dataesr/scientific-paragraphs-categorization huggingface dataset:
Whereas predicted disorder features are available in Mo-biDB  (18) , which has recently been integrated in UniPro-tKB  (23) , the crux of understanding protein disorder is the availability of manually curated, experimentally verified disorder annotations. The previous release of the database, DisProt 7  (24) , held data of ∼800 entries of IDPs/IDRs. Other databases, like IDEAL  (22) , ELM  (25) , DIBS  (26)  and MFIB  (27) , also include curated disorder information but are somehow different capturing specific functional aspects, or protein classes, and the overlap with DisProt is minimal  (28). To reflect on the above-noted breakthroughs and the recent explosion of the related liquid-liquid phase separation (LLPS) field  (29) , we present a significant update and upgrade of the DisProt database, which is now at version 8.
DisProt 8 holds almost two-times as many entries as DisProt 7, including the majority of those available in aforementioned databases. DisProt has been completely redesigned with an extended and updated functional classification scheme that relies on functional/structural aspects of annotated regions and incorporates a novel functional class 'biological condensation'. Annotation concepts have been formalized in a new Disorder Ontology (DO), which is maintained by the entire DisProt community. DisProt 8 also has many novel features that make it easier to search. The graphical interface has been redesigned and a new entry format provides greater flexibility, simplifies maintenance and allows the capture of more information from the literature. Lastly, we made significant improvements on the new annotation interface used by DisProt curators to populate the database.
Database records The major change compared to the previous release is the new annotation paradigm. In DisProt 7, experimen-tal methods represented the annotation core of a DisProt region and function terms were used as attributes. Now the core of an annotation is the functional/structural aspect of a region and the experimental method is an attribute representing the quality of the annotation. Both functional/structural aspects and the type of evidence are encoded in a controlled vocabulary, in line with other core data resources (e. g. UniProtKB). In the new DisProt region format, a 'statement' field has been introduced to track the literature text supporting the evidence. When the text is too long or complicated, a curator statement is provided instead. All 'statements' are available from the website and could be used to train text-mining algorithms and to highlight sentence-based annotations on abstracts and full text articles.
Disorder ontology In order to describe the different functional aspects of IDPs and the experimental methods used to characterize them, an annotation scheme was introduced in DisProt 7. A more formalized version of the disorder ontology was implemented in DisProt 8, to move towards a descriptive, interoperable and collaborative ontology of IDPs. This is the first release of the Disorder Ontology in the specific Biomedical Ontology (OBO) or the Web Ontology Language (OWL) formats  (32, 33). Besides improving the ability to reuse and share the ontology, these formats allow definition of label attributes such as 'xterm' (cross-references to external databases or ontologies) and 'synonym EXACT' (alternative names). They also support assignment of relationships among terms (including for example 'disjoint from' to mark terms that should not be linked together).
An identifier was assigned to each term in the ontology. It gives each label an 8-character accession code (e. g. 'DO:00001'), with the string 'DO:' to indicate the disorder ontology and five numeric characters to indicate the term unambiguously. Mirroring the Gene Ontology, accession numbers are assigned incrementally and there is no relationship between accession codes and the ontology topology. We have reviewed the terms and organization of the whole ontology, paying particular attention to the 'Function' category. We made some straightforward changes, for example, we split 'Fatty acylation (myristoylation and palmitylation)' into a renamed parent class 'Fatty acylation' and D272 Nucleic Acids Research, 2020, Vol. 48, Database issue its new children terms 'Myristoylation' and 'Palmitoylation'.
Curation process and updates DisProt data is provided by a community effort and annotations are collected through a web interface, which has been improved drastically compared to the previous version in terms of field validation, autocompletion and Named Entity Recognition (NER). In particular, curators can use a dedicated service from the NextA 5 literature triage infrastructure  (34)  to rank relevant literature starting from a gene name. In complement, when curators start from an article, the DisProt interface exploits the SciLite software through the EuropePMC API  (35)  to automatically retrieve biological entities and identifiers in the manuscript. The annotation interface implements the concept of ownership and user privileges. DisProt distinguishes two types of users, curators and reviewers.
Curators can edit only entries that they have created, while reviewers can modify all entries. Before release, the reviewers check all annotations to ensure high quality of the data. Curators are experts in the field and trained to meet DisProt annotation standards. As a community database, DisProt looks for new curators. Curator candidates are enrolled upon an evaluation of the curriculum and curation skills. Access to the annotation interface is restricted to registered curators and provided through Google Authentication (based on the OAuth 2. 0 protocol) or the ELIXIR authentication and authorization infrastructure system  (36). In the past, the DisProt interface had been kept open for limited time slots. Now the new DisProt interface is always open and new releases will be delivered more frequently, i.
