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  1. AU="Natale, Darren A"
  2. AU="Neznanov, Nickolay S"
  3. AU="Richards, Emily D"
  4. AU="Cho, Kyung-Jin"
  5. AU=Hodos Rachel A
  6. AU="Sahin, Kazim"
  7. AU="Bieri, Jan"
  8. AU="Procter-Murphy, R"
  9. AU=Hauguel-Moreau Marie
  10. AU="Cheng, Delfine"
  11. AU="Stephanie C. Pennington"
  12. AU="O'Hanlon, Karen A"
  13. AU="Heide Glaesmer"
  14. AU="Paget-Bailly, Philippe"
  15. AU="Rory J McCrimmon"
  16. AU="Ahdoot, Aaron I."
  17. AU="Neote, Kuldeep S"
  18. AU="Shen, Congcong"
  19. AU="Rahi, Kosar"
  20. AU="Channabasavaiah, Jagadish Puralae"
  21. AU="Anselmi, Maurizio"
  22. AU="Chauhan, D."
  23. AU="Nicoll, Roger A"
  24. AU="Kwon, Young-Sam"
  25. AU="Mihwa Lee"
  26. AU="Yuanting Jin"
  27. AU="Ter Haar, Eva"
  28. AU="Wolin, Dan L"
  29. AU="Zhang, Tenan"
  30. AU="Piedrafita, Lídia"
  31. AU="Nandy, Ananya"
  32. AU="Bansemer, Sven"
  33. AU="Kochetov, O"
  34. AU="Liu, Fen"

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  1. Artikel ; Online: Perspectives on tracking data reuse across biodata resources.

    Ross, Karen E / Bastian, Frederic B / Buys, Matt / Cook, Charles E / D'Eustachio, Peter / Harrison, Melissa / Hermjakob, Henning / Li, Donghui / Lord, Phillip / Natale, Darren A / Peters, Bjoern / Sternberg, Paul W / Su, Andrew I / Thakur, Matthew / Thomas, Paul D / Bateman, Alex

    Bioinformatics advances

    2024  Band 4, Heft 1, Seite(n) vbae057

    Abstract: Motivation: Data reuse is a common and vital practice in molecular biology and enables the knowledge gathered over recent decades to drive discovery and innovation in the life sciences. Much of this knowledge has been collated into molecular biology ... ...

    Abstract Motivation: Data reuse is a common and vital practice in molecular biology and enables the knowledge gathered over recent decades to drive discovery and innovation in the life sciences. Much of this knowledge has been collated into molecular biology databases, such as UniProtKB, and these resources derive enormous value from sharing data among themselves. However, quantifying and documenting this kind of data reuse remains a challenge.
    Results: The article reports on a one-day virtual workshop hosted by the UniProt Consortium in March 2023, attended by representatives from biodata resources, experts in data management, and NIH program managers. Workshop discussions focused on strategies for tracking data reuse, best practices for reusing data, and the challenges associated with data reuse and tracking. Surveys and discussions showed that data reuse is widespread, but critical information for reproducibility is sometimes lacking. Challenges include costs of tracking data reuse, tensions between tracking data and open sharing, restrictive licenses, and difficulties in tracking commercial data use. Recommendations that emerged from the discussion include: development of standardized formats for documenting data reuse, education about the obstacles posed by restrictive licenses, and continued recognition by funding agencies that data management is a critical activity that requires dedicated resources.
    Availability and implementation: Summaries of survey results are available at: https://docs.google.com/forms/d/1j-VU2ifEKb9C-sW6l3ATB79dgHdRk5v_lESv2hawnso/viewanalytics (survey of data providers) and https://docs.google.com/forms/d/18WbJFutUd7qiZoEzbOytFYXSfWFT61hVce0vjvIwIjk/viewanalytics (survey of users).
    Sprache Englisch
    Erscheinungsdatum 2024-04-25
    Erscheinungsland England
    Dokumenttyp Editorial
    ISSN 2635-0041
    ISSN (online) 2635-0041
    DOI 10.1093/bioadv/vbae057
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Protein ontology on the semantic web for knowledge discovery.

    Chen, Chuming / Huang, Hongzhan / Ross, Karen E / Cowart, Julie E / Arighi, Cecilia N / Wu, Cathy H / Natale, Darren A

    Scientific data

    2020  Band 7, Heft 1, Seite(n) 337

    Abstract: The Protein Ontology (PRO) provides an ontological representation of protein-related entities, ranging from protein families to proteoforms to complexes. Protein Ontology Linked Open Data (LOD) exposes, shares, and connects knowledge about protein- ... ...

