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Toti D, Macari G, Barbierato E, Polticelli F. FGDB: a comprehensive graph database of ligand fragments from the Protein Data Bank. Database (Oxford) 2022; 2022:6619197. [PMID: 35763362 PMCID: PMC9239314 DOI: 10.1093/database/baac044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 05/06/2022] [Accepted: 05/31/2022] [Indexed: 11/22/2022]
Abstract
This work presents Fragment Graph DataBase (FGDB), a graph database of ligand fragments extracted and generated from the protein entries available in the Protein Data Bank (PDB). FGDB is meant to support and elicit campaigns of fragment-based drug design, by enabling users to query it in order to construct ad hoc, target-specific libraries. In this regard, the database features more than 17 000 fragments, typically small, highly soluble and chemically stable molecules expressed via their canonical Simplified Molecular Input Line Entry System (SMILES) representation. For these fragments, the database provides information related to their contact frequencies with the amino acids, the ligands they are contained in and the proteins the latter bind to. The graph database can be queried via standard web forms and textual searches by a number of identifiers (SMILES, ligand and protein PDB ids) as well as via graphical queries that can be performed against the graph itself, providing users with an intuitive and effective view upon the underlying biological entities. Further search mechanisms via advanced conjunctive/disjunctive/negated textual queries are also possible, in order to allow scientists to look for specific relationships and export their results for further studies. This work also presents two sample use cases where maternal embryonic leucine zipper kinase and mesotrypsin are used as a target, being proteins of high biomedical relevance for the development of cancer therapies. Database URL: http://biochimica3.bio.uniroma3.it/fragments-web/
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Affiliation(s)
- Daniele Toti
- Department of Mathematics and Physics, Catholic University of the Sacred Heart, Faculty of Mathematical, Physical and Natural Sciences , via della Garzetta 48, Brescia 25133, Italy
| | - Gabriele Macari
- Department of Sciences, Roma Tre University , viale Marconi 446, Roma, Lazio 00146, Italy
| | - Enrico Barbierato
- Department of Mathematics and Physics, Catholic University of the Sacred Heart, Faculty of Mathematical, Physical and Natural Sciences , via della Garzetta 48, Brescia 25133, Italy
| | - Fabio Polticelli
- Department of Sciences, Roma Tre University , viale Marconi 446, Roma, Lazio 00146, Italy
- Roma Tre Section, National Institute of Nuclear Physics , via della Vasca Navale 84, Roma 00146, Italy
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Timón-Reina S, Rincón M, Martínez-Tomás R. An overview of graph databases and their applications in the biomedical domain. Database (Oxford) 2021; 2021:baab026. [PMID: 34003247 PMCID: PMC8130509 DOI: 10.1093/database/baab026] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 03/24/2021] [Accepted: 04/30/2021] [Indexed: 01/18/2023]
Abstract
Over the past couple of decades, the explosion of densely interconnected data has stimulated the research, development and adoption of graph database technologies. From early graph models to more recent native graph databases, the landscape of implementations has evolved to cover enterprise-ready requirements. Because of the interconnected nature of its data, the biomedical domain has been one of the early adopters of graph databases, enabling more natural representation models and better data integration workflows, exploration and analysis facilities. In this work, we survey the literature to explore the evolution, performance and how the most recent graph database solutions are applied in the biomedical domain, compiling a great variety of use cases. With this evidence, we conclude that the available graph database management systems are fit to support data-intensive, integrative applications, targeted at both basic research and exploratory tasks closer to the clinic.
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Affiliation(s)
- Santiago Timón-Reina
- Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia (UNED), C/Juan del Rosal, 16 Ciudad Universitaria, Madrid 28040, Spain
| | - Mariano Rincón
- Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia (UNED), C/Juan del Rosal, 16 Ciudad Universitaria, Madrid 28040, Spain
| | - Rafael Martínez-Tomás
- Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia (UNED), C/Juan del Rosal, 16 Ciudad Universitaria, Madrid 28040, Spain
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3
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Simpson CM, Gnad F. Applying graph database technology for analyzing perturbed co-expression networks in cancer. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2020:6029398. [PMID: 33306799 PMCID: PMC7731929 DOI: 10.1093/database/baaa110] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 09/20/2020] [Accepted: 11/30/2020] [Indexed: 11/13/2022]
Abstract
Graph representations provide an elegant solution to capture and analyze complex molecular mechanisms in the cell. Co-expression networks are undirected graph representations of transcriptional co-behavior indicating (co-)regulations, functional modules or even physical interactions between the corresponding gene products. The growing avalanche of available RNA sequencing (RNAseq) data fuels the construction of such networks, which are usually stored in relational databases like most other biological data. Inferring linkage by recursive multiple-join statements, however, is computationally expensive and complex to design in relational databases. In contrast, graph databases store and represent complex interconnected data as nodes, edges and properties, making it fast and intuitive to query and analyze relationships. While graph-based database technologies are on their way from a fringe domain to going mainstream, there are only a few studies reporting their application to biological data. We used the graph database management system Neo4j to store and analyze co-expression networks derived from RNAseq data from The Cancer Genome Atlas. Comparing co-expression in tumors versus healthy tissues in six cancer types revealed significant perturbation tracing back to erroneous or rewired gene regulation. Applying centrality, community detection and pathfinding graph algorithms uncovered the destruction or creation of central nodes, modules and relationships in co-expression networks of tumors. Given the speed, accuracy and straightforwardness of managing these densely connected networks, we conclude that graph databases are ready for entering the arena of biological data.
