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Touré V, Unni D, Krauss P, Abdelwahed A, Buchhorn J, Hinderling L, Geiger TR, Österle S. The SPHN Schema Forge - transform healthcare semantics from human-readable to machine-readable by leveraging semantic web technologies. J Biomed Semantics 2025; 16:9. [PMID: 40341005 PMCID: PMC12063216 DOI: 10.1186/s13326-025-00330-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2025] [Accepted: 04/30/2025] [Indexed: 05/10/2025] Open
Abstract
BACKGROUND The Swiss Personalized Health Network (SPHN) adopted the Resource Description Framework (RDF), a core component of the Semantic Web technology stack, for the formal encoding and exchange of healthcare data in a medical knowledge graph. The SPHN RDF Schema defines the semantics on how data elements should be represented. While RDF is proven to be machine readable and interpretable, it can be challenging for individuals without specialized background to read and understand the knowledge represented in RDF. For this reason, the semantics described in the SPHN RDF Schema are primarily defined in a user-accessible tabular format, the SPHN Dataset, before being translated into its RDF representation. However, this translation process was previously manual, time-consuming and labor-intensive. RESULT To automate and streamline the translation from tabular to RDF representation, the SPHN Schema Forge web service was developed. With a few clicks, this tool automatically converts an SPHN-compliant Dataset spreadsheet into an RDF schema. Additionally, it generates SHACL rules for data validation, an HTML visualization of the schema and SPARQL queries for basic data analysis. CONCLUSION The SPHN Schema Forge significantly reduces the manual effort and time required for schema generation, enabling researchers to focus on more meaningful tasks such as data interpretation and analysis within the SPHN framework.
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Affiliation(s)
- Vasundra Touré
- Personalized Health Informatics, SIB Swiss Institute of Bioinformatics, Basel, 4051, Switzerland.
| | - Deepak Unni
- Personalized Health Informatics, SIB Swiss Institute of Bioinformatics, Basel, 4051, Switzerland
| | | | | | | | | | | | - Sabine Österle
- Personalized Health Informatics, SIB Swiss Institute of Bioinformatics, Basel, 4051, Switzerland
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2
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Orchard SE. What have Data Standards ever done for us? Mol Cell Proteomics 2025:100933. [PMID: 40024375 DOI: 10.1016/j.mcpro.2025.100933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2024] [Revised: 02/21/2025] [Accepted: 02/24/2025] [Indexed: 03/04/2025] Open
Abstract
The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has been successfully developing guidelines, data formats, and controlled vocabularies for both the field of molecular interaction and that of mass spectrometry for more than 20 years. This review explores some of the ways that the proteomics community has benefitted from the development of community standards and takes a look at some of the tools and resources that have been improved or developed as a result of the work of the HUPO-PSI.
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Affiliation(s)
- S E Orchard
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
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3
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Hoyt CT, Gyori BM. The O3 guidelines: open data, open code, and open infrastructure for sustainable curated scientific resources. Sci Data 2024; 11:547. [PMID: 38811583 PMCID: PMC11136952 DOI: 10.1038/s41597-024-03406-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 05/22/2024] [Indexed: 05/31/2024] Open
Affiliation(s)
- Charles Tapley Hoyt
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Benjamin M Gyori
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA.
- Department of Bioengineering, College of Engineering, Northeastern University, Boston, MA, USA.
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4
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Attrill H, Antonazzo G, Goodman JL, Thurmond J, Strelets VB, Brown NH, the FlyBase Consortium. A new experimental evidence-weighted signaling pathway resource in FlyBase. Development 2024; 151:dev202255. [PMID: 38230566 PMCID: PMC10911275 DOI: 10.1242/dev.202255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 01/09/2024] [Indexed: 01/18/2024]
Abstract
Research in model organisms is central to the characterization of signaling pathways in multicellular organisms. Here, we present the comprehensive and systematic curation of 17 Drosophila signaling pathways using the Gene Ontology framework to establish a dynamic resource that has been incorporated into FlyBase, providing visualization and data integration tools to aid research projects. By restricting to experimental evidence reported in the research literature and quantifying the amount of such evidence for each gene in a pathway, we captured the landscape of empirical knowledge of signaling pathways in Drosophila.
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Affiliation(s)
- Helen Attrill
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK
| | - Giulia Antonazzo
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK
| | - Joshua L. Goodman
- Department of Biology, Indiana University, Bloomington, IN 47405, USA
| | - Jim Thurmond
- Department of Biology, Indiana University, Bloomington, IN 47405, USA
| | | | - Nicholas H. Brown
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK
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5
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Csabai L, Bohár B, Türei D, Prabhu S, Földvári-Nagy L, Madgwick M, Fazekas D, Módos D, Ölbei M, Halka T, Poletti M, Kornilova P, Kadlecsik T, Demeter A, Szalay-Bekő M, Kapuy O, Lenti K, Vellai T, Gul L, Korcsmáros T. AutophagyNet: high-resolution data source for the analysis of autophagy and its regulation. Autophagy 2024; 20:188-201. [PMID: 37589496 PMCID: PMC10761021 DOI: 10.1080/15548627.2023.2247737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 07/31/2023] [Accepted: 08/06/2023] [Indexed: 08/18/2023] Open
Abstract
Macroautophagy/autophagy is a highly-conserved catabolic procss eliminating dysfunctional cellular components and invading pathogens. Autophagy malfunction contributes to disorders such as cancer, neurodegenerative and inflammatory diseases. Understanding autophagy regulation in health and disease has been the focus of the last decades. We previously provided an integrated database for autophagy research, the Autophagy Regulatory Network (ARN). For the last eight years, this resource has been used by thousands of users. Here, we present a new and upgraded resource, AutophagyNet. It builds on the previous database but contains major improvements to address user feedback and novel needs due to the advancement in omics data availability. AutophagyNet contains updated interaction curation and integration of over 280,000 experimentally verified interactions between core autophagy proteins and their protein, transcriptional and post-transcriptional regulators as well as their potential upstream pathway connections. AutophagyNet provides annotations for each core protein about their role: 1) in different types of autophagy (mitophagy, xenophagy, etc.); 2) in distinct stages of autophagy (initiation, expansion, termination, etc.); 3) with subcellular and tissue-specific localization. These annotations can be used to filter the dataset, providing customizable download options tailored to the user's needs. The resource is available in various file formats (e.g. CSV, BioPAX and PSI-MI), and data can be analyzed and visualized directly in Cytoscape. The multi-layered regulation of autophagy can be analyzed by combining AutophagyNet with tissue- or cell type-specific (multi-)omics datasets (e.g. transcriptomic or proteomic data). The resource is publicly accessible at http://autophagynet.org.Abbreviations: ARN: Autophagy Regulatory Network; ATG: autophagy related; BCR: B cell receptor pathway; BECN1: beclin 1; GABARAP: GABA type A receptor-associated protein; IIP: innate immune pathway; LIR: LC3-interacting region; lncRNA: long non-coding RNA; MAP1LC3B: microtubule associated protein 1 light chain 3 beta; miRNA: microRNA; NHR: nuclear hormone receptor; PTM: post-translational modification; RTK: receptor tyrosine kinase; TCR: T cell receptor; TLR: toll like receptor.
