric subset of representative proteins for a given genome, as opposed to the full proteome which includes all proteins (e.g. including isoforms) that map to the genome. Figure 2. Bacillus subtilis proteomes viewed on the Proteomes webpage with BUSCO and CPD scores. The left-hand panel suggests further option by which the user could filter the data, for example by only selecting reference proteomes. phenomenonmechanismscontent_tagsAs previously described ( 3 ), concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗we continue to remove almost identical proteomes of the same species from UniProtKB ( https://www.uniprot.org/help/proteome_redundancy ), leaving only a relevant representative proteome in that section of the database. unhoused_relationsThe redundant proteome sequences are available through UniParc to researchers and stable proteome identifiers (of the form UPXXXXXXXXX, where Xs are integers) are maintained for each redundant proteome to ensure findability. UniRef protein sequence clusters facilitate sequence similarity searches, functional annotation, gene prediction, and genome and proteome comparisons. concept_edgesWe have adopted the MMseqs2 algorithm to improve the speed of UniRef production ( 11 ), decreasing the time taken to perform UniRef50 clustering from four weeks to 60 hours, and improved procedures to compute proteome clusters and identify representative proteomes, pan proteomes, and core and accessory proteomes. concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗sensesExpert curation The evaluation of experimental data published in the scientific literature, and summarizing key points of biological relevance in the appropriate reviewed UniProtKB/Swiss-Prot record, is fundamental to the operation of the UniProt database. The functional information extracted from the literature is added both in the form of human readable summaries and via structured vocabularies, such as the Gene Ontology (GO) ( 12 ). The curators also work to improve the computational accessibility of UniProt records, for example updating and replacing the existing textual descriptions of biochemical reactions in UniProtKB using the Rhea knowledgebase of biochemical reactions ( 13 ). Rhea ( 14 ) uses the chemical ontology ChEBI (Chemical Entities of Biological Interest) ( 15 ) to describe reaction participants, their chemical structures and chemical transformations. The complete ChEBI ontology is indexed to support hierarchical searches on the UniProt website so that a user searching on a top-level term such as ‘phosphatidylinositol’ (CHEBI:28874) will find reactions involving derivatives such as 1-phosphatidyl-1D-myo-inositol 3,4,5-trisphosphate (CHEBI:16618). Users can also search using Rhea identifiers as well as identifiers, names, synonyms and chemical structures (encoded as InChIKeys) from ChEBI. A user-friendly visualization for chemical reactions has also been developed to enable Rhea reactions to be viewed on the website within the appropriate UniProt entry (Figure 3 ). UniProtKB/Swiss-Prot currently includes annotations for 8,058 unique Rhea reactions, which feature in 220 003 distinct UniProtKB/Swiss-Prot protein records (39.1% of all UniProtKB/Swiss-Prot records are annotated with Rhea) (release 2020_04 of 12 August 2020). The annotation of pseudoenzymes has also been reviewed and updated, in collaboration with experts in this field ( 16 ). Figure 3. Catalytic activity comment and Rhea reaction visualized by the reaction graphic for the histone-lysine N -methyltransferase EHMT2 (UniProtKB: Q96KQ7 ). concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗To further ensure our data is both human-readable and also computationally-tractable and continues to adhere to the FAIR principles ( 2 ), we are working to standardize the representation of all existing UniProt data on the functional impact of human variation. UniProtKB/Swiss-Prot has curated over 81 000 variants in 13,000 human protein sequence records from peer-reviewed literature, annotated with information on the functional impact and clinical significance, when known. Over 30 000 of these variants have been associated with Mendelian diseases. Clinical significance is evaluated using the guidelines of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG-AMP) ( 17 ) and ClinGen tools such as the pathogenicity calculator ( 18 ), with all clinical interpretations routinely submitted to ClinVar to promote reuse ( 19 ). In addition to the increased use of structured vocabularies to enhance accessibility to UniProtKB records, we have also improved the presentation of the information within each entry. concept_edgesThe vast majority of multi-exon genes undergo alternative splicing to produce a variety of splice isoform proteins, which can potentially increase the functional diversity of proteins. Currently at least 95% of human genes are believed to be alternatively spliced ( 20 , 21 ) resulting in an estimated 75 000 distinct protein coding sequences. concept_edgesPost-translational proteolytic cleavage, where proteins are cleaved to remove some additional amino acid(s) or portion of protein, creates yet more mature amino-acid chains as a single polyprotein may generate multiple bioactive proteins or peptides. We have adapted our data model to capture machine-readable functional annotations for specific isoforms and polyprotein cleavage products, and now provide such knowledge for >5000 protein sequence entries. This enables the users to mine the data to identify cases where alternative protein sequences generated from the same gene have different functions. UniProtKB provides stable identifiers to both isoforms and to post-translationally cleaved chains which are used by many of our collaborators to identify these specific protein sequences in their own resources. This, in turn, has allowed us to further improve our mapping of imported data, for example binary protein interactions imported from the IMEx Consortium of molecular interaction databases are now displayed at the specific isoform/post-processed chain level. The representation of isoform-specific annotations and sequence features has been enhanced in the website to facilitate the exploration of this information in UniProt (Figure 4 ). Figure 4. Information extracted from an entry describing Hepatitis C viral protein (UniProtKB: P27958 ) highlighting annotation added at the processed mature chain level, describing the p21 core protein. (PRO_0000037566). concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗sensesResponding to the COVID-19 pandemic The broad expert knowledge of our curation team enables us to quickly respond to areas of new and rapidly expanding biology. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was identified as the cause of the 2019–2020 COVID-19 viral outbreak and ensuing pandemic. Its genome sequence was first made publicly available by the International Nucleotide Sequence Database Collaboration (INSDC) on 10 January 2020 (accession number MN908947 ). UniProt curators specializing in viral proteomes rapidly annotated the proteins encoded by this viral genome, first by similarity to other closely related coronaviruses, and subsequently by updating relevant entries with experimental data as soon as this was published. A pre-release dataset was made publicly available, first as text files on the UniProt FTP site, followed by the launch of a dedicated COVID-19 disease portal in March 2020 ( https://covid-19.uniprot.org ), providing the latest available pre-release UniProtKB data for the SARS-CoV-2 coronavirus and other viral and human entries relating to the COVID-19 outbreak. As the pandemic has progressed, these entries have been prioritized for further update to support global research efforts, with information relating to a more granular understanding of protein functionality, 3D-structure, binary interactions, and complex formation being added when either published or deposited in collaborating resources. concept_edgesThe COVID portal is currently being updated more frequently than the standard 8-weekly UniProt release cycle to ensure the research community accesses these data in a timely manner. The COVID-19 disease portal is a prototype of our intention to provide disease-centric access points to the wealth of data contained in UniProtKB records. Another recent example of a disease-focused curation effort has been the update of records relating to Alzheimer's disease, including proteins containing a disease-related amino-acid variant, their interacting partners and model organism proteins important for our understanding of disease initiation and progression. This work has been supported by clinical researchers active in the field who contributed to a number of workshops held in both the USA and UK and have suggested key protein targets for focused curation ( 22 , 23 ) and provided valuable user input into how this data should be accessed. concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗sensessensesAutomatic annotation The ever-increasing amount of genomic data arising from current sequencing projects means that the proportion of unreviewed records in UniProtKB/TrEMBL describing largely predicted proteins represents by far the largest, and most rapidly growing, section of UniProtKB. These unreviewed records are enriched with functional annotation by systems using the protein classification tool InterPro ( 24 ), which classifies sequences at superfamily, family and subfamily levels, and predicts the occurrence of functional domains and important sites. In UniProtKB/TrEMBL entries, domains predicted by the InterPro member databases PROSITE, SMART or Pfam are used to automatically provide domain annotations. The semi-automated rule-based computational annotation UniRule system ( 25 ) annotates experimentally uncharacterized proteins based on similarity to known experimentally characterized proteins, adding properties, such as protein name, functional annotation, catalytic activity, pathway, GO terms and subcellular location. We continue to increase the number of UniRules used for annotation and this set has now grown to 6768 (release 2020_04) rules in total. To complement the expert guided process of creating UniRules, we have recently (release 2020_04) unhoused_relationsintroduced the Association-Rule-Based Annotator (ARBA), a multiclass, self-training annotation system for automatic classification and annotation of UniProtKB proteins ( 3 ). This replaces the previous rule-based SAAS system. ARBA is trained on UniProtKB/Swiss-Prot, then uses rule mining techniques to generate concise annotation models with the highest representativeness and coverage based on the properties of InterPro group membership and taxonomy. ARBA employs a data exclusion set that censors data not suitable for computational annotation (such as specific biophysical or chemical properties) and generates human-readable rules for each release which are made available at https://www.uniprot.org/arba/ . 