dataesr/scientific-paragraphs-categorization huggingface dataset:
Three-dimensional structures Because a protein's structure provides the key to understanding the mechanism of its function, insights can often be gained by exploring structures, especially for proteins complexed with physiologically relevant molecules such as interacting proteins or peptides, nucleic acids, substrates or cofactors, or small-molecule inhibitors. The definitive resource for protein 3D structures is the Protein Data Bank (PDB), for which records can be readily retrieved from the Research Collaboratory for Structural Bioinformatics (RCSB) website  (Rose et al. , 2013) , its partner site PDB in Europe (PDBe;  Gutmanas et al. , 2014) , and the NCBI's Molecular Modeling Database (MMDB;  Madej et al. , 2012 ; Supplemental Table  S11 ). These resources offer complementary search and display options, but all allow the inspection of structures online using interactive Web-based viewers and the download of structure data files for offline viewing using the latest 3D-structure exploration software.
In many cases there are multiple PDB records for a given protein; these often correspond to protein constructs of different lengths and proteins complexed with different molecules. Searching the foregoing resources by gene or protein ID yields the set of relevant records, each title being a brief description of the protein plus any complexed molecules. Alternatively, a graphical overview of PDB records corresponding to a given gene product is provided by PDBsum (de  Beer et al. , 2014). Here a domain diagram of the fulllength protein is shown, and immediately below, a graphical alignment of constructs whose structures have been solved, with secondary structural elements depicted schematically. For a given construct, clicking its schematic diagram opens a sequence alignment with the full-length protein, whereas clicking its PDB code opens a page displaying a wealth of structural, biochemical, and functional information, with links to structure viewers and to downloading the structure file.
Returning to DASty, this program cleverly integrates sequencebased domain and feature prediction with 3D modeling. When a protein's 3D structure is available, this appears above the graphical multialignment in an interactive Jmol viewer  (Herraez, 2006) , allowing zooming in and out, rotation, and display in different formats. Clicking a domain or feature within the alignment highlights corresponding residues within the primary sequence and on the 3D structure, allowing researchers to identify 3D juxtapositions of domains and features within the protein.
Several resources providing for the identification of protein domains have been developed; these differ in approaches, definitions, and algorithms, thus generating complementary sets of classifications. Major protein-domain resources driven primarily by sequence data include Pfam  (Finn et al. , 2014) , SMART  (Letunic et al. , 2012) , PANTHER  (Mi et al. , 2013) , and InterPro  (Hunter et al. , 2012 ; Supplemental Table  S10 ). In contrast, domain classification by CATH-Gene3D  (Sillitoe et al. , 2013; Lees et al. , 2014)  is driven mainly by protein three-dimensional (3D) structure data, including the overall architecture, subdomain folding, and secondary structural elements. Programs for identifying SLiMs within a protein of interest include PROSITE  (Sigrist et al. , 2013) , the Eukaryotic Linear Motif (ELM) database  (Dinkel et al.
, 2014) , Minimotif Miner  (Mi et al. , 2012) , and Scansite  (Obenauer et al. , 2003). Each of these domain or motif identification resources can be accessed independently. However, a more efficient approach is to employ a program that uses several methods and generates a graphical output integrating all identified features in a single display. Typically with these programs, "mousing over" a feature triggers a pop-up box providing further information. The ideal program would use all of the foregoing domain and motif identification methods, generating a single clear and concise diagram; because no single program includes all of these methods, the prominent resources are described separately. The NCBI's Conserved Domain Database (CDD; Marchler-Bauer et al. , 2013) generates a very compact diagram representing superfamilies, domains, and functional sites of the protein, together with annotation of specific residues required for activities such as enzymatic catalysis or binding to DNA.
Parameters used to define matches can be refined to allow identification of features at different sensitivity thresholds. Mousing over a feature opens a box providing a functional description and (where available) a 3D structure image. Proteins harboring similar domain architectures can be displayed via a single-click link to the CDART website  (Geer et al. , 2002). The CDD is thus an excellent starting point for identifying key features of a protein, before using other programs to perform deeper exploration. The InterPro resource  (Hunter et al. , 2012)  identifies protein features in four categories: families, domains, repeats, and sites, based on signatures defined by multiple partner databases. The outputs are displayed in a clear graphical multialignment, each feature hit being a Web link to the relevant entry in the home resource.
to exist runs into the hundreds (UniProt lists >450) and continues to increase. Because several residues within a protein may potentially be modified, with different PTMs present in various combinations, the potential repertoire of distinct species of modified protein in a cell is astronomical. The modification status of a protein is highly dynamic, in many cases depending on the activities of enzymes that catalyze the addition and removal of the PTMs, those of proteins that bind depending on modification status, and the relative colocalization of all these players within the cell-sometimes to different parts of an organelle. These properties often in turn depend on cellular contexts such as cell type, cell-cycle phase, and cellular stresses, including drug treatments. Thus researchers wanting to know whether their gene product of interest is modified in vivo need to take into consideration this biological and experimental contextual information; the storage and retrieval of these metadata poses a particular challenge for PTM databases.
