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Gawron P, Hoksza D, Piñero J, Peña-Chilet M, Esteban-Medina M, Fernandez-Rueda JL, Colonna V, Smula E, Heirendt L, Ancien F, Groues V, Satagopam VP, Schneider R, Dopazo J, Furlong LI, Ostaszewski M. Visualization of automatically combined disease maps and pathway diagrams for rare diseases. FRONTIERS IN BIOINFORMATICS 2023; 3:1101505. [PMID: 37502697 PMCID: PMC10369067 DOI: 10.3389/fbinf.2023.1101505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 05/05/2023] [Indexed: 07/29/2023] Open
Abstract
Introduction: Investigation of molecular mechanisms of human disorders, especially rare diseases, require exploration of various knowledge repositories for building precise hypotheses and complex data interpretation. Recently, increasingly more resources offer diagrammatic representation of such mechanisms, including disease-dedicated schematics in pathway databases and disease maps. However, collection of knowledge across them is challenging, especially for research projects with limited manpower. Methods: In this article we present an automated workflow for construction of maps of molecular mechanisms for rare diseases. The workflow requires a standardized definition of a disease using Orphanet or HPO identifiers to collect relevant genes and variants, and to assemble a functional, visual repository of related mechanisms, including data overlays. The diagrams composing the final map are unified to a common systems biology format from CellDesigner SBML, GPML and SBML+layout+render. The constructed resource contains disease-relevant genes and variants as data overlays for immediate visual exploration, including embedded genetic variant browser and protein structure viewer. Results: We demonstrate the functionality of our workflow on two examples of rare diseases: Kawasaki disease and retinitis pigmentosa. Two maps are constructed based on their corresponding identifiers. Moreover, for the retinitis pigmentosa use-case, we include a list of differentially expressed genes to demonstrate how to tailor the workflow using omics datasets. Discussion: In summary, our work allows for an ad-hoc construction of molecular diagrams combined from different sources, preserving their layout and graphical style, but integrating them into a single resource. This allows to reduce time consuming tasks of prototyping of a molecular disease map, enabling visual exploration, hypothesis building, data visualization and further refinement. The code of the workflow is open and accessible at https://gitlab.lcsb.uni.lu/minerva/automap/.
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Affiliation(s)
- Piotr Gawron
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - David Hoksza
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
- Faculty of Mathematics and Physics, Charles University, Prague, Czechia
| | - Janet Piñero
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), Barcelona, Spain
- MedBioinformatics Solutions SL, Barcelona, Spain
| | - Maria Peña-Chilet
- Computational Medicine Platform, Fundacion Progreso y Salud, Sevilla, Spain
- Spanish Network of Research in Rare Diseases (CIBERER), Sevilla, Spain
| | | | | | - Vincenza Colonna
- Institute of Genetics and Biophysics, National Research Council of Italy, Naples, Rome
- Department of Genetics, Genomics and Informatics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Ewa Smula
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - Laurent Heirendt
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - François Ancien
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - Valentin Groues
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - Venkata P. Satagopam
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - Joaquin Dopazo
- Computational Medicine Platform, Fundacion Progreso y Salud, Sevilla, Spain
- Spanish Network of Research in Rare Diseases (CIBERER), Sevilla, Spain
| | - Laura I. Furlong
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), Barcelona, Spain
- MedBioinformatics Solutions SL, Barcelona, Spain
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
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Li J, He X, Gao S, Liang Y, Qi Z, Xi Q, Zuo Y, Xing Y. The Metal-binding Protein Atlas (MbPA): an integrated database for curating metalloproteins in all aspects. J Mol Biol 2023:168117. [PMID: 37086947 DOI: 10.1016/j.jmb.2023.168117] [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: 10/29/2022] [Revised: 04/14/2023] [Accepted: 04/17/2023] [Indexed: 04/24/2023]
Abstract
Metal-binding proteins are essential for the vital activities and engage in their roles by acting in concert with metal cations. MbPA (The Metal-binding Protein Atlas) is the most comprehensive resource up to now dedicated to curating metal-binding proteins. Currently, it contains 106373 entries and 440187 sites related to 54 metals and 8169 species. Users can view all metal-binding proteins and species-specific proteins in MbPA. There are also metal-proteomics data that quantitatively describes protein expression in different tissues and organs. By analyzing the data of the amino acid residues at the metal-binding site, it is found that about 80% of the metal ions tend to bind to cysteine, aspartic acid, glutamic acid, and histidine. Moreover, we use Diversity Measure to confirm that the diversity of metal-binding is specific in different area of periodic table, and further elucidate the binding modes of 19 transition metals on 20 amino acids. In addition, MbPA also embraces 6855 potential pathogenic mutations related to metalloprotein. The resource is freely available at http://bioinfor.imu.edu.cn/mbpa.
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Affiliation(s)
- Jinzhao Li
- The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of life sciences, Inner Mongolia University, Hohhot, 010021, China
| | - Xiang He
- The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of life sciences, Inner Mongolia University, Hohhot, 010021, China
| | - Shuang Gao
- The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of life sciences, Inner Mongolia University, Hohhot, 010021, China
| | - Yuchao Liang
- The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of life sciences, Inner Mongolia University, Hohhot, 010021, China
| | - Zhi Qi
- The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of life sciences, Inner Mongolia University, Hohhot, 010021, China; Key Laboratory of Forage and Endemic Crop Biotechnology, Ministry of Education, School of Life Sciences, Inner Mongolia University, Hohhot, 010021, China
| | - Qilemuge Xi
- The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of life sciences, Inner Mongolia University, Hohhot, 010021, China
| | - Yongchun Zuo
- The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of life sciences, Inner Mongolia University, Hohhot, 010021, China.
| | - Yongqiang Xing
- The Inner Mongolia Key Laboratory of Functional Genome Bioinformatics, School of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou 014010, China.