22 894 concept_edgesARBA rules were used to annotate 87 325 890 proteins in release 2020_04, increasing the combined coverage of the rule-based annotation systems from 35% to 49% in UniProtKB/TrEMBL. Sequence feature predictions are currently excluded from annotation by ARBA. Additionally, in release 2020_04, more than 15 million uncharacterized protein names have been improved using InterPro member database signatures, updating their name to ‘domain X containing protein’ following the International Protein Nomenclature Guidelines ( https://www.uniprot.org/docs/International_Protein_Nomenclature_Guidelines.pdf ). For example, the uncharacterized Western lowland gorilla protein UniProtKB:G3RLC3 has now been renamed ‘SH2 domain-containing protein’ giving biological information to the user. content_tagsThis system includes adding names based on domains of unknown function (e.g. UniProtKB:A0A009EMH9 DUF4372 domain-containing protein) as, although not immediately informative, it enables protein grouping thus improving the chances of eventually assigning a function to that domain. controlscontent_tagsAll automatic annotations are labelled with their evidence/source. As previously described ( 3 ), UniFIRE is an open-source Java-based framework and tool developed to apply the UniProt annotation rules on given protein sequences and provided by UniProt to share our knowledge in computational annotation and our rule-based systems ( https://gitlab.ebi.ac.uk/uniprot-public/unifire ). This system is freely available for groups to use for in-house protein annotation projects ( 26 ) or to contribute their own rules in the URML (UniProt Rule Markup Language) format which may be reused for the annotation of UniProtKB entries. The automatic annotation systems described above require the presence of an ordered region of protein that can be recognized as a domain or provide a signature of family membership which has been identified by an InterPro member database. However, over 20% of unreviewed proteins in UniProt do not contain any InterPro signature regions, and many InterPro signatures are not associated with transferable annotation. The largest part of missing annotation seems to derive from intrinsically disordered (ID) protein regions, therefore we have collaborated with the MobiDB-lite resource to provide a consensus-based prediction of long disorder ( 27 ). The method uses eight different predictors to derive a consensus which is then filtered for spurious short predictions. 109 144 661 predictions of regions of disorder, plus those described as ‘Basic’, ‘Polar’, ‘Acidic’, ‘Polyampholyte’ and ‘Pro-’ or ’Cys-rich’ have been added to 37 286 893 unreviewed entries and it is planned to also import these annotations into the appropriate UniProtKB/Swiss-Prot entries. concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗sensesData Integration UniProtKB integrates large-scale datasets, mapping these data onto the appropriate protein sequence records and displaying the mappings via the ProtVista visualisation tool ( 28 ) and downloadable via FTP and APIs ( 29 ). Clinically relevant sources of variation (e.g. 100K genomes, gnomAD and ClinVar SNPs) are mapped to protein features and variants using a pre-calculated mapping of the genomic coordinates for the amino acids at the beginning and end of each exon and the conversion of UniProt position annotations to their genomic coordinates ( 30 ). Functional positional annotations from the UniProt human reference proteome are now being mapped to the corresponding genomic coordinates on the GRCh38 version of the human genome for each release of UniProt. These mappings are also available as BED files or as part of a UniProt genomic track hub and can be downloaded from the UniProt FTP site ( www.uniprot.org/downloads ). unhoused_relationsAligning variants to protein features, such as functional domains and active sites, ligand binding sites and PTMs in the UniProt record, can provide mechanistic insights into how specific variants can lead to disease or resistance to a drug or to a pathogen. UniProt additionally integrates and visualizes unique and non-unique peptides identified by mass spectrometry proteomic data deposited through the ProteomeXchange Consortium ( 31 ) (e.g. PeptideAtlas ( 32 ), MassIVE ( 33 ) and jPOST ( 34 )) and other large-scale initiatives (CPTAC ( 35 ), ProteomicsDB ( 36 ), MaxQB ( 37 ), ETD and CTDP ( 38 )). mechanismsconcepts↗concepts↗sensesconcept_edgescontent_tagscontent_tags↗content_tagscontent_tags↗Peptide identifications can be taken as evidence that a protein has been validated (PE = 1) using a variation of the HPP guidelines (in brief, at least two unique peptides of seven amino acids or more or, for proteins where this cannot be achieved due to sequence constraints, one unique peptide of ten amino acids or more has been mapped to a protein). concept_edgesUniProt also provides the new format PEFF (PSI Extended FASTA Format) proposed by the HUPO-PSI (Human Proteome Organization-Proteomics Standard Initiative) for sequence databases ( 39 ) to be used by sequence search engines and other associated tools (e.g. spectral libraries search tools). The UniProt PEFF format currently represents variation and sequence data and is available through the Proteins API service ( https://www.ebi.ac.uk/proteins/api/doc/ ) for 31 species. concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗sensesCommunity curation in UniProt UniProt users have always actively engaged with us and provide important feedback to the resource. unhoused_relationsThe significant number of requests we receive through the Help Desk for articles and annotations to be added to protein entries prompted the development of the ‘Community submission’ page, where researchers are able to add articles that they deem relevant to an entry and provide optional basic annotation by selecting the topics relevant to each paper from a controlled list and/or adding short statements about protein name, function, and disease, as described in the publication. sensesContributors are asked to supply their ORCID ( https://orcid.org/ ), a researcher personal ID, which is used to both validate that the submission is genuine and to give credit to the submitter for their work (Figure 5 ). This enables us to leverage the scientific community as a resource for enhancing our curated content, emulating a model already adopted by a number of model organism databases, such as WormBase ( 40 ), PomBase ( 41 ) and FlyBase ( 42 ). Figure 5. Community contribution. (i) Use ‘Add a publication’ functionality (red box) in the UniProtKB entry. (ii) Partial snapshot of the submission form, a sample available here: https://community.uniprot.org/bbsub/sampleform.html . (iii) After submission and review the publication and information are displayed in the relevant UniProtKB entry with attribution to submitter (red box) in a future public release. Submissions are minimally checked by an experienced curator before being added to the Publications section of the record. A tracker tool has also been developed ( https://community.uniprot.org/bbsub/bbsubinfo.html ) to enable users to access and search this wealth of additional bibliography. Researchers are encouraged to add relevant publications to entries of interest to them. Since we started the pilot in release 2019_08 we have seen a continuing increase in user submissions. As of release 2020_04 there have been 674 submissions relating to 424 publications and 557 entries, from 149 unique users ( https://community.uniprot.org/bbsub/STATS.html ). concepts↗concepts↗concepts↗concepts↗concepts↗sensesWebsite updates Users are at the center of the UniProt website design and development process. We follow a user-centered design process, conducting regular workshops, user testing, surveys and user research activities involving many users worldwide with varied research backgrounds and use cases. We are currently redesigning the website to take advantage of new technologies and paradigms in web development and welcome the UniProt user community to participate in user feedback and testing activities by contacting us at help@uniprot.org and contributing your user stories and workflows. As part of the redesign process, we have redeveloped both the UniProt interaction viewer (Figure 6A ) and ProtVista viewer, which graphically represents protein sequence features, such as domains, sites, post-translational modifications and variants, and structures. These are now part of the ‘Nightingale’ visualization web component library ( https://ebi-webcomponents.github.io/nightingale/#/ ) and are publicly available as lightweight, flexible, and modular components that can be more easily extended with new features, modified and implemented by users in their own resources (Figure 6B ). The ProtVista viewer has already been implemented by the Open Targets ( 43 ) and the Pharos ( 44 ) databases of unstudied and understudied drug targets amongst others. Figure 6. ( A ) The UniProtKB interaction viewer as seen in entry UniProtKB: Q9NSA3 , the beta-catenin-interacting protein 1. ( B ) The interaction viewer reusable web component in the Nightingale library. concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗DATA AVAILABILITY Due to the ever-increasing number of sequence records UniProt is processing with every release cycle, as of release 2020_01 (26 February 2020), UniProt releases are now published every eight weeks. This gives our production team the time required to