dataesr/scientific-paragraphs-categorization huggingface dataset:
Orthology and phylogeny Orthology ontology development and application. Orthologs are defined as genes derived from a common ancestral gene by speciation. Orthology information can play a central role in predicting gene function in newly sequenced genomes and can also help unravel the evolutionary history of genes and organisms. Orthology resources have been represented in a variety of formats, including the OrthoXML 27 that is used by several orthology databases such as InParanoid  28  , Orthologous MAtrix (OMA,  29  ), and TreeFam  30. The interest in exchanging orthology data with other communities has provided the impetus for research on applying the Semantic Web and using common ontologies for making the meaning of the content explicit. Thus, on the basis of previous studies on the semantic representation of orthology  31, 32  , we made efforts during BH15 towards semantic standardization of orthology content  33.
We developed the Orthology Ontology (ORTH,  34  and  35  ) to capture essential concepts pertaining to orthology, including clusters of orthologs derived from speciation events. ORTH was designed following best practices in ontology engineering, i. e. , reusing related ontologies such as the Semanticscience Integrated Ontology (SIO, 36 ), the Relations Ontology (RO,  37  ), and the Comparative Data Analysis Ontology (CDAO,  38  ). We used the Semantic Web Integration Tool (SWIT, 39 and 40 ), a generic tool for generating semantic repositories from relational databases and XML sources, to convert InParanoid, OMA, and TreeFam datasets in OrthoXML format into RDF. More details and sample queries for the datasets using ORTH are on the source code repository  41, 42. Although the standard mapping and transformation by SWIT was largely able to transform the content of the three databases, though a few resource-specific rules were necessary because: (1) OrthoXML offers generic tags that are used by orthology databases in a heterogeneous way, e.
g. for describing the taxonomic range of a cluster of orthologs; and (2) different orthology resources use identifiers of genes or proteins from different databases, so the corresponding prefixes for URIs had to be adapted. The next steps include: (1) evaluation of the results by the Quest for Orthologs (QfO,  43  ) community, which could lead to the development of a QfO semantic infrastructure for sharing orthology resources; (2) examining the interoperability of semantic orthology datasets using additional databases such as UniProt  44  , KEGG OC  45  , and the Microbial Genome Database (MBGD,  46  ); and (3) developing applications and tools for comparative analysis of genomes/proteomes utilizing the ORTH. Molecular evolutionary process calibration. Not only qualitative but also quantitative representation of evolutionary events, i.
Recently, a working group at the National Evolutionary Synthesis Center (NESCent, 53 ) initiated a project to address this 54 and to establish a database of reference fossils with a web service API 55. To evaluate whether this new resource can indeed be usefully applied in the analysis of molecular data we developed a proof-of-concept pipeline 55 (based on Bio::Phylo 56 and SUPERSMART 57 ) that includes a reconciliation between fossil taxa from the FossilCalibrations database and extant taxa from in the TreeFam orthology database. The steps are as follows: 1. Download a data dump release from TreeFam. 2. For each TreeFam gene family, fetch fossils from FossilCalibrations through the API. This was done by querying for the taxonomic names, e. g. "Mammalia", that are applied to internal node labels in gene family trees.
Given that baseline substitution rates differ between lineages we performed an assessment of whether this prediction could be detected confined to a single lineage, that of C. elegans. Figure  2  suggests that this is indeed the case (this is in essence a different way of obtaining, roughly, some of the findings of 60 ). As a proof of concept to test whether it is possible to include fossil data from this new resource we conclude that this is indeed possible, but we note several drawbacks: • The FossilCalibrations database makes its data available as simple JSON. This is convenient for programmers but it also means that certain concepts used in the JSON are ambiguous as they are not linked to any controlled vocabulary or ontology. • The distinction between stem and crown fossils is made using magic numbers whose values and their meanings are poorly documented (we could only discover their semantics by inspecting the source code of FossilCalibrations).
