dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, a user might like to see all tissue blocks for 50-year-old females or search for cell types in a specific spatial location that have high gene-expression values in experimental datasets. The interlinkage of spatial reference objects and ASCT+B tables makes it possible to associate cell-type and biomarker data from experimental data to 3D anatomical structure data and to compute how many cells of what type are commonly found in which anatomical structures and what their typical biomarker expression values are. As the number of HRAregistered datasets increases, prediction accuracy will increase. As the HRA is spatially explicit, the spatial location of cell-type populations can be visually presented in 3D. Initially, cells are placed randomly; as more data on cell-neighborhoods becomes available  23, 24  , cells can be placed in spatially explicit patterns.
Validation. The anatomical structures and cell types in the ASCT+B tables are validated against reference ontologies on a weekly basis (see Technical Validation section). Validation results are used to improve the ASCT+B tables and to issue ontology change requests to slowly and steadily modify existing reference ontologies (e. g. , Uberon and CL) so they accurately represent healthy human adult data. When completed, the CCFO will be a proper, HRA-labeled subset (using the human_reference_atlas subset tag) of major reference ontologies that are widely used by a diverse range of bioinformatics applications. HRA will evolve over time. As new experimental datasets and analysis results become available, the HRA data structures and the CCFO will need to be updated to capture new entity types and linkages and to serve qualitatively new user needs.
Lastly, each 3D organ object instance in the ontology receives a set of spatial placement parameters that states its position, size, and orientation in relation to the HRA coordinate space. The Specimen Ontology has four main classes: Tissue Block, Tissue Section, Dataset, Donor. Only donor is used for HRA construction; all four are used for HRA usage. For HRA construction, we keep track of the Visible Human Project specimen data. For HRA usage, we record Tissue Block, Tissue Section, Dataset, Donor info for each tissue block. In terms of Donor data that is collected by independent laboratories, we capture sex and age, among other properties.
Comparison to Uberon, FMA, CL, HGNC When comparing the ASCT+B tables on June 13, 2022 with Uberon (May 17, 2022 version) and Cell Ontology (CL, Feb 16, 2022 version), there were 1,620 terms that do not validate; 1,290 of these are from blood vasculature and peripheral nervous system. Many of the part_of relationships between anatomical structures validate, but few of the anatomical structures to cell type relationships validate (a large proportion is due to immune cells, which may or may not be tissue specific making it hard to model location in ontologies). For the extended blood vasculature tables, only 390 (41%) of the final 953 vessels in the HRA database are in Uberon. The FMA ontology has many terms that are missing in Uberon and we are in the process of adding those to Uberon. All 1,397 unique anatomical structures in the 26 organs 3D Reference Library link either to Uberon or FMA.
Spatial, biological structure, and specimen data is generated in JSON format. A stand-alone version of the RUI is available  13  and data can be downloaded locally and shared freely as LOD so it becomes available via the CCF-API. The Exploration User Interface (EUI, see Fig. 6b ) uses the CCF-API to let users explore RUI-annotated data using specimen, biological structure, and spatial attributes. For example, it is possible to filter tissue data collected by different consortia from different donors using properties such as donor sex and age, cell types, or spatial location. The code for both applications is freely available  11. The EUI has been successfully integrated into the GTEx portal  26.
An in memory ondemand approach was used for data management, and an efficient collision detection algorithm  15  was employed to compute the type and volume of anatomical structures within each tissue block. Last but not least, we computed the volume that is not inside any existing anatomical structures (e. g. , that is outside of the organ, possibly indicating that a re-registration via the RUI is needed). All code was made available on GitHub  14. CCF Virtual Reality (VR) Organ Gallery. The VR Organ Gallery lets users explore the male and female human reference bodies in a 3D virtual environment using an Oculus Quest 2 virtual reality headset. The VR setup makes it possible to explore the HRA data across different scales-from encountering a whole body in real size (see Fig. 6d ), zooming into single organs and studying their many anatomical structures, or examining cell-type populations within specific anatomical structures  12.