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Affiliation(s)
- Luca Csabai
- Earlham Institute, Norwich, UK
- Department of Genetics, ELTE Eötvös Loránd University, Budapest, Hungary
| | - Balázs Bohár
- Earlham Institute, Norwich, UK
- Department of Genetics, ELTE Eötvös Loránd University, Budapest, Hungary
| | - Dénes Türei
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany
| | | | - László Földvári-Nagy
- Department of Morphology and Physiology, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary
| | - Matthew Madgwick
- Earlham Institute, Norwich, UK
- Quadram Institute, Norwich Research Park, Norwich, UK
| | - Dávid Fazekas
- Earlham Institute, Norwich, UK
- Department of Genetics, ELTE Eötvös Loránd University, Budapest, Hungary
| | - Dezső Módos
- Earlham Institute, Norwich, UK
- Quadram Institute, Norwich Research Park, Norwich, UK
| | - Márton Ölbei
- Earlham Institute, Norwich, UK
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Themis Halka
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Martina Poletti
- Earlham Institute, Norwich, UK
- Quadram Institute, Norwich Research Park, Norwich, UK
| | | | - Tamás Kadlecsik
- Department of Genetics, ELTE Eötvös Loránd University, Budapest, Hungary
| | | | | | - Orsolya Kapuy
- Department of Molecular Biology, Semmelweis University, Budapest, Hungary
| | - Katalin Lenti
- Department of Morphology and Physiology, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary
| | - Tibor Vellai
- Department of Genetics, ELTE Eötvös Loránd University, Budapest, Hungary
- ELKH/MTA-ELTE Genetics Research Group, Budapest, Hungary
| | - Lejla Gul
- Earlham Institute, Norwich, UK
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Tamás Korcsmáros
- Earlham Institute, Norwich, UK
- Department of Genetics, ELTE Eötvös Loránd University, Budapest, Hungary
- Quadram Institute, Norwich Research Park, Norwich, UK
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
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6
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Li H, Ma T, Hao M, Guo W, Gu J, Zhang X, Wei L. Decoding functional cell-cell communication events by multi-view graph learning on spatial transcriptomics. Brief Bioinform 2023; 24:bbad359. [PMID: 37824741 DOI: 10.1093/bib/bbad359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/25/2023] [Accepted: 09/18/2023] [Indexed: 10/14/2023] Open
Abstract
Cell-cell communication events (CEs) are mediated by multiple ligand-receptor (LR) pairs. Usually only a particular subset of CEs directly works for a specific downstream response in a particular microenvironment. We name them as functional communication events (FCEs) of the target responses. Decoding FCE-target gene relations is: important for understanding the mechanisms of many biological processes, but has been intractable due to the mixing of multiple factors and the lack of direct observations. We developed a method HoloNet for decoding FCEs using spatial transcriptomic data by integrating LR pairs, cell-type spatial distribution and downstream gene expression into a deep learning model. We modeled CEs as a multi-view network, developed an attention-based graph learning method to train the model for generating target gene expression with the CE networks, and decoded the FCEs for specific downstream genes by interpreting trained models. We applied HoloNet on three Visium datasets of breast cancer and liver cancer. The results detangled the multiple factors of FCEs by revealing how LR signals and cell types affect specific biological processes, and specified FCE-induced effects in each single cell. We conducted simulation experiments and showed that HoloNet is more reliable on LR prioritization in comparison with existing methods. HoloNet is a powerful tool to illustrate cell-cell communication landscapes and reveal vital FCEs that shape cellular phenotypes. HoloNet is available as a Python package at https://github.com/lhc17/HoloNet.
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Affiliation(s)
- Haochen Li
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Tianxing Ma
- MOE Key Lab of Bioinformatics, Bioinformatics Division of BNRIST and Department of Automation, Tsinghua University, Beijing 100084, China
| | - Minsheng Hao
- MOE Key Lab of Bioinformatics, Bioinformatics Division of BNRIST and Department of Automation, Tsinghua University, Beijing 100084, China
| | - Wenbo Guo
- MOE Key Lab of Bioinformatics, Bioinformatics Division of BNRIST and Department of Automation, Tsinghua University, Beijing 100084, China
| | - Jin Gu
- MOE Key Lab of Bioinformatics, Bioinformatics Division of BNRIST and Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xuegong Zhang
- School of Medicine, Tsinghua University, Beijing 100084, China
- MOE Key Lab of Bioinformatics, Bioinformatics Division of BNRIST and Department of Automation, Tsinghua University, Beijing 100084, China
| | - Lei Wei
- MOE Key Lab of Bioinformatics, Bioinformatics Division of BNRIST and Department of Automation, Tsinghua University, Beijing 100084, China
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7
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Attrill H, Antonazzo G, Goodman JL, Thurmond J, Strelets VB, Brown NH. A new experimental evidence-weighted signaling pathway resource in FlyBase. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.10.552786. [PMID: 37645956 PMCID: PMC10461922 DOI: 10.1101/2023.08.10.552786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Research in model organisms is central to the characterization of signaling pathways in multicellular organisms. Here, we present the systematic curation of 17 Drosophila signaling pathways using the Gene Ontology framework to establish a comprehensive and dynamic resource that has been incorporated into FlyBase, providing visualization and data integration tools to aid research projects. By restricting to experimental evidence reported in the research literature and quantifying the amount of such evidence for each gene in a pathway, we captured the landscape of empirical knowledge of signaling pathways in Drosophila . Summary statement Comprehensive curation of Drosophila signaling pathways and new visual displays of the pathways provides a new FlyBase resource for researchers, and new insights into signaling pathway architecture.
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8
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Giuseppe A, Chiara P, Yun N, Igor J. Pathway integration and annotation: building a puzzle with non-matching pieces and no reference picture. Brief Bioinform 2022; 23:6691914. [PMID: 36063560 DOI: 10.1093/bib/bbac368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 07/25/2022] [Accepted: 08/05/2022] [Indexed: 11/13/2022] Open
Abstract
Biological pathways are a broadly used formalism for representing and interpreting the cascade of biochemical reactions underlying cellular and biological mechanisms. Pathway representation provides an ontological link among biomolecules such as RNA, DNA, small molecules, proteins, protein complexes, hormones and genes. Frequently, pathway annotations are used to identify mechanisms linked to genes within affected biological contexts. This important role and the simplicity and elegance in representing complex interactions led to an explosion of pathway representations and databases. Unfortunately, the lack of overlap across databases results in inconsistent enrichment analysis results, unless databases are integrated. However, due to absence of consensus, guidelines or gold standards in pathway definition and representation, integration of data across pathway databases is not straightforward. Despite multiple attempts to provide consolidated pathways, highly related, redundant, poorly overlapping or ambiguous pathways continue to render pathways analysis inconsistent and hard to interpret. Ontology-based integration will promote unbiased, comprehensive yet streamlined analysis of experiments, and will reduce the number of enriched pathways when performing pathway enrichment analysis. Moreover, appropriate and consolidated pathways provide better training data for pathway prediction algorithms. In this manuscript, we describe the current methods for pathway consolidation, their strengths and pitfalls, and highlight directions for future improvements to this research area.