Database content Since the last release, both the number of proteins and regions has almost doubled. DisProt 8 contains 1556 proteins and 3511 sequence segments annotated as disordered, which cover 19. 7% of the number of residues. These numbers become 1390 proteins, 3041 regions and 18. 7% of disorder content when ambiguous evidence is not considered. Previous annotations have been fixed and updated. Regions shorter than ten residues are no longer allowed and existing short regions were marked as obsolete as the majority are flexible loops annotated from X-ray experiments that do not represent disorder-related functional sites. Regions ending outside the sequence, regions with a start index of zero instead of one and entries for which the reference sequence in UniProtKB changed, were corrected and, when necessary, new records were created manually.
In red the 'structural state' terms show as the majority of region records in DisProt are annotated as disordered. Only five proteins are annotated with the 'order' term. In the future, curators will be encouraged to also track information about order, in particular when relevant for structural transitions. Transitions are mainly covering folding events ('disorder to order'), 365 proteins and 36 200 residues, and not the contrary. The majority of interaction partner annotations refers protein and nucleic acid binding. Binding residues are, however, overestimated since in the previous DisProt version, due to hard constraints in the database schema, it was not possible to narrow region boundaries to real interacting positions. Binding positions will become more precise in the  Darker segments in Figure  2  indicate the fraction of proteins (left plot) and residues (right plot) for which more than one experimental evidence is available.
Entry page The entry page is composed of three main sections. On the top, general information of the protein including name, Dis-Prot ID, organism, sequence length, MobiDB and UniPro-tKB accession numbers are provided. On the top right, it is possible to select the DisProt version and hide/show ambiguous/obsolete evidence. A download dropdown button allows saving the whole entry data in JSON, TSV (tabseparated) or the corresponding sequence in FASTA format. A new dynamic feature viewer allows to visualize DisProt evidence mapped onto sequence. The feature viewer shows two tracks by default, DisProt consensus and domains, the latter including Pfam (37) and Gene3D (38) annotation. DisProt consensus is generated by merging region annotation following the hierarchy of the ontology terms. In the last step, when merging the four main ontology branches, priority is given to 'interaction partner', 'structural transition', 'structural state' and 'disorder function', respectively.
Browsing and searching data DisProt implements both a database and a BLAST search  (39) , both available from the 'browse' page. The database search allows to compose a query against several fields, which can be combined to meet multiple criteria. All search fields accept regular expressions, and 'Free text' allows to search against the entire database content. For example, by searching 'p53' in 'free text' and 'homo | mus' in 'organism' will return all human and mouse proteins with the 'p53' string somewhere in the corresponding database records (protein name, annotation reference title, etc. ). Query results are displayed in the table below the search box. Table  columns  are customizable and the result can be downloaded in JSON, TSV or FASTA format.
CONCLUSIONS AND FUTURE WORK In the previous release, DisProt disorder annotations were polished and major errors were fixed but the number of newly annotated proteins was limited. In DisProt 8, disorder annotations doubled and a robust infrastructure has been put in place to leverage and accelerate the annotation process. The database format has been improved to be flexible enough to capture essential information from the literature but, at the same time, keeping disorder representation simple and clear. A new disorder ontology has been formalized with the aim of improving maintenance and data exchange with core data resources. The new ontology is versioned and provides a hierarchy to facilitate term traversal. Article sentences tracking statements about disorder experimental evidence are now captured providing a corpus for the implementation of new text-mining models.
Dataset used to build the dendrograms We retrieved from the KEGG database  [16]  a large set of organisms genes, and we identified those associated with enzymes. For each enzyme in a given organism, we identified all the chemical reactions associated with that enzyme, such that, for each organism we were able to build a list of all identified chemical reactions potentially present in its metabolism. Moreover, for each gene we obtained their corresponding nucleic acid and amino acid sequences. Details on the procedures used to obtain information from KEGG can be found in  [17]. Separately, for each prokaryotic organism in our dataset, we searched the NCBI database for its 16S rRNA subunit sequence using an automatized script including the terms Genus species[Orgn] AND 16S ribosomal RNA[Titl] NOT partial sequence  [Titl] , where Genus species was the binomial nomenclature of each organism in the dataset obtained from KEGG.
This search was designed to retrieve only complete sequences and discard partial ones. Our original data set built with KEGG's information comprised originally 4803 organisms. From these, the metabolic networks of 3972 organisms were completed, whereby NCBI searches retrieved 16S rRNA subunit sequences for 1537 of them. The intersection of all these sets resulted in a dataset with 1506 prokaryote organisms for which we had complete information, i. e. we had all sequences for their enzymes, the complete list of chemical reactions and 16S rRNA nucleotide sequences.