complete data import, proteome redundancy removal, data checking, integration of external data and automatic annotation of unreviewed records prior to starting the release process. In addition to providing customizable views and downloads in a range of formats via the website, and file sets at the FTP site ( www.uniprot.org/downloads ), UniProt supplies users with a number of different options for computational access to the data ( www.uniprot.org/help/programmatic_access ). These include the website RESTful Application Programming Interface (API), stable URLs that can be bookmarked, linked, and reused, the Proteins extended REST API providing genomic coordinates of UniProtKB sequences and annotations imported and mapped from large-scale data imports ( 29 ), and the SPARQL API that allows users to perform complex queries across all UniProt data and also other resources that provide a SPARQL endpoint, such as DisGeNET ( 45 ), Bgee ( 46 ), or Wikidata ( 47 ). The Proteins API has recently been extended to serve the HUPO Proteomics Standards Initiative Extended FASTA Format (PEFF) for the proteomics community which enables more metadata, such as detail of amino-acid variants in the FASTA file header section ( 39 ). Outreach and training The training of our users to make the best use of our data continues to be of critical importance to the UniProt Consortium. In order to access our global user base, we are increasingly moving away from classroom style training towards use of distance learning techniques. We regularly run webinars, which range from basic introductions to the use of the web pages aimed at new users and students, to more specialist sessions on, for example, use of the API or 3D structural representation in UniProtKB. The webinars are recorded and are subsequently made available online ( https://www.ebi.ac.uk/training/online/ ) where they are supported by related online training materials and YouTube videos ( https://www.youtube.com/user/uniprotvideos/ ). In order to reach new communities, the webinars are widely advertised through social media forums such as Twitter (@uniprot) and Facebook ( https://www.facebook.com/uniprot.org/ ) as well as established mailing lists. concepts↗concepts↗concepts↗concepts↗concepts↗CONCLUSIONS UniProt continues to play its pivotal role in the fields of biology and biomedicine, collecting, standardizing and organizing knowledge of proteins and their functions to create a reference framework for multiscale biomedical data integration and analysis. Organisms are being routinely sequenced at the whole genome level, and eukaryotic, prokaryotic, and metagenomic sequencing projects are all contributing to the increased diversity of sequence data in the UniProt databases. It is of increasing importance that our automatic annotation pipelines continue to develop in parallel to ensure that these unreviewed genomes, the vast majority of which are not being experimentally studied at the protein level, are richly and comprehensively annotated with functional information. unhoused_relationssensesExpert curation of those proteins biochemically characterized remains a key focus of our activities, to both inform on these well-studied entities and also to act as template entries for information transfer to proteins in related species. As the complexity and depth of our value-added data increases, we are exploring new ways to present the data to users and will continue to serve the community with new and improved website access designed to improve and enhance the user experience and upgraded programmatic access, with ease of use always a priority. We greatly value the feedback and annotation updates from our user community. Please send your feedback and suggestions to the e-mail address help@uniprot.org or via the contact link on the UniProt website. ACKNOWLEDGEMENTS The UniProt publication has been prepared by Alex Bateman, Maria-Jesus Martin, Sandra Orchard, Michele Magrane, Rahat Agivetova, Shadab Ahmad, Emanuele Alpi, Emily H. Bowler-Barnett, Ramona Britto, Borisas Bursteinas, Hema Bye-A-Jee, Ray Coetzee, Austra Cukura, Alan Da Silva, Paul Denny, Tunca Dogan, ThankGod Ebenezer, Jun Fan, Leyla Garcia Castro, Penelope Garmiri, George Georghiou, Leonardo Gonzales, Emma Hatton-Ellis, Abdulrahman Hussein, Alexandr Ignatchenko, Giuseppe Insana, Rizwan Ishtiaq, Petteri Jokinen, Vishal Joshi, Dushyanth Jyothi, Antonia Lock, Rodrigo Lopez, Aurelien Luciani, Jie Luo, Yvonne Lussi, Alistair MacDougall, Fabio Madeira, Mahdi Mahmoudy, Manuela Menchi, Alok Mishra, Katie Moulang, Andrew Nightingale, Carla Susana Oliveira, Sangya Pundir, Guoying Qi, Shriya Raj, Daniel Rice, Milagros Rodriguez Lopez, Rabie Saidi, Joseph Sampson, Tony Sawford, Elena Speretta, Edward Turner, Nidhi Tyagi, Preethi Vasudev, Vladimir Volynkin, Kate Warner, Xavier Watkins, Rossana Zaru, and Hermann Zellner at the EMBL- European Bioinformatics Institute; Alan Bridge, Sylvain Poux, Nicole Redaschi, Lucila Aimo, Ghislaine Argoud-Puy, Andrea Auchincloss, Kristian Axelsen, Parit Bansal, Delphine Baratin, Marie-Claude Blatter, Jerven Bolleman, Emmanuel Boutet, Lionel Breuza, Cristina Casals-Casas, Edouard de Castro, Kamal Chikh Echioukh, Elisabeth Coudert, Beatrice Cuche, Mikael Doche, Dolnide Dornevil, Anne Estreicher, Maria Livia Famiglietti, Marc Feuermann, Elisabeth Gasteiger, Sebastien Gehant, Vivienne Gerritsen, Arnaud Gos, Nadine Gruaz-Gumowski, Ursula Hinz, Chantal Hulo, Nevila Hyka-Nouspikel, Florence Jungo, Guillaume Keller, Arnaud Kerhornou, Vicente Lara, Philippe Le Mercier, Damien Lieberherr, Thierry Lombardot, Xavier Martin, Patrick Masson, Anne Morgat, Teresa Batista Neto, Salvo Paesano, Ivo Pedruzzi, Sandrine Pilbout, Lucille Pourcel, Monica Pozzato, Manuela Pruess, Catherine Rivoire, Christian Sigrist, Karin Sonesson, Andre Stutz, Shyamala Sundaram, Michael Tognolli, and Laure Verbregue at the SIB Swiss Institute of Bioinformatics; Cathy H. Wu, Cecilia N. Arighi, Leslie Arminski, Chuming Chen, Yongxing Chen, John S. Garavelli, Hongzhan Huang, Kati Laiho, Peter McGarvey, Darren A. Natale, Karen Ross, C. R. Vinayaka, Qinghua Wang, Yuqi Wang, Lai-Su Yeh, and Jian Zhang at the Protein Information Resource. The UniProt consortium also acknowledges the contributions of Patrick Ruch and Douglas Teodoro from the SIB Swiss Institute of Bioinformatics who developed the UPCLASS publication classification tool and the ChEBI team at EMBL-EBI for their support in small molecule annotation. Contributor Information The UniProt Consortium, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK; Protein Information Resource, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA; Protein Information Resource, University of Delaware, Ammon-Pinizzotto Biopharmaceutical Innovation Building, Suite 147, 590 Avenue 1743, Newark, DE 19713, USA; SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 rue Michel Servet, CH-1211 Geneva 4, Switzerland. The UniProt Consortium: Alex Bateman , Maria-Jesus Martin , Sandra Orchard , Michele Magrane , Rahat Agivetova , Shadab Ahmad , Emanuele Alpi , Emily H Bowler-Barnett , Ramona Britto , Borisas Bursteinas , Hema Bye-A-Jee , Ray Coetzee , Austra Cukura , Alan Da Silva , Paul Denny , Tunca Dogan , ThankGod Ebenezer , Jun Fan , Leyla Garcia Castro , Penelope Garmiri , George Georghiou , Leonardo Gonzales , Emma Hatton-Ellis , Abdulrahman Hussein , Alexandr Ignatchenko , Giuseppe Insana , Rizwan Ishtiaq , Petteri Jokinen , Vishal Joshi , Dushyanth Jyothi , Antonia Lock , Rodrigo Lopez , Aurelien Luciani , Jie Luo , Yvonne Lussi , Alistair MacDougall , Fabio Madeira , Mahdi Mahmoudy , Manuela Menchi , Alok Mishra , Katie Moulang , Andrew Nightingale , Carla Susana Oliveira , Sangya Pundir , Guoying Qi , Shriya Raj , Daniel Rice , Milagros Rodriguez Lopez , Rabie Saidi , Joseph Sampson , Tony Sawford , Elena Speretta , Edward Turner , Nidhi Tyagi , Preethi Vasudev , Vladimir Volynkin , Kate Warner , Xavier Watkins , Rossana Zaru , Hermann Zellner , Alan Bridge , Sylvain Poux , Nicole Redaschi , Lucila Aimo , Ghislaine Argoud-Puy , Andrea Auchincloss , Kristian Axelsen , Parit Bansal , Delphine Baratin , Marie-Claude Blatter , Jerven Bolleman , Emmanuel Boutet , Lionel Breuza , Cristina Casals-Casas , Edouard de Castro , Kamal Chikh Echioukh , Elisabeth Coudert , Beatrice Cuche , Mikael Doche , Dolnide Dornevil , Anne Estreicher , Maria Livia Famiglietti , Marc Feuermann , Elisabeth Gasteiger , Sebastien Gehant , Vivienne Gerritsen , Arnaud Gos , Nadine Gruaz-Gumowski , Ursula Hinz , Chantal Hulo , Nevila Hyka-Nouspikel , Florence Jungo , Guillaume Keller , Arnaud Kerhornou , Vicente Lara , Philippe Le Mercier , Damien Lieberherr , Thierry Lombardot , Xavier Martin , Patrick Masson , Anne Morgat , Teresa Batista Neto , Salvo Paesano , Ivo Pedruzzi , Sandrine Pilbout , Lucille Pourcel , Monica Pozzato , Manuela Pruess , Catherine Rivoire , Christian Sigrist , Karin Sonesson , Andre Stutz , Shyamala Sundaram , Michael Tognolli , Laure Verbregue , Cathy H Wu , Cecilia N Arighi , Leslie Arminski , Chuming Chen , Yongxing Chen , John S Garavelli , Hongzhan Huang , Kati Laiho , Peter McGarvey , Darren A Natale , Karen Ross , C R Vinayaka , Qinghua Wang , Yuqi Wang , Lai-Su Yeh , Jian Zhang , Patrick Ruch , and Douglas Teodoro FUNDING National Eye Institute, National Human Genome Research Institute, National Heart, Lung, and Blood Institute, National Institute of Allergy and Infectious Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of General Medical Sciences, National Cancer Institute, National Institute On Aging, and National Institute of Mental Health of the National Institutes of Health [U24HG007822]; National Human Genome Research Institute [U41HG002273]; National Institute of General Medical Sciences [R01GM080646, P20GM103446] (the content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health); Biotechnology and Biological Sciences Research Council [BB/T010541/1]; British Heart Foundation [RG/13/5/30112]; Open Targets; Swiss Federal Government through the State Secretariat for Education, Research and Innovation SERI; European Molecular Biology Laboratory core funds. Funding for open access charge: National Institutes of Health [U24HG007822]. Conflict of interest statement . None declared.
Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; 10 of 12 relations here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.
| facet | tag | field | conf. | whose words |
|---|---|---|---|---|
| object_of_study | A KNOWLEDGEBASE THAT DELETES FROM ITSELF AND KEEPS WHAT IT DELETED - 190 million protein sequences in two provenance tiers, with a separate archive holding everything the knowledgebase has dropped | object_of_study | the paper's words | |
| research_question | what UniProt now contains, how completeness and quality are assessed, and how annotations are evidenced | research_question | the paper's words | |
| contribution | a two-year status report: proteome completeness metrics, automated annotation, a credit-based community submission route, and a COVID-19 curation portal | contribution | the paper's words | |
| claim_type | method | claim_type | the paper's words | |
| phenomenon | protein knowledge as something that must be tiered by provenance, judged for completeness against neighbours, and deleted without being lost | phenomenon | the paper's words | |
| method | system-or-tool-building also in: system-or-tool-building | method | the paper's words | |
| mechanisms | IT SOLVES THE PROBLEM THE GENE ONTOLOGY REFUSES. `aleksander2023` in this corpus: the knowledgebase must be `THE CURRENT STATE OF KNOWLEDGE AND NOT JUST AN ARCHIVE OF ALL PUBLIC DATA`. UniProt: `the UniProt Archive (UniParc) delivers a complete set of known sequences, including historical obsolete sequences`. GO REFUSES TO BE AN ARCHIVE; UNIPROT RUNS A SEPARATE ONE SO IT NEED NOT BE | mechanisms[0] | 0.95 | the tagger's words |
| mechanisms | and the deletion is reversible by construction: `we continue to remove almost identical proteomes of the same species from UniProtKB ... The redundant proteome sequences are available through UniParc ... and stable proteome identifiers ... are maintained for each redundant proteome to ensure findability`. Removed from the knowledgebase, kept in the archive, IDENTIFIER PRESERVED | mechanisms[1] | 0.95 | the tagger's words |
| mechanisms | AND WHETHER A PROTEIN IS KNOWN TO EXIST IS A COUNTING RULE WITH TWO THRESHOLDS: `Peptide identifications can be taken as evidence that a protein has been validated (PE = 1) ... at least two unique peptides of seven amino acids or more or, for proteins where this cannot be achieved due to sequence constraints, one unique peptide of ten amino acids or more`. The fallback exists because some proteins fail the first rule for reasons that have nothing to do with whether they exist | mechanisms[2] | 0.95 | the tagger's words |
| mechanisms | and completeness is judged AGAINST THE NEIGHBOURS: the `Complete Proteome Detector` `statistically evaluates the completeness and quality of each proteome by directly comparing it to those of a group of at least three closely taxonomically related species`, classifying it as `STANDARD`, `close to standard` or an `OUTLIER` | mechanisms[3] | 0.95 | the tagger's words |
| limitations | A NEW INSTRUMENT CAUSED DELETIONS FROM THE KNOWLEDGEBASE: `As a result of our adoption of the BUSCO algorithm, a number of incomplete and poor-quality proteomes were identified and removed from UniProtKB`. The proteomes did not change; the ruler did | limitations[0] | 0.95 | the tagger's words |
| limitations | and the object being counted is hedged in its own definition: a proteome is `the complete set of proteins believed to be expressed by an organism`. Completeness and belief in the same phrase | limitations[1] | 0.95 | the tagger's words |
| limitations | THE TWO TIERS ARE A PROVENANCE DISTINCTION, NOT A QUALITY ONE ON ITS FACE: `UniProtKB COMBINES REVIEWED UniProtKB/Swiss-Prot entries, to which data have been added by our expert biocuration team, with the UNREVIEWED UniProtKB/TrEMBL entries that are annotated by automated systems`. A user who does not read the tier reads both as the same kind of claim | limitations[2] | 0.95 | the tagger's words |
| limitations | and the scale makes the tiering unavoidable: entries grew `by >65 million records, an increase of >50% in just 2 years`. No biocuration team can review that | limitations[3] | 0.95 | the tagger's words |
| controls | BUSCO scoring against a defined set of near-universal single-copy orthologs eliminates: incomplete proteomes passing as complete - and it worked in the destructive direction: 'a number of incomplete and poor-quality proteomes were identified and removed from UniProtKB' · outcome: held | controls[0] | 0.95 | the tagger's words |
| controls | the Complete Proteome Detector, comparing each proteome to at least three closely related species eliminates: the absence of a per-organism expectation - there is no independent count of how many proteins an organism should have, so the neighbours supply one · outcome: partial | controls[1] | 0.95 | the tagger's words |
| controls | labelling every automatic annotation with its evidence and source eliminates: the confusion of an inferred annotation with a curated one - 'All automatic annotations are labelled with their evidence/source' · outcome: held | controls[2] | 0.95 | the tagger's words |
| concepts | UniProt Knowledgebase also in: UniProt Knowledgebase | concepts[0] | 0.95 | the paper's words |
| concepts | UniProt Archive also in: UniProt Archive | concepts[1] | 0.95 | the paper's words |
| concepts | historical obsolete sequences also in: historical obsolete sequences | concepts[2] | 0.95 | the paper's words |
| concepts | Complete Proteome Detector also in: Complete Proteome Detector | concepts[3] | 0.95 | the paper's words |
| concepts | BUSCO also in: BUSCO | concepts[4] | 0.95 | the paper's words |
| concepts | validated also in: validated | concepts[5] | 0.95 | the paper's words |
| concepts | Peptide identifications also in: Peptide identifications | concepts[6] | 0.95 | the paper's words |
| concepts | comprehensive, high-quality and freely accessible set of protein sequences | concepts[7] | 0.95 | the paper's words |
| concepts | functional information also in: functional information | concepts[8] | 0.95 | the paper's words |
| concepts | sequence redundancy also in: sequence redundancy | concepts[9] | 0.95 | the paper's words |
| concepts | proteome completeness and quality also in: proteome completeness and quality | concepts[10] | 0.95 | the paper's words |
| concepts | reviewed entries also in: reviewed entries | concepts[11] | 0.95 | the paper's words |
| concepts | unreviewed entries also in: unreviewed entries | concepts[12] | 0.95 | the paper's words |
| concepts | credit-based publication submission interface | concepts[13] | 0.95 | the paper's words |
| concepts | dedicated portal also in: dedicated portal | concepts[14] | 0.95 | the paper's words |
| concepts | Association-Rule-Based Annotator (ARBA) | concepts[15] | 0.95 | the paper's words |
| concepts | ARBA also in: ARBA | concepts[16] | 0.95 | the paper's words |
| concepts | complete compendium of all known protein sequence data | concepts[17] | 0.95 | the paper's words |
| concepts | experimentally verified also in: experimentally verified | concepts[18] | 0.95 | the paper's words |
| concepts | computationally predicted also in: computationally predicted | concepts[19] | 0.95 | the paper's words |
| concepts | reviewed UniProtKB/Swiss-Prot entries | concepts[20] | 0.95 | the paper's words |
| concepts | unreviewed UniProtKB/TrEMBL entries also in: unreviewed UniProtKB/TrEMBL entries | concepts[21] | 0.95 | the paper's words |
| concepts | expert biocuration team also in: expert biocuration team | concepts[22] | 0.95 | the paper's words |
| concepts | automated systems also in: automated systems | concepts[23] | 0.95 | the paper's words |
| concepts | UniRef databases also in: UniRef databases | concepts[24] | 0.95 | the paper's words |
| concepts | sequence identity also in: sequence identity | concepts[25] | 0.95 | the paper's words |
| concepts | UniParc also in: UniParc | concepts[26] | 0.95 | the paper's words |
| concepts | central hub also in: central hub | concepts[27] | 0.95 | the paper's words |
| concepts | 180 other resources also in: 180 other resources | concepts[28] | 0.95 | the paper's words |
| concepts | Findable, Accessible, Interoperable and Reusable | concepts[29] | 0.95 | the paper's words |
| concepts | FAIR also in: FAIR | concepts[30] | 0.95 | the paper's words |
| concepts | stable and traceable identifiers also in: stable and traceable identifiers | concepts[31] | 0.95 | the paper's words |
| concepts | Application Programming Interfaces also in: Application Programming Interfaces | concepts[32] | 0.95 | the paper's words |
| concepts | File Transfer Protocol (FTP) downloads | concepts[33] | 0.95 | the paper's words |
| concepts | proteomes also in: proteomes | concepts[34] | 0.95 | the paper's words |