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Affiliation(s)
- Agapito Giuseppe
- Department of Law, Economics and Social Sciences, University Magna Græcia of Catanzaro, Italy.,Data Analytic Research Center, University Magna Græcia of Catanzaro, Italy
| | - Pastrello Chiara
- Osteoarthritis Research Program, Division of Orthopaedics, Schroeder Arthritis Institute, University Health Network, Toronto, Canada
| | - Niu Yun
- Osteoarthritis Research Program, Division of Orthopaedics, Schroeder Arthritis Institute, University Health Network, Toronto, Canada
| | - Jurisica Igor
- Osteoarthritis Research Program, Division of Orthopaedics, Schroeder Arthritis Institute, University Health Network, Toronto, Canada.,Departments of Medical Biophysics and Computer Science Canada, University of Toronto, Toronto, Canada.,Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, Toronto, Canada.,Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia
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9
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Multilayered Networks of SalmoNet2 Enable Strain Comparisons of the Salmonella Genus on a Molecular Level. mSystems 2022; 7:e0149321. [PMID: 35913188 PMCID: PMC9426430 DOI: 10.1128/msystems.01493-21] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Serovars of the genus Salmonella primarily evolved as gastrointestinal pathogens in a wide range of hosts. Some serotypes later evolved further, adopting a more invasive lifestyle in a narrower host range associated with systemic infections. A system-level knowledge of these pathogens could identify the complex adaptations associated with the evolution of serovars with distinct pathogenicity, host range, and risk to human health. This promises to aid the design of interventions and serve as a knowledge base in the Salmonella research community. Here, we present SalmoNet2, a major update to SalmoNet1, the first multilayered interaction resource for Salmonella strains, containing protein-protein, transcriptional regulatory, and enzyme-enzyme interactions. The new version extends the number of Salmonella networks from 11 to 20. We now include a strain from the second species in the Salmonella genus, a strain from the Salmonella enterica subspecies arizonae and additional strains of importance from the subspecies enterica, including S. Typhimurium strain D23580, an epidemic multidrug-resistant strain associated with invasive nontyphoidal salmonellosis (iNTS). The database now uses strain specific metabolic models instead of a generalized model to highlight differences between strains. The update has increased the coverage of high-quality protein-protein interactions, and enhanced interoperability with other computational resources by adopting standardized formats. The resource website has been updated with tutorials to help researchers analyze their Salmonella data using molecular interaction networks from SalmoNet2. SalmoNet2 is accessible at http://salmonet.org/. IMPORTANCE Multilayered network databases collate interaction information from multiple sources, and are powerful both as a knowledge base and subject of analysis. Here, we present SalmoNet2, an integrated network resource containing protein-protein, transcriptional regulatory, and metabolic interactions for 20 Salmonella strains. Key improvements to the update include expanding the number of strains, strain-specific metabolic networks, an increase in high-quality protein-protein interactions, community standard computational formats to help interoperability, and online tutorials to help users analyze their data using SalmoNet2.
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10
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Milano M, Agapito G, Cannataro M. Challenges and Limitations of Biological Network Analysis. BIOTECH 2022; 11:24. [PMID: 35892929 PMCID: PMC9326688 DOI: 10.3390/biotech11030024] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 07/04/2022] [Accepted: 07/06/2022] [Indexed: 11/17/2022] Open
Abstract
High-Throughput technologies are producing an increasing volume of data that needs large amounts of data storage, effective data models and efficient, possibly parallel analysis algorithms. Pathway and interactomics data are represented as graphs and add a new dimension of analysis, allowing, among other features, graph-based comparison of organisms' properties. For instance, in biological pathway representation, the nodes can represent proteins, RNA and fat molecules, while the edges represent the interaction between molecules. Otherwise, biological networks such as Protein-Protein Interaction (PPI) Networks, represent the biochemical interactions among proteins by using nodes that model the proteins from a given organism, and edges that model the protein-protein interactions, whereas pathway networks enable the representation of biochemical-reaction cascades that happen within the cells or tissues. In this paper, we discuss the main models for standard representation of pathways and PPI networks, the data models for the representation and exchange of pathway and protein interaction data, the main databases in which they are stored and the alignment algorithms for the comparison of pathways and PPI networks of different organisms. Finally, we discuss the challenges and the limitations of pathways and PPI network representation and analysis. We have identified that network alignment presents a lot of open problems worthy of further investigation, especially concerning pathway alignment.
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Affiliation(s)
- Marianna Milano
- Department of Medical and Clinical Surgery, University Magna Græcia, 88100 Catanzaro, Italy; (M.M.); (M.C.)
- Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy
| | - Giuseppe Agapito
- Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy
- Department of Law, Economics and Social Sciences, University Magna Græcia, 88100 Catanzaro, Italy
| | - Mario Cannataro
- Department of Medical and Clinical Surgery, University Magna Græcia, 88100 Catanzaro, Italy; (M.M.); (M.C.)
- Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy
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11
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Kalman ZE, Dudola D, Mészáros B, Gáspári Z, Dobson L. PSINDB: the postsynaptic protein-protein interaction database. Database (Oxford) 2022; 2022:baac007. [PMID: 35234850 PMCID: PMC9216581 DOI: 10.1093/database/baac007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 01/21/2022] [Accepted: 02/07/2022] [Indexed: 06/14/2023]
Abstract
The postsynaptic region is the receiving part of the synapse comprising thousands of proteins forming an elaborate and dynamically changing network indispensable for the molecular mechanisms behind fundamental phenomena such as learning and memory. Despite the growing amount of information about individual protein-protein interactions (PPIs) in this network, these data are mostly scattered in the literature or stored in generic databases that are not designed to display aspects that are fundamental to the understanding of postsynaptic functions. To overcome these limitations, we collected postsynaptic PPIs complemented by a high amount of detailed structural and biological information and launched a freely available resource, the Postsynaptic Interaction Database (PSINDB), to make these data and annotations accessible. PSINDB includes tens of thousands of binding regions together with structural features, mediating and regulating the formation of PPIs, annotated with detailed experimental information about each interaction. PSINDB is expected to be useful for various aspects of molecular neurobiology research, from experimental design to network and systems biology-based modeling and analysis of changes in the protein network upon various stimuli. Database URL https://psindb.itk.ppke.hu/.
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Affiliation(s)
- Zsofia E Kalman
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, Budapest 1083, Hungary
| | - Dániel Dudola
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, Budapest 1083, Hungary
| | - Bálint Mészáros
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstraße 1, Heidelberg 69117, Germany
| | - Zoltán Gáspári
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, Budapest 1083, Hungary
| | - Laszlo Dobson
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstraße 1, Heidelberg 69117, Germany
- Institute of Enzymology, Research Centre for Natural Sciences, Magyar Tudósok Körútja 2, Budapest 1117, Hungary
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12
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Porras P, Orchard S, Licata L. IMEx Databases: Displaying Molecular Interactions into a Single, Standards-Compliant Dataset. Methods Mol Biol 2022; 2449:27-42. [PMID: 35507258 DOI: 10.1007/978-1-0716-2095-3_2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Molecular interaction databases aim to systematically capture and organize the experimental interaction information described in the scientific literature. These data can then be used to perform network analysis, to assign putative roles to uncharacterized proteins and to investigate their involvement in cellular pathways.This chapter gives a brief overview of publicly available molecular interaction databases and focuses on the members of the IMEx Consortium, on their curation policies and standard data formats. All of the goals achieved by IMEx databases over the last 15 years, the data types provided and the many different ways in which such data can be utilized by the research community, are described in detail. The IMEx databases curate molecular interaction data to the highest caliber, following a detailed curation model and supplying rich metadata by employing common curation rules and harmonized standards. The IMEx Consortium provides comprehensively annotated molecular interaction data integrated into a single, non-redundant, open access dataset.
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Affiliation(s)
- Pablo Porras
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK
| | - Sandra Orchard
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK
| | - Luana Licata
- Department of Biology, University of Rome Tor Vergata, Rome, Italy.
