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1. An open source knowledge graph ecosystem for the life sciences

Author

Tiffany J. Callahan, Ignacio J. Tripodi, Adrianne L. Stefanski, Luca Cappelletti, Sanya B. Taneja, Jordan M. Wyrwa, Elena Casiraghi, Nicolas A. Matentzoglu, Justin Reese, Jonathan C. Silverstein, Charles Tapley Hoyt, Richard D. Boyce, Scott A. Malec, Deepak R. Unni, Marcin P. Joachimiak, Peter N. Robinson, Christopher J. Mungall, Emanuele Cavalleri, Tommaso Fontana, Giorgio Valentini, Marco Mesiti, Lucas A. Gillenwater, Brook Santangelo, Nicole A. Vasilevsky, Robert Hoehndorf, Tellen D. Bennett, Patrick B. Ryan, George Hripcsak, Michael G. Kahn, Michael Bada, William A. Baumgartner, and Lawrence E. Hunter

Subjects
Science
Abstract

Abstract Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

Published
2024
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2. Unifying the identification of biomedical entities with the Bioregistry

Author

Charles Tapley Hoyt, Meghan Balk, Tiffany J. Callahan, Daniel Domingo-Fernández, Melissa A. Haendel, Harshad B. Hegde, Daniel S. Himmelstein, Klas Karis, John Kunze, Tiago Lubiana, Nicolas Matentzoglu, Julie McMurry, Sierra Moxon, Christopher J. Mungall, Adriano Rutz, Deepak R. Unni, Egon Willighagen, Donald Winston, and Benjamin M. Gyori

Subjects
Science
Abstract

Abstract The standardized identification of biomedical entities is a cornerstone of interoperability, reuse, and data integration in the life sciences. Several registries have been developed to catalog resources maintaining identifiers for biomedical entities such as small molecules, proteins, cell lines, and clinical trials. However, existing registries have struggled to provide sufficient coverage and metadata standards that meet the evolving needs of modern life sciences researchers. Here, we introduce the Bioregistry, an integrative, open, community-driven metaregistry that synthesizes and substantially expands upon 23 existing registries. The Bioregistry addresses the need for a sustainable registry by leveraging public infrastructure and automation, and employing a progressive governance model centered around open code and open data to foster community contribution. The Bioregistry can be used to support the standardized annotation of data, models, ontologies, and scientific literature, thereby promoting their interoperability and reuse. The Bioregistry can be accessed through https://bioregistry.io and its source code and data are available under the MIT and CC0 Licenses at https://github.com/biopragmatics/bioregistry .

Published
2022
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3. Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science

Author

Deepak R. Unni, Sierra A. T. Moxon, Michael Bada, Matthew Brush, Richard Bruskiewich, J. Harry Caufield, Paul A. Clemons, Vlado Dancik, Michel Dumontier, Karamarie Fecho, Gustavo Glusman, Jennifer J. Hadlock, Nomi L. Harris, Arpita Joshi, Tim Putman, Guangrong Qin, Stephen A. Ramsey, Kent A. Shefchek, Harold Solbrig, Karthik Soman, Anne E. Thessen, Melissa A. Haendel, Chris Bizon, Christopher J. Mungall, and The Biomedical Data Translator Consortium

Subjects
Therapeutics. Pharmacology, RM1-950, Public aspects of medicine, RA1-1270
Abstract

Abstract Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph‐based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these “knowledge graphs” (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open‐access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open‐source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object‐oriented classification and graph‐oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

Published
2022
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4. MaizeMine: A Data Mining Warehouse for the Maize Genetics and Genomics Database

Author

Md Shamimuzzaman, Jack M. Gardiner, Amy T. Walsh, Deborah A. Triant, Justin J. Le Tourneau, Aditi Tayal, Deepak R. Unni, Hung N. Nguyen, John L. Portwood, Ethalinda K. S. Cannon, Carson M. Andorf, and Christine G. Elsik

Subjects
data mining, genome database, InterMine, maize, Zea mays, Plant culture, SB1-1110
Abstract