Limitations and future directions The CCF. OWL v2. 0. 1 and the CCF API are used both in several production applications across consortia and in research and training. Yet, the current ontology model and API code have a number of limitations that have become obvious when using them in different contexts and for different use cases. Some modeling decisions (e. g. , representing spatial entities and relationships using 3D meshes registered into a common coordinate system) have proven valuable while other decisions (e. g. , representing landmarks as an Extraction Site) have been confusing for our team and other users and will be revised in future iterations. We discuss key limitations, lessons learned, and next steps here to empower other investigators who work on atlas design and/or applications that aim to facilitate access to large amounts of heterogeneous data.
Going forward, we expect to see multiple biomarker sets (e. g. , generated using different celltype annotation tools) per cell-type population; these can be encoded using Boolean AND between all required biomarkers from one set and OR between biomarker sets; this will likely lead to ontology reasoner scaling issues. Future CCF. OWL versions will support complete provenance chains for all digital objects and code using W3C Provenance  63, 64. Spatial accuracy. To understand the limitations of tissue registration using the Registration User Interface (RUI), we performed a user study with 42 subjects  55. Results show that RUI users can perform tissue block matching tasks at an average of 5. 88 degrees rotation accuracy and 1. 32 mm position accuracy (at an average of 22. 6 seconds per task after 8.
3 tasks in a series of 30 identical tasks). Going forward, we will implement Stage 2 registration using biomolecular data that is only now becoming available for some anatomical structures. Over time, with sufficient high-quality data from thousands of human donors, the HRA will become more stable and data registration more accurate. Proper separation between ontologies and data instances. CCF. OWL v3. 0 will model HRArelevant vocabularies, application ontologies, and references using the LinkML  65  general purpose modeling language. The HRA will use CCFO to detail all specimen-related, structural biology, and spatial data. Experimental data from various consortia will be modeled using LinkML and advertised as 5-star Linked Open Data  66. That is, the CCFO defines the vocabularies shared by HRA and experimental data; LinkML is used for the data specification.
Novel use cases. A series of qualitatively new use cases will be developed to showcase the value of combining HRA with other LOD data-e. g, from the Gene-Centric Common Fund Data Ecosystem (CFDE) Knowledge Graph  67  , the STRING -Protein-Protein Interaction Networks Functional Enrichment Analysis database  68  , and the Scalable Precision Medicine Oriented Knowledge Engine (SPOKE) graph database that federates more than 30 open datasets into a public data commons of health relevant knowledge  16, 69. Scaling up. Efficient data management, spatial queries and algorithm implementations are needed to scale up to the 37 trillion cells that make up the human body 2 and to truly capture human diversity. CCF data is a core component that will be accessed highly frequently by many database queries.
Documentation and training. This paper details the CCFO and the CCF API for the first time. Going forward, we plan to publish simple examples of how the CCFO is currently used (or will be used) in different applications. We will organize hands-on workshops that train other experts to run proper SPARQL queries against the CCF. OWL graph database or use the standard HTTP CCF-API to support a whole ecosystem of collaborative and compatible APIs, libraries, and user interfaces.
Usage Notes The CCF API supports programmatic access to CCF. OWL v2. 0. 1 data; for exemplary queries see the companion website at https://cns-iu. github. io/ccf. owl-paper-supporting-information. The CCF. OWL v2. 0. 1 data and API are used in the HuBMAP and GTEx portals, the Registration and the Exploration User Interfaces, 3D mesh-level collision detection, and the VR Organ Gallery. We welcome others to join our effort in constructing a user-friendly and useful HRA. Please sign up for the ASCT+B Working Group at https://iu. co1. qualtrics. com/jfe/form/SV_bpaBhIr8XfdiNRH to receive updates and meeting invites.