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3
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Mohammed Y, Goodlett D, Borchers CH. Bioinformatics Tools and Knowledgebases to Assist Generating Targeted Assays for Plasma Proteomics. Methods Mol Biol 2023; 2628:557-577. [PMID: 36781806 DOI: 10.1007/978-1-0716-2978-9_32] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
Abstract
In targeted proteomics experiments, selecting the appropriate proteotypic peptides as surrogate for the target protein is a crucial pre-acquisition step. This step is largely a bioinformatics exercise that involves integrating information on the peptides and proteins and using various software tools and knowledgebases. We present here a few resources that automate and simplify the selection process to a great degree. These tools and knowledgebases were developed primarily to streamline targeted proteomics assay development and include PeptidePicker, PeptidePickerDB, MRMAssayDB, MouseQuaPro, and PeptideTracker. We have used these tools to develop and document thousands of targeted proteomics assays, many of them for plasma proteins with focus on human and mouse. An important aspect in all these resources is the integrative approach on which they are based. Using these tools in the first steps of designing a singleplexed or multiplexed targeted proteomic experiment can reduce the necessary experimental steps tremendously. All the tools and knowledgebases we describe here are Web-based and freely accessible so scientists can query the information conveniently from the browser. This chapter provides an overview of these software tools and knowledgebases, their content, and how to use them for targeted plasma proteomics. We further demonstrate how to use them with the results of the HUPO Human Plasma Proteome Project to produce a new database of 3.8 k targeted assays for known human plasma proteins. Upon experimental validation, these assays should help in the further quantitative characterizing of the plasma proteome.
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Affiliation(s)
- Yassene Mohammed
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, ZA, Netherlands. .,University of Victoria - Genome BC Proteomics Centre, Victoria, BC, Canada. .,Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, Canada.
| | - David Goodlett
- University of Victoria - Genome BC Proteomics Centre, Victoria, BC, Canada.,Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, Canada.,University of Gdansk, International Centre for Cancer Vaccine Science, Gdansk, Poland
| | - Christoph H Borchers
- Proteomics Centre, Segal Cancer Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada.,Gerald Bronfman Department of Oncology, Jewish General Hospital, Montreal, QC, Canada.,Division of Experimental Medicine, McGill University, Montreal, QC, Canada.,Department of Pathology, McGill University, Montreal, QC, Canada
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4
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Iqbal S, Brünger T, Pérez-Palma E, Macnee M, Brunklaus A, Daly MJ, Campbell AJ, Hoksza D, May P, Lal D. Delineation of functionally essential protein regions for 242 neurodevelopmental genes. Brain 2023; 146:519-533. [PMID: 36256779 PMCID: PMC9924913 DOI: 10.1093/brain/awac381] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/12/2022] [Accepted: 09/04/2022] [Indexed: 01/25/2023] Open
Abstract
Neurodevelopmental disorders (NDDs), including severe paediatric epilepsy, autism and intellectual disabilities are heterogeneous conditions in which clinical genetic testing can often identify a pathogenic variant. For many of them, genetic therapies will be tested in this or the coming years in clinical trials. In contrast to first-generation symptomatic treatments, the new disease-modifying precision medicines require a genetic test-informed diagnosis before a patient can be enrolled in a clinical trial. However, even in 2022, most identified genetic variants in NDD genes are 'variants of uncertain significance'. To safely enrol patients in precision medicine clinical trials, it is important to increase our knowledge about which regions in NDD-associated proteins can 'tolerate' missense variants and which ones are 'essential' and will cause a NDD when mutated. In addition, knowledge about functionally indispensable regions in the 3D structure context of proteins can also provide insights into the molecular mechanisms of disease variants. We developed a novel consensus approach that overlays evolutionary, and population based genomic scores to identify 3D essential sites (Essential3D) on protein structures. After extensive benchmarking of AlphaFold predicted and experimentally solved protein structures, we generated the currently largest expert curated protein structure set for 242 NDDs and identified 14 377 Essential3D sites across 189 gene disorders associated proteins. We demonstrate that the consensus annotation of Essential3D sites improves prioritization of disease mutations over single annotations. The identified Essential3D sites were enriched for functional features such as intermembrane regions or active sites and discovered key inter-molecule interactions in protein complexes that were otherwise not annotated. Using the currently largest autism, developmental disorders, and epilepsies exome sequencing studies including >360 000 NDD patients and population controls, we found that missense variants at Essential3D sites are 8-fold enriched in patients. In summary, we developed a comprehensive protein structure set for 242 NDDs and identified 14 377 Essential3D sites in these. All data are available at https://es-ndd.broadinstitute.org for interactive visual inspection to enhance variant interpretation and development of mechanistic hypotheses for 242 NDDs genes. The provided resources will enhance clinical variant interpretation and in silico drug target development for NDD-associated genes and encoded proteins.
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Affiliation(s)
- Sumaiya Iqbal
- The Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Tobias Brünger
- Cologne Center for Genomics, University of Cologne, 50923 Köln, Germany
| | - Eduardo Pérez-Palma
- Universidad del Desarrollo, Centro de Genética y Genómica, Facultad de Medicina Clínica Alemana, 7610658 Las Condes, Santiago de Chile, Chile
| | - Marie Macnee
- Cologne Center for Genomics, University of Cologne, 50923 Köln, Germany
| | - Andreas Brunklaus
- The Paediatric Neurosciences Research Group, Royal Hospital for Children, Glasgow G12 8QQ, UK
- School of Health and Wellbeing, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK
| | - Mark J Daly
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Institute for Molecular Medicine Finland (FIMM), Centre of Excellence in Complex Disease Genetics, University of Helsinki, 00100 Helsinki, Finland
| | - Arthur J Campbell
- The Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - David Hoksza
- Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, 110 00 Staré Město, Czechia, Czech Republic
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
| | - Dennis Lal
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Cologne Center for Genomics, University of Cologne, 50923 Köln, Germany
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Genomic Medicine Institute, Lerner Research Institute Cleveland Clinic, Cleveland, OH 44106, USA
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5
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Gawron P, Smula E, Schneider R, Ostaszewski M. Exploration and comparison of molecular mechanisms across diseases using MINERVA Net. Protein Sci 2023; 32:e4565. [PMID: 36648161 PMCID: PMC9885449 DOI: 10.1002/pro.4565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 12/16/2022] [Accepted: 01/11/2023] [Indexed: 01/18/2023]
Abstract
Protein function is often interpreted using molecular interaction diagrams, encoding roles a given protein plays in various molecular mechanisms. Information about disease-related mechanisms can be inferred from disease maps, knowledge repositories containing manually constructed systems biology diagrams. Disease maps hosted on the Molecular Interaction Network VisuAlization (MINERVA) Platform are individually accessible through a REST API interface of each instance, making it challenging to systematically explore their contents. To address this challenge, we introduce the MINERVA Net web service, a repository of open-access disease maps allowing users to publicly share minimal information about their maps. The MINERVA Net repository provides REST API endpoints of particular disease maps, which then can be individually queried for content. In this article, we describe the concept of MINERVA Net and illustrate its use by comparing proteins and their interactions in three different disease maps.