However, whereas the resultant data explosion can be readily accommodated by specialist PTM databases, those protein resources that rely on manual curation to ensure quality are likely to lag behind in terms of up-to-dateness. Arguably the most extensively studied protein PTM is the phosphorylation of tyrosine, serine, and threonine residues, and protein phosphorylation resources have taken the lead regarding the curation of PTM data and their retrieval with the necessary contextual information (Supplemental Table  S13 ). The most comprehensive of these is PhosphoSitePlus  (Hornbeck et al. , 2012) , in which each protein record includes a functional summary, and then modification sites (phosphorylation, plus several other types) are shown graphically in the context of the protein's domains.
An accompanying table lists PTM sites with their surrounding amino acid sequences, comparing those from orthologous proteins in related species. Each modified residue has its own record page, displaying experimental and contextual information and literature references. Complementary information on protein phosphorylation is provided by the Phospho. ELM  (Dinkel et al. , 2011)  and PHOSIDA  (Gnad et al. , 2011)  resources. Several databases include information about specific PTM types, but trawling though each individually would be an unnecessarily tedious exercise. A more efficient approach is to search a meta-database, one bringing together data originating from several separate databases. For PTM-related information the most comprehensive resource is dbPTM  (Lu et al. , 2013) , whose entries cover 96 types of modification and originate from UniProt, with specialist databases of PTMs including protein phosphorylation, glycosylation, S-nitrosylation, ubiquitylation, and methylation, plus their own literature textmining efforts.
Here the location of each experimentally determined PTM is shown in a protein-domain diagram; beneath is a table listing the PTMs with some sequence context, Web links to the databases of origin, and literature references. pathways is due to a large degree to interactions between signaling molecules and scaffold proteins  (Good et al. , 2011). Thus information about interactions of gene products can provide useful physiological context to understanding their biological functions. Numerous protein-protein interaction databases have been developed, and thus a plethora of information is accessible, with a variety of options for retrieval and display (Supplemental Table  S12 ). Some standardization between these resources is being achieved, as 12 prominent interaction databases have joined the International Molecular Exchange (IMEx) consortium to share curation and annotation of interaction data  (Orchard et al.
This can be achieved via the color coding of the network edges by interaction type, and a filter can be applied to restrict displayed interactions to those of a certain type-for example, "experiments. " Interaction databases are invaluable resources for providing information about known partners and networks in which the gene products are involved. However, protein-protein interactions are certainly not always constitutive, and cell contexts-for example, cell-cycle and developmental stages and responses to stressescount for a great deal. Currently, as with ontology terms, these dynamic aspects are rarely conveyed by interaction databases, leaving room for future developments to these resources.
Although efforts are underway to standardize such data and centralize them in a single portal such as the NCBI's ClinVar  (Landrum et al. , 2014) , the current diversity of complementary disease resources means that these still warrant separate descriptions. A comprehensive categorization of associations between human genes and diseases is provided by the Genetic Association Database  (Becker et al. , 2004; Supplemental Table S15). Querying by gene term generates a table of published instances in which the involvement of that gene in a disease has been tested. Each entry reports the disease name and class and associated terms, plus numerous links, including one to the corresponding publication, often with a one-line summary of the study's conclusions. For well-studied genes the full output may contain considerable redundancy, and the database also reports negative associations (i.
e. , when a gene-disease association was tested and not found to exist), although these can be filtered from the output. Another valuable resource linking genes to diseases is KEGG  (Kanehisa et al. , 2014) , in which a list of diseases associated with a gene of interest is shown in the relevant entry in the KEGG GENES database. Clicking a disease identifier links to the relevant entry in KEGG DISEASE, providing a full description of that disorder, the nature of the genetic association, etiological factors, and molecular markers, plus pharmacological agents with which it can be treated. The Comparative Toxicogenomics Database  (Davis et al. , 2013)  incorporates a complementary approach: in addition to recording associations between genes and disease from manual curation, this resource contains gene-disease associations inferred on the basis of reported interactions between gene products and compounds and between compounds and disease.