52ass pairs are linked through inverse relationships (e. g. , a Donor provides Sample; Sample comes_from Donor). Most relationships are of single cardinality (indicated by a 1 on the arrow), whereas some are of multiple cardinality (indicated by an asterisk). Examples of multiple cardinality include: a donor can provide multiple tissue samples, a sample can be used to generate multiple datasets, a tissue block can be subdivided into multiple tissue sections. Document for Ontology Editor" 46 details how to (1) download and setup the development environment using Git, (2) add new reference organs to the CCFO, (3) update an existing reference organ in the CCFO, (4) build the CCFO, and (5) release the CCFO. The general CCFO generation pipeline is shown in Fig. 5. The ASCT+B tables (in top left) are read by an external Validation Tool 47 that compares whether anatomical structure and cell type terms are valid and if these relationships are true according to Uberon and CL/PCL using UberGraph48.
g. , GTEx has 29 Extraction Sets for more than 5,000 tissue blocks). The asctb2ccf tool reads the anatomical structure tags and generates Biological Structure Ontology data (e. g. , predictions of cell types commonly found in these anatomical structures). The spatial2ccf tool reads spatial data and generates Spatial Ontology data. The specimen2ccf52reads Donor, Tissue Block, and Sample data and generates Specimen Ontology data. There are 266,576 triples in the graph database that holds the content of the CCF. OWL v2. 0. 1. The size of all 3D object GLB files is 2. 56 GB. CCF. OWL v2. 0. 1 represents major digital objects of the HRA v1. 2, including 26 ASCT+B tables, 53 3D reference objects, and a crosswalk that associates anatomical structures in both. In addition, the data covers landmark organ sets which are used in the tissue registration user interface to facilitate tissue block placement in 3D organ models; these were not published with DOIs, but do appear in the CCFO.
Registered Tissue Data. This table shows 364 Extraction Site entities that either represent the registration location for Tissue Blocks or Registration Sites. For each, we provide consortium name and entity ID, HuBMAP ID if available, and experimental data donor sex as well as HRA data such as organ name, sex, Uberon ID, and GLB file. The data comes from four consortia. HuBMAP data includes 306 tissue blocks, and many of these have derived tissue sections with one or more associated datasets. The 29 well-defined GTEx extraction set sites link to 5,397 datasets. The 26 SPARC blocks link to one paper while the three KPMP tissue blocks link to two data publications.
Acknowledgments We thank the more than 200 authors that contributed to the ASCT+B table authoring and review process;  Kristen Browne  and  Heidi Schlehlein  for creating the 3D reference objects;  Devin M. Wright  and the  HuBMAP team  for registering 306 tissue blocks and  Peter Hanna  from  SPARC ,  Seth Winfree  from  KPMP , and  Kristin Ardlie from GTEx  for close collaborations on using the CCF in their consortia efforts. We deeply appreciate work by  Tracey Theriault  for figure design,  Todd Theriault  for copy editing, and  Medina Sydykanova  and  Naval Pandey  for procuring images. This research has been funded by the  National Institutes of Health Human BioMolecular Atlas Program (HuBMAP)  award  OT2OD026671 ,  Cellular Senescence Network (SenNet) Consortium Organization and Data Coordinating Center (CODCC)  award  1U24CA268108-01 ,  National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Kidney Precision Medicine Project  grant  U2CDK114886 ,  NIH U01CA242936 , and  Common Fund Data Ecosystem (CFDE)   OT2 OD030545.
http://purl. org/ccf/biological_sex Refers to a biological sex. Class Female http://purl. bioontology. org/ontology/LNC/LA3-6 Refers to a female person. Class Male http://purl. bioontology. org/ontology/LNC/LA2-8 Refers to a male person. Class Collection http://purl. org/ccf/collection Refers to a data collection. Class Dataset http://purl. org/ccf/dataset Refers to a dataset. Class Ethnicity http://purl. org/ccf/ethnicity Refers to an ethnicity group. Class Hispanic or Latino http://purl. bioontology. org/ontology/LNC/LA6214.
Code Availability All data and standard operating procedures are released under Creative Commons Attribution 4. 0 International (CC BY 4. 0). All code was released under the MIT License and can be accessed in GitHub at https://github. com/hubmapconsortium/ccf-ontology. Female VH_F_small_intestine Female http://purl. obolibrary. org/obo/UBERON_0002108 https://ccf-ontology. hubmapconsortium. org/objects/v1. 2/VH_F_Small_Intestine. glb HuBMAP https://entity. api. hubmapconsortium. org/entities/35e16f13caab262f446836f63cf4ad42 HBM229. NKWB. 488 Female VH_F_small_intestine Female http://purl. obolibrary. org/obo/UBERON_0002108 https://ccf-ontology. hubmapconsortium. org/objects/v1. 2/VH_F_Small_Intestine. glb HuBMAP https://entity. api. hubmapconsortium. org/entities/0ccf43ecc685e2ce2bd339fb87a240a2 HBM663.