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Affiliation(s)
- Piotr Gawron
- LCSB, Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Ewa Smula
- LCSB, Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Reinhard Schneider
- LCSB, Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Marek Ostaszewski
- LCSB, Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
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6
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Malladi S, Powell HR, David A, Islam SA, Copeland MM, Kundrotas PJ, Sternberg MJ, Vakser IA. GWYRE: A resource for mapping variants onto experimental and modeled structures of human protein complexes. J Mol Biol 2022; 434:167608. [PMID: 35662458 PMCID: PMC9188266 DOI: 10.1016/j.jmb.2022.167608] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 03/31/2022] [Accepted: 04/20/2022] [Indexed: 02/08/2023]
Abstract
Structure of protein complexes is important for interpreting genetic variation. Data on single amino acid variants is available from high-throughput sequencing. Integrated modeling approach was applied to proteins and their complexes. GWYRE resource incorporates predicted protein complexes with mapped mutations.
Rapid progress in structural modeling of proteins and their interactions is powered by advances in knowledge-based methodologies along with better understanding of physical principles of protein structure and function. The pool of structural data for modeling of proteins and protein–protein complexes is constantly increasing due to the rapid growth of protein interaction databases and Protein Data Bank. The GWYRE (Genome Wide PhYRE) project capitalizes on these developments by advancing and applying new powerful modeling methodologies to structural modeling of protein–protein interactions and genetic variation. The methods integrate knowledge-based tertiary structure prediction using Phyre2 and quaternary structure prediction using template-based docking by a full-structure alignment protocol to generate models for binary complexes. The predictions are incorporated in a comprehensive public resource for structural characterization of the human interactome and the location of human genetic variants. The GWYRE resource facilitates better understanding of principles of protein interaction and structure/function relationships. The resource is available at http://www.gwyre.org.
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7
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Segura J, Rose Y, Bittrich S, Burley SK, Duarte JM. OUP accepted manuscript. Bioinformatics 2022; 38:3304-3305. [PMID: 35543462 PMCID: PMC9191206 DOI: 10.1093/bioinformatics/btac317] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 05/06/2022] [Indexed: 11/17/2022] Open
Abstract
Motivation Mapping positional features from one-dimensional (1D) sequences onto three-dimensional (3D) structures of biological macromolecules is a powerful tool to show geometric patterns of biochemical annotations and provide a better understanding of the mechanisms underpinning protein and nucleic acid function at the atomic level. Results We present a new library designed to display fully customizable interactive views between 1D positional features of protein and/or nucleic acid sequences and their 3D structures as isolated chains or components of macromolecular assemblies. Availability and implementation https://github.com/rcsb/rcsb-saguaro-3d. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Yana Rose
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
| | - Sebastian Bittrich
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
| | - Jose M Duarte
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
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Glavaški M, Velicki L. Humans and machines in biomedical knowledge curation: hypertrophic cardiomyopathy molecular mechanisms' representation. BioData Min 2021; 14:45. [PMID: 34600580 PMCID: PMC8487578 DOI: 10.1186/s13040-021-00279-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 09/14/2021] [Indexed: 11/25/2022] Open
Abstract
Background Biomedical knowledge is dispersed in scientific literature and is growing constantly. Curation is the extraction of knowledge from unstructured data into a computable form and could be done manually or automatically. Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease, with genotype–phenotype associations still incompletely understood. We compared human- and machine-curated HCM molecular mechanisms’ models and examined the performance of different machine approaches for that task. Results We created six models representing HCM molecular mechanisms using different approaches and made them publicly available, analyzed them as networks, and tried to explain the models’ differences by the analysis of factors that affect the quality of machine-curated models (query constraints and reading systems’ performance). A result of this work is also the Interactive HCM map, the only publicly available knowledge resource dedicated to HCM. Sizes and topological parameters of the networks differed notably, and a low consensus was found in terms of centrality measures between networks. Consensus about the most important nodes was achieved only with respect to one element (calcium). Models with a reduced level of noise were generated and cooperatively working elements were detected. REACH and TRIPS reading systems showed much higher accuracy than Sparser, but at the cost of extraction performance. TRIPS proved to be the best single reading system for text segments about HCM, in terms of the compromise between accuracy and extraction performance. Conclusions Different approaches in curation can produce models of the same disease with diverse characteristics, and they give rise to utterly different conclusions in subsequent analysis. The final purpose of the model should direct the choice of curation techniques. Manual curation represents the gold standard for information extraction in biomedical research and is most suitable when only high-quality elements for models are required. Automated curation provides more substance, but high level of noise is expected. Different curation strategies can reduce the level of human input needed. Biomedical knowledge would benefit overwhelmingly, especially as to its rapid growth, if computers were to be able to assist in analysis on a larger scale. Supplementary Information The online version contains supplementary material available at 10.1186/s13040-021-00279-2.
