dataesr/scientific-paragraphs-categorization huggingface dataset:
This enables resources to concentrate on the curation effort, rather than development of curation pipelines and annotation tools  16  , and also increases curation consistency. Members enter data through a web-based editorial platform designed to allow collaborative curation by physically remote partners. A sophisticated institute manager module links individual curators to their resource and/or funding body to enable full accreditation of the curation effort. The IntAct team is responsible for updating the data and producing a regular data release. The full IMEx dataset is made publicly available under an open Creative Commons Attribution 4. 0 International licence (CC-BY4. 0) as a single PSICQUIC service that can be accessed and searched via the IMEx Consortium website (ww. imexconsortium.
org) and any other resource implementing the IMEx PSICQUIC webservice. These include the IntAct website (www. ebi. ac. uk/intact/) and the mentha web resource  22  , maintained by the MINT group. Member databases may import some or all of the IMEx dataset back into their own resources. Partners may add other information to the IMEx dataset on their own websites or choose to only provide access to a subset of data that is of interest to their specific resource. For example, IID complements the IMEx data with interactions predicted by multiple machine learning and data mining algorithms, tissue and disease annotation context  23, 24  , while MatrixDB only provides IMEx data pertaining to extracellular matrix proteins and glycosaminoglycans  25. This model enables not only large interaction databases to contribute to the overall effort of IMEx but also allows annotation of molecular interactions by groups in data resources that do not maintain an interaction database (e.
g. the UniProt Consortium). It also ensures that the dataset will continue to be maintained should funding be withdrawn or a resource decide to focus on a different area of research. For example, the data curated by the Microbial Protein Interaction Database (MPIDB), a former IMEx member, has been maintained and updated within the IntAct database since this resource ceased curation in 2013. The IntAct database additionally holds a considerable number of records curated by several of the IMEx Consortium members (primarily IntAct, MINT, DIP and UniProt), which pre-date the IMEx agreement. There is currently no concerted effort planned to bring this legacy data up to IMEx standards, although individual records may be re-curated on demand. The Consortium benefits as a whole from the expertise of individual member databases.
Furthermore, experimental techniques to capture ncRNA-protein and miRNA-mRNA interactions have been included in the CVs and curation guidelines. The Consortium is currently also examining the structured description of downstream effects of molecular interactions, such as the up-or down-regulation of gene expression. Proteins are curated at the sequence level, using the UniProtKB database as the reference resource for proteins and peptides. The use of UniProtKB enables the curator, for each publication, to accurately describe the level of detail provided about the proteins and to use identifiers for the unambiguous annotation of each protein interactor. For instance, a publication may only give enough detail for an interactor to be mapped to any or all of the protein isoform products of a specific gene, or more specifically to a single protein isoform, or to a post-translationally cleaved peptide chain.
UniProtKB supplies appropriate identifiers for all of these, and in each case supplies the corresponding underlying sequence. Binding regions can be aligned to known protein domains, as described by InterPro  28. The effects of point mutations can be captured down to the amino acid level, using a CV to describe their effect on an interaction. To capture this level of detail, the use of a high-quality protein reference resource is essential. Reverse engineering protein to gene identifiers to enable network analysis of, for example, RNA-Seq data is a relatively trivial task but it is considerably more difficult, if not impossible, to map isoforms and binding domain data directly to a gene model or genomic sequence. Databases that curate PPI data directly to gene identifiers simply do not capture this wealth of information.
The detailed biocuration of binding domains, mutations  29  and post-translational modifications (PTMs) requires that these coordinate-level mappings are kept synchronised with changes to the underlying protein sequence database. An update to a predictive gene model may result in a corresponding change to the protein sequence(s) derived from it. Interactions involving domains and/or residues of that protein sequence then require a corresponding update to ensure that the mapping to the updated sequence is correct. Update pipelines need to be run regularly, in line with the release cycle of the sequence database, namely every 8 weeks in the case of UniProtKB. This is a computationally complex set of procedures run at the EMBL-EBI on the entire IMEx content, ensuring its consistency and representing one advantage of maintaining this as a single dataset.
Similarly, all CVs used to describe an aspect of an interaction are updated with every release. Small molecule interactors, including carbohydrates and lipids, are mapped to the ChEBI database  30  , protein complexes to the Complex Portal  31  , mRNAs to Ensembl 32 or ENA  33  , ncRNAs to RNAcentral  34  and genes to Ensembl. Whilst in many cases the database representation of these entities is largely more stable than that of proteins, update pipelines for each of these will be enhanced and improved with time. For analyses to be meaningful, data quality and full representation of experimental detail are of paramount importance. This is particularly required when working in an area of high data complexity and heterogeneity. However, capturing these data poses a significant challenge for curators who need to be familiar with an ever-growing set of experimental techniques.
