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Chiliński M, Sengupta K, Plewczynski D. From DNA human sequence to the chromatin higher order organisation and its biological meaning: Using biomolecular interaction networks to understand the influence of structural variation on spatial genome organisation and its functional effect. Semin Cell Dev Biol 2021; 121:171-185. [PMID: 34429265 DOI: 10.1016/j.semcdb.2021.08.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 08/06/2021] [Accepted: 08/12/2021] [Indexed: 12/30/2022]
Abstract
The three-dimensional structure of the human genome has been proven to have a significant functional impact on gene expression. The high-order spatial chromatin is organised first by looping mediated by multiple protein factors, and then it is further formed into larger structures of topologically associated domains (TADs) or chromatin contact domains (CCDs), followed by A/B compartments and finally the chromosomal territories (CTs). The genetic variation observed in human population influences the multi-scale structures, posing a question regarding the functional impact of structural variants reflected by the variability of the genes expression patterns. The current methods of evaluating the functional effect include eQTLs analysis which uses statistical testing of influence of variants on spatially close genes. Rarely, non-coding DNA sequence changes are evaluated by their impact on the biomolecular interaction network (BIN) reflecting the cellular interactome that can be analysed by the classical graph-theoretic algorithms. Therefore, in the second part of the review, we introduce the concept of BIN, i.e. a meta-network model of the complete molecular interactome developed by integrating various biological networks. The BIN meta-network model includes DNA-protein binding by the plethora of protein factors as well as chromatin interactions, therefore allowing connection of genomics with the downstream biomolecular processes present in a cell. As an illustration, we scrutinise the chromatin interactions mediated by the CTCF protein detected in a ChIA-PET experiment in the human lymphoblastoid cell line GM12878. In the corresponding BIN meta-network the DNA spatial proximity is represented as a graph model, combined with the Proteins-Interaction Network (PIN) of human proteome using the Gene Association Network (GAN). Furthermore, we enriched the BIN with the signalling and metabolic pathways and Gene Ontology (GO) terms to assert its functional context. Finally, we mapped the Single Nucleotide Polymorphisms (SNPs) from the GWAS studies and identified the chromatin mutational hot-spots associated with a significant enrichment of SNPs related to autoimmune diseases. Afterwards, we mapped Structural Variants (SVs) from healthy individuals of 1000 Genomes Project and identified an interesting example of the missing protein complex associated with protein Q6GYQ0 due to a deletion on chromosome 14. Such an analysis using the meta-network BIN model is therefore helpful in evaluating the influence of genetic variation on spatial organisation of the genome and its functional effect in a cell.
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Affiliation(s)
- Mateusz Chiliński
- Laboratory of Bioinformatics and Computational Genomics, Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland; Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland
| | - Kaustav Sengupta
- Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland
| | - Dariusz Plewczynski
- Laboratory of Bioinformatics and Computational Genomics, Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland; Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland.
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2
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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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Bolduc B, Hodgkins SB, Varner RK, Crill PM, McCalley CK, Chanton JP, Tyson GW, Riley WJ, Palace M, Duhaime MB, Hough MA, Saleska SR, Sullivan MB, Rich VI. The IsoGenie database: an interdisciplinary data management solution for ecosystems biology and environmental research. PeerJ 2020. [DOI: 10.7717/peerj.9467] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Modern microbial and ecosystem sciences require diverse interdisciplinary teams that are often challenged in “speaking” to one another due to different languages and data product types. Here we introduce the IsoGenie Database (IsoGenieDB; https://isogenie-db.asc.ohio-state.edu/), a de novo developed data management and exploration platform, as a solution to this challenge of accurately representing and integrating heterogenous environmental and microbial data across ecosystem scales. The IsoGenieDB is a public and private data infrastructure designed to store and query data generated by the IsoGenie Project, a ~10 year DOE-funded project focused on discovering ecosystem climate feedbacks in a thawing permafrost landscape. The IsoGenieDB provides (i) a platform for IsoGenie Project members to explore the project’s interdisciplinary datasets across scales through the inherent relationships among data entities, (ii) a framework to consolidate and harmonize the datasets needed by the team’s modelers, and (iii) a public venue that leverages the same spatially explicit, disciplinarily integrated data structure to share published datasets. The IsoGenieDB is also being expanded to cover the NASA-funded Archaea to Atmosphere (A2A) project, which scales the findings of IsoGenie to a broader suite of Arctic peatlands, via the umbrella A2A Database (A2A-DB). The IsoGenieDB’s expandability and flexible architecture allow it to serve as an example ecosystems database.
