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Evangelista JE, Clarke DJB, Xie Z, Lachmann A, Jeon M, Chen K, Jagodnik KM, Jenkins SL, Kuleshov MV, Wojciechowicz ML, Schürer SC, Medvedovic M, Ma'ayan A. SigCom LINCS: data and metadata search engine for a million gene expression signatures. Nucleic Acids Res 2022; 50:W697-W709. [PMID: 35524556 PMCID: PMC9252724 DOI: 10.1093/nar/gkac328] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/04/2022] [Accepted: 04/20/2022] [Indexed: 12/13/2022] Open
Abstract
Millions of transcriptome samples were generated by the Library of Integrated Network-based Cellular Signatures (LINCS) program. When these data are processed into searchable signatures along with signatures extracted from Genotype-Tissue Expression (GTEx) and Gene Expression Omnibus (GEO), connections between drugs, genes, pathways and diseases can be illuminated. SigCom LINCS is a webserver that serves over a million gene expression signatures processed, analyzed, and visualized from LINCS, GTEx, and GEO. SigCom LINCS is built with Signature Commons, a cloud-agnostic skeleton Data Commons with a focus on serving searchable signatures. SigCom LINCS provides a rapid signature similarity search for mimickers and reversers given sets of up and down genes, a gene set, a single gene, or any search term. Additionally, users of SigCom LINCS can perform a metadata search to find and analyze subsets of signatures and find information about genes and drugs. SigCom LINCS is findable, accessible, interoperable, and reusable (FAIR) with metadata linked to standard ontologies and vocabularies. In addition, all the data and signatures within SigCom LINCS are available via a well-documented API. In summary, SigCom LINCS, available at https://maayanlab.cloud/sigcom-lincs, is a rich webserver resource for accelerating drug and target discovery in systems pharmacology.
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Affiliation(s)
- John Erol Evangelista
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Daniel J B Clarke
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Zhuorui Xie
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Alexander Lachmann
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Minji Jeon
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Kerwin Chen
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Kathleen M Jagodnik
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Sherry L Jenkins
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Maxim V Kuleshov
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Megan L Wojciechowicz
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Stephan C Schürer
- Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Mario Medvedovic
- Department of Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Avi Ma'ayan
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
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Parvanova I, Borziak K, Guarino J, Finkelstein J. A Platform for Integrating and Sharing Cancer Stem Cell Data. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2320-2325. [PMID: 34891750 DOI: 10.1109/embc46164.2021.9630083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Advancements in cancer research and treatment have highlighted the need for standardization and sharing of cancer stem cell (CSC) data to facilitate research transparency and to promote collaboration within the scientific community. Although previous applications have attempted to gather and disseminate these data, currently no platform organizes the heterogeneous CSC information into a harmonized project-based framework. The aim of our platform, ReMeDy, is to provide an intelligent informatics solution integrating diverse CSC characteristics, outcomes information, and omics data across clinical, preclinical and in vitro studies. These heterogeneous data streams are organized within a multi-modular framework, subjected to a stringent validation by using standardized ontologies, and stored in a searchable format. To test usefulness of our approach for capturing diverse data related to CSCs, we integrated data from 52 publicly-available CSC projects. We validated the robustness of the platform, by efficiently organizing diverse data elements, and demonstrated its potential for promoting future knowledge discovery driven by aggregation of published data. Next steps include expanding number of uploaded CSC projects and developing additional data visualization tools. The platform is accessible through https://remedy.mssm.edu/.
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