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Affiliation(s)
- Claire M Simpson
- Department of Bioinformatics and Data Science, Cell Signaling Technology Inc., 3 Trask Lane, Danvers, MA 01923, USA
| | - Florian Gnad
- Department of Bioinformatics and Data Science, Cell Signaling Technology Inc., 3 Trask Lane, Danvers, MA 01923, USA
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Thapa I, Ali H. A Multiomics Graph Database System for Biological Data Integration and Cancer Informatics. J Comput Biol 2020; 28:209-219. [PMID: 32783648 DOI: 10.1089/cmb.2020.0231] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
The multiomics data are heterogeneous and come from different biological levels such as epigenetics, genomics, transcriptomics and proteomics. The development of high-throughput technologies has enabled researchers not only to study all the entities together but also to utilize information from different levels spanning DNA methylation, copy number variation (CNV), mutation, gene expression, and miRNA expression. With the recent advancement in image informatics, the field of radiomics is rapidly emerging. It can be expected that the information from microscopic images of the tissue will soon be part of many multiomics studies. Meanwhile, integration of different kinds of multiomics data to extract relevant biological information is currently a big challenge. This study is our ongoing effort to develop a model that properly integrates multiomics data and allows easy retrieval of information relevant to biological processes. In this article, we have enriched our previous graph database model to store gene expression, miRNA expression, DNA methylation, mutation, CNV, clinical data, including information of the image of tissue slides. To show that the model is working, we used data from the Cancer Genome Atlas for three cancer types.
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Affiliation(s)
- Ishwor Thapa
- College of Information Science and Technology, University of Nebraska at Omaha, Omaha, Nebraska, USA
| | - Hesham Ali
- College of Information Science and Technology, University of Nebraska at Omaha, Omaha, Nebraska, USA
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Satti FA, Ali T, Hussain J, Khan WA, Khattak AM, Lee S. Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability. COMPUTING 2020; 102. [PMCID: PMC7437110 DOI: 10.1007/s00607-020-00837-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
The lack of Interoperable healthcare data presents a major challenge, towards achieving ubiquitous health care. The plethora of diverse medical standards, rather than common standards, is widening the gap of interoperability. While many organizations are working towards a standardized solution, there is a need for an alternate strategy, which can intelligently mediate amongst a variety of medical systems, not complying with any mainstream healthcare standards while utilizing the benefits of several standard merging initiates, to eventually create digital health personas. The existence and efficiency of such a platform is dependent upon the underlying storage and processing engine, which can acquire, manage and retrieve the relevant medical data. In this paper, we present the Ubiquitous Health Profile (UHPr), a multi-dimensional data storage solution in a semi-structured data curation engine, which provides foundational support for archiving heterogeneous medical data and achieving partial data interoperability in the healthcare domain. Additionally, we present the evaluation results of this proposed platform in terms of its timeliness, accuracy, and scalability. Our results indicate that the UHPr is able to retrieve an error free comprehensive medical profile of a single patient, from a set of slightly over 116.5 million serialized medical fragments for 390,101 patients while maintaining a good scalablity ratio between amount of data and its retrieval speed.