MaizeMine is the data mining resource of the Maize Genetics and Genome Database (MaizeGDB; http://maizemine.maizegdb.org). It enables researchers to create and export customized annotation datasets that can be merged with their own research data for use in downstream analyses. MaizeMine uses the InterMine data warehousing system to integrate genomic sequences and gene annotations from the Zea mays B73 RefGen_v3 and B73 RefGen_v4 genome assemblies, Gene Ontology annotations, single nucleotide polymorphisms, protein annotations, homologs, pathways, and precomputed gene expression levels based on RNA-seq data from the Z. mays B73 Gene Expression Atlas. MaizeMine also provides database cross references between genes of alternative gene sets from Gramene and NCBI RefSeq. MaizeMine includes several search tools, including a keyword search, built-in template queries with intuitive search menus, and a QueryBuilder tool for creating custom queries. The Genomic Regions search tool executes queries based on lists of genome coordinates, and supports both the B73 RefGen_v3 and B73 RefGen_v4 assemblies. The List tool allows you to upload identifiers to create custom lists, perform set operations such as unions and intersections, and execute template queries with lists. When used with gene identifiers, the List tool automatically provides gene set enrichment for Gene Ontology (GO) and pathways, with a choice of statistical parameters and background gene sets. With the ability to save query outputs as lists that can be input to new queries, MaizeMine provides limitless possibilities for data integration and meta-analysis.

Published
2020
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5. An Open-Source Knowledge Graph Ecosystem for the Life Sciences.

Author

Tiffany J. Callahan, Ignacio J. Tripodi, Adrianne L. Stefanski, Luca Cappelletti, Sanya Bathla Taneja, Jordan M. Wyrwa, Elena Casiraghi, Nicolas A. Matentzoglu, Justin T. Reese, Jonathan C. Silverstein, Charles Tapley Hoyt, Richard D. Boyce, Scott A. Malec, Deepak R. Unni, Marcin P. Joachimiak, Peter N. Robinson, Christopher J. Mungall, Emanuele Cavalleri, Tommaso Fontana, Giorgio Valentini, Marco Mesiti, Lucas A. Gillenwater, Brook E. Santangelo, Nicole A. Vasilevsky, Robert Hoehndorf, Tellen D. Bennett, Patrick B. Ryan, George Hripcsak, Michael G. Kahn, Michael Bada, William A. Baumgartner Jr., and Lawrence E. Hunter

Published
2023
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6. KG-Hub - Building and Exchanging Biological Knowledge Graphs.

Author

J. Harry Caufield, Tim E. Putman, Kevin Schaper, Deepak R. Unni, Harshad Hegde, Tiffany J. Callahan, Luca Cappelletti, Sierra A. T. Moxon, Vida Ravanmehr, Seth Carbon, Lauren E. Chan, Katherina Cortes, Kent A. Shefchek, Glass Elsarboukh, James P. Balhoff, Tommaso Fontana, Nicolas Matentzoglu, Richard M. Bruskiewich, Anne E. Thessen, Nomi L. Harris, Monica C. Munoz-Torres, Melissa A. Haendel, Peter N. Robinson, Marcin P. Joachimiak, Christopher J. Mungall, and Justin T. Reese

Published
2023
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8. Leveraging Ontologies within the National Microbiome Data Collaborative.

Author

William D. Duncan, Faiza Ahmed, Fnu Anubhav, Jeffrey Baumes, Jonathan Beezley, Mark Borkum, Lisa M. Bramer, Shane Canon, Patrick S. G. Chain, Danielle Christianson, Yuri Corilo, Karen Davenport, Brandon Davis, Meghan Drake, Kjiersten Fagnan, Mark Flynn, David Hays, Bin Hu, Marcel Huntemann, Julia Kelliher, Sofya Lebedeva, Po-E Li, Mary Lipton, Chien-Chi Lo, Douglas Mans, Stanton Martin, Lee Ann McCue, David Millard 0002, Kayd Miller, Nigel Mouncey, Paul D. Piehowski, Elais Player Jackson, Anastasiya Prymolenna, Samuel O. Purvine, T. B. K. Reddy, Rachel Richardson, Migun Shakya, Montana Smith, Jagadish Chandrabose Sundaramurthi, Mark A. Miller, Deepak R. Unni, Pajau Vangay, Bruce Wilson, Donald Winston, Elisha M. Wood-Charlson, Yan Xu, Emiley A. Eloe-Fadrosh, and Christopher J. Mungall