    Abstract The Protein Ontology (PRO) provides an ontological representation of protein-related entities, ranging from protein families to proteoforms to complexes. Protein Ontology Linked Open Data (LOD) exposes, shares, and connects knowledge about protein-related entities on the Semantic Web using Resource Description Framework (RDF), thus enabling integration with other Linked Open Data for biological knowledge discovery. For example, proteins (or variants thereof) can be retrieved on the basis of specific disease associations. As a community resource, we strive to follow the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles, disseminate regular updates of our data, support multiple methods for accessing, querying and downloading data in various formats, and provide documentation both for scientists and programmers. PRO Linked Open Data can be browsed via faceted browser interface and queried using SPARQL via YASGUI. RDF data dumps are also available for download. Additionally, we developed RESTful APIs to support programmatic data access. We also provide W3C HCLS specification compliant metadata description for our data. The PRO Linked Open Data is available at https://lod.proconsortium.org/ .
    Mesh-Begriff(e) Datasets as Topic ; Knowledge Discovery ; Proteins/chemistry ; Semantic Web ; Software
    Chemische Substanzen Proteins
    Sprache Englisch
    Erscheinungsdatum 2020-10-12
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2775191-0
    ISSN 2052-4463 ; 2052-4463
    ISSN (online) 2052-4463
    ISSN 2052-4463
    DOI 10.1038/s41597-020-00679-9
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel ; Online: BpForms and BcForms: a toolkit for concretely describing non-canonical polymers and complexes to facilitate global biochemical networks.

    Lang, Paul F / Chebaro, Yassmine / Zheng, Xiaoyue / P Sekar, John A / Shaikh, Bilal / Natale, Darren A / Karr, Jonathan R

    Genome biology

    2020  Band 21, Heft 1, Seite(n) 117

    Abstract: Non-canonical residues, caps, crosslinks, and nicks are important to many functions of DNAs, RNAs, proteins, and complexes. However, we do not fully understand how networks of such non-canonical macromolecules generate behavior. One barrier is our ... ...

    Abstract Non-canonical residues, caps, crosslinks, and nicks are important to many functions of DNAs, RNAs, proteins, and complexes. However, we do not fully understand how networks of such non-canonical macromolecules generate behavior. One barrier is our limited formats for describing macromolecules. To overcome this barrier, we develop BpForms and BcForms, a toolkit for representing the primary structure of macromolecules as combinations of residues, caps, crosslinks, and nicks. The toolkit can help omics researchers perform quality control and exchange information about macromolecules, help systems biologists assemble global models of cells that encompass processes such as post-translational modification, and help bioengineers design cells.
    Mesh-Begriff(e) Macromolecular Substances/chemistry ; Macromolecular Substances/standards ; Molecular Structure ; Proteomics ; Software ; Synthetic Biology ; Systems Biology
    Chemische Substanzen Macromolecular Substances
    Sprache Englisch
    Erscheinungsdatum 2020-05-18
    Erscheinungsland England
    Dokumenttyp Evaluation Study ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2040529-7
    ISSN 1474-760X ; 1474-760X
    ISSN (online) 1474-760X
    ISSN 1474-760X
    DOI 10.1186/s13059-020-02025-z
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  4. Artikel: BpForms and BcForms: a toolkit for concretely describing non-canonical polymers and complexes to facilitate global biochemical networks

    Lang, Paul F / Chebaro, Yassmine / Zheng, Xiaoyue / P. Sekar, John A / Shaikh, Bilal / Natale, Darren A / Karr, Jonathan R

    Genome biology. 2020 Dec., v. 21, no. 1

    2020  

    Abstract: Non-canonical residues, caps, crosslinks, and nicks are important to many functions of DNAs, RNAs, proteins, and complexes. However, we do not fully understand how networks of such non-canonical macromolecules generate behavior. One barrier is our ... ...

    Abstract Non-canonical residues, caps, crosslinks, and nicks are important to many functions of DNAs, RNAs, proteins, and complexes. However, we do not fully understand how networks of such non-canonical macromolecules generate behavior. One barrier is our limited formats for describing macromolecules. To overcome this barrier, we develop BpForms and BcForms, a toolkit for representing the primary structure of macromolecules as combinations of residues, caps, crosslinks, and nicks. The toolkit can help omics researchers perform quality control and exchange information about macromolecules, help systems biologists assemble global models of cells that encompass processes such as post-translational modification, and help bioengineers design cells.
    Schlagwörter RNA ; crosslinking ; models ; polymers ; post-translational modification ; proteins ; quality control
    Sprache Englisch
    Erscheinungsverlauf 2020-12
    Umfang p. 117.
    Erscheinungsort BioMed Central
    Dokumenttyp Artikel
    ZDB-ID 2040529-7
    ISSN 1474-760X ; 1465-6906
    ISSN (online) 1474-760X
    ISSN 1465-6906
    DOI 10.1186/s13059-020-02025-z
    Datenquelle NAL Katalog (AGRICOLA)

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  5. Artikel ; Online: PIRSitePredict for protein functional site prediction using position-specific rules.