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Affiliation(s)
- Mila Glavaški
- Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia.
| | - Lazar Velicki
- Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia.,Institute of Cardiovascular Diseases Vojvodina, Sremska Kamenica, Serbia
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Reardon HV, Che A, Luke BT, Ravichandran S, Collins JR, Mudunuri US. AVIA 3.0: interactive portal for genomic variant and sample level analysis. Bioinformatics 2021; 37:2467-2469. [PMID: 33289511 DOI: 10.1093/bioinformatics/btaa994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 11/09/2020] [Accepted: 11/17/2020] [Indexed: 11/13/2022] Open
Abstract
SUMMARY The Annotation, Visualization and Impact Analysis (AVIA) is a web application combining multiple features to annotate and visualize genomic variant data. Users can investigate functional significance of their genetic alterations across samples, genes and pathways. Version 3.0 of AVIA offers filtering options through interactive charts and by linking disease relevant data sources. Newly incorporated services include gene, variant and sample level reporting, literature and functional correlations among impacted genes, comparative analysis across samples and against data sources such as TCGA and ClinVar, and cohort building. Sample and data management is now feasible through the application, which allows greater flexibility with sharing, reannotating and organizing data. Most importantly, AVIA's utility stems from its convenience for allowing users to upload and explore results without any a priori knowledge or the need to install, update and maintain software or databases. Together, these enhancements strengthen AVIA as a comprehensive, user-driven variant analysis portal. AVAILABILITYAND IMPLEMENTATION AVIA is accessible online at https://avia-abcc.ncifcrf.gov.
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Affiliation(s)
- Hue V Reardon
- Advanced Biomedical Computational Science, Biomedical Informatics & Data Science, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Anney Che
- Advanced Biomedical Computational Science, Biomedical Informatics & Data Science, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Brian T Luke
- Advanced Biomedical Computational Science, Biomedical Informatics & Data Science, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Sarangan Ravichandran
- Advanced Biomedical Computational Science, Biomedical Informatics & Data Science, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Jack R Collins
- Advanced Biomedical Computational Science, Biomedical Informatics & Data Science, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Uma S Mudunuri
- Advanced Biomedical Computational Science, Biomedical Informatics & Data Science, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
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10
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Postic G, Andreani J, Marcoux J, Reys V, Guerois R, Rey J, Mouton-Barbosa E, Vandenbrouck Y, Cianferani S, Burlet-Schiltz O, Labesse G, Tufféry P. Proteo3Dnet: a web server for the integration of structural information with interactomics data. Nucleic Acids Res 2021; 49:W567-W572. [PMID: 33963857 PMCID: PMC8262742 DOI: 10.1093/nar/gkab332] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 04/09/2021] [Accepted: 04/19/2021] [Indexed: 01/01/2023] Open
Abstract
Proteo3Dnet is a web server dedicated to the analysis of mass spectrometry interactomics experiments. Given a flat list of proteins, its aim is to organize it in terms of structural interactions to provide a clearer overview of the data. This is achieved using three means: (i) the search for interologs with resolved structure available in the protein data bank, including cross-species remote homology search, (ii) the search for possibly weaker interactions mediated through Short Linear Motifs as predicted by ELM-a unique feature of Proteo3Dnet, (iii) the search for protein-protein interactions physically validated in the BioGRID database. The server then compiles this information and returns a graph of the identified interactions and details about the different searches. The graph can be interactively explored to understand the way the core complexes identified could interact. It can also suggest undetected partners to the experimentalists, or specific cases of conditionally exclusive binding. The interest of Proteo3Dnet, previously demonstrated for the difficult cases of the proteasome and pragmin complexes data is, here, illustrated in the context of yeast precursors to the small ribosomal subunits and the smaller interactome of 14-3-3zeta frequent interactors. The Proteo3Dnet web server is accessible at http://bioserv.rpbs.univ-paris-diderot.fr/services/Proteo3Dnet/.
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Affiliation(s)
- Guillaume Postic
- Université de Paris, CNRS UMR 8251, INSERM U1133, RPBS, Paris 75205, France.,Institut Français de Bioinformatique (IFB), UMS 3601-CNRS, Université Paris-Saclay, Orsay, France
| | - Jessica Andreani
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Julien Marcoux
- Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, UPS, Toulouse, France
| | - Victor Reys
- Centre de Biochimie Structurale (CBS), CNRS, INSERM, Univ Montpellier, Montpellier, France
| | - Raphaël Guerois
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Julien Rey
- Université de Paris, CNRS UMR 8251, INSERM U1133, RPBS, Paris 75205, France
| | - Emmanuelle Mouton-Barbosa
- Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, UPS, Toulouse, France
| | - Yves Vandenbrouck
- Univ. Grenoble Alpes, INSERM, CEA, UMR BioSanté U1292, CNRS, CEA, FR2048, 38000 Grenoble, France
| | - Sarah Cianferani
- Laboratoire de Spectrométrie de Masse BioOrganique, Université de Strasbourg, CNRS, IPHC UMR 7178, 67000 Strasbourg, France
| | - Odile Burlet-Schiltz
- Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, UPS, Toulouse, France
| | - Gilles Labesse
- Centre de Biochimie Structurale (CBS), CNRS, INSERM, Univ Montpellier, Montpellier, France
| | - Pierre Tufféry
- Université de Paris, CNRS UMR 8251, INSERM U1133, RPBS, Paris 75205, France
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11
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Miszta P, Pasznik P, Niewieczerzał S, Jakowiecki J, Filipek S. GPCRsignal: webserver for analysis of the interface between G-protein-coupled receptors and their effector proteins by dynamics and mutations. Nucleic Acids Res 2021; 49:W247-W256. [PMID: 34060630 PMCID: PMC8262697 DOI: 10.1093/nar/gkab434] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/20/2021] [Accepted: 05/07/2021] [Indexed: 01/04/2023] Open
Abstract
GPCRsignal (https://gpcrsignal.biomodellab.eu/) is a webserver devoted to signaling complexes of G-protein–coupled receptors (GPCRs). The recent improvement in cryo-electron microscopy resulted in the determination of a large number of high-resolution structures of GPCRs bound to their effector proteins: G proteins or arrestins. Analyzing the interfaces between receptor and an effector protein is of high importance since a selection of proper G protein or specific conformation of arrestin leads to changes of signaling that can significantly affect action of drugs. GPCRsignal provides a possibility of running molecular dynamics simulations of all currently available GPCR-effector protein complexes for curated structures: wild-type, with crystal/cryo-EM mutations, or with mutations introduced by the user. The simulations are performed in an implicit water-membrane environment, so they are rather fast. User can run several simulations to obtain statistically valid results. The simulations can be analyzed separately using dynamic FlarePlots for particular types of interactions. One can also compare groups of simulations in Interaction frequency analysis as HeatMaps and also in interaction frequency difference analysis as sticks, linking the interacting residues, of different color and size proportional to differences in contact frequencies.