| concepts | complete set of proteins believed to be expressed by an organism | concepts[35] | 0.95 | the paper's words |
| concepts | INSDC source databases also in: INSDC source databases | concepts[36] | 0.95 | the paper's words |
| concepts | viral proteomes also in: viral proteomes | concepts[37] | 0.95 | the paper's words |
| concepts | Benchmarking Universal Single-Copy Orthologs about this quote: A FIGURE/TABLE CAPTION IS JOINED INTO THE 'evidence' QUOTE, beginning at character 824: 'Figure 1. Growth in the number of entries in the UniProt databases ove…'. The page prints the caption apart from the paragraph; read the quote as the paragraph, then the caption. | concepts[38] | 0.95 | the paper's words |
| concepts | complete, duplicated, fragmented, and potentially missing genes about this quote: A FIGURE/TABLE CAPTION IS JOINED INTO THE 'evidence' QUOTE, beginning at character 824: 'Figure 1. Growth in the number of entries in the UniProt databases ove…'. The page prints the caption apart from the paragraph; read the quote as the paragraph, then the caption. | concepts[39] | 0.95 | the paper's words |
| concepts | near-universal single copy orthologs about this quote: A FIGURE/TABLE CAPTION IS JOINED INTO THE 'evidence' QUOTE, beginning at character 824: 'Figure 1. Growth in the number of entries in the UniProt databases ove…'. The page prints the caption apart from the paragraph; read the quote as the paragraph, then the caption. also in: near-universal single copy orthologs | concepts[40] | 0.95 | the paper's words |
| concepts | incomplete and poor-quality proteomes about this quote: A FIGURE/TABLE CAPTION IS JOINED INTO THE 'evidence' QUOTE, beginning at character 824: 'Figure 1. Growth in the number of entries in the UniProt databases ove…'. The page prints the caption apart from the paragraph; read the quote as the paragraph, then the caption. | concepts[41] | 0.95 | the paper's words |
| concepts | different quality and completeness metrics | concepts[42] | 0.95 | the paper's words |
| concepts | standard also in: standard | concepts[43] | 0.95 | the paper's words |
| concepts | close to standard also in: close to standard | concepts[44] | 0.95 | the paper's words |
| concepts | outlier also in: outlier | concepts[45] | 0.95 | the paper's words |
| concepts | Reference Proteome also in: Reference Proteome | concepts[46] | 0.95 | the paper's words |
| concepts | Reference Proteome set also in: Reference Proteome set | concepts[47] | 0.95 | the paper's words |
| concepts | research community also in: research community | concepts[48] | 0.95 | the paper's words |
| concepts | computational clustering also in: computational clustering | concepts[49] | 0.95 | the paper's words |
| concepts | best annotated proteome also in: best annotated proteome | concepts[50] | 0.95 | the paper's words |
| concepts | gene-centric subset of representative proteins | concepts[51] | 0.95 | the paper's words |
| concepts | full proteome also in: full proteome | concepts[52] | 0.95 | the paper's words |
| concepts | isoforms also in: isoforms | concepts[53] | 0.95 | the paper's words |
| concepts | representative proteome also in: representative proteome | concepts[54] | 0.95 | the paper's words |
| concepts | redundant proteome sequences also in: redundant proteome sequences | concepts[55] | 0.95 | the paper's words |
| concepts | stable proteome identifiers also in: stable proteome identifiers | concepts[56] | 0.95 | the paper's words |
| concepts | findability also in: findability | concepts[57] | 0.95 | the paper's words |
| concepts | UniRef protein sequence clusters also in: UniRef protein sequence clusters | concepts[58] | 0.95 | the paper's words |
| concepts | sequence similarity searches also in: sequence similarity searches | concepts[59] | 0.95 | the paper's words |
| concepts | gene prediction also in: gene prediction | concepts[60] | 0.95 | the paper's words |
| concepts | pan proteomes also in: pan proteomes | concepts[61] | 0.95 | the paper's words |
| concepts | core and accessory proteomes also in: core and accessory proteomes | concepts[62] | 0.95 | the paper's words |
| concepts | time taken to perform UniRef50 clustering | concepts[63] | 0.95 | the paper's words |
| concepts | MMseqs2 algorithm also in: MMseqs2 algorithm | concepts[64] | 0.95 | the paper's words |
| concepts | UniRef50 clustering also in: UniRef50 clustering | concepts[65] | 0.95 | the paper's words |
| concepts | Expert curation also in: Expert curation | concepts[66] | 0.95 | the paper's words |
| concepts | hierarchical searches also in: hierarchical searches | concepts[67] | 0.95 | the paper's words |
| concepts | experimental data also in: experimental data | concepts[68] | 0.95 | the paper's words |
| concepts | human readable summaries also in: human readable summaries | concepts[69] | 0.95 | the paper's words |
| concepts | structured vocabularies also in: structured vocabularies | concepts[70] | 0.95 | the paper's words |
| concepts | Gene Ontology (GO) also in: Gene Ontology (GO) | concepts[71] | 0.95 | the paper's words |
| concepts | Rhea knowledgebase also in: Rhea knowledgebase | concepts[72] | 0.95 | the paper's words |
| concepts | biochemical reactions also in: biochemical reactions | concepts[73] | 0.95 | the paper's words |
| concepts | chemical ontology ChEBI also in: chemical ontology ChEBI | concepts[74] | 0.95 | the paper's words |
| concepts | reaction participants also in: reaction participants | concepts[75] | 0.95 | the paper's words |
| concepts | chemical structures also in: chemical structures | concepts[76] | 0.95 | the paper's words |
| concepts | chemical transformations also in: chemical transformations | concepts[77] | 0.95 | the paper's words |
| concepts | InChIKeys also in: InChIKeys | concepts[78] | 0.95 | the paper's words |
| concepts | pseudoenzymes also in: pseudoenzymes | concepts[79] | 0.95 | the paper's words |
| concepts | functional impact of human variation also in: functional impact of human variation | concepts[80] | 0.95 | the paper's words |
| concepts | clinical significance also in: clinical significance | concepts[81] | 0.95 | the paper's words |
| concepts | ACMG-AMP also in: ACMG-AMP | concepts[82] | 0.95 | the paper's words |
| concepts | pathogenicity calculator also in: pathogenicity calculator | concepts[83] | 0.95 | the paper's words |
| concepts | ClinVar also in: ClinVar | concepts[84] | 0.95 | the paper's words |
| concepts | alternative splicing also in: alternative splicing | concepts[85] | 0.95 | the paper's words |
| concepts | splice isoform proteins also in: splice isoform proteins | concepts[86] | 0.95 | the paper's words |
| concepts | functional diversity of proteins also in: functional diversity of proteins | concepts[87] | 0.95 | the paper's words |
| concepts | post-translational proteolytic cleavage | concepts[88] | 0.95 | the paper's words |
| concepts | mature amino-acid chains also in: mature amino-acid chains | concepts[89] | 0.95 | the paper's words |
| concepts | polyprotein also in: polyprotein | concepts[90] | 0.95 | the paper's words |
| concepts | multiple bioactive proteins or peptides | concepts[91] | 0.95 | the paper's words |
| concepts | machine-readable functional annotations | concepts[92] | 0.95 | the paper's words |
| concepts | specific isoforms also in: specific isoforms | concepts[93] | 0.95 | the paper's words |
| concepts | polyprotein cleavage products also in: polyprotein cleavage products | concepts[94] | 0.95 | the paper's words |
| concepts | binary protein interactions also in: binary protein interactions | concepts[95] | 0.95 | the paper's words |
| concepts | IMEx Consortium also in: IMEx Consortium | concepts[96] | 0.95 | the paper's words |
| concepts | pre-release dataset also in: pre-release dataset | concepts[97] | 0.95 | the paper's words |
| concepts | COVID-19 disease portal also in: COVID-19 disease portal | concepts[98] | 0.95 | the paper's words |
| concepts | COVID portal also in: COVID portal | concepts[99] | 0.95 | the paper's words |
| concepts | protein functionality also in: protein functionality | concepts[100] | 0.95 | the paper's words |
| concepts | 3D-structure also in: 3D-structure | concepts[101] | 0.95 | the paper's words |
| concepts | complex formation also in: complex formation | concepts[102] | 0.95 | the paper's words |
| concepts | standard 8-weekly UniProt release cycle | concepts[103] | 0.95 | the paper's words |
| concepts | disease-centric access points also in: disease-centric access points | concepts[104] | 0.95 | the paper's words |
| concepts | Alzheimer's disease also in: Alzheimer's disease | concepts[105] | 0.95 | the paper's words |
| concepts | disease-related amino-acid variant also in: disease-related amino-acid variant | concepts[106] | 0.95 | the paper's words |