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13
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Kuiper M, Bonello J, Fernández-Breis JT, Bucher P, Futschik ME, Gaudet P, Kulakovskiy IV, Licata L, Logie C, Lovering RC, Makeev VJ, Orchard S, Panni S, Perfetto L, Sant D, Schulz S, Zerbino DR, Lægreid A. The Gene Regulation Knowledge Commons: The action area of GREEKC. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2021; 1865:194768. [PMID: 34757206 DOI: 10.1016/j.bbagrm.2021.194768] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 10/18/2021] [Accepted: 10/20/2021] [Indexed: 02/08/2023]
Abstract
The COST Action Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC, CA15205, www.greekc.org) organized nine workshops in a four-year period, starting September 2016. The workshops brought together a wide range of experts from all over the world working on various parts of the knowledge cycle that is central to understanding gene regulatory mechanisms. The discussions between ontologists, curators, text miners, biologists, bioinformaticians, philosophers and computational scientists spawned a host of activities aimed to update and standardise existing knowledge management workflows, encourage new experimental approaches and thoroughly involve end-users in the process to design the Gene Regulation Knowledge Commons (GRKC). The GREEKC consortium describes its main achievements, contextualised in a state-of-the-art of current tools and resources that today represent the GRKC.
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Affiliation(s)
- Martin Kuiper
- Systems Biology Group, Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Joseph Bonello
- Faculty of Information & Communication Technology, University of Malta, Msida, Malta
| | | | - Philipp Bucher
- Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Amphipôle, 1015 Lausanne, Switzerland
| | - Matthias E Futschik
- Systems Biology and Bioinformatics Laboratory (SysBioLab), Centre of Marine Sciences (CCMAR), University of Algarve, 8005-139 Faro, Portugal
| | - Pascale Gaudet
- SIB Swiss Institute of Bioinformatics, 1 Rue Michel-Servet, 1204 Geneva, Switzerland
| | - Ivan V Kulakovskiy
- Institute of Protein Research, Russian Academy of Sciences, Institutskaya 4, 142290 Pushchino, Russia
| | - Luana Licata
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Colin Logie
- Department of Molecular Biology, Faculty of Science, Radboud University, PO Box 9101, Nijmegen 6500HG, the Netherlands
| | - Ruth C Lovering
- Functional Gene Annotation, Pre-clinical and Fundamental Science, Institute of Cardiovascular Science, University College London, 5 University Street, London WC1E 6JF, UK
| | - Vsevolod J Makeev
- Vavilov Institute of General Genetics, Russian Academy of Sciences, Gubkina 3, 119991 Moscow, Russia
| | - Sandra Orchard
- European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Simona Panni
- Department DIBEST, University of Calabria, Rende, Italy
| | - Livia Perfetto
- Fondazione Human Technopole, Department of Biology, Via Cristina Belgioioso, 171, 20157 Milan, Italy
| | - David Sant
- Department of Biomedical Informatics, University of Utah, 421 Wakara Way #140, Salt Lake City, UT 84108, United States
| | - Stefan Schulz
- Institute of Medical Informatics, Statistics and Documentation, Medical University of Graz, Auenbruggerpl. 2, Graz, Austria
| | - Daniel R Zerbino
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Astrid Lægreid
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, 7491 Trondheim, Norway
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14
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Csabai L, Fazekas D, Kadlecsik T, Szalay-Bekő M, Bohár B, Madgwick M, Módos D, Ölbei M, Gul L, Sudhakar P, Kubisch J, Oyeyemi OJ, Liska O, Ari E, Hotzi B, Billes VA, Molnár E, Földvári-Nagy L, Csályi K, Demeter A, Pápai N, Koltai M, Varga M, Lenti K, Farkas IJ, Türei D, Csermely P, Vellai T, Korcsmáros T. SignaLink3: a multi-layered resource to uncover tissue-specific signaling networks. Nucleic Acids Res 2021; 50:D701-D709. [PMID: 34634810 PMCID: PMC8728204 DOI: 10.1093/nar/gkab909] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/16/2021] [Accepted: 09/22/2021] [Indexed: 12/26/2022] Open
Abstract
Signaling networks represent the molecular mechanisms controlling a cell's response to various internal or external stimuli. Most currently available signaling databases contain only a part of the complex network of intertwining pathways, leaving out key interactions or processes. Hence, we have developed SignaLink3 (http://signalink.org/), a value-added knowledge-base that provides manually curated data on signaling pathways and integrated data from several types of databases (interaction, regulation, localisation, disease, etc.) for humans, and three major animal model organisms. SignaLink3 contains over 400 000 newly added human protein-protein interactions resulting in a total of 700 000 interactions for Homo sapiens, making it one of the largest integrated signaling network resources. Next to H. sapiens, SignaLink3 is the only current signaling network resource to provide regulatory information for the model species Caenorhabditis elegans and Danio rerio, and the largest resource for Drosophila melanogaster. Compared to previous versions, we have integrated gene expression data as well as subcellular localization of the interactors, therefore uniquely allowing tissue-, or compartment-specific pathway interaction analysis to create more accurate models. Data is freely available for download in widely used formats, including CSV, PSI-MI TAB or SQL.
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Affiliation(s)
- Luca Csabai
- Earlham Institute, Norwich NR4 7UZ, UK.,Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | - Dávid Fazekas
- Earlham Institute, Norwich NR4 7UZ, UK.,Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | - Tamás Kadlecsik
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | | | - Balázs Bohár
- Earlham Institute, Norwich NR4 7UZ, UK.,Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | - Matthew Madgwick
- Earlham Institute, Norwich NR4 7UZ, UK.,Gut Microbes and Health Programme, Quadram Institute Bioscience, Norwich, NR4 7UQ, UK
| | - Dezső Módos
- Earlham Institute, Norwich NR4 7UZ, UK.,Gut Microbes and Health Programme, Quadram Institute Bioscience, Norwich, NR4 7UQ, UK
| | - Márton Ölbei
- Earlham Institute, Norwich NR4 7UZ, UK.,Gut Microbes and Health Programme, Quadram Institute Bioscience, Norwich, NR4 7UQ, UK
| | - Lejla Gul
- Earlham Institute, Norwich NR4 7UZ, UK
| | - Padhmanand Sudhakar
- Earlham Institute, Norwich NR4 7UZ, UK.,Translational Research in GastroIntestinal Disorders, Leuven BE-3000, Belgium
| | - János Kubisch
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | | | - Orsolya Liska
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary.,HCEMM-BRC Metabolic Systems Biology Lab, Szeged H-6726, Hungary.,Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Eötvös Loránd Research Network (ELKH), Szeged H-6726, Hungary.,Doctoral School in Biology, University of Szeged, Szeged H-6720 Hungary
| | - Eszter Ari
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary.,HCEMM-BRC Metabolic Systems Biology Lab, Szeged H-6726, Hungary.,Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Eötvös Loránd Research Network (ELKH), Szeged H-6726, Hungary
| | - Bernadette Hotzi
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | - Viktor A Billes
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary.,ELKH/MTA-ELTE Genetics Research Group, Budapest H-1117, Hungary
| | - Eszter Molnár
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | - László Földvári-Nagy
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary.,Department of Morphology and Physiology, Semmelweis University, Budapest H-1088, Hungary
| | - Kitti Csályi
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | - Amanda Demeter
- Earlham Institute, Norwich NR4 7UZ, UK.,Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | - Nóra Pápai
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary.,Institute of Molecular Biotechnology, Vienna A-1030, Austria
| | - Mihály Koltai
- Centre for the Mathematical Modelling of Infectious Diseases (CMMID), London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Máté Varga
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | - Katalin Lenti
- Department of Morphology and Physiology, Semmelweis University, Budapest H-1088, Hungary
| | - Illés J Farkas
- Citibank Europe plc Hungarian Branch Office, Budapest H-1133, Hungary
| | - Dénes Türei
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Péter Csermely
- Department of Molecular Biology, Semmelweis University, Budapest H-1094, Hungary
| | - Tibor Vellai
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary.,ELKH/MTA-ELTE Genetics Research Group, Budapest H-1117, Hungary
| | - Tamás Korcsmáros
- Earlham Institute, Norwich NR4 7UZ, UK.,Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary.,Gut Microbes and Health Programme, Quadram Institute Bioscience, Norwich, NR4 7UQ, UK
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15
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Touré V, Flobak Å, Niarakis A, Vercruysse S, Kuiper M. The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling. Brief Bioinform 2021; 22:bbaa390. [PMID: 33378765 PMCID: PMC8294520 DOI: 10.1093/bib/bbaa390] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 11/26/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022] Open
Abstract
Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. Logical regulatory networks can be used to predict biological and cellular behaviors by system perturbations and in silico simulations. Today, broad sets of causal interactions are available in a variety of biological knowledge resources. However, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. It can therefore be challenging to efficiently explore various resources of causal interaction and maintain an overview of recorded contextual information that ensures valid use of the data. This review lists the different types of public resources with causal interactions, the different views on biological processes that they represent, the various data formats they use for data representation and storage, and the data exchange and conversion procedures that are available to extract and download these interactions. This may further raise awareness among the targeted audience, i.e. logical modelers and other scientists interested in molecular causal interactions, but also database managers and curators, about the abundance and variety of causal molecular interaction data, and the variety of tools and approaches to convert them into one interoperable resource.