    Chen, Chuming / Wang, Qinghua / Huang, Hongzhan / Vinayaka, Cholanayakanahalli R / Garavelli, John S / Arighi, Cecilia N / Natale, Darren A / Wu, Cathy H

    Database : the journal of biological databases and curation

    2019  Band 2019

    Abstract: Methods focused on predicting 'global' annotations for proteins (such as molecular function, biological process and presence of domains or membership in a family) have reached a relatively mature stage. Methods to provide fine-grained 'local' annotation ... ...

    Abstract Methods focused on predicting 'global' annotations for proteins (such as molecular function, biological process and presence of domains or membership in a family) have reached a relatively mature stage. Methods to provide fine-grained 'local' annotation of functional sites (at the level of individual amino acid) are now coming to the forefront, especially in light of the rapid accumulation of genetic variant data. We have developed a computational method and workflow that predicts functional sites within proteins using position-specific conditional template annotation rules (namely PIR Site Rules or PIRSRs for short). Such rules are curated through review of known protein structural and other experimental data by structural biologists and are used to generate high-quality annotations for the UniProt Knowledgebase (UniProtKB) unreviewed section. To share the PIRSR functional site prediction method with the broader scientific community, we have streamlined our workflow and developed a stand-alone Java software package named PIRSitePredict. We demonstrate the use of PIRSitePredict for functional annotation of de novo assembled genome/transcriptome by annotating uncharacterized proteins from Trinity RNA-seq assembly of embryonic transcriptomes of the following three cartilaginous fishes: Leucoraja erinacea (Little Skate), Scyliorhinus canicula (Small-spotted Catshark) and Callorhinchus milii (Elephant Shark). On average about 1200 lines of annotations were predicted for each species.
    Mesh-Begriff(e) Amino Acid Sequence ; Animals ; Databases, Protein ; Embryo, Nonmammalian/metabolism ; Fishes/embryology ; Fishes/genetics ; Genome ; Molecular Sequence Annotation ; Software ; Transcriptome/genetics
    Sprache Englisch
    Erscheinungsdatum 2019-02-11
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2496706-3
    ISSN 1758-0463 ; 1758-0463
    ISSN (online) 1758-0463
    ISSN 1758-0463
    DOI 10.1093/database/baz026
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  6. Artikel ; Online: Tutorial on Protein Ontology Resources.

    Arighi, Cecilia N / Drabkin, Harold / Christie, Karen R / Ross, Karen E / Natale, Darren A

    Methods in molecular biology (Clifton, N.J.)

    2017  Band 1558, Seite(n) 57–78

    Abstract: The Protein Ontology (PRO) is the reference ontology for proteins in the Open Biomedical Ontologies (OBO) foundry and consists of three sub-ontologies representing protein classes of homologous genes, proteoforms (e.g., splice isoforms, sequence variants, ...

    Abstract The Protein Ontology (PRO) is the reference ontology for proteins in the Open Biomedical Ontologies (OBO) foundry and consists of three sub-ontologies representing protein classes of homologous genes, proteoforms (e.g., splice isoforms, sequence variants, and post-translationally modified forms), and protein complexes. PRO defines classes of proteins and protein complexes, both species-specific and species nonspecific, and indicates their relationships in a hierarchical framework, supporting accurate protein annotation at the appropriate level of granularity, analyses of protein conservation across species, and semantic reasoning. In the first section of this chapter, we describe the PRO framework including categories of PRO terms and the relationship of PRO to other ontologies and protein resources. Next, we provide a tutorial about the PRO website ( proconsortium.org ) where users can browse and search the PRO hierarchy, view reports on individual PRO terms, and visualize relationships among PRO terms in a hierarchical table view, a multiple sequence alignment view, and a Cytoscape network view. Finally, we describe several examples illustrating the unique and rich information available in PRO.
    Mesh-Begriff(e) Animals ; Biological Ontologies ; Computational Biology/methods ; Databases, Genetic ; Humans ; Molecular Sequence Annotation ; Proteins/chemistry ; Proteins/genetics ; Proteins/metabolism ; Software ; User-Computer Interface ; Web Browser
    Chemische Substanzen Proteins
    Sprache Englisch
    Erscheinungsdatum 2017-01-31
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural
    ISSN 1940-6029
    ISSN (online) 1940-6029
    DOI 10.1007/978-1-4939-6783-4_3
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  7. Artikel ; Online: COVID-19 biomarkers and their overlap with comorbidities in a disease biomarker data model.