| concepts | Automatic annotation also in: Automatic annotation | concepts[107] | 0.95 | the paper's words |
| concepts | InterPro also in: InterPro | concepts[108] | 0.95 | the paper's words |
| concepts | UniRule system also in: UniRule system | concepts[109] | 0.95 | the paper's words |
| concepts | rule mining techniques also in: rule mining techniques | concepts[110] | 0.95 | the paper's words |
| concepts | UniFIRE also in: UniFIRE | concepts[111] | 0.95 | the paper's words |
| concepts | UniProt Rule Markup Language also in: UniProt Rule Markup Language | concepts[112] | 0.95 | the paper's words |
| concepts | eight different predictors also in: eight different predictors | concepts[113] | 0.95 | the paper's words |
| concepts | superfamily, family and subfamily levels | concepts[114] | 0.95 | the paper's words |
| concepts | functional domains also in: functional domains | concepts[115] | 0.95 | the paper's words |
| concepts | important sites also in: important sites | concepts[116] | 0.95 | the paper's words |
| concepts | experimentally uncharacterized proteins | concepts[117] | 0.95 | the paper's words |
| concepts | known experimentally characterized proteins | concepts[118] | 0.95 | the paper's words |
| concepts | multiclass, self-training annotation system | concepts[119] | 0.95 | the paper's words |
| concepts | SAAS system also in: SAAS system | concepts[120] | 0.95 | the paper's words |
| concepts | concise annotation models also in: concise annotation models | concepts[121] | 0.95 | the paper's words |
| concepts | data exclusion set also in: data exclusion set | concepts[122] | 0.95 | the paper's words |
| concepts | human-readable rules also in: human-readable rules | concepts[123] | 0.95 | the paper's words |
| concepts | ARBA rules also in: ARBA rules | concepts[124] | 0.95 | the paper's words |
| concepts | combined coverage of the rule-based annotation systems | concepts[125] | 0.95 | the paper's words |
| concepts | uncharacterized protein names also in: uncharacterized protein names | concepts[126] | 0.95 | the paper's words |
| concepts | domain X containing protein also in: domain X containing protein | concepts[127] | 0.95 | the paper's words |
| concepts | intrinsically disordered (ID) protein regions | concepts[128] | 0.95 | the paper's words |
| concepts | consensus-based prediction of long disorder | concepts[129] | 0.95 | the paper's words |
| concepts | ProtVista visualisation tool also in: ProtVista visualisation tool | concepts[130] | 0.95 | the paper's words |
| concepts | pre-calculated mapping also in: pre-calculated mapping | concepts[131] | 0.95 | the paper's words |
| concepts | Aligning variants to protein features | concepts[132] | 0.95 | the paper's words |
| concepts | PSI Extended FASTA Format also in: PSI Extended FASTA Format | concepts[133] | 0.95 | the paper's words |
| concepts | PEFF also in: PEFF | concepts[134] | 0.95 | the paper's words |
| concepts | Proteins API also in: Proteins API | concepts[135] | 0.95 | the paper's words |
| concepts | large-scale datasets also in: large-scale datasets | concepts[136] | 0.95 | the paper's words |
| concepts | genomic coordinates also in: genomic coordinates | concepts[137] | 0.95 | the paper's words |
| concepts | GRCh38 version of the human genome also in: GRCh38 version of the human genome | concepts[138] | 0.95 | the paper's words |
| concepts | specific variants also in: specific variants | concepts[139] | 0.95 | the paper's words |
| concepts | disease also in: disease | concepts[140] | 0.95 | the paper's words |
| concepts | resistance to a drug also in: resistance to a drug | concepts[141] | 0.95 | the paper's words |
| concepts | mechanistic insights also in: mechanistic insights | concepts[142] | 0.95 | the paper's words |
| concepts | mass spectrometry proteomic data also in: mass spectrometry proteomic data | concepts[143] | 0.95 | the paper's words |
| concepts | ProteomeXchange Consortium also in: ProteomeXchange Consortium | concepts[144] | 0.95 | the paper's words |
| concepts | Community submission also in: Community submission | concepts[145] | 0.95 | the paper's words |
| concepts | controlled list also in: controlled list | concepts[146] | 0.95 | the paper's words |
| concepts | tracker tool about this quote: A FIGURE/TABLE CAPTION IS JOINED INTO THE 'evidence' QUOTE, beginning at character 1070: 'Figure 5. Community contribution. (i) Use ‘Add a publication’ function…'. The page prints the caption apart from the paragraph; read the quote as the paragraph, then the caption. also in: tracker tool | concepts[147] | 0.95 | the paper's words |
| concepts | ORCID also in: ORCID | concepts[148] | 0.95 | the paper's words |
| concepts | researcher personal ID also in: researcher personal ID | concepts[149] | 0.95 | the paper's words |
| concepts | model organism databases also in: model organism databases | concepts[150] | 0.95 | the paper's words |
| concepts | experienced curator about this quote: A FIGURE/TABLE CAPTION IS JOINED INTO THE 'evidence' QUOTE, beginning at character 1070: 'Figure 5. Community contribution. (i) Use ‘Add a publication’ function…'. The page prints the caption apart from the paragraph; read the quote as the paragraph, then the caption. also in: experienced curator | concepts[151] | 0.95 | the paper's words |
| concepts | community contribution about this quote: A FIGURE/TABLE CAPTION IS JOINED INTO THE 'evidence' QUOTE, beginning at character 1070: 'Figure 5. Community contribution. (i) Use ‘Add a publication’ function…'. The page prints the caption apart from the paragraph; read the quote as the paragraph, then the caption. also in: community contribution | concepts[152] | 0.95 | the paper's words |
| concepts | articles and annotations also in: articles and annotations | concepts[153] | 0.95 | the paper's words |
| concepts | user-centered design process also in: user-centered design process | concepts[154] | 0.95 | the paper's words |
| concepts | UniProt interaction viewer also in: UniProt interaction viewer | concepts[155] | 0.95 | the paper's words |
| concepts | ProtVista viewer also in: ProtVista viewer | concepts[156] | 0.95 | the paper's words |
| concepts | Nightingale also in: Nightingale | concepts[157] | 0.95 | the paper's words |
| concepts | lightweight, flexible, and modular components | concepts[158] | 0.95 | the paper's words |
| concepts | proteome redundancy removal also in: proteome redundancy removal | concepts[159] | 0.95 | the paper's words |
| concepts | RESTful Application Programming Interface | concepts[160] | 0.95 | the paper's words |
| concepts | stable URLs also in: stable URLs | concepts[161] | 0.95 | the paper's words |
| concepts | SPARQL API also in: SPARQL API | concepts[162] | 0.95 | the paper's words |
| concepts | SPARQL endpoint also in: SPARQL endpoint | concepts[163] | 0.95 | the paper's words |
| concepts | eight weeks also in: eight weeks | concepts[164] | 0.95 | the paper's words |
| concepts | reference framework for multiscale biomedical data integration and analysis | concepts[165] | 0.95 | the paper's words |
| concepts | automatic annotation pipelines also in: automatic annotation pipelines | concepts[166] | 0.95 | the paper's words |
| concepts | template entries also in: template entries | concepts[167] | 0.95 | the paper's words |
| concepts | information transfer to proteins in related species | concepts[168] | 0.95 | the paper's words |
| concepts | programmatic access also in: programmatic access | concepts[169] | 0.95 | the paper's words |
| unhoused_relations | Reference Proteome → research community A Reference Proteome may be selected by the research community; selected-by is absent from the eight edge types. | unhoused_relations[0] | 0.95 | the tagger's words |
| unhoused_relations | Reference Proteome → computational clustering A Reference Proteome may be selected by computational clustering; selected-by is absent from the eight edge types. | unhoused_relations[1] | 0.95 | the tagger's words |
| unhoused_relations | Association-Rule-Based Annotator (ARBA) → SAAS system ARBA replaces the previous SAAS system; replaces is absent from the eight edge types. | unhoused_relations[2] | 0.95 | the tagger's words |
| unhoused_relations | redundant proteome sequences → UniParc Redundant sequences removed from UniProtKB remain available through UniParc; stored/available-through is absent from the eight edge types. | unhoused_relations[3] | 0.95 | the tagger's words |
| unhoused_relations | central hub → 180 other resources The central hub links users out to 180 resources; links-to is absent from the eight edge types. | unhoused_relations[4] | 0.95 | the tagger's words |
| unhoused_relations | Aligning variants to protein features → mechanistic insights Mapping variants to protein features provides mechanistic insights; supports-inference-of is absent from the eight edge types. | unhoused_relations[5] | 0.95 | the tagger's words |
