Computational protocol to identify shared transcriptional risks and mutually beneficial compounds between diseases
Summary: The accumulation of omics and biobank resources allows for a genome-wide understanding of the shared pathologic mechanisms between diseases and for strategies to identify drugs that could be repurposed as novel treatments. Here, we present a computational protocol, implemented as a Snakemak...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-03-01
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| Series: | STAR Protocols |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666166724000480 |
| _version_ | 1797311532505235456 |
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| author | Hua Gao Mao Zhang Richard A. Baylis Fudi Wang Johan L.M. Björkegren Jason J. Kovacic Arno Ruusalepp Nicholas J. Leeper |
| author_facet | Hua Gao Mao Zhang Richard A. Baylis Fudi Wang Johan L.M. Björkegren Jason J. Kovacic Arno Ruusalepp Nicholas J. Leeper |
| author_sort | Hua Gao |
| collection | DOAJ |
| description | Summary: The accumulation of omics and biobank resources allows for a genome-wide understanding of the shared pathologic mechanisms between diseases and for strategies to identify drugs that could be repurposed as novel treatments. Here, we present a computational protocol, implemented as a Snakemake workflow, to identify shared transcriptional processes and screen compounds that could result in mutual benefit. This protocol also includes a description of a pharmacovigilance study designed to validate the effect of compounds using electronic health records.For complete details on the use and execution of this protocol, please refer to Gao et al.1 and Baylis et al.2 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. |
| first_indexed | 2024-03-08T02:01:07Z |
| format | Article |
| id | doaj.art-aec9b6c76c0d4355bc39bed0e3b9b685 |
| institution | Directory Open Access Journal |
| issn | 2666-1667 |
| language | English |
| last_indexed | 2024-03-08T02:01:07Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | STAR Protocols |
| spelling | doaj.art-aec9b6c76c0d4355bc39bed0e3b9b6852024-02-14T05:18:57ZengElsevierSTAR Protocols2666-16672024-03-0151102883Computational protocol to identify shared transcriptional risks and mutually beneficial compounds between diseasesHua Gao0Mao Zhang1Richard A. Baylis2Fudi Wang3Johan L.M. Björkegren4Jason J. Kovacic5Arno Ruusalepp6Nicholas J. Leeper7Department of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cardiovascular Institute, Stanford, CA 94305, USA; Corresponding authorStanford Cardiovascular Institute, Stanford, CA 94305, USADepartment of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cardiovascular Institute, Stanford, CA 94305, USA; Department of Medicine, Division of Cardiology, University of California, San Francisco, San Francisco, CA 94143, USADepartment of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cardiovascular Institute, Stanford, CA 94305, USADepartment of Medicine, Karolinska Institute, Huddinge, Sweden; Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USACardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA; Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia; St. Vincent’s Clinical School, University of NSW, Sydney, NSW, AustraliaDepartment of Cardiac Surgery and The Heart Clinic, Tartu University Hospital and Department of Cardiology, Institute of Clinical Medicine, Tartu University, Tartu, EstoniaDepartment of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cardiovascular Institute, Stanford, CA 94305, USA; Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA; Corresponding authorSummary: The accumulation of omics and biobank resources allows for a genome-wide understanding of the shared pathologic mechanisms between diseases and for strategies to identify drugs that could be repurposed as novel treatments. Here, we present a computational protocol, implemented as a Snakemake workflow, to identify shared transcriptional processes and screen compounds that could result in mutual benefit. This protocol also includes a description of a pharmacovigilance study designed to validate the effect of compounds using electronic health records.For complete details on the use and execution of this protocol, please refer to Gao et al.1 and Baylis et al.2 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.http://www.sciencedirect.com/science/article/pii/S2666166724000480BioinformaticsCancerHealth SciencesRNAseq |
| spellingShingle | Hua Gao Mao Zhang Richard A. Baylis Fudi Wang Johan L.M. Björkegren Jason J. Kovacic Arno Ruusalepp Nicholas J. Leeper Computational protocol to identify shared transcriptional risks and mutually beneficial compounds between diseases STAR Protocols Bioinformatics Cancer Health Sciences RNAseq |
| title | Computational protocol to identify shared transcriptional risks and mutually beneficial compounds between diseases |
| title_full | Computational protocol to identify shared transcriptional risks and mutually beneficial compounds between diseases |
| title_fullStr | Computational protocol to identify shared transcriptional risks and mutually beneficial compounds between diseases |
| title_full_unstemmed | Computational protocol to identify shared transcriptional risks and mutually beneficial compounds between diseases |
| title_short | Computational protocol to identify shared transcriptional risks and mutually beneficial compounds between diseases |
| title_sort | computational protocol to identify shared transcriptional risks and mutually beneficial compounds between diseases |
| topic | Bioinformatics Cancer Health Sciences RNAseq |
| url | http://www.sciencedirect.com/science/article/pii/S2666166724000480 |
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