Published
2021

9. The Gene Ontology resource: enriching a GOld mine.

Author

Seth Carbon, Eric Douglass, Benjamin M. Good, Deepak R. Unni, Nomi L. Harris, Christopher J. Mungall, Siddartha Basu, Rex L. Chisholm, Robert J. Dodson, Eric Hartline, Petra Fey, Paul D. Thomas, Laurent-Philippe Albou, Dustin Ebert, Michael J. Kesling, Huaiyu Mi, Anushya Muruganujan, Xiaosong Huang, Tremayne Mushayahama, Sandra A. LaBonte, Deborah A. Siegele, Giulia Antonazzo, Helen Attrill, Nick H. Brown, Phani V. Garapati, Steven J. Marygold, Vitor Trovisco, Gilberto dos Santos, Kathleen Falls, Christopher J. Tabone, Pinglei Zhou, Joshua L. Goodman, Victor B. Strelets, Jim Thurmond, Penelope Garmiri, Rizwan Ishtiaq, Milagros Rodríguez-López, Marcio Luis Acencio, Martin Kuiper, Astrid Lægreid, Colin Logie, Ruth C. Lovering, Barbara Kramarz, Shirin C. C. Saverimuttu, Sandra M. Pinheiro, Heather Gunn, Renzhi Su, Katherine E. Thurlow, Marcus C. Chibucos, Michelle G. Giglio, Suvarna Nadendla, James B. Munro, Rebecca C. Jackson, Margaret J. Duesbury, Noemi del-Toro, Birgit H. M. Meldal, Kalpana Paneerselvam, Livia Perfetto, Pablo Porras, Sandra E. Orchard, Anjali Shrivastava, Hsin-Yu Chang, Robert D. Finn, Alex L. Mitchell, Neil D. Rawlings, Lorna J. Richardson, Amaia Sangrador-Vegas, Judith A. Blake, Karen R. Christie, Mary E. Dolan, Harold J. Drabkin, David P. Hill, Li Ni, Dmitry M. Sitnikov, Midori A. Harris, Stephen G. Oliver, Kim Rutherford, Valerie Wood, Jaqueline Hayles, Jürg Bähler, Elizabeth R. Bolton, Jeffrey DePons, Melinda R. Dwinell, G. Thomas Hayman, Mary L. Kaldunski, Anne E. Kwitek, Stanley J. F. Laulederkind, Cody Plasterer, Marek Tutaj, Mahima Vedi, Shur-Jen Wang, Peter D'Eustachio, Lisa Matthews, James P. Balhoff, Suzi A. Aleksander, Michael J. Alexander, J. Michael Cherry, Stacia R. Engel, Felix Gondwe, Kalpana Karra, Stuart R. Miyasato, Robert S. Nash, Matt Simison, Marek S. Skrzypek, Shuai Weng, Edith D. Wong, Marc Feuermann, Pascale Gaudet, Anne Morgat, Erica Bakker, Tanya Z. Berardini, Leonore Reiser, Shabari Subramaniam, Eva Huala, Cecilia N. Arighi, Andrea H. Auchincloss, Kristian B. Axelsen, Ghislaine Argoud-Puy, Alex Bateman, Marie-Claude Blatter, Emmanuel Boutet, Emily Bowler, Lionel Breuza, Alan J. Bridge, Ramona Britto, Hema Bye-A-Jee, Cristina Casals-Casas, Elisabeth Coudert, Paul Denny 0002, Anne Estreicher, Maria Livia Famiglietti, George E. Georghiou, Arnaud Gos, Nadine Gruaz-Gumowski, Emma Hatton-Ellis, Chantal Hulo, Alexandr Ignatchenko, Florence Jungo, Kati Laiho, Philippe Le Mercier, Damien Lieberherr, Antonia Lock, Yvonne Lussi, Alistair MacDougall, Michele Magrane, Maria Jesus Martin, Patrick Masson, Darren A. Natale, Nevila Hyka-Nouspikel, Ivo Pedruzzi, Lucille Pourcel, Sylvain Poux, Sangya Pundir, Catherine Rivoire, Elena Speretta, Shyamala Sundaram, Nidhi Tyagi, Kate Warner, Rossana Zaru, Cathy H. Wu, Alexander D. Diehl, Juancarlos Chan, Christian A. Grove, Raymond Y. N. Lee, Hans-Michael Müller, Daniela Raciti, Kimberly Van Auken, Paul W. Sternberg, Matthew Berriman, Michael Paulini, Kevin L. Howe, Sibyl Gao, Adam Wright, Lincoln Stein, Douglas G. Howe, Sabrina Toro, Monte Westerfield, Pankaj Jaiswal, Laurel Cooper, and Justin Elser