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Affiliation(s)
- Przemysław Miszta
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, 02-093 Warsaw, Poland
| | - Paweł Pasznik
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, 02-093 Warsaw, Poland
| | - Szymon Niewieczerzał
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, 02-093 Warsaw, Poland
| | - Jakub Jakowiecki
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, 02-093 Warsaw, Poland
| | - Sławomir Filipek
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, 02-093 Warsaw, Poland
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12
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Bernhofer M, Dallago C, Karl T, Satagopam V, Heinzinger M, Littmann M, Olenyi T, Qiu J, Schütze K, Yachdav G, Ashkenazy H, Ben-Tal N, Bromberg Y, Goldberg T, Kajan L, O’Donoghue S, Sander C, Schafferhans A, Schlessinger A, Vriend G, Mirdita M, Gawron P, Gu W, Jarosz Y, Trefois C, Steinegger M, Schneider R, Rost B. PredictProtein - Predicting Protein Structure and Function for 29 Years. Nucleic Acids Res 2021; 49:W535-W540. [PMID: 33999203 PMCID: PMC8265159 DOI: 10.1093/nar/gkab354] [Citation(s) in RCA: 103] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/06/2021] [Accepted: 05/10/2021] [Indexed: 12/12/2022] Open
Abstract
Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings.
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Affiliation(s)
- Michael Bernhofer
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
- TUM Graduate School CeDoSIA, Boltzmannstr 11, 85748 Garching, Germany
| | - Christian Dallago
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
- TUM Graduate School CeDoSIA, Boltzmannstr 11, 85748 Garching, Germany
| | - Tim Karl
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
| | - Venkata Satagopam
- Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
- ELIXIR Luxembourg (ELIXIR-LU) Node, University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Michael Heinzinger
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
- TUM Graduate School CeDoSIA, Boltzmannstr 11, 85748 Garching, Germany
| | - Maria Littmann
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
- TUM Graduate School CeDoSIA, Boltzmannstr 11, 85748 Garching, Germany
| | - Tobias Olenyi
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
| | - Jiajun Qiu
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
- Department of Otolaryngology Head & Neck Surgery, The Ninth People's Hospital & Ear Institute, School of Medicine & Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai Jiao Tong University, Shanghai, China
| | - Konstantin Schütze
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
| | - Guy Yachdav
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
| | - Haim Ashkenazy
- Department of Molecular Biology, Max Planck Institute for Developmental Biology, Tübingen, Germany
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, 69978 Tel Aviv, Israel
| | - Nir Ben-Tal
- Department of Biochemistry & Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, 69978 Tel Aviv, Israel
| | - Yana Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ 08901, USA
| | - Tatyana Goldberg
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
| | - Laszlo Kajan
- Roche Polska Sp. z o.o., Domaniewska 39B, 02–672 Warsaw, Poland
| | | | - Chris Sander
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA 02215, USA
- Broad Institute of MIT and Harvard, Boston, MA 02142, USA
| | - Andrea Schafferhans
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
- HSWT (Hochschule Weihenstephan Triesdorf | University of Applied Sciences), Department of Bioengineering Sciences, Am Hofgarten 10, 85354 Freising, Germany
| | - Avner Schlessinger
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | - Milot Mirdita
- Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Piotr Gawron
- Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Wei Gu
- Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
- ELIXIR Luxembourg (ELIXIR-LU) Node, University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Yohan Jarosz
- Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
- ELIXIR Luxembourg (ELIXIR-LU) Node, University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Christophe Trefois
- Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
- ELIXIR Luxembourg (ELIXIR-LU) Node, University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Martin Steinegger
- School of Biological Sciences, Seoul National University, Seoul, South Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, South Korea
| | - Reinhard Schneider
- Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
- ELIXIR Luxembourg (ELIXIR-LU) Node, University of Luxembourg, Campus Belval, House of Biomedicine II, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Burkhard Rost
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany
- Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748 Garching/Munich, Germany
- TUM School of Life Sciences Weihenstephan (WZW), Alte Akademie 8, Freising, Germany
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13
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Koulouras G, Frith MC. Significant non-existence of sequences in genomes and proteomes. Nucleic Acids Res 2021; 49:3139-3155. [PMID: 33693858 PMCID: PMC8034619 DOI: 10.1093/nar/gkab139] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 02/11/2021] [Accepted: 02/25/2021] [Indexed: 12/22/2022] Open
Abstract
Minimal absent words (MAWs) are minimal-length oligomers absent from a genome or proteome. Although some artificially synthesized MAWs have deleterious effects, there is still a lack of a strategy for the classification of non-occurring sequences as potentially malicious or benign. In this work, by using Markovian models with multiple-testing correction, we reveal significant absent oligomers, which are statistically expected to exist. This suggests that their absence is due to negative selection. We survey genomes and proteomes covering the diversity of life and find thousands of significant absent sequences. Common significant MAWs are often mono- or dinucleotide tracts, or palindromic. Significant viral MAWs are often restriction sites and may indicate unknown restriction motifs. Surprisingly, significant mammal genome MAWs are often present, but rare, in other mammals, suggesting that they are suppressed but not completely forbidden. Significant human MAWs are frequently present in prokaryotes, suggesting immune function, but rarely present in human viruses, indicating viral mimicry of the host. More than one-fourth of human proteins are one substitution away from containing a significant MAW, with the majority of replacements being predicted harmful. We provide a web-based, interactive database of significant MAWs across genomes and proteomes.