• Some of the taxon names used by FossilCalibrations are not scientific names from any explicitly identified taxonomy. For example, some fossil calibration points have names such as "Chimpanzee-Human", or "Humanity". Such names are difficult to resolve using taxonomic name resolution services. • There are large biases in taxon sampling in the database. In fossil databases this is nearly inevitable as some taxa fossilize much better than others, but even where a relatively rich fossil record is known to exist, e. g. in the sea urchins (Takeshi Kawashima, pers. comm. ), no records were available in the database. The first three drawbacks we identified can all be traced back to poorly defined semantics, which we therefore characterize as the key current issue in LOD representation of fossil specimens.
To fill this gap, firstly we need to semantically curate FossilCalibrations data manually, which of course may take time, then export curated information in RDF so that analyses proposed in this section can be integrated in the automated pipeline. Proteomics Protein semantic representation. Many datasets on the Semantic Web are available as RDF, but often lack the explicit modeltheoretic semantics provided by languages such as OWL. For complex datasets, the additional semantics of OWL, which includes assertions of disjointness, i. e. the explicit semantic distinction between classes and their instances, and axioms restricting the use of classes and object properties, may be particularly beneficial. The main limitation of languages such as OWL is that querying them is often highly computationally intensive and therefore not feasible for large datasets.
Our aim was to evaluate how well formal languages like OWL scale in representing very large datasets. We chose the UniProt database  44  , as it currently constitutes one of the larger RDF datasets, is used throughout biology, and has rigorous quality checks. Our aim was to find a representation of proteins and their functions using OWL. As automated reasoning over OWL knowledge bases is highly complex (2-NEXPTIME complete), we limited ourselves to the OWL 2 EL profile. However, widely used ontology design patterns for representing functions are not expressible in OWL 2 EL, as certain types of restrictions (in particular universal quantification) do not fall within the OWL 2 EL expressivity. As a consequence of these limitations, we decided to develop a novel representation pattern for asserting that proteins have a function that would fall in OWL 2 EL and would enable us to convert all of UniProtKB into OWL (though for testing purposes we converted only a subset on the order of 10 5 OWL axioms).
Proteome assay annotation. In proteomics, expressed proteins are usually identified by mass spectrometry. In most common workflows, proteins are digested into peptides with a protease. The peptides are ionized and then fragmented. Their precursor mass-to-charge ratios and fragment ion spectra are experimentally measured and compared with theoretical masses and fragmentation patterns of peptides calculated from a protein database. Information about experimental protocols and data analysis methods is thus important for understanding the raw and processed data. An identified protein list has substantial amounts of metadata such as labels used for quantification, e. g. iTRAQ,  63  , or SILAC,  64  , protease used for protein digestion (most commonly trypsin), pre-separation method (LC, 2D-gel electrophoresis, etc.
), ionization and ion detection method of the mass spectrometer (MALDI-TOF-TOF, etc. ), peak-processing software (ProteoWizard,  65  ; MaxQuant, 66 ; etc. ), protein database used for theoretical peptide mass calculation (UniProt,  44  ; Ensembl, 67 ; etc. ), database search software for peptide-spectral matches (Mascot,  68  ; X!Tandem,  69  ; MaxQuant, 66 ; etc. ), and parameters and thresholds of the software. These experimental protocol-and data analysis method-related terms are necessary metadata for submissions to proteome databases/repositories. To describe these metadata, the Human Proteome Organization Proteomics Standards Initiative (HUPO-PSI) has developed the PSI-MS controlled vocabulary  70  and ProteomeXchange  71  , which is a consortium of mass spectrometry proteomics data repositories including PRIDE, the Peptide Atlas SRM Experiment Library (PASSEL,  72  ), and MassIVE 73 , has established a core set of metadata for dataset deposition using PSI-MS.
The Japanese proteome community is now developing the Japan Proteome Standard (jPOST) repository  74  , which is a mass spectrometry proteomics data repository. The salient feature of jPOST is the ability to re-analyze data from deposited raw data; by using raw data and a jPOST-original re-analysis workflow, the community plans to integrate data from various experiments to construct a standardized proteome database (jPOST database). Original analytical results from submitters are not suitable for integration because they were performed using various different protein databases and peak-identification/ database search software with various different parameters. For re-analysis, it is necessary to describe detailed information about experimental procedures. However, current controlled vocabularies (CVs) such as PSI-MS are insufficient for metadata description, and so we have attempted to reorganize and extend the current CVs for jPOST.
