Managing Large-Scale Genomic Datasets and Translation into Clinical Practice

• Published in: Yearbook of medical informatics Vol. 23; no. 1; pp. 212 - 214
• Main Authors: Lecroq, T., Soualmia, L. F.
• Format: Journal Article
• Language: English; German
• Published: Stuttgart Schattauer Verlag für Medizin und Naturwissenschaften 15.08.2014; Georg Thieme Verlag KG; Schattauer; Schattauer GmbH
• Subjects: big data management; Bioinformatics; Computational Biology; Computer Science; Electronic Health Records; gene expression; genome; Genomics; Humans; Male; Medical Informatics; Original Article; Translational Medical Research; computational biology; genome; medical informatics; big data management; gene expression; Translational medical research
• ISSN: 0943-4747; 2364-0502
• DOI: 10.15265/IY-2014-0039

Objective:To summarize excellent current research in the field of Bioinformatics and Translational Informatics with application in the health domain. Method: We provide a synopsis of the articles selected for the IMIA Yearbook 2014, from which we attempt to derive a synthetic overview of current and future activities in the field. A first step of selection was performed by querying MEDLINE with a list of MeSH descriptors completed by a list of terms adapted to the section. Each section editor evaluated independently the set of 1,851 articles and 15 articles were retained for peer-review. Results: The selection and evaluation process of this Yearbook's section on Bioinformatics and Translational Informatics yielded three excellent articles regarding data management and genome medicine. In the first article, the authors present VEST (Variant Effect Scoring Tool) which is a supervised machine learning tool for prioritizing variants found in exome sequencing projects that are more likely involved in human Mendelian diseases. In the second article, the authors show how to infer surnames of male individuals by crossing anonymous publicly available genomic data from the Y chromosome and public genealogy data banks. The third article presents a statistical framework called iCluster+ that can perform pattern discovery in integrated cancer genomic data. This framework was able to determine different tumor subtypes in colon cancer. Conclusions: The current research activities still attest the continuous convergence of Bioinformatics and Medical Informatics, with a focus this year on large-scale biological, genomic, and Electronic Health Records data. Indeed, there is a need for powerful tools for managing and interpreting complex data, but also a need for user-friendly tools developed for the clinicians in their daily practice. All the recent research and development efforts are contributing to the challenge of impacting clinically the results and even going towards a personalized medicine in the near future.