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Affiliation(s)
- Benjamin Bolduc
- Department of Microbiology, The Ohio State University, Columbus, OH, USA
| | | | - Ruth K. Varner
- Earth Systems Research Center, Institute for the Study of Earth, Oceans and Space, University of New Hampshire, Durham, NH, USA
- Department of Earth Sciences, College of Engineering and Physical Sciences, University of New Hampshire, Durham, NH, USA
| | - Patrick M. Crill
- Department of Geological Sciences and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
| | - Carmody K. McCalley
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY, USA
| | - Jeffrey P. Chanton
- Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, FL, USA
| | - Gene W. Tyson
- Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia
| | - William J. Riley
- Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Michael Palace
- Earth Systems Research Center, Institute for the Study of Earth, Oceans and Space, University of New Hampshire, Durham, NH, USA
- Department of Earth Sciences, College of Engineering and Physical Sciences, University of New Hampshire, Durham, NH, USA
| | - Melissa B. Duhaime
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA
| | - Moira A. Hough
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
| | - Scott R. Saleska
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
| | - Matthew B. Sullivan
- Department of Microbiology, The Ohio State University, Columbus, OH, USA
- Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH, USA
| | - Virginia I. Rich
- Department of Microbiology, The Ohio State University, Columbus, OH, USA
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Wercelens P, da Silva W, Hondo F, Castro K, Walter ME, Araújo A, Lifschitz S, Holanda M. Bioinformatics Workflows With NoSQL Database in Cloud Computing. Evol Bioinform Online 2019; 15:1176934319889974. [PMID: 31839702 PMCID: PMC6896126 DOI: 10.1177/1176934319889974] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Accepted: 10/29/2019] [Indexed: 12/29/2022] Open
Abstract
Scientific workflows can be understood as arrangements of managed activities executed by different processing entities. It is a regular Bioinformatics approach applying workflows to solve problems in Molecular Biology, notably those related to sequence analyses. Due to the nature of the raw data and the in silico environment of Molecular Biology experiments, apart from the research subject, 2 practical and closely related problems have been studied: reproducibility and computational environment. When aiming to enhance the reproducibility of Bioinformatics experiments, various aspects should be considered. The reproducibility requirements comprise the data provenance, which enables the acquisition of knowledge about the trajectory of data over a defined workflow, the settings of the programs, and the entire computational environment. Cloud computing is a booming alternative that can provide this computational environment, hiding technical details, and delivering a more affordable, accessible, and configurable on-demand environment for researchers. Considering this specific scenario, we proposed a solution to improve the reproducibility of Bioinformatics workflows in a cloud computing environment using both Infrastructure as a Service (IaaS) and Not only SQL (NoSQL) database systems. To meet the goal, we have built 3 typical Bioinformatics workflows and ran them on 1 private and 2 public clouds, using different types of NoSQL database systems to persist the provenance data according to the Provenance Data Model (PROV-DM). We present here the results and a guide for the deployment of a cloud environment for Bioinformatics exploring the characteristics of various NoSQL database systems to persist provenance data.
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Affiliation(s)
- Polyane Wercelens
- Department of Computer Science, University of Brasília, Brasília, Brazil
| | - Waldeyr da Silva
- Department of Computer Science, University of Brasília, Brasília, Brazil.,NEPBIO (Group of Biological Studies and Research on Cerrado), Federal Institute of Goiás (IFG), Formosa, Goiás, Brazil
| | - Fernanda Hondo
- Department of Computer Science, University of Brasília, Brasília, Brazil
| | - Klayton Castro
- Department of Computer Science, University of Brasília, Brasília, Brazil
| | | | - Aletéia Araújo
- Department of Computer Science, University of Brasília, Brasília, Brazil
| | - Sergio Lifschitz
- Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Maristela Holanda
- Department of Computer Science, University of Brasília, Brasília, Brazil
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Deffur A, Wilkinson RJ, Mayosi BM, Mulder NM. ANIMA: Association network integration for multiscale analysis. Wellcome Open Res 2018; 3:27. [PMID: 30271886 PMCID: PMC6134339 DOI: 10.12688/wellcomeopenres.14073.3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/30/2018] [Indexed: 11/20/2022] Open
Abstract
Contextual functional interpretation of -omics data derived from clinical samples is a classical and difficult problem in computational systems biology. The measurement of thousands of data points on single samples has become routine but relating ‘big data’ datasets to the complexities of human pathobiology is an area of ongoing research. Complicating this is the fact that many publicly available datasets use bulk transcriptomics data from complex tissues like blood. The most prevalent analytic approaches derive molecular ‘signatures’ of disease states or apply modular analysis frameworks to the data. Here we describe ANIMA (association network integration for multiscale analysis), a network-based data integration method using clinical phenotype and microarray data as inputs. ANIMA is implemented in R and Neo4j and runs in Docker containers. In short, the build algorithm iterates over one or more transcriptomics datasets to generate a large, multipartite association network by executing multiple independent analytic steps (differential expression, deconvolution, modular analysis based on co-expression, pathway analysis) and integrating the results. Once the network is built, it can be queried directly using Cypher (a graph query language), or by custom functions that communicate with the graph database via language-specific APIs. We developed a web application using Shiny, which provides fully interactive, multiscale views of the data. Using our approach, we show that we can reconstruct multiple features of disease states at various scales of organization, from transcript abundance patterns of individual genes through co-expression patterns of groups of genes to patterns of cellular behaviour in whole blood samples, both in single experiments as well in meta-analyses of multiple datasets.