    Gogate, Nikhita / Lyman, Daniel / Bell, Amanda / Cauley, Edmund / Crandall, Keith A / Joseph, Ashia / Kahsay, Robel / Natale, Darren A / Schriml, Lynn M / Sen, Sabyasach / Mazumder, Raja

    Briefings in bioinformatics

    2021  Band 22, Heft 6

    Abstract: In response to the COVID-19 outbreak, scientists and medical researchers are capturing a wide range of host responses, symptoms and lingering postrecovery problems within the human population. These variable clinical manifestations suggest differences in ...

    Abstract In response to the COVID-19 outbreak, scientists and medical researchers are capturing a wide range of host responses, symptoms and lingering postrecovery problems within the human population. These variable clinical manifestations suggest differences in influential factors, such as innate and adaptive host immunity, existing or underlying health conditions, comorbidities, genetics and other factors-compounding the complexity of COVID-19 pathobiology and potential biomarkers associated with the disease, as they become available. The heterogeneous data pose challenges for efficient extrapolation of information into clinical applications. We have curated 145 COVID-19 biomarkers by developing a novel cross-cutting disease biomarker data model that allows integration and evaluation of biomarkers in patients with comorbidities. Most biomarkers are related to the immune (SAA, TNF-∝ and IP-10) or coagulation (D-dimer, antithrombin and VWF) cascades, suggesting complex vascular pathobiology of the disease. Furthermore, we observe commonality with established cancer biomarkers (ACE2, IL-6, IL-4 and IL-2) as well as biomarkers for metabolic syndrome and diabetes (CRP, NLR and LDL). We explore these trends as we put forth a COVID-19 biomarker resource (https://data.oncomx.org/covid19) that will help researchers and diagnosticians alike.
    Sprache Englisch
    Erscheinungsdatum 2021-05-12
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 2068142-2
    ISSN 1477-4054 ; 1467-5463
    ISSN (online) 1477-4054
    ISSN 1467-5463
    DOI 10.1093/bib/bbab191
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  8. Artikel ; Online: COVID-19 Biomarkers in research: Extension of the OncoMX cancer biomarker data model to capture biomarker data from other diseases.

    Gogate, Nikhita / Lyman, Daniel / Crandall, Keith A / Kahsay, Robel / Natale, Darren A. / Sen, Sabyasachi / Mazumder, Raja

    bioRxiv

    Abstract: Scientists, medical researchers, and health care workers have mobilized worldwide in response to the outbreak of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; SCoV2). Preliminary data have captured a wide range of host ... ...

    Abstract Scientists, medical researchers, and health care workers have mobilized worldwide in response to the outbreak of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; SCoV2). Preliminary data have captured a wide range of host responses, symptoms, and lingering problems post-recovery within the human population. These variable clinical manifestations suggest differences in influential factors, such as innate and adaptive host immunity, existing or underlying health conditions, co-morbidities, genetics, and other factors. As COVID-19-related data continue to accumulate from disparate groups, the heterogeneous nature of these datasets poses challenges for efficient extrapolation of meaningful observations, hindering translation of information into clinical applications. Attempts to utilize, analyze, or combine biomarker datasets from multiple sources have shown to be inefficient and complicated, without a unifying resource. As such, there is an urgent need within the research community for the rapid development of an integrated and harmonized COVID-19 Biomarker Knowledgebase. By leveraging data collection and integration methods, backed by a robust data model developed to capture cancer biomarker data we have rapidly crowdsourced the collection and harmonization of COVID-19 biomarkers. Our resource currently has 138 unique biomarkers. We found multiple instances of the same biomarker substance being suggested as multiple biomarker types during our extensive cross-validation and manual curation. As a result, our Knowledgebase currently has 265 biomarker type combinations. Every biomarker entry is made comprehensive by bringing in together ancillary data from multiple sources such as biomarker accessions (canonical UniProtKB accession, PubChem Compound ID, Cell Ontology ID, Protein Ontology ID, NCI Thesaurus Code, and Disease Ontology ID), BEST biomarker category, and specimen type (Uberon Anatomy Ontology) unified with ontology standards. Our preliminary observations show distinct trends in the collated biomarkers. Most biomarkers are related to the immune system (SAA, TNF-α, and IP-10) or coagulopathies (D-dimer, antithrombin, and VWF) and a few have already been established as cancer biomarkers (ACE2, IL-6, IL-4, and IL-2). These trends align with the proposed hypotheses of clinical manifestations compounding the complexity of COVID-19 pathobiology. We explore these trends as we put forth a COVID-19 biomarker resource that will help researchers and diagnosticians alike. All biomarker data are freely available from https://data.oncomx.org/covid19.
    Schlagwörter covid19
    Sprache Englisch
    Erscheinungsdatum 2020-09-10
    Verlag Cold Spring Harbor Laboratory
    Dokumenttyp Artikel ; Online
    DOI 10.1101/2020.09.09.196220
    Datenquelle COVID19