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Affiliation(s)
- Grigorios Koulouras
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26 Aomi, Koto-ku, Tokyo 135-0064, Japan
| | - Martin C Frith
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26 Aomi, Koto-ku, Tokyo 135-0064, Japan
- Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Chiba, Japan
- Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), AIST, Shinjuku-ku, Tokyo, Japan
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14
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Bhowmick P, Roome S, Borchers CH, Goodlett DR, Mohammed Y. An Update on MRMAssayDB: A Comprehensive Resource for Targeted Proteomics Assays in the Community. J Proteome Res 2021; 20:2105-2115. [PMID: 33683131 PMCID: PMC8041396 DOI: 10.1021/acs.jproteome.0c00961] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
Precise multiplexed
quantification of proteins in biological samples
can be achieved by targeted proteomics using multiple or parallel
reaction monitoring (MRM/PRM). Combined with internal standards, the
method achieves very good repeatability and reproducibility enabling
excellent protein quantification and allowing longitudinal and cohort
studies. A laborious part of performing such experiments lies in the
preparation steps dedicated to the development and validation of individual
protein assays. Several public repositories host information on targeted
proteomics assays, including NCI’s Clinical Proteomic Tumor
Analysis Consortium assay portals, PeptideAtlas SRM Experiment Library,
SRMAtlas, PanoramaWeb, and PeptideTracker, with all offering varying
levels of details. We introduced MRMAssayDB in 2018 as an integrated
resource for targeted proteomics assays. The Web-based application
maps and links the assays from the repositories, includes comprehensive
up-to-date protein and sequence annotations, and provides multiple
visualization options on the peptide and protein level. We have extended
MRMAssayDB with more assays and extensive annotations. Currently it
contains >828 000 assays covering >51 000 proteins
from
94 organisms, of which >17 000 proteins are present in >2400
biological pathways, and >48 000 mapping to >21 000
Gene Ontology terms. This is an increase of about four times the number
of assays since introduction. We have expanded annotations of interaction,
biological pathways, and disease associations. A newly added visualization
module for coupled molecular structural annotation browsing allows
the user to interactively examine peptide sequence and any known PTMs
and disease mutations, and map all to available protein 3D structures.
Because of its integrative approach, MRMAssayDB enables a holistic
view of suitable proteotypic peptides and commonly used transitions
in empirical data. Availability: http://mrmassaydb.proteincentre.com.
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Affiliation(s)
- Pallab Bhowmick
- University of Victoria - Genome BC Proteomics Centre, Victoria, British Columbia V8Z 7X8, Canada.,University of Victoria, Victoria, British Columbia V8P 5C2, Canada
| | - Simon Roome
- University of Victoria - Genome BC Proteomics Centre, Victoria, British Columbia V8Z 7X8, Canada.,University of Victoria, Victoria, British Columbia V8P 5C2, Canada
| | - Christoph H Borchers
- Proteomics Centre, Segal Cancer Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec H3T 1E2, Canada.,Gerald Bronfman Department of Oncology, Jewish General Hospital, Montreal, Quebec H3T 1E2, Canada.,Department of Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Nobel Street, Moscow 121205, Russia
| | - David R Goodlett
- University of Victoria - Genome BC Proteomics Centre, Victoria, British Columbia V8Z 7X8, Canada.,University of Victoria, Victoria, British Columbia V8P 5C2, Canada.,University of Gdansk, International Centre for Cancer Vaccine Science, 80-309 Gdansk, Poland
| | - Yassene Mohammed
- University of Victoria - Genome BC Proteomics Centre, Victoria, British Columbia V8Z 7X8, Canada.,University of Victoria, Victoria, British Columbia V8P 5C2, Canada.,Center for Proteomics and Metabolomics, Leiden University Medical Center, 2333 ZA Leiden, Netherlands
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15
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Visualizing protein structures - tools and trends. Biochem Soc Trans 2021; 48:499-506. [PMID: 32196545 DOI: 10.1042/bst20190621] [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: 01/12/2020] [Revised: 03/01/2020] [Accepted: 03/04/2020] [Indexed: 02/06/2023]
Abstract
Molecular visualization is fundamental in the current scientific literature, textbooks and dissemination materials. It provides an essential support for presenting results, reasoning on and formulating hypotheses related to molecular structure. Tools for visual exploration of structural data have become easily accessible on a broad variety of platforms thanks to advanced software tools that render a great service to the scientific community. These tools are often developed across disciplines bridging computer science, biology and chemistry. This mini-review was written as a short and compact overview for scientists who need to visualize protein structures and want to make an informed decision which tool they should use. Here, we first describe a few 'Swiss Army knives' geared towards protein visualization for everyday use with an existing large user base, then focus on more specialized tools for peculiar needs that are not yet as broadly known. Our selection is by no means exhaustive, but reflects a diverse snapshot of scenarios that we consider informative for the reader. We end with an account of future trends and perspectives.
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16
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MINERVA, A Platform for the Exploration of Disease Maps. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11685-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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17
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Iqbal S, Hoksza D, Pérez-Palma E, May P, Jespersen JB, Ahmed SS, Rifat ZT, Heyne HO, Rahman MS, Cottrell JR, Wagner FF, Daly MJ, Campbell AJ, Lal D. MISCAST: MIssense variant to protein StruCture Analysis web SuiTe. Nucleic Acids Res 2020; 48:W132-W139. [PMID: 32402084 PMCID: PMC7319582 DOI: 10.1093/nar/gkaa361] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 04/17/2020] [Accepted: 05/11/2020] [Indexed: 12/19/2022] Open
Abstract
Human genome sequencing efforts have greatly expanded, and a plethora of missense variants identified both in patients and in the general population is now publicly accessible. Interpretation of the molecular-level effect of missense variants, however, remains challenging and requires a particular investigation of amino acid substitutions in the context of protein structure and function. Answers to questions like 'Is a variant perturbing a site involved in key macromolecular interactions and/or cellular signaling?', or 'Is a variant changing an amino acid located at the protein core or part of a cluster of known pathogenic mutations in 3D?' are crucial. Motivated by these needs, we developed MISCAST (missense variant to protein structure analysis web suite; http://miscast.broadinstitute.org/). MISCAST is an interactive and user-friendly web server to visualize and analyze missense variants in protein sequence and structure space. Additionally, a comprehensive set of protein structural and functional features have been aggregated in MISCAST from multiple databases, and displayed on structures alongside the variants to provide users with the biological context of the variant location in an integrated platform. We further made the annotated data and protein structures readily downloadable from MISCAST to foster advanced offline analysis of missense variants by a wide biological community.