For well-studied organisms, much relevant information on genetic variations and corresponding phenotypes is present in species-specific gene databases (Supplemental Table  S1 ), and so for exploring known variations in hits from model experimental organisms and their biological consequences, the relevant gene page from such a resource is often the best place to start. More comprehensive, multispecies repositories of genetic variations are stored in specialist resources such as dbSNP for SNPs  (Bhagwat, 2010)  and DGVa and dbVAR for GSVs  (Lappalainen et al. , 2013 ; Supplemental Table  S14 ). Information from these and other sources is available via Ensembl, for which gene pages can be accessed from multiple ID types. In the relevant Ensembl gene page, under Gene-based displays, one option is the Variation table; this provides an overview of all genetic variations identified for that gene.
Here initially types of variation are listed, each accompanied by a brief description and the number of times a variation of that type has been found in the gene of interest. Clicking Show for a variation type opens a table displaying full information about all occurrences for that gene. At the protein level, reviewed UniProt entries list documented amino acid variants under the Sequence annotation (Features) section, within three categories: Alternative sequence, Natural variant, and Mutagenesis. Alongside each variant are links to literature and source databases (where appropriate) and a graphic illustrating the position of the variation within the protein. When the variant has functional or pathological consequences, these are briefly described.
The canSAR resource offers a complementary functionality, providing for a given protein a table of druggable or tractable domains, linking to diagrams detailing the interactions between these and relevant ligand molecules. The set of possible combinations between proteins and smallmolecule compounds recorded in public databases presents billions of potential docking interactions, a large proportion of which are uncharacterized, many possibly harboring significant pharmaceutical potential. Massive in silico efforts are underway to assess these potential interactions, and the recently launched Drugable portal allows access to data relating to compounds predicted to dock with each target protein, correlated with tissue-expression profiles  (Reardon, 2013).
QuESTION 10: I WOuLd LIkE TO kNOW EVERyTHINg ABOuT THIS gENE (OR PROTEIN)! HOW CAN I ACCESS AS muCH RELEVANT INfORmATION AS POSSIBLE? The primary bioinformatic databases described so far provide a wealth of data relevant to the gene or protein of interest, but for the information-hungry biologist, visiting each site in turn is not likely to be the most efficient way of accessing these resources. A more effective approach is to perform a cross-database search (which queries multiple resources in parallel, generating many outputs) or to access a summary website (which provides an overview of information gathered from primary database resources). Both the NCBI's "GQuery" Global Cross-Database Search and the EBI Search (also known as EB-eye) allow the user to query all of their hosted databases in one operation; these multisearches can be initiated via a direct Web link (Supplemental Table  S17 ).
In both cases, for each database the number of hits is shown, alongside links to results lists for that database. The Bioinformatic Harvester  (Liebel et al. , 2005)  is a utility that launches searches of multiple resources simultaneously, retrieving information about the gene or protein of interest from human, mouse, rat, zebrafish, or Arabidopsis. Queries of any type (names or IDs of genes or proteins, or any text term) are first used to generate a list of relevant proteins (IPI identifiers and one-line descriptions). On selection of a protein, Bioinformatic Harvester performs the searches, displaying results from each resource in a separate frame, helpfully organized in a multitabbed Web page. A subsequent project from the Harvester team demonstrated a proof-of-principle that all publicly accessible scientific data from literature, databases, and laboratory-hosted Web pages could be made accessible via a Google-style interface, using distributed search engine technologies  (Lütjohann et al.
Blocking the action of specific proteins involved in pathological processes is of course one principal means of treating disease. For a drug or chemical database to be useful for biological data mining, it must be queryable on the basis of target IDs, using standard gene or protein nomenclature. One such resource is ChEMBL  (Bento et al. , 2014) , a database of bioactive small molecules (Supplemental Table  S16 ). Because dozens or hundreds of interacting molecules may be recorded for a given target, ChEMBL provides interactive graphs displaying distributions of their properties, allowing the user to narrow a set of compounds for examination. Selected compounds are presented in a sortable table, which includes structure diagrams, physicochemical properties, and results of relevant bioassays.
Another such resource is DrugBank  (Law et al. , 2014) , a comprehensive database of pharmaceuticals and inhibitors. When a protein is recorded as a target of such a molecule, a UniProt IDbased search reveals protein information, followed by a table of relevant interacting molecules, each being a link to the full record from the main database. The Comparative Toxicogenomics Database records links between genes and interacting chemicals, including effects of compounds on the expression of genes, as well as the activities of their products. Each gene page displays the top 10 interacting chemicals (by number of literature references) in a bar chart. Clicking a chemical name opens a list of corresponding interactions; alternatively, clicking the Chemical Interactions tab opens a large sortable table of all interacting chemicals, with one-line descriptions of each interaction, plus literature references.
