A multi-omics graph database for data integration and knowledge extraction

Suyeon Kim; Ishwor Thapa; Hesham Ali

doi:10.1145/3535508.3545517

A multi-omics graph database for data integration and knowledge extraction

Suyeon Kim, Ishwor Thapa, Hesham Ali

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

Major recent advances in sequencing technologies have created new opportunities for studying the complex microbiome domain. However, microbial communities have many unknown roles and unclear impacts on their host environment. The increased availability of microbial omics data associated with heterogeneous metadata has the potential to revolutionize microbiome research. This study proposes a novel data-integration model and a practical pipeline to explore microbial community functions with the integration of omics data. Three case studies were employed to highlight the advanced abilities and applications of our graph database model. Furthermore, we show that a variety of information can be queried against our model and easily extracted using the proposed analysis pipeline. Our findings suggest that the proposed model is highly queryable and provides a critical analytical platform to extract useful knowledge from multi-omics data. We show that such knowledge extraction can lead to new discoveries, particularly when utilizing all available datasets.

Original language	English (US)
Title of host publication	Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2022
Publisher	Association for Computing Machinery, Inc
ISBN (Electronic)	9781450393867
DOIs	https://doi.org/10.1145/3535508.3545517
State	Published - Aug 7 2022
Event	13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2022 - Chicago, United States Duration: Aug 7 2022 → Aug 8 2022

Publication series

Name	Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2022

Conference

Conference	13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2022
Country/Territory	United States
City	Chicago
Period	8/7/22 → 8/8/22

Keywords

Data integration
Data mining
Graph-based database models
Inflammatory bowel disease
Information systems
Microbiome
Neo4j

ASJC Scopus subject areas

Computer Science Applications
Software
Biomedical Engineering
Health Informatics

Access to Document

10.1145/3535508.3545517

Cite this

Kim, S., Thapa, I., & Ali, H. (2022). A multi-omics graph database for data integration and knowledge extraction. In Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2022 Article 56 (Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2022). Association for Computing Machinery, Inc. https://doi.org/10.1145/3535508.3545517

A multi-omics graph database for data integration and knowledge extraction. / Kim, Suyeon; Thapa, Ishwor; Ali, Hesham.
Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2022. Association for Computing Machinery, Inc, 2022. 56 (Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, S, Thapa, I & Ali, H 2022, A multi-omics graph database for data integration and knowledge extraction. in Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2022., 56, Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2022, Association for Computing Machinery, Inc, 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2022, Chicago, United States, 8/7/22. https://doi.org/10.1145/3535508.3545517

Kim S, Thapa I, Ali H. A multi-omics graph database for data integration and knowledge extraction. In Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2022. Association for Computing Machinery, Inc. 2022. 56. (Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2022). doi: 10.1145/3535508.3545517

Kim, Suyeon ; Thapa, Ishwor ; Ali, Hesham. / A multi-omics graph database for data integration and knowledge extraction. Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2022. Association for Computing Machinery, Inc, 2022. (Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2022).

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abstract = "Major recent advances in sequencing technologies have created new opportunities for studying the complex microbiome domain. However, microbial communities have many unknown roles and unclear impacts on their host environment. The increased availability of microbial omics data associated with heterogeneous metadata has the potential to revolutionize microbiome research. This study proposes a novel data-integration model and a practical pipeline to explore microbial community functions with the integration of omics data. Three case studies were employed to highlight the advanced abilities and applications of our graph database model. Furthermore, we show that a variety of information can be queried against our model and easily extracted using the proposed analysis pipeline. Our findings suggest that the proposed model is highly queryable and provides a critical analytical platform to extract useful knowledge from multi-omics data. We show that such knowledge extraction can lead to new discoveries, particularly when utilizing all available datasets.",

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AB - Major recent advances in sequencing technologies have created new opportunities for studying the complex microbiome domain. However, microbial communities have many unknown roles and unclear impacts on their host environment. The increased availability of microbial omics data associated with heterogeneous metadata has the potential to revolutionize microbiome research. This study proposes a novel data-integration model and a practical pipeline to explore microbial community functions with the integration of omics data. Three case studies were employed to highlight the advanced abilities and applications of our graph database model. Furthermore, we show that a variety of information can be queried against our model and easily extracted using the proposed analysis pipeline. Our findings suggest that the proposed model is highly queryable and provides a critical analytical platform to extract useful knowledge from multi-omics data. We show that such knowledge extraction can lead to new discoveries, particularly when utilizing all available datasets.

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A multi-omics graph database for data integration and knowledge extraction

Abstract

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Conference

Keywords

ASJC Scopus subject areas

Access to Document

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