| unhoused_relations | Community submission → articles and annotations The submission page enables users to contribute publications and basic annotations; enables-contribution is absent from the eight edge types. | unhoused_relations[6] | 0.95 | the tagger's words |
| unhoused_relations | Expert curation → template entries Expert curation produces template entries for transferring information to related proteins; produces/serves-as-template is absent from the eight edge types. | unhoused_relations[7] | 0.95 | the tagger's words |
| senses | `validated` (PE = 1) - A COUNTING RULE: `at least two unique peptides of seven amino acids or more or ... one unique peptide of ten amino acids or more has been mapped to a protein`. Existence, operationalised, with a fallback for proteins the first rule cannot reach | senses[0] | 0.95 | the tagger's words |
| senses | `complete proteome` - JUDGED AGAINST THE NEIGHBOURS RATHER THAN AGAINST THE ORGANISM: the CPD compares each proteome `to those of a group of at least three closely taxonomically related species` and returns `standard`, `close to standard` or `outlier` | senses[1] | 0.95 | the tagger's words |
| senses | `reviewed` vs `unreviewed` - A PROVENANCE TIER: reviewed entries carry data added `by our expert biocuration team`, unreviewed ones are `annotated by automated systems`. The same field in both, with a different warrant behind it | senses[2] | 0.95 | the tagger's words |
| senses | `UniParc` - THE ARCHIVE THAT KEEPS WHAT THE KNOWLEDGEBASE DROPS, `a complete set of known sequences, including historical obsolete sequences`. GO declines to be an archive; UniProt separates the two roles | senses[3] | 0.95 | the tagger's words |
| senses | reviewed UniProtKB/Swiss-Prot entries: records whose data are added by expert biocurators | senses[4] | 0.95 | the tagger's words |
| senses | unreviewed UniProtKB/TrEMBL entries: records annotated by automated systems | senses[5] | 0.95 | the tagger's words |
| senses | UniParc: the archive retaining known sequences including obsolete sequences | senses[6] | 0.95 | the tagger's words |
| senses | proteome: the set of proteins currently believed to be expressed by an organism | senses[7] | 0.95 | the tagger's words |
| senses | Reference Proteome: a stably maintained, best-annotated representative selected by community choice or clustering | senses[8] | 0.95 | the tagger's words |
| senses | gene-centric subset: one algorithmically selected representative protein per gene, unlike the isoform-inclusive full proteome | senses[9] | 0.95 | the tagger's words |
| senses | expert curation: evaluation of experimental literature and summarization in reviewed Swiss-Prot records | senses[10] | 0.95 | the tagger's words |
| senses | ARBA: a multiclass self-training system that mines concise automatic-annotation rules | senses[11] | 0.95 | the tagger's words |
| senses | UniRule: similarity-based semi-automated annotation from experimentally characterized proteins | senses[12] | 0.95 | the tagger's words |
| senses | PEFF: extended FASTA format carrying variation and sequence metadata for proteomic tools | senses[13] | 0.95 | the tagger's words |
| senses | ORCID: personal researcher identifier used to validate submissions and assign contribution credit | senses[14] | 0.95 | the tagger's words |
| senses | COVID-19 disease portal: pre-release disease-centric access updated faster than the ordinary UniProt cycle | senses[15] | 0.95 | the tagger's words |
| senses | community curation: user-submitted publications and basic annotations minimally checked by an experienced curator about this quote: A FIGURE/TABLE CAPTION IS JOINED INTO THE 'evidence' QUOTE, beginning at character 1070: 'Figure 5. Community contribution. (i) Use ‘Add a publication’ function…'. The page prints the caption apart from the paragraph; read the quote as the paragraph, then the caption. | senses[16] | 0.95 | the tagger's words |
| senses | Nightingale components: lightweight modular visualization components reusable in external resources | senses[17] | 0.95 | the tagger's words |
| senses | template entries: expert-curated records that support information transfer to related-species proteins | senses[18] | 0.95 | the tagger's words |
| alternatives | claim_type: method a second reading reaching the same value. Recorded: method — note: A BLIND second reading by the project on 2026-08-25: the record's own claim type/method/phenomenon were not looked at until every value here was written and substring-verified. Not applied - a person decides. Sampling frame: the 504 staged records carrying no alternatives block, so this never re-measures the 202. All 11 records with 130+ tags were taken as a census and 19 more spread one per discipline across 22 disciplines - the dense non-neuroscience cell is the one the 202 do not contain (inside them, 94-100% of the densest bands are neuroscience; among these 11, one is)., | alternatives.claim_type | 0.75 | the tagger's words |
| alternatives | method: database-resource-description a second reading DIFFERING from the recorded value. Recorded: system-or-tool-building — note: A BLIND second reading by the project on 2026-08-25: the record's own claim type/method/phenomenon were not looked at until every value here was written and substring-verified. Not applied - a person decides. Sampling frame: the 504 staged records carrying no alternatives block, so this never re-measures the 202. All 11 records with 130+ tags were taken as a census and 19 more spread one per discipline across 22 disciplines - the dense non-neuroscience cell is the one the 202 do not contain (inside them, 94-100% of the densest bands are neuroscience; among these 11, one is)., | alternatives.method | 0.8 | the tagger's words |
| alternatives | phenomenon: the UniProt knowledgebase of protein sequence and functional annotation a second reading DIFFERING from the recorded value. Recorded: protein knowledge as something that must be tiered by provenance, judged for completeness against neighbours, and delete — note: A BLIND second reading by the project on 2026-08-25: the record's own claim type/method/phenomenon were not looked at until every value here was written and substring-verified. Not applied - a person decides. Sampling frame: the 504 staged records carrying no alternatives block, so this never re-measures the 202. All 11 records with 130+ tags were taken as a census and 19 more spread one per discipline across 22 disciplines - the dense non-neuroscience cell is the one the 202 do not contain (inside them, 94-100% of the densest bands are neuroscience; among these 11, one is)., | alternatives.phenomenon | 0.8 | the tagger's words |
| claim_relations | extends → UniProt's own 2019 update in the same journal, which described the response to growth in microbial sequence records | claim_relations[0] | 0.95 | the tagger's words |
| claim_relations | supports → the FAIR data principles - 'providing stable and traceable identifiers ... and by fully evidencing our data sources throughout' | claim_relations[1] | 0.95 | the tagger's words |
| concept_edges | historical obsolete sequences —part-of→ UniProt Archive strength of the relation: 0.95 · note: THE ARCHIVE IS DEFINED BY HOLDING WHAT THE KNOWLEDGEBASE DOES NOT. 'aleksander2023' in this corpus says a knowledgebase must NOT be an archive of all public data; UniProt runs both and keeps the identifiers stable across the boundary also in: historical obsolete sequences · UniProt Archive | concept_edges[0] | 0.95 | the tagger's words |
| concept_edges | validated —operationalised-as→ Peptide identifications strength of the relation: 0.95 · note: 'at least two unique peptides of seven amino acids or more or, for proteins where this cannot be achieved due to sequence constraints, one unique peptide of ten amino acids or more'. Two thresholds and a fallback, and the fallback is there because the first rule fails for reasons unrelated to existence also in: validated · Peptide identifications | concept_edges[1] | 0.95 | the tagger's words |
| concept_edges | gene-centric subset of representative proteins —contrasted-with→ full proteome the relation itself is separately evidenced · strength of the relation: 1 · note: The paper explicitly contrasts one protein per gene with the isoform-inclusive full proteome. | concept_edges[2] | 0.95 | the tagger's words |
| concept_edges | alternative splicing —causes→ functional diversity of proteins direction on the measure: increases · the relation itself is separately evidenced · strength of the relation: 0.7 · direction on the measure (stated by the tagger, no quotation): increases · note: The paper says splicing can potentially increase functional diversity; weight reflects both modals.
Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but splicing 'can potentially increase' functional diversity; hedged. | concept_edges[3] | 0.95 | the tagger's words |
| concept_edges | polyprotein —causes→ multiple bioactive proteins or peptides the relation itself is separately evidenced · strength of the relation: 0.7 · about this quote: DOUBTFUL, found by an audit that read the edge against its span: signed 'increases' with no direction word ('may generate'); its effect direction was retired for that reason and is kept under a dated before-key. The edge is kept live; a reader should weigh it with this in mind. · note: A single polyprotein may generate multiple products; weight reflects 'may'. | concept_edges[4] | 0.95 | the tagger's words |
| concept_edges | MMseqs2 algorithm —causes→ time taken to perform UniRef50 clustering direction on the measure: reduces · the relation itself is separately evidenced · strength of the relation: 1 · direction on the measure (stated by the tagger, no quotation): reduces · note: Adoption decreased production time from four weeks to 60 hours. | concept_edges[5] | 0.95 | the tagger's words |
| concept_edges | UniProt Archive —same-as→ UniParc the relation itself is separately evidenced · strength of the relation: 1 · note: The paper gives UniParc as the parenthetical name of the UniProt Archive. also in: UniProt Archive · UniParc | concept_edges[6] | 0.95 | the tagger's words |
| concept_edges | BUSCO —same-as→ Benchmarking Universal Single-Copy Orthologs the relation itself is separately evidenced · strength of the relation: 1 · note: Parenthetical acronym expansion. | concept_edges[7] | 0.95 | the tagger's words |
| concept_edges | Findable, Accessible, Interoperable and Reusable —same-as→ FAIR the relation itself is separately evidenced · strength of the relation: 1 · note: Parenthetical acronym expansion for the data principles. | concept_edges[8] | 0.95 | the tagger's words |
| concept_edges | PEFF —same-as→ PSI Extended FASTA Format the relation itself is separately evidenced · strength of the relation: 1 · note: Parenthetical format-name expansion. also in: PEFF · PSI Extended FASTA Format | concept_edges[9] | 0.95 | the tagger's words |
| concept_edges | COVID portal —contrasted-with→ standard 8-weekly UniProt release cycle differs in: update frequency · the relation itself is separately evidenced · strength of the relation: 1 · differs in (stated by the tagger, no quotation): update frequency · note: The portal is directly compared with the normal release cycle. | concept_edges[10] | 0.95 | the tagger's words |
| concept_edges | ARBA rules —causes→ combined coverage of the rule-based annotation systems direction on the measure: increases · the relation itself is separately evidenced · strength of the relation: 1 · direction on the measure (stated by the tagger, no quotation): increases · note: Use of ARBA rules increased combined rule-based coverage from 35% to 49%. | concept_edges[11] | 0.95 | the tagger's words |
| content_tags | UniParc delivers a complete set of known sequences, including historical obsolete ones | content_tags.findings[0] | 0.95 | the tagger's words |
| content_tags | AND THE DELETION IS REVERSIBLE BY CONSTRUCTION: removed proteomes `are available through UniParc ... and stable proteome identifiers ... are maintained for each redundant proteome to ensure findability` | content_tags.findings[1] | 0.95 | the tagger's words |
| content_tags | WHETHER A PROTEIN IS KNOWN TO EXIST IS A COUNTING RULE WITH TWO THRESHOLDS AND A FALLBACK: `at least two unique peptides of seven amino acids or more or, for proteins where this cannot be achieved due to sequence constraints, one unique peptide of ten amino acids or more` | content_tags.findings[2] | 0.95 | the tagger's words |
| content_tags | COMPLETENESS IS JUDGED AGAINST THE NEIGHBOURS: the CPD compares each proteome `to those of a group of at least three closely taxonomically related species` and returns `STANDARD`, `close to standard` or `OUTLIER` | content_tags.findings[3] | 0.95 | the tagger's words |
| content_tags | adopting BUSCO led to removing a number of incomplete and poor-quality proteomes from UniProtKB | content_tags.findings[4] | 0.95 | the tagger's words |
| content_tags | and the object counted is hedged in its own definition - a proteome is `the complete set of proteins believed to be expressed by an organism` | content_tags.findings[5] | 0.95 | the tagger's words |
| content_tags | THE TWO TIERS ARE A PROVENANCE DISTINCTION: reviewed entries have data added `by our expert biocuration team`, unreviewed ones are `annotated by automated systems` | content_tags.findings[6] | 0.95 | the tagger's words |
| content_tags | and every automatic annotation carries its warrant: `All automatic annotations are labelled with their evidence/source` | content_tags.findings[7] | 0.95 | the tagger's words |
| content_tags | UniProtKB grew by more than 65 million entries, over 50%, in just 2 years | content_tags.findings[8] | 0.95 | the tagger's words |
| content_tags | AND AN UNINFORMATIVE NAME IS KEPT ON PURPOSE: a `domain-containing protein` name is retained `as, although not immediately informative, it enables protein grouping thus improving the chances of eventually assigning a function to that domain`. A placeholder name defended by what it makes possible later | content_tags.findings[9] | 0.95 | the tagger's words |
| content_tags | UniProtKB aims to be comprehensive, high-quality and freely accessible | content_tags.findings[10] | 0.95 | the tagger's words |
| content_tags | and the functional information is `experimentally verified, or computationally predicted` - one field, two warrants, stated in the first sentence of the introduction | content_tags.findings[11] | 0.95 | the tagger's words |
| content_tags | protein existence level 1: `at least two unique peptides of seven amino acids or more or ... one unique peptide of ten amino acids or more has been mapped to a protein` also in: validated | content_tags.definitions[0] | 0.95 | the tagger's words |
| content_tags | `an in-house algorithm, which statistically evaluates the completeness and quality of each proteome by directly comparing it to those of a group of at least three closely taxonomically related species`, returning `standard`, `close to standard` or `outlier` also in: Complete Proteome Detector | content_tags.definitions[1] | 0.95 | the tagger's words |
| content_tags | `delivers a complete set of known sequences, including historical obsolete sequences` also in: UniProt Archive | content_tags.definitions[2] | 0.95 | the tagger's words |
| content_tags | 2 — of seven amino acids or more, the primary rule for calling a protein validated at PE=1 | content_tags.quantities[0] | 0.95 | the tagger's words |
| content_tags | 3 — the minimum size of the comparison group the CPD judges a proteome's completeness against | content_tags.quantities[1] | 0.95 | the tagger's words |
| content_tags | 65 — growth in UniProtKB entries in two years - an increase of over 50% | content_tags.quantities[2] | 0.95 | the tagger's words |
| content_tags | the evidence that decides whether a protein counts as validated also in: Peptide identifications | content_tags.variables[0] | 0.95 | the tagger's words |
Tags that could not be placed in the text:
What was deliberately not tagged, and why, is recorded per paper by the tagger. Where a paper has no such record, this page says so rather than implying that nothing was declined.