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Affiliation(s)
- Vasundra Touré
- Department of Biology of the Norwegian University of Science and Technology
| | | | - Anna Niarakis
- Department of Biology, Univ Evry, University of Paris-Saclay, affiliated with the laboratory GenHotel in Genopole campus, and a delegate at the Lifeware Group, INRIA Saclay
| | - Steven Vercruysse
- Researcher in computer science and computational biology and focuses on building a bridge between human and computer understanding
| | - Martin Kuiper
- systems biology at the Department of Biology of the Norwegian University of Science and Technology
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16
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Cesareni G, Sacco F, Perfetto L. Assembling Disease Networks From Causal Interaction Resources. Front Genet 2021; 12:694468. [PMID: 34178043 PMCID: PMC8226215 DOI: 10.3389/fgene.2021.694468] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 05/19/2021] [Indexed: 12/27/2022] Open
Abstract
The development of high-throughput high-content technologies and the increased ease in their application in clinical settings has raised the expectation of an important impact of these technologies on diagnosis and personalized therapy. Patient genomic and expression profiles yield lists of genes that are mutated or whose expression is modulated in specific disease conditions. The challenge remains of extracting from these lists functional information that may help to shed light on the mechanisms that are perturbed in the disease, thus setting a rational framework that may help clinical decisions. Network approaches are playing an increasing role in the organization and interpretation of patients' data. Biological networks are generated by connecting genes or gene products according to experimental evidence that demonstrates their interactions. Till recently most approaches have relied on networks based on physical interactions between proteins. Such networks miss an important piece of information as they lack details on the functional consequences of the interactions. Over the past few years, a number of resources have started collecting causal information of the type protein A activates/inactivates protein B, in a structured format. This information may be represented as signed directed graphs where physiological and pathological signaling can be conveniently inspected. In this review we will (i) present and compare these resources and discuss the different scope in comparison with pathway resources; (ii) compare resources that explicitly capture causality in terms of data content and proteome coverage (iii) review how causal-graphs can be used to extract disease-specific Boolean networks.
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Affiliation(s)
- Gianni Cesareni
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Francesca Sacco
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Livia Perfetto
- Department of Biology, Fondazione Human Technopole, Milan, Italy
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17
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Meldal BHM, Pons C, Perfetto L, Del-Toro N, Wong E, Aloy P, Hermjakob H, Orchard S, Porras P. Analysing the yeast complexome-the Complex Portal rising to the challenge. Nucleic Acids Res 2021; 49:3156-3167. [PMID: 33677561 PMCID: PMC8034636 DOI: 10.1093/nar/gkab077] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 01/22/2021] [Accepted: 01/27/2021] [Indexed: 02/06/2023] Open
Abstract
The EMBL-EBI Complex Portal is a knowledgebase of macromolecular complexes providing persistent stable identifiers. Entries are linked to literature evidence and provide details of complex membership, function, structure and complex-specific Gene Ontology annotations. Data are freely available and downloadable in HUPO-PSI community standards and missing entries can be requested for curation. In collaboration with Saccharomyces Genome Database and UniProt, the yeast complexome, a compendium of all known heteromeric assemblies from the model organism Saccharomyces cerevisiae, was curated. This expansion of knowledge and scope has led to a 50% increase in curated complexes compared to the previously published dataset, CYC2008. The yeast complexome is used as a reference resource for the analysis of complexes from large-scale experiments. Our analysis showed that genes coding for proteins in complexes tend to have more genetic interactions, are co-expressed with more genes, are more multifunctional, localize more often in the nucleus, and are more often involved in nucleic acid-related metabolic processes and processes where large machineries are the predominant functional drivers. A comparison to genetic interactions showed that about 40% of expanded co-complex pairs also have genetic interactions, suggesting strong functional links between complex members.
