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Saparov, A. ; Zech, M.

Big data and transformative bioinformatics in genomic diagnostics and beyond.

Parkinsonism Relat. Disord.:107311 (2025)
Article in press DOI PMC
Open Access Gold (Paid Option)
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
Korrespondenzautor
Schlagwörter Bioinformatics ; Dystonia ; Genomics ; Multi-omics ; Phenomics ; Big Data
ISSN (print) / ISBN 1353-8020
e-ISSN 1873-5126
Zeitschrift Parkinsonism & Related Disorders
Quellenangaben Band: , Heft: , Seiten: , Artikelnummer: 107311 Supplement: ,
Verlag Elsevier
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Neurogenomics (ING)