At BH15, we enumerated required categories of metadata, such as Instrument mode and Quantification platform, and collected vocabularies with the cooperation of experimental proteomics scientists. The collected vocabularies were mapped to existing CVs where possible, and we began to develop an ontology for unmapped vocabularies  75. We also developed an RDF schema based on the CVs and ontology (Figure  3 ) for jPOST datasets. Constructing an ontology that is compatible with existing CVs such as PSI-MS is important for integrating jPOST data with other proteomics data stored in the databases of the ProteomeXchange Consortium  71. In addition, by using common ontologies/CVs such as Taxonomy and disease name and a standardized data model like RDF, the proteomics datasets can also be linked and integrated with datasets derived from other technologies such as transcriptomics and epigenomics.
Scientists in natural products chemistry use the accurate mass and chemical shifts in Nuclear Magnetic Resonance (NMR) spectra to elucidate the structure of unknown natural chemical compounds. In contrast, researchers in metabolomics commonly try to provisionally identify chromatographic peaks by comparing their retention time (or retention indices), and/or the mass spectra, with those present in a mass spectral library database generated from the data of authentic standards  81. The Metabolomics Standards Initiative defined four levels of reporting metabolite identification and annotation: identified metabolite (Level 1), putatively annotated metabolites (Level 2), putatively annotated metabolite classes (Level 3), and unknown compounds (Level 4)  82. This indicates that the confidence levels of metabolite identification reported in metabolomics studies can vary largely, because of different extraction protocols, different instruments and measurement parameters, different pre-processing methods, and the diversity of annotation expertise  83.
This hampers the reusability/reanalysis of published metabolomics datasets, although there are public repositories for metabolomics data such as MetaboLights  84  and MetabolomeExpress. org  85. Biological interpretation of changes in metabolite levels is very important and is still challenging, because such metabolite pools are the resulting output of many biological processes. To facilitate biological interpretation by existing biological knowledge, e. g. biochemical pathways, pathway-based analysis like Metabolite Set Enrichment Analysis (MSEA,  86  ) is available. This approach highly depends on predefined biological pathways such as KEGG  87  , Pathway Commons 88 , BioCyc  89  , and WikiPathways  90. Molecular interactions can be regarded as a network by calculating association between molecules in omics data.
Correlation-based approaches are behind for construction of association networks such as gene co-expression networks in transcriptomics (for example, see 91,92). There are many software tools for pathway visualization and integration of different omics data (for example, see 93). Examples include KEGG Mapper 94 , KEGGViewer  95  , PathVisio  96  , WikiPathways App  97  , and KEGGScape  98. Metscape is a Cytoscape App for network analysis and visualization of genemetabolite associations  99. MetaMapR 100 can be used for integrating biochemical reaction with chemical structural and mass spectral similarity to analyze pathway-independent associations including unknown metabolites. MetaboAnalyst 101 provides a user-friendly, web-based analytical platform for metabolome data pre-processing, normalization, statistical analysis, and metabolite annotation.
DeviumWeb 102 is also a user-friendly web application for integrating statistical multivariate analysis with biochemical domain knowledge using R-Shiny  103  , a web application framework for R. Plant metabolome database development. Unlike compound and mass spectral databases such as KEGG  87  and MassBank 104 , metabolite-profile oriented databases still remain relatively undeveloped and under-used in plants  81. The data and metadata for more than 140 mutants of Arabidopsis thaliana, an important model plant, are archived at the Plant and Microbial Metabolomics Resource (PMR,  105  ). It is a flexible database that is designed for data sharing in metabolomics and implements data analysis tools  106. Information on phenotypic screening of Arabidopsis chloroplast mutants using assays of amino acids and fatty acids of more than 10,000 T-DNA insertion mutants using mass spectrometry are stored in Chloroplast 2010  [107] [108] [109].