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Affiliation(s)
- Birgit H M Meldal
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Carles Pons
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, 08028 Barcelona, Catalonia, Spain
| | - Livia Perfetto
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Noemi Del-Toro
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Edith Wong
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305-5477, USA
| | - Patrick Aloy
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, 08028 Barcelona, Catalonia, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Catalonia, Spain
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Sandra Orchard
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Pablo Porras
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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18
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Touré V, Vercruysse S, Acencio ML, Lovering RC, Orchard S, Bradley G, Casals-Casas C, Chaouiya C, Del-Toro N, Flobak Å, Gaudet P, Hermjakob H, Hoyt CT, Licata L, Lægreid A, Mungall CJ, Niknejad A, Panni S, Perfetto L, Porras P, Pratt D, Saez-Rodriguez J, Thieffry D, Thomas PD, Türei D, Kuiper M. The Minimum Information about a Molecular Interaction CAusal STatement (MI2CAST). Bioinformatics 2021; 36:5712-5718. [PMID: 32637990 PMCID: PMC8023674 DOI: 10.1093/bioinformatics/btaa622] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 06/06/2020] [Accepted: 06/30/2020] [Indexed: 12/30/2022] Open
Abstract
Motivation A large variety of molecular interactions occurs between biomolecular components in cells. When a molecular interaction results in a regulatory effect, exerted by one component onto a downstream component, a so-called ‘causal interaction’ takes place. Causal interactions constitute the building blocks in our understanding of larger regulatory networks in cells. These causal interactions and the biological processes they enable (e.g. gene regulation) need to be described with a careful appreciation of the underlying molecular reactions. A proper description of this information enables archiving, sharing and reuse by humans and for automated computational processing. Various representations of causal relationships between biological components are currently used in a variety of resources. Results Here, we propose a checklist that accommodates current representations, called the Minimum Information about a Molecular Interaction CAusal STatement (MI2CAST). This checklist defines both the required core information, as well as a comprehensive set of other contextual details valuable to the end user and relevant for reusing and reproducing causal molecular interaction information. The MI2CAST checklist can be used as reporting guidelines when annotating and curating causal statements, while fostering uniformity and interoperability of the data across resources. Availability and implementation The checklist together with examples is accessible at https://github.com/MI2CAST/MI2CAST Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Vasundra Touré
- Department of Biology, Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway
| | - Steven Vercruysse
- Department of Biology, Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway
| | - Marcio Luis Acencio
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway
| | - Ruth C Lovering
- Functional Gene Annotation, Preclinical and Fundamental Science, Institute of Cardiovascular Science, UCL, University College London, London WC1E 6JF, UK
| | - Sandra Orchard
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Glyn Bradley
- Computational Biology, Functional Genomics, GSK, Stevenage SG1 2NY, UK
| | | | - Claudine Chaouiya
- Aix Marseille Univ, CNRS, Centrale Marseille, I2M Marseille 13331, France
| | - Noemi Del-Toro
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Åsmund Flobak
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway.,The Cancer Clinic, St. Olav's Hospital, Trondheim University Hospital, Trondheim 7030, Norway
| | - Pascale Gaudet
- SIB Swiss Institute of Bioinformatics, Geneva 1211, Switzerland
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | | | - Luana Licata
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 00133 Rome, Italy
| | - Astrid Lægreid
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway
| | - Christopher J Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Anne Niknejad
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, Amphipole Building, 1015 Lausanne, Switzerland
| | - Simona Panni
- Department of Biology, Ecology and Earth Sciences, University of Calabria, Ecology and Earth Science, Via Pietro Bucci Cubo 6/C, Rende 87036, CS, Italy
| | - Livia Perfetto
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Pablo Porras
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Dexter Pratt
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Julio Saez-Rodriguez
- Institute of Computational Biomedicine, Heidelberg University, Faculty of Medicine, 69120 Heidelberg, Germany.,Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH Aachen University, Aachen 52062, Germany
| | - Denis Thieffry
- Institut de Biologie de l'ENS (IBENS), Département de Biologie, École Normale Supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France
| | - Paul D Thomas
- Division of Bioinformatics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90007, USA
| | - Dénes Türei
- Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH Aachen University, Aachen 52062, Germany
| | - Martin Kuiper
- Department of Biology, Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway
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19
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Touré V, Zobolas J, Kuiper M, Vercruysse S. CausalBuilder: bringing the MI2CAST causal interaction annotation standard to the curator. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2021:6129748. [PMID: 33547799 PMCID: PMC7904049 DOI: 10.1093/database/baaa107] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 11/16/2020] [Accepted: 12/07/2020] [Indexed: 12/23/2022]
Abstract
Molecular causal interactions are defined as regulatory connections between biological components. They are commonly retrieved from biological experiments and can be used for connecting biological molecules together to enable the building of regulatory computational models that represent biological systems. However, including a molecular causal interaction in a model requires assessing its relevance to that model, based on the detailed knowledge about the biomolecules, interaction type and biological context. In order to standardize the representation of this knowledge in 'causal statements', we recently developed the Minimum Information about a Molecular Interaction Causal Statement (MI2CAST) guidelines. Here, we introduce causalBuilder: an intuitive web-based curation interface for the annotation of molecular causal interactions that comply with the MI2CAST standard. The causalBuilder prototype essentially embeds the MI2CAST curation guidelines in its interface and makes its rules easy to follow by a curator. In addition, causalBuilder serves as an original application of the Visual Syntax Method general-purpose curation technology and provides both curators and tool developers with an interface that can be fully configured to allow focusing on selected MI2CAST concepts to annotate. After the information is entered, the causalBuilder prototype produces genuine causal statements that can be exported in different formats.
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Affiliation(s)
- Vasundra Touré
- Department of Biology, Norwegian University of Science and Technology (NTNU), Høgskoleringen 5, 7491 Trondheim, Norway
| | - John Zobolas
- Department of Biology, Norwegian University of Science and Technology (NTNU), Høgskoleringen 5, 7491 Trondheim, Norway
| | - Martin Kuiper
- Department of Biology, Norwegian University of Science and Technology (NTNU), Høgskoleringen 5, 7491 Trondheim, Norway
| | - Steven Vercruysse
- Department of Biology, Norwegian University of Science and Technology (NTNU), Høgskoleringen 5, 7491 Trondheim, Norway
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20
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Porras P, Barrera E, Bridge A, Del-Toro N, Cesareni G, Duesbury M, Hermjakob H, Iannuccelli M, Jurisica I, Kotlyar M, Licata L, Lovering RC, Lynn DJ, Meldal B, Nanduri B, Paneerselvam K, Panni S, Pastrello C, Pellegrini M, Perfetto L, Rahimzadeh N, Ratan P, Ricard-Blum S, Salwinski L, Shirodkar G, Shrivastava A, Orchard S. Towards a unified open access dataset of molecular interactions. Nat Commun 2020; 11:6144. [PMID: 33262342 PMCID: PMC7708836 DOI: 10.1038/s41467-020-19942-z] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 11/09/2020] [Indexed: 12/16/2022] Open
Abstract
The International Molecular Exchange (IMEx) Consortium provides scientists with a single body of experimentally verified protein interactions curated in rich contextual detail to an internationally agreed standard. In this update to the work of the IMEx Consortium, we discuss how this initiative has been working in practice, how it has ensured database sustainability, and how it is meeting emerging annotation challenges through the introduction of new interactor types and data formats. Additionally, we provide examples of how IMEx data are being used by biomedical researchers and integrated in other bioinformatic tools and resources.
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Affiliation(s)
- Pablo Porras
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Elisabet Barrera
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Alan Bridge
- SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 rue Michel Servet, CH-1211, Geneva, Switzerland
| | - Noemi Del-Toro
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Gianni Cesareni
- University of Rome Tor Vergata, Rome, Italy
- IRCCS Fondazione Santa Lucia, 00143, Rome, Italy
| | - Margaret Duesbury
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Campus, Hinxton, Cambridge, CB10 1SD, UK
- UCLA-DOE Institute, University of California, Los Angeles, CA, 90095, USA
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Campus, Hinxton, Cambridge, CB10 1SD, UK
| | | | - Igor Jurisica
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, and Krembil Research Institute, University Health Network, 60 Leonard Avenue, 5KD-407, Toronto, ON, M5T 0S8, Canada
- Departments of Medical Biophysics, and Computer Science, University of Toronto, Toronto, ON, Canada
- Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Max Kotlyar
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, and Krembil Research Institute, University Health Network, 60 Leonard Avenue, 5KD-407, Toronto, ON, M5T 0S8, Canada
| | | | - Ruth C Lovering
- Functional Gene Annotation, Preclinical and Fundamental Science, UCL Institute of Cardiovascular Science, University College London, London, WC1E 6JF, UK
| | - David J Lynn
- Computational and Systems Biology Program, Precision Medicine Theme, South Australian Health and Medical Research Institute, Adelaide, SA, 5000, Australia
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, 5042, Australia
| | - Birgit Meldal
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Bindu Nanduri
- Institute for Genomics, Biocomputing and Biotechnology, Mississippi State University, Starkville, MS, USA
| | - Kalpana Paneerselvam
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Simona Panni
- Università della Calabria, Dipartimento di Biologia, Ecologia e Scienze della Terra, Via Pietro Bucci Cubo 6/C, Rende, CS, Italy
| | - Chiara Pastrello
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, and Krembil Research Institute, University Health Network, 60 Leonard Avenue, 5KD-407, Toronto, ON, M5T 0S8, Canada
| | - Matteo Pellegrini
- Department of Molecular, Cell and Developmental Biology, UCLA, Box 951606, Los Angeles, CA, 90095-1606, USA
| | - Livia Perfetto
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Negin Rahimzadeh
- UCLA-DOE Institute, University of California, Los Angeles, CA, 90095, USA
| | - Prashansa Ratan
- UCLA-DOE Institute, University of California, Los Angeles, CA, 90095, USA
| | - Sylvie Ricard-Blum
- ICBMS, UMR 5246 University Lyon 1 - CNRS, Univ. Lyon, 69622, Villeurbanne, France
| | - Lukasz Salwinski
- UCLA-DOE Institute, University of California, Los Angeles, CA, 90095, USA
| | - Gautam Shirodkar
- UCLA-DOE Institute, University of California, Los Angeles, CA, 90095, USA
| | - Anjalia Shrivastava
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Campus, Hinxton, Cambridge, CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Sandra Orchard
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Campus, Hinxton, Cambridge, CB10 1SD, UK.