Given a set of N organisms the first step in our proposed dendrogram reconstruction is the evaluation of a symmetric N × N distance matrix (D), where each element element D ij is a measure of the "evolutionary" distance between organism i and j. The evaluation of this matrix follows different methodologies that are described in the following subsections. Here we explain the reconstruction of the dendrogram once the D matrix is calculated. For this, we follow the same procedure as in  [12]. The matrix D can be viewed as a complete weighted graph G = (V, E, w). The set of nodes V stands for all the organisms in the dataset. Each pair of different organisms are linked by an edge in E. A non-negative function w : E → R + 0 associates a weight to each edge, according to the distance between the organisms connected by that edge.
Dendrogram comparisons Since different methods have been proposed for generating dendrograms from the same set of organisms, a measure is needed for comparing them. Robinson-Foulds metric, introduced in  [1] , allows to measure how similar two dendrograms are. This metric has been widely used since it is not limited to binary trees and is based on counting elementary operations which transform one dendrogram into another. The lower the difference between two dendrograms is, the more similar the two dendrograms are. A more detailed description can be found in the Supplementary Material. Several algorithms have been described for efficiently compute this metric  [2, 3]. In this work we have considered the implementation in the Python library DendroPy  [23]. Two ensembles were constructed by randomly choosing organisms from the 1506 organisms set for which there was complete information.
Results and Discussion We have worked with two ensembles, all constructed by randomly selecting organisms from the 1506 organisms dataset for which we had complete information (see the aforementioned dataset subsection). The first ensemble contained ten organisms sets with twenty organisms in each set while the second ensemble contained ten organisms sets with thirty organisms in each set. For each set in an ensemble, we construct 3 gene-based dendrograms (denoted by DRIBO, DENZS and D1ENZ), 6 network dendrograms (denoted by DS1, DS2, DNET1, DNET2, DNET3 and DNET4) and also 100 random dendrograms (DRAND). Then we compare each dendrogram with each random dendrogram and with each other (calculate the symmetric difference, i. e. Robinson-Foulds Metric). We evaluate the average and standard deviation for every pair of PT for each organisms set, so that all comparisons are covered.
From this diversity of DENZS dendrograms, we chose to use for the following comparison only the DENZS built with the BLOSUM55 matrix. In order to visualize the comparison of results, dendrograms were done from the tables using pvclust package  [27]  in R statistical software using Ward. D clustering method and euclidean distance on the Robinson-Foulds values for each dendrogram pair. Two different methods of significance are shown: approximately unbiased p-value (AU) and bootstrap probability value (BP). AU p-value is computed by multiscale bootstrap resampling and is generally a better approximation to unbiased p-value than BP value that is computed by normal bootstrap resampling  [27]. In Table  2  and Figure  3  we have compared gene-based and network methods for the first ensemble with ten sets of twenty organisms each and in Table  3  and Figure  4  we have shown the same results for the second ensemble with ten sets of thirty organisms each.
Research Infrastructures (RI) and associated centres of excellence currently build harmonized multidisciplinary workflows that support academic and private sector users. The European Marine Biological Research Infrastructure Cluster (EMBRIC) has brought together six such RIs in a European project to promote the blue bio-economy. The overarching objective is to develop coherent chains of high-quality services for access to biological, analytical and data resources providing improvements in the throughput and efficiency of workflows for discovery of novel marine products. In order to test the efficiency of this prototype pipeline for discovery, 248 rarely-grown organisms were isolated and analysed, some extracts demonstrated interesting biochemical properties and are currently undergoing further analysis.
In response to this issue EMBRIC 1 3 is facilitating the search by exploring ways to integrate and mine metabolomic/genomic/metagenomic data. There is also a critical need to bridge the gap between 'feature identification' and compound chemical characterisation. Data analysis tools to integrate genome reading with metabolite information are seen as a crucial success factor. Thus, an essential first step is combining parallel, complementary streams to speed discovery (see Fig. 1 ). An additional problem is that the majority of microorganisms in the environment are yet to be cultured and this is compounded further by the fact that those from marine environments are renowned slow growers  (Joint et al. 2010). In order to address this, DSMZ researchers have been developing culturing technologies for less tractable microbes.