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Affiliation(s)
- Sumaiya Iqbal
- Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - David Hoksza
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.,Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic
| | - Eduardo Pérez-Palma
- Genomic Medicine Institute, Lerner Research Institute Cleveland Clinic, Cleveland, OH 44195, USA
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Jakob B Jespersen
- Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark
| | - Shehab S Ahmed
- Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, West Palashi, Dhaka-1205, Bangladesh
| | - Zaara T Rifat
- Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, West Palashi, Dhaka-1205, Bangladesh
| | - Henrike O Heyne
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA.,Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00100 Helsinki, Finland
| | - M Sohel Rahman
- Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, West Palashi, Dhaka-1205, Bangladesh
| | - Jeffrey R Cottrell
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Florence F Wagner
- Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Mark J Daly
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA.,Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00100 Helsinki, Finland
| | - Arthur J Campbell
- Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Dennis Lal
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Genomic Medicine Institute, Lerner Research Institute Cleveland Clinic, Cleveland, OH 44195, USA.,Cologne Center for Genomics, University of Cologne, Cologne, Germany.,Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
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18
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Serhan CN, Gupta SK, Perretti M, Godson C, Brennan E, Li Y, Soehnlein O, Shimizu T, Werz O, Chiurchiù V, Azzi A, Dubourdeau M, Gupta SS, Schopohl P, Hoch M, Gjorgevikj D, Khan FM, Brauer D, Tripathi A, Cesnulevicius K, Lescheid D, Schultz M, Särndahl E, Repsilber D, Kruse R, Sala A, Haeggström JZ, Levy BD, Filep JG, Wolkenhauer O. The Atlas of Inflammation Resolution (AIR). Mol Aspects Med 2020; 74:100894. [PMID: 32893032 PMCID: PMC7733955 DOI: 10.1016/j.mam.2020.100894] [Citation(s) in RCA: 89] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Acute inflammation is a protective reaction by the immune system in response to invading pathogens or tissue damage. Ideally, the response should be localized, self-limited, and returning to homeostasis. If not resolved, acute inflammation can result in organ pathologies leading to chronic inflammatory phenotypes. Acute inflammation and inflammation resolution are complex coordinated processes, involving a number of cell types, interacting in space and time. The biomolecular complexity and the fact that several biomedical fields are involved, make a multi- and interdisciplinary approach necessary. The Atlas of Inflammation Resolution (AIR) is a web-based resource capturing an essential part of the state-of-the-art in acute inflammation and inflammation resolution research. The AIR provides an interface for users to search thousands of interactions, arranged in inter-connected multi-layers of process diagrams, covering a wide range of clinically relevant phenotypes. By mapping experimental data onto the AIR, it can be used to elucidate drug action as well as molecular mechanisms underlying different disease phenotypes. For the visualization and exploration of information, the AIR uses the Minerva platform, which is a well-established tool for the presentation of disease maps. The molecular details of the AIR are encoded using international standards. The AIR was created as a freely accessible resource, supporting research and education in the fields of acute inflammation and inflammation resolution. The AIR connects research communities, facilitates clinical decision making, and supports research scientists in the formulation and validation of hypotheses. The AIR is accessible through https://air.bio.informatik.uni-rostock.de.
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Affiliation(s)
- Charles N Serhan
- Center for Experimental Therapeutics and Reperfusion Injury, Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Shailendra K Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany
| | - Mauro Perretti
- The William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Catherine Godson
- Diabetes Complications Research Centre, Conway Institute & School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Eoin Brennan
- Diabetes Complications Research Centre, Conway Institute & School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Yongsheng Li
- Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Oliver Soehnlein
- Department of Physiology and Pharmacology (FyFA), Karolinska Institutet, 17177, Stockholm, Sweden; German Center for Cardiovascular Research (DZHK), München, Germany; Institute for Cardiovascular Prevention (IPEK), Ludwig Maximilian University, 80336, München, Germany
| | - Takao Shimizu
- Department of Lipidomics, Graduate School of Medicine, The University of Tokyo, 113-0033 Tokyo, Japan; National Center for Global Health and Medicine, 1-21-1 Toyama, Shinjuku-ku, Tokyo, Japan
| | - Oliver Werz
- Department of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, Friedrich Schiller University Jena, 07743, Jena, Germany
| | - Valerio Chiurchiù
- Institute of Translational Pharmacology, National Research Council, 00133, Rome, Italy; Laboratory of Resolution of Neuroinflammation, IRCCS Santa Lucia Foundation, 00143, Rome, Italy
| | - Angelo Azzi
- School of Graduate Biomedical Pharmacology and Drug Development Program at Tufts University, Boston, MA 02111, USA
| | - Marc Dubourdeau
- Ambiotis, Canal Biotech 2 - 3 Rue des Satellites, 31400, Toulouse, France
| | - Suchi Smita Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany
| | - Patrick Schopohl
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany
| | - Matti Hoch
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany
| | - Dragana Gjorgevikj
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany
| | - Faiz M Khan
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany
| | - David Brauer
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany
| | - Anurag Tripathi
- CSIR - Indian Institute of Toxicology Research, 226001, Lucknow, India
| | | | - David Lescheid
- Department of Medical Affairs & Research, Heel GmbH, 76532, Baden-Baden, Germany
| | - Myron Schultz
- Department of Medical Affairs & Research, Heel GmbH, 76532, Baden-Baden, Germany
| | - Eva Särndahl
- iRiSC - Inflammatory Response and Infection Susceptibility Centre, Faculty of Medicine and Health, Örebro University, SE-701 82, Örebro, Sweden; School of Medical Sciences, Örebro University, SE-701 82, Örebro, Sweden
| | - Dirk Repsilber
- School of Medical Sciences, Örebro University, SE-701 82, Örebro, Sweden
| | - Robert Kruse
- iRiSC - Inflammatory Response and Infection Susceptibility Centre, Faculty of Medicine and Health, Örebro University, SE-701 82, Örebro, Sweden; Department of Clinical Research Laboratory, Faculty of Medicine and Health, Örebro University, SE-701 82, Örebro, Sweden
| | - Angelo Sala
- Department of Pharmaceutical Sciences, University of Milan, 20133 Milano, and IRIB, C.N.R, 90146, Palermo, Italy
| | - Jesper Z Haeggström
- Department of Medical Biochemistry and Biophysics, Karolinska Institute, SE-171 77, Stockholm, Sweden
| | - Bruce D Levy
- Brigham and Women's Hospital, Department of Medicine, Pulmonary and Critical Care Medicine and Harvard Medical School, Boston, MA, 02115, USA
| | - János G Filep
- Department of Pathology and Cell Biology, University of Montreal, and Research Center, Maisonneuve-Rosemont Hospital, Montreal, QC, H1T 2M4, Canada
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany; Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, Stellenbosch, South Africa.