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21
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Licata L, Lo Surdo P, Iannuccelli M, Palma A, Micarelli E, Perfetto L, Peluso D, Calderone A, Castagnoli L, Cesareni G. SIGNOR 2.0, the SIGnaling Network Open Resource 2.0: 2019 update. Nucleic Acids Res 2020; 48:D504-D510. [PMID: 31665520 PMCID: PMC7145695 DOI: 10.1093/nar/gkz949] [Citation(s) in RCA: 105] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 09/30/2019] [Accepted: 10/09/2019] [Indexed: 01/11/2023] Open
Abstract
The SIGnaling Network Open Resource 2.0 (SIGNOR 2.0) is a public repository that stores signaling information as binary causal relationships between biological entities. The captured information is represented graphically as a signed directed graph. Each signaling relationship is associated to an effect (up/down-regulation) and to the mechanism (e.g. binding, phosphorylation, transcriptional activation, etc.) causing the up/down-regulation of the target entity. Since its first release, SIGNOR has undergone a significant content increase and the number of annotated causal interactions have almost doubled. SIGNOR 2.0 now stores almost 23 000 manually-annotated causal relationships between proteins and other biologically relevant entities: chemicals, phenotypes, complexes, etc. We describe here significant changes in curation policy and a new confidence score, which is assigned to each interaction. We have also improved the compliance to the FAIR data principles by providing (i) SIGNOR stable identifiers, (ii) programmatic access through REST APIs, (iii) bioschemas and (iv) downloadable data in standard-compliant formats, such as PSI-MI CausalTAB and GMT. The data are freely accessible and downloadable at https://signor.uniroma2.it/.
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Affiliation(s)
- Luana Licata
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 00133 Rome, Italy
| | - Prisca Lo Surdo
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 00133 Rome, Italy
| | - Marta Iannuccelli
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 00133 Rome, Italy
| | - Alessandro Palma
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 00133 Rome, Italy
| | - Elisa Micarelli
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 00133 Rome, Italy
| | - Livia Perfetto
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 00133 Rome, Italy
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | | | - Alberto Calderone
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 00133 Rome, Italy
| | - Luisa Castagnoli
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 00133 Rome, Italy
| | - Gianni Cesareni
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 00133 Rome, Italy
- IRCSS Fondazione Santa Lucia, 00142 Rome, Italy
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22
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Iannuccelli M, Micarelli E, Surdo PL, Palma A, Perfetto L, Rozzo I, Castagnoli L, Licata L, Cesareni G. CancerGeneNet: linking driver genes to cancer hallmarks. Nucleic Acids Res 2020; 48:D416-D421. [PMID: 31598703 PMCID: PMC6943052 DOI: 10.1093/nar/gkz871] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 09/12/2019] [Accepted: 09/30/2019] [Indexed: 12/28/2022] Open
Abstract
CancerGeneNet (https://signor.uniroma2.it/CancerGeneNet/) is a resource that links genes that are frequently mutated in cancers to cancer phenotypes. The resource takes advantage of a curation effort aimed at embedding a large fraction of the gene products that are found altered in cancer cells into a network of causal protein relationships. Graph algorithms, in turn, allow to infer likely paths of causal interactions linking cancer associated genes to cancer phenotypes thus offering a rational framework for the design of strategies to revert disease phenotypes. CancerGeneNet bridges two interaction layers by connecting proteins whose activities are affected by cancer drivers to proteins that impact on the 'hallmarks of cancer'. In addition, CancerGeneNet annotates curated pathways that are relevant to rationalize the pathological consequences of cancer driver mutations in selected common cancers and 'MiniPathways' illustrating regulatory circuits that are frequently altered in different cancers.
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Affiliation(s)
- Marta Iannuccelli
- Department of Biology, University of Rome, Tor Vergata, 00133 Rome, Italy
| | - Elisa Micarelli
- Department of Biology, University of Rome, Tor Vergata, 00133 Rome, Italy
| | - Prisca Lo Surdo
- Department of Biology, University of Rome, Tor Vergata, 00133 Rome, Italy
| | - Alessandro Palma
- Department of Biology, University of Rome, Tor Vergata, 00133 Rome, Italy
| | - Livia Perfetto
- Department of Biology, University of Rome, Tor Vergata, 00133 Rome, Italy
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Ilaria Rozzo
- Department of Biology, University of Rome, Tor Vergata, 00133 Rome, Italy
| | - Luisa Castagnoli
- Department of Biology, University of Rome, Tor Vergata, 00133 Rome, Italy
| | - Luana Licata
- Department of Biology, University of Rome, Tor Vergata, 00133 Rome, Italy
| | - Gianni Cesareni
- Department of Biology, University of Rome, Tor Vergata, 00133 Rome, Italy
- IRCSS Fondazione Santa Lucia, 00142 Rome, Italy
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23
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Kovács D, Sigmond T, Hotzi B, Bohár B, Fazekas D, Deák V, Vellai T, Barna J. HSF1Base: A Comprehensive Database of HSF1 (Heat Shock Factor 1) Target Genes. Int J Mol Sci 2019; 20:ijms20225815. [PMID: 31752429 PMCID: PMC6888953 DOI: 10.3390/ijms20225815] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 11/11/2019] [Accepted: 11/15/2019] [Indexed: 12/28/2022] Open
Abstract
HSF1 (heat shock factor 1) is an evolutionarily conserved master transcriptional regulator of the heat shock response (HSR) in eukaryotic cells. In response to high temperatures, HSF1 upregulates genes encoding molecular chaperones, also called heat shock proteins, which assist the refolding or degradation of damaged intracellular proteins. Accumulating evidence reveals however that HSF1 participates in several other physiological and pathological processes such as differentiation, immune response, and multidrug resistance, as well as in ageing, neurodegenerative demise, and cancer. To address how HSF1 controls these processes one should systematically analyze its target genes. Here we present a novel database called HSF1Base (hsf1base.org) that contains a nearly comprehensive list of HSF1 target genes identified so far. The list was obtained by manually curating publications on individual HSF1 targets and analyzing relevant high throughput transcriptomic and chromatin immunoprecipitation data derived from the literature and the Yeastract database. To support the biological relevance of HSF1 targets identified by high throughput methods, we performed an enrichment analysis of (potential) HSF1 targets across different tissues/cell types and organisms. We found that general HSF1 functions (targets are expressed in all tissues/cell types) are mostly related to cellular proteostasis. Furthermore, HSF1 targets that are conserved across various animal taxa operate mostly in cellular stress pathways (e.g., autophagy), chromatin remodeling, ribosome biogenesis, and ageing. Together, these data highlight diverse roles for HSF1, expanding far beyond the HSR.