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Affiliation(s)
- Fahad Ahmed Satti
- Ubiquitous Computing Lab, Department of Computer Engineering, Kyung Hee University, Global Campus, Yongin, South Korea
| | - Taqdir Ali
- Division of ICT, College of Science and Engineering, Hamad Bin Khalifa University (HBKU), Education City, Doha, Qatar
| | - Jamil Hussain
- Ubiquitous Computing Lab, Department of Computer Engineering, Kyung Hee University, Global Campus, Yongin, South Korea
| | - Wajahat Ali Khan
- College of Engineering and Technology, University of Derby, Markeaton Street, Derby, DE223AW UK
| | | | - Sungyoung Lee
- Ubiquitous Computing Lab, Department of Computer Engineering, Kyung Hee University, Global Campus, Yongin, South Korea
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Costa RL, Gadelha L, Ribeiro-Alves M, Porto F. GeNNet: an integrated platform for unifying scientific workflows and graph databases for transcriptome data analysis. PeerJ 2017; 5:e3509. [PMID: 28695067 PMCID: PMC5501156 DOI: 10.7717/peerj.3509] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2017] [Accepted: 06/06/2017] [Indexed: 12/28/2022] Open
Abstract
There are many steps in analyzing transcriptome data, from the acquisition of raw data to the selection of a subset of representative genes that explain a scientific hypothesis. The data produced can be represented as networks of interactions among genes and these may additionally be integrated with other biological databases, such as Protein-Protein Interactions, transcription factors and gene annotation. However, the results of these analyses remain fragmented, imposing difficulties, either for posterior inspection of results, or for meta-analysis by the incorporation of new related data. Integrating databases and tools into scientific workflows, orchestrating their execution, and managing the resulting data and its respective metadata are challenging tasks. Additionally, a great amount of effort is equally required to run in-silico experiments to structure and compose the information as needed for analysis. Different programs may need to be applied and different files are produced during the experiment cycle. In this context, the availability of a platform supporting experiment execution is paramount. We present GeNNet, an integrated transcriptome analysis platform that unifies scientific workflows with graph databases for selecting relevant genes according to the evaluated biological systems. It includes GeNNet-Wf, a scientific workflow that pre-loads biological data, pre-processes raw microarray data and conducts a series of analyses including normalization, differential expression inference, clusterization and gene set enrichment analysis. A user-friendly web interface, GeNNet-Web, allows for setting parameters, executing, and visualizing the results of GeNNet-Wf executions. To demonstrate the features of GeNNet, we performed case studies with data retrieved from GEO, particularly using a single-factor experiment in different analysis scenarios. As a result, we obtained differentially expressed genes for which biological functions were analyzed. The results are integrated into GeNNet-DB, a database about genes, clusters, experiments and their properties and relationships. The resulting graph database is explored with queries that demonstrate the expressiveness of this data model for reasoning about gene interaction networks. GeNNet is the first platform to integrate the analytical process of transcriptome data with graph databases. It provides a comprehensive set of tools that would otherwise be challenging for non-expert users to install and use. Developers can add new functionality to components of GeNNet. The derived data allows for testing previous hypotheses about an experiment and exploring new ones through the interactive graph database environment. It enables the analysis of different data on humans, rhesus, mice and rat coming from Affymetrix platforms. GeNNet is available as an open source platform at https://github.com/raquele/GeNNet and can be retrieved as a software container with the command docker pull quelopes/gennet.
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Affiliation(s)
- Raquel L. Costa
- DEXL Lab, National Laboratory for Scientific Computing (LNCC), Petrópolis, Rio de Janeiro, Brazil
- National Institute of Cancer (INCA), Rio de Janeiro, RJ, Brazil
| | - Luiz Gadelha
- DEXL Lab, National Laboratory for Scientific Computing (LNCC), Petrópolis, Rio de Janeiro, Brazil
| | - Marcelo Ribeiro-Alves
- Laboratory of Clinical Research in DST- AIDS, National Institute of Infectology Evandro Chagas, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Fábio Porto
- DEXL Lab, National Laboratory for Scientific Computing (LNCC), Petrópolis, Rio de Janeiro, Brazil
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Yoon BH, Kim SK, Kim SY. Use of Graph Database for the Integration of Heterogeneous Biological Data. Genomics Inform 2017; 15:19-27. [PMID: 28416946 PMCID: PMC5389944 DOI: 10.5808/gi.2017.15.1.19] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Revised: 02/02/2017] [Accepted: 02/02/2017] [Indexed: 12/15/2022] Open
Abstract
Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data.
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Affiliation(s)
- Byoung-Ha Yoon
- Personalized Genomic Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Korea.,Department of Functional Genomics, University of Science and Technology (UST), Daejeon 34113, Korea
| | - Seon-Kyu Kim
- Personalized Genomic Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Korea
| | - Seon-Young Kim
- Personalized Genomic Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Korea.,Department of Functional Genomics, University of Science and Technology (UST), Daejeon 34113, Korea
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Lin Y, Chen J, Shen B. Interactions Between Genetics, Lifestyle, and Environmental Factors for Healthcare. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 1005:167-191. [PMID: 28916933 DOI: 10.1007/978-981-10-5717-5_8] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The occurrence and progression of diseases are strongly associated with a combination of genetic, lifestyle, and environmental factors. Understanding the interplay between genetic and nongenetic components provides deep insights into disease pathogenesis and promotes personalized strategies for people healthcare. Recently, the paradigm of systems medicine, which integrates biomedical data and knowledge at multidimensional levels, is considered to be an optimal way for disease management and clinical decision-making in the era of precision medicine. In this chapter, epigenetic-mediated genetics-lifestyle-environment interactions within specific diseases and different ethnic groups are systematically discussed, and data sources, computational models, and translational platforms for systems medicine research are sequentially presented. Moreover, feasible suggestions on precision healthcare and healthy longevity are kindly proposed based on the comprehensive review of current studies.
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Affiliation(s)
- Yuxin Lin
- Center for Systems Biology, Soochow University, No.1 Shizi Street, Suzhou, Jiangsu, 215006, China
| | - Jiajia Chen
- School of Chemistry, Biology and Materials Engineering, Suzhou University of Science and Technology, No.1 Kerui road, Suzhou, Jiangsu, 215011, China
| | - Bairong Shen
- Center for Systems Biology, Soochow University, No.1 Shizi Street, Suzhou, Jiangsu, 215006, China. .,Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, 215163, China. .,Medical College of Guizhou University, Guiyang, 550025, China.
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