Published
2021
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11. The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species.

Author

Kent A. Shefchek, Nomi L. Harris, Michael A. Gargano, Nicolas Matentzoglu, Deepak R. Unni, Matthew H. Brush, Dan Keith, Tom Conlin, Nicole A. Vasilevsky, Xingmin Aaron Zhang, James P. Balhoff, Larry Babb, Susan M. Bello, Hannah Blau, Yvonne M. Bradford, Seth Carbon, Leigh Carmody, Lauren E. Chan, Valentina Cipriani, Alayne Cuzick, Maria G. Della Rocca, Nathan A. Dunn, Shahim Essaid, Petra Fey, Christian A. Grove, Jean-Philippe F. Gourdine, Ada Hamosh, Midori A. Harris, Ingo Helbig, Maureen E. Hoatlin, Marcin P. Joachimiak, Simon Jupp, Kenneth B. Lett, Suzanna E. Lewis, Craig McNamara, Zoë May Pendlington, Clare Pilgrim, Tim E. Putman, Vida Ravanmehr, Justin T. Reese, Erin Rooney Riggs, Sofia M. C. Robb, Paola Roncaglia, James Seager, Erik Segerdell, Morgan Similuk, Andrea L. Storm, Courtney Thaxon, Anne E. Thessen, Julius O. B. Jacobsen, Julie A. McMurry, Tudor Groza, Sebastian Köhler 0001, Damian Smedley, Peter N. Robinson, Christopher J. Mungall, Melissa A. Haendel, Monica C. Munoz-Torres, and David Osumi-Sutherland

Published
2020
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12. Apollo: Democratizing genome annotation.

Author

Nathan A Dunn, Deepak R Unni, Colin Diesh, Monica Munoz-Torres, Nomi L Harris, Eric Yao, Helena Rasche, Ian H Holmes, Christine G Elsik, and Suzanna E Lewis

Subjects
Biology (General), QH301-705.5
Abstract

Genome annotation is the process of identifying the location and function of a genome's encoded features. Improving the biological accuracy of annotation is a complex and iterative process requiring researchers to review and incorporate multiple sources of information such as transcriptome alignments, predictive models based on sequence profiles, and comparisons to features found in related organisms. Because rapidly decreasing costs are enabling an ever-growing number of scientists to incorporate sequencing as a routine laboratory technique, there is widespread demand for tools that can assist in the deliberative analytical review of genomic information. To this end, we present Apollo, an open source software package that enables researchers to efficiently inspect and refine the precise structure and role of genomic features in a graphical browser-based platform. Some of Apollo's newer user interface features include support for real-time collaboration, allowing distributed users to simultaneously edit the same encoded features while also instantly seeing the updates made by other researchers on the same region in a manner similar to Google Docs. Its technical architecture enables Apollo to be integrated into multiple existing genomic analysis pipelines and heterogeneous laboratory workflow platforms. Finally, we consider the implications that Apollo and related applications may have on how the results of genome research are published and made accessible.