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Affiliation(s)
- Dániel Kovács
- Department of Genetics, Institute of Biology, Eötvös Loránd University, Pázmány Péter stny. 1/C, H-1117 Budapest, Hungary; (D.K.); (T.S.); (B.H.); (B.B.); (D.F.)
| | - Tímea Sigmond
- Department of Genetics, Institute of Biology, Eötvös Loránd University, Pázmány Péter stny. 1/C, H-1117 Budapest, Hungary; (D.K.); (T.S.); (B.H.); (B.B.); (D.F.)
| | - Bernadette Hotzi
- Department of Genetics, Institute of Biology, Eötvös Loránd University, Pázmány Péter stny. 1/C, H-1117 Budapest, Hungary; (D.K.); (T.S.); (B.H.); (B.B.); (D.F.)
| | - Balázs Bohár
- Department of Genetics, Institute of Biology, Eötvös Loránd University, Pázmány Péter stny. 1/C, H-1117 Budapest, Hungary; (D.K.); (T.S.); (B.H.); (B.B.); (D.F.)
- Earlham Institute, Norwich NR4 7UZ, UK
- Quadram Institute, Norwich NR4 7UA, UK
| | - Dávid Fazekas
- Department of Genetics, Institute of Biology, Eötvös Loránd University, Pázmány Péter stny. 1/C, H-1117 Budapest, Hungary; (D.K.); (T.S.); (B.H.); (B.B.); (D.F.)
- Earlham Institute, Norwich NR4 7UZ, UK
- Quadram Institute, Norwich NR4 7UA, UK
| | - Veronika Deák
- Department of Applied Biotechnology and Food Science, Laboratory of Biochemistry and Molecular Biology, University of Technology, H-1111 Budapest, Hungary;
| | - Tibor Vellai
- Department of Genetics, Institute of Biology, Eötvös Loránd University, Pázmány Péter stny. 1/C, H-1117 Budapest, Hungary; (D.K.); (T.S.); (B.H.); (B.B.); (D.F.)
- MTA-ELTE Genetics Research Group, Eötvös Loránd University, H-1117 Budapest, Hungary
- Correspondence: (T.V.); (J.B.); Tel.: +36-1-372-2500 (ext. 8684) (T.V.); +36-1-372-2500 (ext. 8349) (J.B.); Fax: +36-1-372-2641 (T.V.)
| | - János Barna
- Department of Genetics, Institute of Biology, Eötvös Loránd University, Pázmány Péter stny. 1/C, H-1117 Budapest, Hungary; (D.K.); (T.S.); (B.H.); (B.B.); (D.F.)
- MTA-ELTE Genetics Research Group, Eötvös Loránd University, H-1117 Budapest, Hungary
- Correspondence: (T.V.); (J.B.); Tel.: +36-1-372-2500 (ext. 8684) (T.V.); +36-1-372-2500 (ext. 8349) (J.B.); Fax: +36-1-372-2641 (T.V.)
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24
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Palma A, Cerquone Perpetuini A, Ferrentino F, Fuoco C, Gargioli C, Giuliani G, Iannuccelli M, Licata L, Micarelli E, Paoluzi S, Perfetto L, Petrilli LL, Reggio A, Rosina M, Sacco F, Vumbaca S, Zuccotti A, Castagnoli L, Cesareni G. Myo-REG: A Portal for Signaling Interactions in Muscle Regeneration. Front Physiol 2019; 10:1216. [PMID: 31611808 PMCID: PMC6776608 DOI: 10.3389/fphys.2019.01216] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 09/06/2019] [Indexed: 12/12/2022] Open
Abstract
Muscle regeneration is a complex process governed by the interplay between several muscle-resident mononuclear cell populations. Following acute or chronic damage these cell populations are activated, communicate via cell-cell interactions and/or paracrine signals, influencing fate decisions via the activation or repression of internal signaling cascades. These are highly dynamic processes, occurring with distinct temporal and spatial kinetics. The main challenge toward a system level description of the muscle regeneration process is the integration of this plethora of inter- and intra-cellular interactions. We integrated the information on muscle regeneration in a web portal. The scientific content annotated in this portal is organized into two information layers representing relationships between different cell types and intracellular signaling-interactions, respectively. The annotation of the pathways governing the response of each cell type to a variety of stimuli/perturbations occurring during muscle regeneration takes advantage of the information stored in the SIGNOR database. Additional curation efforts have been carried out to increase the coverage of molecular interactions underlying muscle regeneration and to annotate cell-cell interactions. To facilitate the access to information on cell and molecular interactions in the context of muscle regeneration, we have developed Myo-REG, a web portal that captures and integrates published information on skeletal muscle regeneration. The muscle-centered resource we provide is one of a kind in the myology field. A friendly interface allows users to explore, approximately 100 cell interactions or to analyze intracellular pathways related to muscle regeneration. Finally, we discuss how data can be extracted from this portal to support in silico modeling experiments.
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Affiliation(s)
- Alessandro Palma
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | | | | | - Claudia Fuoco
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Cesare Gargioli
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Giulio Giuliani
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | | | - Luana Licata
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Elisa Micarelli
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Serena Paoluzi
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Livia Perfetto
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | | | - Alessio Reggio
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Marco Rosina
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Francesca Sacco
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Simone Vumbaca
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | | | - Luisa Castagnoli
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Gianni Cesareni
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
- Fondazione Santa Lucia Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
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25
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Caufield JH, Ping P. New advances in extracting and learning from protein-protein interactions within unstructured biomedical text data. Emerg Top Life Sci 2019; 3:357-369. [PMID: 33523203 DOI: 10.1042/etls20190003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 07/11/2019] [Accepted: 07/16/2019] [Indexed: 12/14/2022]
Abstract
Protein-protein interactions, or PPIs, constitute a basic unit of our understanding of protein function. Though substantial effort has been made to organize PPI knowledge into structured databases, maintenance of these resources requires careful manual curation. Even then, many PPIs remain uncurated within unstructured text data. Extracting PPIs from experimental research supports assembly of PPI networks and highlights relationships crucial to elucidating protein functions. Isolating specific protein-protein relationships from numerous documents is technically demanding by both manual and automated means. Recent advances in the design of these methods have leveraged emerging computational developments and have demonstrated impressive results on test datasets. In this review, we discuss recent developments in PPI extraction from unstructured biomedical text. We explore the historical context of these developments, recent strategies for integrating and comparing PPI data, and their application to advancing the understanding of protein function. Finally, we describe the challenges facing the application of PPI mining to the text concerning protein families, using the multifunctional 14-3-3 protein family as an example.
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Affiliation(s)
- J Harry Caufield
- The NIH BD2K Center of Excellence in Biomedical Computing, University of California at Los Angeles, Los Angeles, CA 90095, U.S.A
- Department of Physiology, University of California at Los Angeles, Los Angeles, CA 90095, U.S.A
| | - Peipei Ping
- The NIH BD2K Center of Excellence in Biomedical Computing, University of California at Los Angeles, Los Angeles, CA 90095, U.S.A
- Department of Physiology, University of California at Los Angeles, Los Angeles, CA 90095, U.S.A
- Department of Medicine/Cardiology, University of California at Los Angeles, Los Angeles, CA 90095, U.S.A
- Department of Bioinformatics, University of California at Los Angeles, Los Angeles, CA 90095, U.S.A
- Scalable Analytics Institute (ScAi), University of California at Los Angeles, Los Angeles, CA 90095, U.S.A
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