IMEx data content and interactome coverage The IMEx data is constantly growing, with a new data release approximately every 8 weeks. Whilst there are interactions captured for a wide range of species, Table  1  highlights the predominance of human PPI data. The significant fraction of human-other species interactions includes a considerable number of interactions tested with human proteins against close mammalian (primarily mouse) orthologues, but also host-pathogen interactions between human and viral/bacterial proteins. Other model organisms, such as S. cerevisiae, E. coli or A. thaliana are also well represented in the dataset and a curation focus for IMEx partners. The human IMEx data set is dominated by PPIs. The number of interactions involving other molecule types is considerably lower (Table  2 ), but is steadily increasing and will provide interesting extensions to biologically relevant networks.
The curation performed by members of the IMEx Consortium represents a significant proportion of the interaction data found in publicly available databases. There is a degree of data redundancy with other databases that directly curate experimental data such as BioGRID  35  , and the IMEx dataset has been imported by meta-databases such as STRING  36  , mentha  22  , IID  14  and HiP-PIE  37. Figure  1  shows the overlap between unique human interacting pairs in all these different resources, plus legacy data curated by IMEx partners as an intersection plot  38. The greatest overlap exists between IMEx and BioGRID, which is well represented in the meta-databases considered, and between BioGRID and the rest of meta-databases. Figure  1  also highlights that the number of interacting pairs in BioGRID is larger than the IMEx dataset.
This is due to the less detailed curation model adopted by BioGRID, which allows for faster curation and inclusion of data sets that are not considered as indicating physical interactions by the stringent IMEx rules (e. g. , co-fractionation studies)  35. Figure  2  compares the number of unique interacting pairs in the main model organisms (Homo sapiens, Mus musculus, Arabidopsis thaliana, Drosophila melanogaster, Caenorhabditis elegans, and Saccharomyces cerevisiae), highlighting also the type of studies from which the data was curated. Most of the interacting pairs hosted in primary databases come from high-throughput publications (>100 interacting pairs in an experiment or >100 interactors in an n-ary interaction). A comparison with BioGRID is included as this is the only other publicly available, manually curated interaction database.
Tools to visualize and query IMEx data ComplexViewer. The ComplexViewer 39 has been designed specifically to visualise detailed data annotated by the IMEx Consortium. Its capabilities include a visual representation of a range of biomolecules as interactor types (proteins, small molecules and nucleic acids), interactions with more than two participants (n-ary interactions), sequence features relevant to the interaction (e. g. , binding domains) and stoichiometry information. The data is taken from a JavaScript Object Notation format MI-JSON, which can be generated from any PSI-MI compliant data source using the Java Molecular Interactions library (JAMI, see below)  40. This tool has been incorporated into the Complex Portal 31 , HumanMine  41  (http://www. humanmine. org) and YeastMine  42  (http:// yeastmine.
yeastgenome. org) data warehouses, and is freely available for implementation by additional resources (https:// www. npmjs. com/package/complexviewer; http://biojs. io/d/ complexviewer). ProtVista. The UniProt team has developed ProtVista  43  , an interactive tool for visualisation of a wide range of protein sequence features together in the same space. ProtVista is implemented using JavaScript and makes extensive use of D3 (https://d3js. org/), a library for producing dynamic, interactive data visualisations in web browsers. Work is currently underway to introduce an interaction track using the IMEx Consortium data, to visualise protein binding domains within the sequence of the protein represented in a UniProtKB entry, and to show the proteins to which it binds. PSICQUIC. PSICQUIC is a webservice created to enable computational access to standards-compliant molecular interaction data resources  20, 21.
PSICQUIC defines a minimum set of standard SOAP (Simple Object Access Protocol) and REST (Representational state transfer) methods which accept a Molecular Interactions Query Language (MIQL) query as input and return molecular interaction information in one of the standard formats (PSI-XML2. 5, MITAB). PSICQUIC enables the IMEx Consortium to make data available to the research community for rapid search and download. The current PSICQUIC implementation provides only limited access, however, to the wealth of data on molecule binding features, such as binding domains and   a link between the detailed information held in these files and the diverse range of analytical tools provided in the Bioconductor software environment.
Examples of uses of the IMEx dataset Building high-quality networks for data analysis. Protein interaction networks are used by biologists to understand the interconnectivity of signalling systems inside and outside of the cellular environments, exploring both physiologically normal and disease states. Network-based analysis is a powerful technique for extracting biological insights from large datasets. It enables researchers to associate proteins of unknown functions to human-curated pathways or identify clusters of interacting molecules, which may participate in the same biological process or belong to the same physical complex  45. Network topology can suggest the biological properties and function of component molecules. Algorithms exist to analyse aspects of network structures (such as their scale-free, small world, geometric random or hierarchical nature), to calculate network metrics (e.