Published
2019
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16. Mondo: Unifying diseases for the world, by the world

Author

Nicole A Vasilevsky, Nicolas A Matentzoglu, Sabrina Toro, Joseph E Flack, Harshad Hegde, Deepak R Unni, Gioconda F Alyea, Joanna S Amberger, Larry Babb, James P Balhoff, Taylor I Bingaman, Gully A Burns, Orion J Buske, Tiffany J Callahan, Leigh C Carmody, Paula Carrio Cordo, Lauren E Chan, George S Chang, Sean L Christiaens, Michel Dumontier, Laura E Failla, May J Flowers, H. Alpha Garrett, Jennifer L Goldstein, Dylan Gration, Tudor Groza, Marc Hanauer, Nomi L Harris, Jason A Hilton, Daniel S Himmelstein, Charles Tapley Hoyt, Megan S Kane, Sebastian Köhler, David Lagorce, Abbe Lai, Martin Larralde, Antonia Lock, Irene López Santiago, Donna R Maglott, Adriana J Malheiro, Birgit H M Meldal, Monica C Munoz-Torres, Tristan H Nelson, Frank W Nicholas, David Ochoa, Daniel P Olson, Tudor I Oprea, David Osumi-Sutherland, Helen Parkinson, Zoë May Pendlington, Ana Rath, Heidi L Rehm, Lyubov Remennik, Erin R Riggs, Paola Roncaglia, Justyne E Ross, Marion F Shadbolt, Kent A Shefchek, Morgan N Similuk, Nicholas Sioutos, Damian Smedley, Rachel Sparks, Ray Stefancsik, Ralf Stephan, Andrea L Storm, Doron Stupp, Gregory S Stupp, Jagadish Chandrabose Sundaramurthi, Imke Tammen, Darin Tay, Courtney L Thaxton, Eloise Valasek, Jordi Valls-Margarit, Alex H Wagner, Danielle Welter, Patricia L Whetzel, Lori L Whiteman, Valerie Wood, Colleen H Xu, Andreas Zankl, Xingmin Aaron Zhang, Christopher G Chute, Peter N Robinson, Christopher J Mungall, Ada Hamosh, and Melissa A Haendel

Abstract

There are thousands of distinct disease entities and concepts, each of which are known by different and sometimes contradictory names. The lack of a unified system for managing these entities poses a major challenge for both machines and humans that need to harmonize information to better predict causes and treatments for disease. The Mondo Disease Ontology is an open, community-driven ontology that integrates key medical and biomedical terminologies, supporting disease data integration to improve diagnosis, treatment, and translational research. Mondo records the sources of all data and is continually updated, making it suitable for research and clinical applications that require up-to-date disease knowledge.

Published
2022
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17. AgBioData consortium recommendations for sustainable genomics and genetics databases for agriculture.

Author

Lisa C. Harper, Jacqueline D. Campbell, Ethalinda K. S. Cannon, Sook Jung, Monica Poelchau, Ramona L. Walls, Carson M. Andorf, Elizabeth Arnaud, Tanya Z. Berardini, Clayton L. Birkett, Steven B. Cannon, James D. Carson, Bradford Condon, Laurel Cooper, Nathan A. Dunn, Christine G. Elsik, Andrew D. Farmer, Stephen P. Ficklin, David M. Grant, Emily S. Grau, Nic Herndon, Zhi-Liang Hu, Jodi L. Humann, Pankaj Jaiswal, Clément Jonquet, Marie-Angélique Laporte, Pierre Larmande, Gerard R. Lazo, Fiona McCarthy, Naama Menda, Christopher J. Mungall, Monica C. Munoz-Torres, Sushma Naithani, Rex T. Nelson, Daureen Nesdill, Carissa A. Park, James M. Reecy, Leonore Reiser, Lacey-Anne Sanderson, Taner Z. Sen, Margaret Staton, Sabarinath Subramaniam, Marcela Karey Tello-Ruiz, Victor Unda, Deepak R. Unni, Liya Wang, Doreen Ware, Jill L. Wegrzyn, Jason Williams 0003, Margaret Woodhouse, Jing Yu 0002, and Doreen Main

Published
2018
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18. KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response

Author

Justin T. Reese, Deepak R. Unni, Tiffany J. Callahan, Luca Cappelletti, Vida Ravanmehr, Seth Carbon, Tommaso Fontana, Hannah Blau, Nicolas Matentzoglu, Nomi L. Harris, Monica C. Munoz-Torres, Peter N. Robinson, Marcin P. Joachimiak, Christopher J. Mungall, and Sarah Callaghan