Using a microdispenser system,  Bruns et al. (2003)  developed a high-throughput innovative technique for the isolation of bacteria in liquid. The MicroDrop ® microdispenser system enables the dispensing of highly reproducible volumes (0. 5 µl to 2. 5 ml) in microtiter plates in less than 1 min per plate allowing the generation of large numbers of bacterial enrichments for downstream cultivation and analysis. Using this technique  Gich et al. (2005)  obtained 570 bacterial cultures from which the majority was closely related to previously uncultured bacteria. It is necessary to bridge the gap between compound identification and compound chemical characterisation and, ultimately, to gain access to the most appropriate methodologies and technologies. Current "state of the art" approaches utilise High Resolution Mass Spectrometry (MS) and database comparison, coupled with genome analysis.
embri c. eu/deliv erabl es specifically: D6. 1 EMBRIC showcases: prototype pipelines from the microorganism to product discovery (Revised 2019) at https ://www. embri c. eu/sites /defau lt/files /deliv erabl es/EMBRI C%20Del ivera ble%20 D6. 1b. pdf. Further analysis of the more recently received strains is ongoing. An overview of the routes to discovery within the EMBRIC project are shown in Fig. 2. Extensive analysis of over 150 cell pellet extracts selected from the 248 strains was carried out via a combination of MS/MS in-house library matching and manual dereplication (using commercial and online opensource databases) allowed the team to assign between 10 and 25% of the detected features to known metabolites. There were a number of outputs of significance from the microbial prototype pipeline: • Flow cytometry and biofilm technologies were used for selection of marine organisms in samples that had greatest potential as candidates for biomass production; • Combined cosmid and BAC library heterologous expression approach developed; • Genome informed use of isotopes to track new metabolites; • USTAN generated cosmid libraries for a series of more tractable and sequenced test strains.
g. short-chain fatty acids to larger polar and nonpolar compounds of primary and secondary metabolism. Lessons learned from the metabolome analysis of microalgae in EMBRIC work package 7 (https ://www. embri c. eu/WP) accelerated their application to the bacteria; • Use of metabolite clustering analysis with a tool made available as an R package to assist in the identification of novel suites of compounds; • Use of molecular networking tool from GNPS combined with manual dereplication and matching with in-house library allowed the faster and more efficient annotation of known metabolites. The visualization of the chemical space at the molecular level gave insights on structural relationship between chemical entities in the whole data set, pointing towards potential novel compounds (unknown metabolites); Raw data and detailed results are to be found on the EMBRIC web site under work package 6 deliverables (https ://www.
e. de-replication) and how might more be discovered in the marine environment? This first step, therefore, may be considered to be identifying the potential. Twenty collections registered with the World Data Centre for Microorganisms (WDCM) hold microorganisms sourced from the marine environment; amongst them, they house over 334,000 strains. Even within the EMBRIC partners, there are collections that are not listed on the WDCM; additionally, there are many organisms held by scientists in non-registered collections around the world that are not visible to the researcher. The rest of the 700 + collections in the WDCM hold over two million strains of microorganism, many of which may well originate from the marine environment but there is no single place  where all catalogues can be searched and these strains easily revealed.
At present, each single collection catalogue would need to be searched and only 85 collections (holding a total of 356,889 strains) are included in the Global Catalogue of Microorganisms (GCM), with only minimal data made available (https ://gcm. wfcc. info/). A concerted effort, therefore, is needed to make these strains more readily accessible: a task that can be undertaken by the MIRRI and EMBRIC consortia. When the species coverage is examined it is very easy to see that there are large gaps between what is known and what is available. There are also estimates that less than 1% of the microorganisms that are in the environment have been discovered, to date. Thus, the question remains: how can this vast resource be tapped? Targeting the organisms and establishing sampling regimes for the chemistry that is needed, is not straightforward.
Characterisation technologies, data analysis and applications are available and have been identified through this process. The key function of the research infrastructures is to give access to the resources needed for a researcher to make discoveries: e. g. the EMBRIC web tool. ELIXIR, a distributed infrastructure for life-science information: https ://www. elixi r-europ e. org/tools tools offer different ways to find and analyse data to enable such compounds to be identified. The remaining elements in discovery pipelines, scaling-up, extraction, purification and delivery of chemically defined compounds are also elements that have been designed for some specific cases. EU-OPENSCREEN (https ://www. euopens creen. eu/) in particular has pulled together much of the best practice for chemical screening, and offers longstanding expertise regarding the setup and implementation of chemical screens and subsequent hit-to-lead optimization.
To date, resource centres and service providers have tried to secondguess what a company needs, or wants, as many companies are reluctant to let others know what they are working on. Enabling companies to access and utilise information in open databases anonymously misses potential and lessens the opportunity to get the collections and their networks to make more targets available. Appropriate business models with non-disclosure agreements are needed and a common agreement enabled for infrastructure partners to allow others to act on their behalf. By pooling expertise, a critical mass can be focused on specific user problems, such as, the development of dedicated isolation protocols (e. g. selective media to access the microorganisms that cannot yet be cultivated). In terms of the bottlenecks associated with specific active molecules, gaps between the identification and chemical characterization of compounds can be addressed and appropriate biological assay technology to comprehensively test for bioactivities is available already.