OWL graph database and associated CCF API shortly thereafter. Experimental datasets compliant with earlier versions of the HRA might need to be re-registered (e. g. , if ASCT+B table terminology or 3D reference organs changed). The "Standard Operating Procedure (SOP)The CCF-Slim Ontology (ccf-slim. owl)captures HRA data exclusively (no experimental data), and it is used in the Registration User Interface (see Applications section). The CCF. OWL v2. 0. 1 is available through the NCBI BioPortal Ontology Browser (https://bioportal. bioontology. org/ontologies/CCF) and the EBI OLS Ontology Browser (https://www. ebi. ac. uk/ols/ontologies/ccf). It is findable via https://purl. org/ccf/ccf. owl.
Bioinformatics web tools and servers are a powerful way for researchers to share valuable computational tools. They are ideal because users can run analyses in their browsers without having to download, set up, and run code on their local system. However, bioinformatics tool development requires web development pro ciency and server costs which can be a barrier to entry. Google Colaboratory (Colab) is an online interactive code environment available to anyone with a free account. Colab is easy for both the developer and user. It allows researchers to make code available without needing web development skills. Users can customize code easily through Colab, which is bene cial since current bioinformatics web tools do not allow the user to edit code and perform customized operations. Also, Colab allows for easy collaboration and editing of code without concerns about platform compatibility between computers.
Due to its user-friendly and customizable properties, we propose using Colab for developing bioinformatics tools. In this case study, we demonstrate implementation of the OKtool on Colab, a Python-based tool for calculating overlapping k-mers of desired length from a list of protein or DNA sequences. Applications include motif analysis and antigenic epitope analysis. It is a highly-customizable tool fully implemented in Colab that provides a FASTA le of k-mers derived from any given list of sequences. Overall, our work demonstrates the potential for bioinformatics tool development on Colab. OKtool is freely accessible at oktool. cloud.
Introduction Web tools and servers have become a staple of the bioinformatics eld, with hundreds of Internet-based tools available to researchers  [1]. For instance, popular web tools and servers include the Expasy suite of tools, BLAST, Clustal Multiple Sequence Alignment, PEP-FOLD, and the Gene Expression Omnibus GEO2R  [2] [3] [4] [5] [6]. Web-based software are valuable to researchers because they streamline many aspects of the research process. While many programs such as BLAST can be run locally, this can be di cult due to the wide variety of operating systems and associated challenges in keeping software updated to be compatible with these operating systems. In comparison, browser-based web services can function on almost any device. Furthermore, browser-based services do not necessarily need to leverage the user's local computing resources, instead drawing from cloud computing.
Services such as Galaxy can help researchers with limited computing resources, expanding access to methods such as protein structure prediction  [7]. Despite the bene ts of bioinformatics web services, there is a barrier to entry. In general, web development skills and pro ciency with frameworks like R Shiny, Django Flask, or HTML/CSS/Javascript are necessary  [8] [9]. Many researchers lack the experience with web development to properly implement web tools. Also, hosting web services can be di cult to maintain for many years and costly to operate. In fact, a 2021 article reports that only about 50% of bioinformatics web services published in 2010 were still accessibl  [10]. Google Colaboratory (Colab) is a free interactive browser code-editing environment focused on Python, a popular programming language in science.
Colab is free and allows users to work with Python in a IPYNB format, like Jupyter Notebooks which organize code in blocks. Colab notebooks are easily shared and collaborators can be added. Importantly, non-collaborators can execute and modify code without changing the code for others. This enables customization of web tools to a user's speci c needs. Colab reduces the need for researchers to learn web development. Recently, Mirdita et al. successfully implemented AlphaFold2 on Colab, demonstrating the practical utility of Colab as a platform for bioinformatics tools and its potential for increasing accessibility  [11]. In the present study, we implement an overlapping k-mer retrieval algorithm in Colab and create a userfriendly interface. K-mers are widely used in bioinformatics for DNA and epitope analysis  [12] [13].