MIscore relies on the available annotation evidence associated with an interaction and represents the degree of confidence in the existence of a particular interaction. The scoring system takes three factors into account, and uses the CV terms added by the IMEx curators: (1) How the interaction was observed (interaction detection method; MI:0001) (2) The type of interaction: e. g. , direct interaction, physical association and colocalization. (interaction type; MI:0190) (3) The number of publications reporting a specific interaction The results are normalised on a 0-1 scale. Searching the IMEx dataset with the query "intact-miscore:[x TO y]" enables the user to select data subsets by confidence score. At the time of writing, the authors recommend a MIscore range of 0. 45-1 to identify medium confidence interactions and 0.
6-1 for high confidence sets. These thresholds approximately correspond to interactions found with at least two distinct pieces of evidence (MIscore > 0. 45) or those found with three or more pieces of evidence, obtained with different methodologies (MIscore > 0. 6). The MIscore functionality is used by the Reactome pathways database Molecular Interaction overlay  50, 51  , which allows protein-protein or protein-small molecule to be superimposed onto a pathway diagram. For example, Fig. 4a  shows the tyrosine kinase ZAP70 (UniProtKB: P43403) in the Reactome TCR signaling pathway overlaid with 9 protein interactors imported from IMEx. The default setting provides fast access to a quarterly updated and locally hosted version of IMEx data with a MIscore threshold of 0. 45, which selects ~50% of all interactions in IMEx.
Interactions with a confidence level equal to or above this threshold will be visible in the viewport. The Reactome pathway analysis service also gives the user the option to set a more stringent MIscore filter using a slider feature, to select alternative interaction databases, and to perform an extended analysis that includes the IMEx interaction dataset served from the IntAct database. Two studies independently applied data filtering to essentially the same network to investigate the biology of LRRK2, a protein linked to familial forms of Parkinson's disease, using the data made publicly available by IMEx partners. Porras et al. 52  filtered the dataset using MIscore to generate a high-confidence subnetwork, which was used to produce a draft list of high confidence LRRK2 interacting partners.
Manzoni et al. 53  filtered by the number of publications reporting an interaction and then performed Gene Ontology network analysis on the LRRK2 interactome with edges identified by 2 or more publications. Both groups showed that the LRRK2 network was associated with terms referring to transport, cellular organization, vesicles and the cytoskeleton. Experimental data have since shown LRRK2 to be associated with selected Rab GTPases, and are now also present in the IMEx dataset  54. MIscore was designed to be customisable and a different version of the algorithm is used to identify binary interactions for export to the UniProtKB Interaction lines and to the Gene Ontology database. All binary interaction evidence in the IntAct database, including the data generated by spoke expansion of co-complex data, are clustered to produce a non-redundant set of protein pairs.
The UniProtKB dataset is then displayed in the appropriate entries in an adjacency viewer (Fig. 4b ). The IMEx dataset has also been used as a gold-standard training set in text-mining exercises such as the Biocreative competitions. These exercises make use of IMEx data partly because a linkage between figures and experiments has been maintained during the process of IMEx biocuration  55. Furthermore, in a subset of IMEx entries, sentences identifying interacting entities and the interaction detection method have been systematically captured in the 'source-text' annotation field. These sentences are highlighted for readers on abstracts and full text articles in Europe PMC via the SciLite tool  56. Characterising protein isoforms and features. Most eukaryotic protein-coding genes transcribe more than one isoform.
The different functions of isoforms are sometimes known or can be inferred (for example specific isoforms do/do not contain certain functional domains), but in many cases the biological significance of multiple isoforms derived from the same gene is not understood. However, the different interaction patterns of associated isoforms may provide an indication of their different biological functions by analysing their respective binding partners. In 2013, Talavera et al. 57  published an editorial stating that "it is crucial to the advance of basic and medical research that interactions are reported on an isoform-to-isoform basis and that databases switch to a similar approach". The IMEx databases curate this information, whenever the data is made available by authors, making isoform comparisons possible.