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Affiliation(s)
- Armin Deffur
- Department of Medicine, University of Cape Town, Cape Town, 7925, South Africa
| | - Robert J Wilkinson
- Department of Medicine, University of Cape Town, Cape Town, 7925, South Africa.,Wellcome Centre for Infectious Diseases Research in Africa, University of Cape Town, Cape Town, 7925, South Africa.,Francis Crick Institute, London, NW1 1AT, UK.,Imperial College London, London, W2 1PG, UK
| | - Bongani M Mayosi
- Department of Medicine, University of Cape Town, Cape Town, 7925, South Africa
| | - Nicola M Mulder
- Computational Biology Division, Department Integrative Biomedical Sciences, IDM, University of Cape Town, Cape Town, 7925, South Africa
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Adetunji MO, Lamont SJ, Schmidt CJ. TransAtlasDB: an integrated database connecting expression data, metadata and variants. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2018; 2018:4904553. [PMID: 29688361 PMCID: PMC5824778 DOI: 10.1093/database/bay014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Accepted: 01/19/2018] [Indexed: 12/21/2022]
Abstract
High-throughput transcriptome sequencing (RNAseq) is the universally applied method for target-free transcript identification and gene expression quantification, generating huge amounts of data. The constraint of accessing such data and interpreting results can be a major impediment in postulating suitable hypothesis, thus an innovative storage solution that addresses these limitations, such as hard disk storage requirements, efficiency and reproducibility are paramount. By offering a uniform data storage and retrieval mechanism, various data can be compared and easily investigated. We present a sophisticated system, TransAtlasDB, which incorporates a hybrid architecture of both relational and NoSQL databases for fast and efficient data storage, processing and querying of large datasets from transcript expression analysis with corresponding metadata, as well as gene-associated variants (such as SNPs) and their predicted gene effects. TransAtlasDB provides the data model of accurate storage of the large amount of data derived from RNAseq analysis and also methods of interacting with the database, either via the command-line data management workflows, written in Perl, with useful functionalities that simplifies the complexity of data storage and possibly manipulation of the massive amounts of data generated from RNAseq analysis or through the web interface. The database application is currently modeled to handle analyses data from agricultural species, and will be expanded to include more species groups. Overall TransAtlasDB aims to serve as an accessible repository for the large complex results data files derived from RNAseq gene expression profiling and variant analysis. Database URL: https://modupeore.github.io/TransAtlasDB/
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Affiliation(s)
- Modupeore O Adetunji
- Department of Animal and Food Sciences, University of Delaware, Newark, DE 19716, USA
| | - Susan J Lamont
- Department of Animal Science, Iowa State University, Ames, IA 50011-3150, USA
| | - Carl J Schmidt
- Department of Animal and Food Sciences, University of Delaware, Newark, DE 19716, USA
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7
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Deffur A, Wilkinson RJ, Mayosi BM, Mulder NM. ANIMA: Association network integration for multiscale analysis. Wellcome Open Res 2018; 3:27. [DOI: 10.12688/wellcomeopenres.14073.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/02/2018] [Indexed: 11/20/2022] Open
Abstract
Contextual functional interpretation of -omics data derived from clinical samples is a classical and difficult problem in computational systems biology. The measurement of thousands of data points on single samples has become routine but relating ‘big data’ datasets to the complexities of human pathobiology is an area of ongoing research. Complicating this is the fact that many publicly available datasets use bulk transcriptomics data from complex tissues like blood. The most prevalent analytic approaches derive molecular ‘signatures’ of disease states or apply modular analysis frameworks to the data. Here we describe ANIMA (association network integration for multiscale analysis), a network-based data integration method using clinical phenotype and microarray data as inputs. ANIMA is implemented in R and Neo4j and runs in Docker containers. In short, the build algorithm iterates over one or more transcriptomics datasets to generate a large, multipartite association network by executing multiple independent analytic steps (differential expression, deconvolution, modular analysis based on co-expression, pathway analysis) and integrating the results. Once the network is built, it can be queried directly using Cypher (a graph query language), or by custom functions that communicate with the graph database via language-specific APIs. We developed a web application using Shiny, which provides fully interactive, multiscale views of the data. Using our approach, we show that we can reconstruct multiple features of disease states at various scales of organization, from transcript abundance patterns of individual genes through co-expression patterns of groups of genes to patterns of cellular behaviour in whole blood samples, both in single experiments as well in meta-analyses of multiple datasets.
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