Implementation A Google Colab Python notebook was created. We used an algorithm to iterate through a Pandas Dataframe column of sequences and obtain all k-mers of a given length using slicing and list comprehensions and add the k-mers to a FASTA le. We used the Form Field functionality within Colab to create a user-friendly panel to specify parameters such as k-mer length, output le name, and the name of the le column where input sequences are found. Users should upload a CSV le where one column contains the input sequences, with one sequence in each row. The CSV le can have other columns. Users upload CSV les through the upload panel on the left-hand side of Colab. We link a Youtube tutorial by Chanin Nantasenamat that explains the upload process  [15]. Also, we provide an example input le containing 15 epitopes for Epstein Barr virus antigen 6 obtained from the IEDB  [16].
Next, the user speci es ve input parameters, the input le name, the desiredKmerLength, the column where sequences are found, the output le name, and the fasta type (detailedFASTA or simpleFASTA). The user clicks the Run circular button to run the code. Finally, the user clicks the Run button on the second code block to begin the k-mer retrieval. We include a statement in the k-mer code to report any length errors. That is, if the desired k-mer length is longer than a sequence, it will be skipped and Colab will output a message such as "GPPAA is too short for the desired k-mer length". The user can download the resulting FASTA le using the left-hand panel of Colab, where the input le was originally uploaded. In order to make OKtool easily shareable, we purchased the short domain name "oktool.
Conclusion In the current study, we demonstrate using Google Colaboratory for easily implementing a bioinformatics web service without knowledge of web development. It is user-friendly, easy to maintain and update, and free. A limitation is that a free Google account is required, which may not be available in certain regions. Accordingly, we provide a Github link to the OKtool source code le. OKtool is accessible at oktool. cloud and is well-documented with step-by-step instructions. We encourage researchers interested in making bioinformatics code interactively available to others to look into Colab.
Availability Short link: oktool. cloud Full link: https://colab. research. google. com/drive/15Z1ZETuJcXW59D4XKDWi9OKwtaEZC1nz? usp=sharing Github link: https://github. com/andrewgcodes/OKtool Implementation Simply open the Google Colab link to run the code. Step by step instructions are provided. No downloads are necessary. If unable to access Colab, download the Github IPYNB le and open in Jupyter on your system. The only needed library is Pandas, which can be installed via pip.
Tube Implementation and the Codac Library Codac  [34]  is a library for constraint programming over reals, trajectories, and sets. Its development was motivated by mobile robotic problems such as the localization of underwater robots. It proposes various tools to deal with constraints on sets of feasible solutions; several contractors are available and can be used on sets for which the library provides numerical implementations. For instance, various contractors are at hand to compute tubes of trajectories of robots. This allows for the reliable bracketing of a set of trajectories compliant with some sensor measurements and a given differential equation  [19, 32, 35]. The library is available in Python and C++. As an example, one can create a tube [x](•) over some temporal domain [t 0 , t f ] = [0, 10], set a restriction on the trajectories at some point: In Codac, tubes are implemented as a sequence of slices (see Figure  6 ).
Discussion The accurate description of viral genetic diversity (major and minor variations) depends on several parameters, such as the quantity and quality of viral genetic material, sequencing quality and depth, as well as the bioinformatics tools used for data analyses  (Sanjuán and Domingo-Calap, 2019). In the present study, we characterized OsHV-1 genetic diversity in three oyster-farming areas during POMS outbreaks. To do so, oysters from the same cohort were exposed to POMS outbreaks in the field and OsHV-1 genetic diversity was investigated in individual infected moribund oysters. To optimize the characterization of OsHV-1 variations, we selected moribund oysters with high viral loads. Then, DNA extracted from these oysters was used to prepare sequencing libraries using a PCR-free kit to remove genomic coverage biases associated with PCR amplification.