We recently developed a new database, the Metabolite profiling database for Knock-Out mutants in Arabidopsis (MeKO,  110  ), to facilitate improvement of gene annotation. The MeKO database 111 can be used to browse and visualize metabolomic data, containing images of mutants, data on differences in metabolite levels, and the results of statistical data analyses. As mentioned above, the metabolomics community is working towards the setup of sharing metabolome data, while mining publicly available information and demonstrating the richness of integration of multiple metabolome datasets that remain largely unexplored. At present we are constructing our database, called AtMetExpress 112 , to store this information. It is freely available and contains detailed information about metabolites detected in Arabidopsis.
It has a small and simple GUI tool for performing meta-analyses, allowing easy metabolome meta-analysis for plant biologists using R-Shiny. Plants produce a diversity of compounds through secondary metabolic pathways. In these secondary compounds, the flavonoids and glucosinolates are useful as herbal medicines to maintain human health. However, a lot of them are still undescribed in public pathway databases. It is therefore important to construct the infrastructure to integrate such metabolites with their pathways in a cross-database manner. Hence, compounds IDs need to be linked rationally for this purpose. To address the above challenge, we focused on the following things at BH15. We tried to implement several web applications with R-Shiny to improve visualization tools in our metabolome database, AtMetExpress.
To reconstruct secondary metabolite pathway maps on WikiPathways we curated metabolite name, database identifiers of metabolites and reactions (KEGG, KNApSAcK, PlantCyc, and PubChem) in Arabidopsis metabolome data. We focused on flavonoids, which is a well-studied secondary metabolite group in Arabidopsis. We developed the following web applications and tools: a webapp called the Prime Visualization Tool using the R-Shiny framework; an integrated "pathview" Bioconductor package with the Prime Visualization Tool 113 ; an R package for the linkdb RDF client 114 to integrate multiple identifiers of major compound databases like PubChem CID, KEGG, and KNApSAcK. In addition, we examined the SummarizedExperiment container  115  in Bioconductor to use assay, and we discussed the possibility of using the SummarizedExperiment in RDF format.
We integrated several Arabidopsis metabolome datasets and partly finished data curations. These curation efforts continue after BH15. Even in the model plant Arabidopsis, the main target of existing large-scale metabolic models was primary metabolism (for example, see 116-119). Our effort to construct curated Arabidopsis flavonoid dataset will help to expand metabolic models of Arabidopsis and lead to a better understanding of the production of flavonoids.
Biochemical molecules Chemical database integration. Small molecules are studied across a broad set of research areas. They are important as a vital component of living systems and are also used in the formulation of pharmaceutical products. Therefore, access to information collected about molecules is key to research and product development. During BH15, we discussed strategies for cooperation between chemical databases. For instance, participants discussed the role of InChIKey 120 in their own databases as a primary key for chemical structure. Other discussions focused on increasing interoperability in two ways: First by including additional database cross-references, and second by harmonizing the RDF representation of chemical data. Chemical databases such as PubChem 121 , Nikkaji 122 , GlyTouCan  123  , and the Protein Database Japan (PDBj,  124  ) store data in atomic level formats such as Molfile  125  , mmCIF  126  , InChI 120 , and InChIKey.
Participants agreed to use ontologies such as SIO, the Chemical Information ontology (CHEMINF, 53 ), and the Simple Knowledge Organization System (SKOS,  127  ). The RDF data of Nikkaji, KNApSAcK  128  and GlyTouCan were modified to use these ontologies. Increased adoption of the ontologybased RDF representation of small molecules will facilitate their integration and reduce the cost of reuse of data from each of the databases.
Glycomics ontology development. Carbohydrates, often referred to as glycans, differ from other biopolymers such as proteins or nucleic acids in the large variety of different building blocks, i. e. , monosaccharides, and in the possibility of linking these building blocks in several ways, which often results in branched structures. Furthermore, experimental techniques for glycan identification often yield underdetermined structures with varying degrees of uncertainties. Many providers of glycoinformatics databases and tools have developed individual and non-compatible formats to store all these properties of glycan structures, such as LINUCS  130  , LinearCode® 131 , KCF 132 , GLYDE 133 , GlycoCT 134 , or WURCS  135. This variety of nomenclature formats is a major reason for a lack of interoperability and data exchange between various glycoinformatics resources  136, 137.
