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Saparov, A. ; Zech, M.

Big data and transformative bioinformatics in genomic diagnostics and beyond.

Parkinsonism Relat. Disord. 134:107311 (2025)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
The current era of high-throughput analysis-driven research offers invaluable insights into disease etiologies, accurate diagnostics, pathogenesis, and personalized therapy. In the field of movement disorders, investigators are facing an increasing growth in the volume of produced patient-derived datasets, providing substantial opportunities for precision medicine approaches based on extensive information accessibility and advanced annotation practices. Integrating data from multiple sources, including phenomics, genomics, and multi-omics, is crucial for comprehensively understanding different types of movement disorders. Here, we explore formats and analytics of big data generated for patients with movement disorders, including strategies to meaningfully share the data for optimized patient benefit. We review computational methods that are essential to accelerate the process of evaluating the increasing amounts of specialized data collected. Based on concrete examples, we highlight how bioinformatic approaches facilitate the translation of multidimensional biological information into clinically relevant knowledge. Moreover, we outline the feasibility of computer-aided therapeutic target evaluation, and we discuss the importance of expanding the focus of big data research to understudied phenotypes such as dystonia.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Bioinformatics ; Dystonia ; Genomics ; Multi-omics ; Phenomics ; Big Data; Integrative Omics; Mutation Database; Whole-genome; Rare; Framework; Archive; Health; Genes
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
ISSN (print) / ISBN 1353-8020
e-ISSN 1873-5126
Zeitschrift Parkinsonism & Related Disorders
Quellenangaben Band: 134, Heft: , Seiten: , Artikelnummer: 107311 Supplement: ,
Verlag Elsevier
Verlagsort 125 London Wall, London, England
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Neurogenomics (ING)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Genetics and Epidemiology
PSP-Element(e) G-503200-001
Förderungen Technical University of Munich-Institute for Advanced Study
German Research Foundation
Free State of Bavaria under the Excellence Strategy of the Federal Government and the Lander
Federal Ministry of Education and Research (BMBF)
Else Kroner-Fresenius-Stiftung
German Federal Ministry of Education and Research (BMBF, Bonn, Germany)
EJP RD (EJP RD Joint Transnational Call 2022)
DOI 10.1016/j.parkreldis.2025.107311
WOS ID 001476121900001
Scopus ID 85217252484
PubMed ID 39924354
Erfassungsdatum 2025-04-09
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