An alternative approach to retrieving such information is to search a database of assays involving compounds and targets, such as PubChem BioAssay  (Wang et al. , 2014). Querying this resource generates a list of titles of biological assays (but not compound names) in which the query gene or protein was a target, plus literature references. Results can be refined with a single click-for example, to select those in which compounds inhibited with submicromolar (or subnanomolar) IC 50 values. Each title links to a full record of information about the assay protocol, compounds used, and results; each compound links to its entry in the PubChem Compound database  (Wang et al. , 2009) , where full chemical data are displayed, and the molecule's structure can be visualized in a 2D or 3D viewer. Returning to the gene product, the term "druggability" refers to "the likelihood of being able to modulate a target with a smallmolecule drug"  (Owens, 2007).
QuICk LINkS fROm HITS TO INTERNET-BASEd RESOuRCES The answers to the foregoing 10 questions describe only a small selection of the dozens or hundreds of freely accessible bioinformatic databases available. However, free and user-friendly software specifically designed to allow quick access to relevant entries within these resources from results tables is not commonplace. One method allowing easy and direct access to appropriate entries within bioinformatic resources is the creation of a hyperlinked results table (Figure  2 ). Because all major spreadsheet programs allow the generation of hyperlinks incorporating contents of cells within the table, additional columns can be created containing custom hyperlinks that use hitbased gene, nucleotide, or protein identifiers, allowing users one-click direct access to relevant pages within Web-based resources.
The creation of these hyperlinks is straightforward (Supplemental Method S1) and can be automated within most spreadsheet programs, thus negating the requirement for specialist data-mining software. Online databases may be based around DNA, transcript, or protein IDs, with the majority using either gene symbols/codes or protein (UniProt) IDs, although some can be accessed using several ID types. To maximize the available options of resources directly accessible from results tables, a worthwhile exercise is to obtain gene symbols for transcripts or protein hits (Supplemental Method S2) or to obtain UniProt IDs for gene or transcript hits (Supplemental Method S3). Clearly, with such a large number of online databases for which hyperlinks can be made, users should develop a familiarity with resources available for their given organism and make the hyperlinks targeted for that species and relevant to the biological questions being investigated.
dATA SET-WIdE INfORmATION RETRIEVAL Hyperlinked results tables allow one-at-a-time direct access to relevant information for each hit, but for larger sets of hits a more EBI Search results, alongside the listing of the number of hits in each database is the EBI Gene & Protein Summary. This provides a useful overview of the properties of a gene and its products, within five tabs: gene, expression, protein, protein structure, and literature (for five species: human, mouse, budding yeast, Drosophila, and C. elegans). InterMine database technology integrates information about genes and proteins from multiple sources  (Smith et al. , 2012)  and powers overview facilities for several experimental species via a series of interconnected Web portals: FlyMine, YeastMine, MouseMine, RatMine, ZebrafishMine, metabolicMine (including human genes), and modMine (flies and worms).
Some indication of interaction significance was provided by table ranking (IntAct by confidence, BioGRID by reporting frequency); for both resources, p53's top interactor was MDM2, its best-characterized regulator. However, which of the 500 are biologically validated, play roles in p53mediated pathways, and regulate cell proliferation? Addressing such questions requires exploration beyond primary databases, integrating several data sources using more sophisticated software tools. Protein feature identification. The outputs of eight protein feature annotation programs were assessed using p53, a transcription factor with three functional domains (N-terminal transactivation, central DNA binding, C-terminal tetramerization), plus sites of interaction with ions, DNA, and regulatory proteins. All three domains are efficient approach for obtaining and managing such information is the annotated results table (Figure  2 ).
This analysis provides an at-a-glance display of which subsets of genes or proteins share membership of complexes or systems. Each protein links to several database resources, and the page contains links to data set-wide overviews of occurrence (gene conservation), coexpression, and evidence for the mutual association of subsets of factors within the data set. Complementing this, input of gene, transcript, or protein hits to DAVID generates an Annotation Summary Results page with a Pathways subsection. Here the presence of one or more gene products from the input list within various pathways (from resources including BioCarta, KEGG, and Reactome) is indicated, and pathway diagrams can be displayed with the relevant proteins highlighted and flashing. More sophisticated analyses of the properties of a set of interacting gene products are possible using the Cytoscape network visualization and analysis software  (Smoot et al.