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19
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Serhan CN, Gupta SK, Perretti M, Godson C, Brennan E, Li Y, Soehnlein O, Shimizu T, Werz O, Chiurchiù V, Azzi A, Dubourdeau M, Gupta SS, Schopohl P, Hoch M, Gjorgevikj D, Khan FM, Brauer D, Tripathi A, Cesnulevicius K, Lescheid D, Schultz M, Särndahl E, Repsilber D, Kruse R, Sala A, Haeggström JZ, Levy BD, Filep JG, Wolkenhauer O. WITHDRAWN: The Atlas of Inflammation Resolution (AIR). Mol Aspects Med 2020:100893. [PMID: 32873427 DOI: 10.1016/j.mam.2020.100893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The Publisher regrets that this article is an accidental duplication of an article that has already been published, https://doi.org/10.1016/j.mam.2020.100894. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.
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Affiliation(s)
- Charles N Serhan
- Center for Experimental Therapeutics and Reperfusion Injury, Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Shailendra K Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany
| | - Mauro Perretti
- The William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Catherine Godson
- Diabetes Complications Research Centre, Conway Institute & School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Eoin Brennan
- Diabetes Complications Research Centre, Conway Institute & School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Yongsheng Li
- Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Oliver Soehnlein
- Department of Physiology and Pharmacology (FyFA), Karolinska Institutet, 17177, Stockholm, Sweden; German Center for Cardiovascular Research (DZHK), München, Germany; Institute for Cardiovascular Prevention (IPEK), Ludwig Maximilian University, 80336, München, Germany
| | - Takao Shimizu
- Department of Lipidomics, Graduate School of Medicine, The University of Tokyo, 113-0033, Tokyo, Japan; National Center for Global Health and Medicine, 1-21-1 Toyama, Shinjuku-ku, Tokyo, Japan
| | - Oliver Werz
- Department of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, Friedrich Schiller University Jena, 07743, Jena, Germany
| | - Valerio Chiurchiù
- Institute of Translational Pharmacology, National Research Council, 00133, Rome, Italy; Laboratory of Resolution of Neuroinflammation, IRCCS Santa Lucia Foundation, 00143, Rome, Italy
| | - Angelo Azzi
- School of Graduate Biomedical Pharmacology and Drug Development Program at Tufts University, Boston, MA, 02111, USA
| | - Marc Dubourdeau
- Ambiotis, Canal Biotech 2 - 3 Rue des Satellites, 31400, Toulouse, France
| | - Suchi Smita Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany
| | - Patrick Schopohl
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany
| | - Matti Hoch
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany
| | - Dragana Gjorgevikj
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany
| | - Faiz M Khan
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany
| | - David Brauer
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany
| | - Anurag Tripathi
- CSIR - Indian Institute of Toxicology Research, 226001, Lucknow, India
| | | | - David Lescheid
- Department of Medical Affairs & Research, Heel GmbH, 76532, Baden-Baden, Germany
| | - Myron Schultz
- Department of Medical Affairs & Research, Heel GmbH, 76532, Baden-Baden, Germany
| | - Eva Särndahl
- IRiSC - Inflammatory Response and Infection Susceptibility Centre, Faculty of Medicine and Health, Örebro University, SE-701 82, Örebro, Sweden
| | - Dirk Repsilber
- School of Medical Sciences, University of Örebro, SE-701 82, Örebro, Sweden
| | - Robert Kruse
- IRiSC - Inflammatory Response and Infection Susceptibility Centre, Faculty of Medicine and Health, Örebro University, SE-701 82, Örebro, Sweden; Department of Clinical Research Laboratory, Faculty of Medicine and Health, Örebro University, SE-701 82, Örebro, Sweden
| | - Angelo Sala
- Department of Pharmaceutical Sciences, University of Milan, 20133 Milano, and IRIB, C.N.R, 90146, Palermo, Italy
| | - Jesper Z Haeggström
- Department of Medical Biochemistry and Biophysics, Karolinska Institute, SE-171 77, Stockholm, Sweden
| | - Bruce D Levy
- Brigham and Women's Hospital, Department of Medicine, Pulmonary and Critical Care Medicine and Harvard Medical School, Boston, MA, 02115, USA
| | - János G Filep
- Department of Pathology and Cell Biology, University of Montreal, Research Center, Maisonneuve-Rosemont Hospital, Montreal, QC, H1T 2M4, Canada
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany; Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, Stellenbosch, South Africa.
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20
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Hoksza D, Gawron P, Ostaszewski M, Hasenauer J, Schneider R. Closing the gap between formats for storing layout information in systems biology. Brief Bioinform 2020; 21:1249-1260. [PMID: 31273380 PMCID: PMC7373180 DOI: 10.1093/bib/bbz067] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 04/23/2019] [Accepted: 05/14/2019] [Indexed: 11/13/2022] Open
Abstract
The understanding of complex biological networks often relies on both a dedicated layout and a topology. Currently, there are three major competing layout-aware systems biology formats, but there are no software tools or software libraries supporting all of them. This complicates the management of molecular network layouts and hinders their reuse and extension. In this paper, we present a high-level overview of the layout formats in systems biology, focusing on their commonalities and differences, review their support in existing software tools, libraries and repositories and finally introduce a new conversion module within the MINERVA platform. The module is available via a REST API and offers, besides the ability to convert between layout-aware systems biology formats, the possibility to export layouts into several graphical formats. The module enables conversion of very large networks with thousands of elements, such as disease maps or metabolic reconstructions, rendering it widely applicable in systems biology.
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Affiliation(s)
- David Hoksza
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing L-4367 Belvaux, Luxembourg
- Faculty of Mathematics and Physics, Charles University, Malostranské nám. 25, 118 00 Prague, Czech Republic
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing L-4367 Belvaux, Luxembourg
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing L-4367 Belvaux, Luxembourg
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Department of Mathematics, Technische Universität München, München, Germany
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing L-4367 Belvaux, Luxembourg
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