Subjects
Coronavirus disease 2019 (COVID-19), Computer science, business.industry, Conflict of interest, Cloud computing, Ontology (information science), computer.software_genre, Task (project management), World Wide Web, Work (electrical), User interface, business, computer, Data integration
Abstract

Integrated, up-to-date data about SARS-CoV-2 and coronavirus disease 2019 (COVID-19) is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community varies drastically for different tasks - the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates biomedical data to produce knowledge graphs (KGs) for COVID-19 response. This KG framework can also be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics. Funding: This work was supported by grants from the Director, Office of Science, Office of Basic Energy Sciences of the U.S. Department of Energy [to J.R., D.U., S.C., N.L.H., M.J., C.J.M], the Laboratory Directed Research and Development (LDRD) Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231, the NIH (Monarch R24 OD011883, Illuminating the Druggable Genome U01 CA239108-01), a Training Grant from the NLM, NIH to the University of Colorado Anschutz Medical Campus Computational Bioscience Training Program [T15LM009451 to T.J.C.], the National Virtual Biotechnology Laboratory (NVBL), and the Google Cloud COVID-19 Research Grants program. Conflict of Interest: The authors declare no competing interests.

Published
2020
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19. Bovine Genome Database: Tools for Mining the Bos taurus Genome

Author

Darren E, Hagen, Deepak R, Unni, Aditi, Tayal, Gregory W, Burns, and Christine G, Elsik

Subjects
Genome, Quantitative Trait Loci, Computational Biology, Gene Expression, Genetic Variation, Molecular Sequence Annotation, Genomics, Web Browser, User-Computer Interface, Meta-Analysis as Topic, Databases, Genetic, Animals, Data Mining, Cattle, Software, Genome-Wide Association Study
Abstract

The Bovine Genome Database (BGD; http://bovinegenome.org ) is a web-accessible resource that supports bovine genomics research by providing genome annotation and data mining tools. BovineMine is a tool within BGD that integrates BGD data, including the genome, genes, precomputed gene expression levels and variant consequences, with external data sources that include quantitative trait loci (QTL), orthologues, Gene Ontology, gene interactions, and pathways. BovineMine enables researchers without programming skills to create custom integrated datasets for use in downstream analyses. This chapter describes how to enhance a bovine genomics project using the Bovine Genome Database, with data mining examples demonstrating BovineMine.

Published
2018

20. Hymenoptera Genome Database: Using HymenopteraMine to Enhance Genomic Studies of Hymenopteran Insects

Author

Christine G, Elsik, Aditi, Tayal, Deepak R, Unni, Gregory W, Burns, and Darren E, Hagen

Subjects
User-Computer Interface, Databases, Genetic, Genome, Insect, Animals, Computational Biology, Data Mining, Genomics, Web Browser, Hymenoptera, Software
Abstract

The Hymenoptera Genome Database (HGD; http://hymenopteragenome.org ) is a genome informatics resource for insects of the order Hymenoptera, which includes bees, ants and wasps. HGD provides genome browsers with manual annotation tools (JBrowse/Apollo), BLAST, bulk data download, and a data mining warehouse (HymenopteraMine). This chapter focuses on the use of HymenopteraMine to create annotation data sets that can be exported for use in downstream analyses. HymenopteraMine leverages the InterMine platform to combine genome assemblies and official gene sets with data from OrthoDB, RefSeq, FlyBase, Gene Ontology, UniProt, InterPro, KEGG, Reactome, dbSNP, PubMed, and BioGrid, as well as precomputed gene expression information based on publicly available RNAseq. Built-in template queries provide starting points for data exploration, while the QueryBuilder tool supports construction of complex custom queries. The List Analysis and Genomic Regions search tools execute queries based on uploaded lists of identifiers and genome coordinates, respectively. HymenopteraMine facilitates cross-species data mining based on orthology and supports meta-analyses by tracking identifiers across gene sets and genome assemblies.

Published
2018

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