To overcome this situation, development of the glycomics standard ontology (GlycoRDF,  138, 139  ) was started during BioHackathon 2012  4. GlycoRDF can represent glycan structure information together with literature references or experimental data. MonosaccharideDB 140 provides GlycoRDF descriptions of monosaccharides generated from various carbohydrate nomenclature formats. During BH15, participants developed routines to generate GlycoRDF data from WURCS 2. 0 nomenclature, which is used by the GlyTouCan structure repository  123. Thus, glycomics data can now be retrieved as GlycoRDF from GlyTouCan, GlycoEpitope 75 , GlycoNAVI 141 and WURCS using database guidelines 142. The group also discussed possible extensions to GlycoRDF that would offer relations between individual monosaccharides.
Glycoinformatics is at the intersection of bioinformatics and chemoinformatics. In the past there have mainly been attempts to establish cross-links between glycan databases and bioinformatics resources, e. g. between UniCarbKB 143 and UniProtKB  44  , which makes sense from the point of view of glycoproteins and protein-carbohydrate complexes. From the small molecules perspective it is coherent to also cross-link with chemoinformatics databases such as PubChem  121  or Nikkaji (now subsumed by J-Global,  122  ). Glycan structures cooperation was discussed at BH15. As part of this process several possible formats for data exchange were discussed, such as SMILES  144  , InChI, mmCIF, WURCS, or mol file. A focus was subsequently put on the conversion of glycan structures to SMILES, and routines to generate SMILES codes from monosaccharide names were developed in a cooperation between PubChem and MonosaccharideDB developers.
This information will be added to the SADI service definition documents, allowing for automated service testing and/or more intuitive service registry browser design with, for example, pre-populated "try it now" functionality. SPARQL query construction. SPARQL 150 has emerged as the most widely used query language for RDF datasets. RDF datasets are often provided with web interfaces, called SPARQL endpoints, through which SPARQL queries can be submitted. However, constructing a SPARQL query is a relatively complex task for inexperienced users. SPARQL Builder 151 is a web application that assists users in writing SPARQL queries through a graphical user interface. The SPARQL Builder system interactively generates a SPARQL query based on a user-specified path across class-class relationships.
At BH15, we worked on the display of candidate paths from metadata, including hierarchical information of the SPARQL endpoint, graphs, classes, properties, class-class relationships, and their statistics, such as the numbers of triples and instances. To be time efficient, we found that it was necessary to pre-compute and store those metadata for fast retrieval. This suggests that it would be ideal that every SPARQL endpoint provides such metadata. We tested our system on datasets drawn from the EBI RDF Platform and Bio2RDF, and our approach could be extended to other RDF datasets. We also developed a prototype 152 of a search interface using SPARQL Builder system for 439 datasets contained in the Life Science Database Archive (LSDB Archive, 153 ). The LSDB Archive is a service to collect, preserve and provide databases generated by life sciences researchers in Japan.
Using the interface, we can now search for data in the LSDB Archive without knowing the data schema for each dataset. LODQA integration with DisGeNET and Bio2RDF. LODQA 154 is another service being developed to provide a natural language interface to SPARQL endpoints. Users can begin their search with a natural language query, e. g. What genes are associated with Alzheimer's disease?, from which the system automatically generates corresponding SPARQL queries. LODQA also features a graph editor that allows users to compose queries in a graph representation. While the system is developed to be highly adaptable to any RDF datasets, it does require lexical terms, e. g. labels, of data sets to be pre-indexed. During BH15, we explored the use of the LODQA system with DisGeNET and Bio2RDF. As a result, we found that LODQA could generate effective SPARQL queries for some natural language questions like "Which genes are involved in calcium binding?" The LODQA interface to Bio2RDF is publicly available  155  , while the LODQA interface to DisGeNET is discontinued due to major revisions to DisGeNET.