UniProtKB identifiers enable curators to differentiate between transcripts being identified at the isoform or canonical (reference sequence) level. Over 100,000 interactions in IMEx (~12% of IMEx data) contain specific isoform information, with more than 11,000 records containing specific isoform-isoform interactions. The UniProtKB database recently (release 2020_02) refactored the Interaction section of their records to improve the display of isoform data imported from IMEx. It is anticipated that the availability of such data will increase as protein identification techniques improve or as authors realise the value of such data and include this level of detail in publications. IMEx also captures so-called negative interactions, which will be of increasing use in the future. These data largely pertain to isoform-specific interactors, and describe cases where certain isoforms of a gene bind to a bait protein, while other isoforms of the same gene do not bind to the same bait in the same assay system.
Using the mature protein chain identifiers supplied by the UniProtKB database allows IMEx curators to accurately capture the form of the protein used in the assay. This is of particular importance for the annotation of the interactions of viral proteins where one gene in the viral genome may encode multiple proteins. The protein interactions of these post-processed protein and peptide chains cannot be meaningfully described when protein interactions are only captured at the gene level. Reversible and transient PTMs transmit and amplify signals in a highly regulated manner by reversible site-specific modulation, and thus play a key role in signal transduction  60. PTMs are often the result of an enzyme acting on a substrate and the enzyme-substrate reaction can be taken as evidence of a direct interaction in the IMEx data model.
The PTM resulting from this interaction is additionally captured, using the 'resulting-PTM' (MI:0639) term. Cell signalling resources, such as the SIGNOR database  61  have used the relationships between enzymes and substrates from the IMEx dataset and the effects of resulting PTMs on interactions to derive causal interactions. More recently, the IMEx Consortium has also started to capture the effects of chemical modifications of RNA molecules, several of which undergo specific nucleotide modifications during their maturation. Point mutations. To understand how amino acid variations influence protein function and stability, researchers have for many years examined the effect of induced point mutations on protein interactions. These targeted changes to the amino acid sequence of a protein may mimic known sequence variants, remove post-translational modification sites, disrupt regions required for protein stability or alter the properties of protein binding domains.
The IMEx Consortium has been collecting these data  29  using CV terms to describe the observed effects such as 'mutation decreasing interaction strength' (MI:0116), 'mutation increasing interaction rate' (MI:1131) or 'mutation causing an interaction' (MI:2227). The curation rules have recently been extended to include deep mutational scanning data such as described by Woodsmith et al. 62. At the time of writing, this set consists of 58,000 point mutations representing 20,000 interaction evidences annotated with differentially reported effects. To make these data more readily available to the user community, the IMEx Consortium has recently concatenated this dataset and made it available in a tab-delimited format (FeatureTAB)  29. This new data format includes details of the position and the amino acid change of the mutation, the interacting molecules and the effect of the mutation on the interaction.
The IMEx Consortium mutation-specific dataset is available to download at http://ftp. ebi. ac. uk/pub/databases/intact/current/ various/mutations. tsv. The data have already been used to provide potential mechanisms of action for disease related amino acid variants, to investigate the destabilising effect of mutations on structural models of protein-protein interfaces, and to benchmark tools predicting the effect of SNPs on protein function  29. In another recent study, both the protein interaction network from IID  23  and the mutation-specific interaction dataset have been used to explain survival and treatment resistance in ovarian cancer  63. Patients with a TP53 p. Arg175His mutation have a poor prognosis, which may be explained by chemoresistance due to a disrupted TP53 interaction with the BCL2 complex  64, 65  as well as several additional mechanisms, as highlighted in Fig.
5. The IMEx database also collects information concerning the nucleotides involved in miRNA-mRNA interactions and the effect of nucleic acid substitutions on binding. These data are accurately mapped on the mRNA sequence, making them a valuable resource for modelling miRNA interactions and the regulation of gene expression at specific targets. To the best of our knowledge, the IMEx Consortium is the only group capturing this information. Protein tags. Protein tags are chemical additions to a molecule to enable its identification and/or purification. They often consist of peptide sequences genetically attached onto a recombinant protein. As the nature and position of a tag may affect its interaction profile, this information must be recorded to enable researchers to fully comprehend the consequences of such changes  66.
For example, a tag expressed as either an N-or C-terminal fusion of Ebola Virus VP24 protein identified 48 and 51 interacting human proteins, respectively, of which only 40 proteins are common to both fusions  67, 68. Contextualising protein interactions with metadata. As detailed above, the interaction repertoire of a protein depends on many factors, not least the cellular environment in which they are expressed. The detailed metadata now captured in an IMEx record have been significantly expanded since the work of the Consortium was first described. Most techniques for measuring interactions do not use native conditions and, when described by the author in the publication, these data are captured in a systematic way using ontologies and CV terms. In addition to full details on the host organism, experimental methodology and construct details, IMEx curators now routinely capture expression level, any full or partial purification of a molecule, and the method by which constructs are delivered or engineered into a  cell or expression system.