    Volltext online

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  9. Buch ; Online: BpForms and BcForms

    Lang, Paul F. / Chebaro, Yassmine / Zheng, Xiaoyue / Sekar, John A. P. / Shaikh, Bilal / Natale, Darren A. / Karr, Jonathan R.

    Tools for concretely describing non-canonical polymers and complexes to facilitate comprehensive biochemical networks

    2019  

    Abstract: Although non-canonical residues, caps, crosslinks, and nicks play an important role in the function of many DNA, RNA, proteins, and complexes, we do not fully understand how networks of non-canonical macromolecules generate behavior. One barrier is our ... ...

    Abstract Although non-canonical residues, caps, crosslinks, and nicks play an important role in the function of many DNA, RNA, proteins, and complexes, we do not fully understand how networks of non-canonical macromolecules generate behavior. One barrier is our limited formats, such as IUPAC, for abstractly describing macromolecules. To overcome this barrier, we developed BpForms and BcForms, a toolkit of ontologies, grammars, and software for abstracting the primary structure of polymers and complexes as combinations of residues, caps, crosslinks, and nicks. The toolkit can help quality control, exchange, and integrate information about the primary structure of macromolecules into fine-grained global networks of intracellular biochemistry.

    Comment: 21 pages, 4 figures, 2 boxes
    Schlagwörter Quantitative Biology - Biomolecules
    Erscheinungsdatum 2019-03-24
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  10. Artikel: Computational clustering for viral reference proteomes

    Chen, Chuming / Huang, Hongzhan / Mazumder, Raja / Natale, Darren A / McGarvey, Peter B / Zhang, Jian / Polson, Shawn W / Wang, Yuqi / Wu, Cathy H

    Bioinformatics. 2016 July 01, v. 32, no. 13

    2016  

    Abstract: Motivation: The enormous number of redundant sequenced genomes has hindered efforts to analyze and functionally annotate proteins. As the taxonomy of viruses is not uniformly defined, viral proteomes pose special challenges in this regard. Grouping ... ...

    Abstract Motivation: The enormous number of redundant sequenced genomes has hindered efforts to analyze and functionally annotate proteins. As the taxonomy of viruses is not uniformly defined, viral proteomes pose special challenges in this regard. Grouping viruses based on the similarity of their proteins at proteome scale can normalize against potential taxonomic nomenclature anomalies. Results: We present Viral Reference Proteomes (Viral RPs), which are computed from complete virus proteomes within UniProtKB. Viral RPs based on 95, 75, 55, 35 and 15% co-membership in proteome similarity based clusters are provided. Comparison of our computational Viral RPs with UniProt’s curator-selected Reference Proteomes indicates that the two sets are consistent and complementary. Furthermore, each Viral RP represents a cluster of virus proteomes that was consistent with virus or host taxonomy. We provide BLASTP search and FTP download of Viral RP protein sequences, and a browser to facilitate the visualization of Viral RPs. Availability and implementation: http://proteininformationresource.org/rps/viruses/ Contact: chenc@udel.edu Supplementary information: Supplementary data are available at Bioinformatics online.
    Schlagwörter amino acid sequences ; bioinformatics ; genome ; proteins ; proteome ; taxonomy ; viruses
    Sprache Englisch
    Erscheinungsverlauf 2016-0701
    Umfang p. 2041-2043.
    Erscheinungsort Oxford University Press
    Dokumenttyp Artikel
    ZDB-ID 1468345-3
    ISSN 1460-2059 ; 1367-4811 ; 1367-4803
    ISSN (online) 1460-2059 ; 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btw110
    Datenquelle NAL Katalog (AGRICOLA)

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