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Moore LR, Caspi R, Campbell DA, Casey JR, Crevecoeur S, Lea-Smith DJ, Long B, Omar NM, Paley SM, Schmelling NM, Torrado A, Zehr JP, Karp PD. CyanoCyc cyanobacterial web portal. Front Microbiol 2024; 15:1340413. [PMID: 38357349 PMCID: PMC10864581 DOI: 10.3389/fmicb.2024.1340413] [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: 11/17/2023] [Accepted: 01/11/2024] [Indexed: 02/16/2024] Open
Abstract
CyanoCyc is a web portal that integrates an exceptionally rich database collection of information about cyanobacterial genomes with an extensive suite of bioinformatics tools. It was developed to address the needs of the cyanobacterial research and biotechnology communities. The 277 annotated cyanobacterial genomes currently in CyanoCyc are supplemented with computational inferences including predicted metabolic pathways, operons, protein complexes, and orthologs; and with data imported from external databases, such as protein features and Gene Ontology (GO) terms imported from UniProt. Five of the genome databases have undergone manual curation with input from more than a dozen cyanobacteria experts to correct errors and integrate information from more than 1,765 published articles. CyanoCyc has bioinformatics tools that encompass genome, metabolic pathway and regulatory informatics; omics data analysis; and comparative analyses, including visualizations of multiple genomes aligned at orthologous genes, and comparisons of metabolic networks for multiple organisms. CyanoCyc is a high-quality, reliable knowledgebase that accelerates scientists' work by enabling users to quickly find accurate information using its powerful set of search tools, to understand gene function through expert mini-reviews with citations, to acquire information quickly using its interactive visualization tools, and to inform better decision-making for fundamental and applied research.
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Affiliation(s)
| | - Ron Caspi
- SRI International, Menlo Park, CA, United States
| | | | - John R. Casey
- Lawrence Livermore National Laboratory, Physical and Life Sciences Directorate, Livermore, CA, United States
| | - Sophie Crevecoeur
- Watershed Hydrology and Ecology Research Division, Environment and Climate Change Canada, Burlington, ON, Canada
| | - David J. Lea-Smith
- School of Biological Sciences, University of East Anglia, Norwich, United Kingdom
| | - Bin Long
- Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, United States
| | | | | | | | - Alejandro Torrado
- Institute of Plant Biochemistry and Photosynthesis, University of Seville and Spanish National Research Council, Sevilla, Spain
| | - Jonathan P. Zehr
- Ocean Sciences Department, University of California, Santa Cruz, Santa Cruz, CA, United States
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Danchin A. In vivo, in vitro and in silico: an open space for the development of microbe-based applications of synthetic biology. Microb Biotechnol 2022; 15:42-64. [PMID: 34570957 PMCID: PMC8719824 DOI: 10.1111/1751-7915.13937] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 09/14/2021] [Indexed: 12/24/2022] Open
Abstract
Living systems are studied using three complementary approaches: living cells, cell-free systems and computer-mediated modelling. Progresses in understanding, allowing researchers to create novel chassis and industrial processes rest on a cycle that combines in vivo, in vitro and in silico studies. This design-build-test-learn iteration loop cycle between experiments and analyses combines together physiology, genetics, biochemistry and bioinformatics in a way that keeps going forward. Because computer-aided approaches are not directly constrained by the material nature of the entities of interest, we illustrate here how this virtuous cycle allows researchers to explore chemistry which is foreign to that present in extant life, from whole chassis to novel metabolic cycles. Particular emphasis is placed on the importance of evolution.
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Affiliation(s)
- Antoine Danchin
- Kodikos LabsInstitut Cochin24 rue du Faubourg Saint‐JacquesParis75014France
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Keseler IM, Gama-Castro S, Mackie A, Billington R, Bonavides-Martínez C, Caspi R, Kothari A, Krummenacker M, Midford PE, Muñiz-Rascado L, Ong WK, Paley S, Santos-Zavaleta A, Subhraveti P, Tierrafría VH, Wolfe AJ, Collado-Vides J, Paulsen IT, Karp PD. The EcoCyc Database in 2021. Front Microbiol 2021; 12:711077. [PMID: 34394059 PMCID: PMC8357350 DOI: 10.3389/fmicb.2021.711077] [Citation(s) in RCA: 128] [Impact Index Per Article: 42.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 07/02/2021] [Indexed: 11/13/2022] Open
Abstract
The EcoCyc model-organism database collects and summarizes experimental data for Escherichia coli K-12. EcoCyc is regularly updated by the manual curation of individual database entries, such as genes, proteins, and metabolic pathways, and by the programmatic addition of results from select high-throughput analyses. Updates to the Pathway Tools software that supports EcoCyc and to the web interface that enables user access have continuously improved its usability and expanded its functionality. This article highlights recent improvements to the curated data in the areas of metabolism, transport, DNA repair, and regulation of gene expression. New and revised data analysis and visualization tools include an interactive metabolic network explorer, a circular genome viewer, and various improvements to the speed and usability of existing tools.
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Affiliation(s)
- Ingrid M. Keseler
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, Menlo Park, CA, United States
| | - Socorro Gama-Castro
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México
| | - Amanda Mackie
- Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia
| | - Richard Billington
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, Menlo Park, CA, United States
| | | | - Ron Caspi
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, Menlo Park, CA, United States
| | - Anamika Kothari
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, Menlo Park, CA, United States
| | - Markus Krummenacker
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, Menlo Park, CA, United States
| | - Peter E. Midford
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, Menlo Park, CA, United States
| | - Luis Muñiz-Rascado
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México
| | - Wai Kit Ong
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, Menlo Park, CA, United States
| | - Suzanne Paley
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, Menlo Park, CA, United States
| | - Alberto Santos-Zavaleta
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México
- Instituto de Energías Renovables, Universidad Nacional Autónoma de México, Temixco, México
| | - Pallavi Subhraveti
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, Menlo Park, CA, United States
| | - Víctor H. Tierrafría
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México
| | - Alan J. Wolfe
- Department of Microbiology and Immunology, Stritch School of Medicine, Loyola University Chicago, Maywood, IL, United States
| | - Julio Collado-Vides
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
| | - Ian T. Paulsen
- Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia
| | - Peter D. Karp
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, Menlo Park, CA, United States
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