2026-05-22T00:44:46Zhttps://repisalud.isciii.es/rest/oai/request
oai:repisalud.isciii.es:20.500.12105/184142024-11-28T14:49:41Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16927col_20.500.12105_16963
Repisalud author Rojano, Elena author Córdoba-Caballero, José author Jabato, Fernando M. author Gallego, Diana author Serrano, Mercedes author Pérez, Belén author Parés-Aguilar, Álvaro author Perkins, James R. author Ranea, Juan A G. author Seoane-Zonjic, Pedro authoraffiliation [Rojano,E; Córdoba-Caballero,J; Parés-Aguilar,Á; Perkins,JR; Ranea,JAG; Seoane-Zonjic,P] Department of Molecular Biology and Biochemistry, University of Málaga, Málaga, Spain. [Rojano,E; Jabato,FM; Perkins,JR; Ranea,JAG; Seoane-Zonjic,P] Institute of Biomedical Research in Málaga (IBIMA), Málaga, Spain. [Jabato,FM] Supercomputation and Bioinformatics (SCBI), University of Malaga, Malaga, Spain. [Jabato,FM] LifeWatch-ERIC, Seville, Spain. [Gallego,D; Serrano,M; Pérez,B; Perkins,JR; Ranea,JAG; Seoane-Zonjic,P] Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), [Madrid, Málaga, Barcelona], Instituto de Salud Carlos III, Madrid, Spain. [Gallego,D; Pérez,B] Centro de Diagnóstico de Enfermedades Moleculares, Centro de Biología Molecular-SO UAM-CSIC, Universidad Autónoma de Madrid, Madrid, Spain. [Gallego,D; Pérez,B] Instituto de Investigación Sanitaria idiPAZ, Madrid, Spain. [Serrano,M] Neuropediatric Department, Institut de Recerca Hospital Sant Joan de Déu, Barcelona, Spain. 2024-02-19T15:29:48Z 2024-02-19T15:29:48Z 2021-07-27 10.3390/jpm11080730 2075-4426 Journals of Personalized Medicine http://hdl.handle.net/10668/3924 34442375 http://hdl.handle.net/20.500.12105/18414 Exhaustive and comprehensive analysis of pathological traits is essential to understanding genetic diseases, performing precise diagnosis and prescribing personalized treatments. It is particularly important for disease cohorts, as thoroughly detailed phenotypic profiles allow patients to be compared and contrasted. However, many disease cohorts contain patients that have been ascribed low numbers of very general and relatively uninformative phenotypes. We present Cohort Analyzer, a tool that measures the phenotyping quality of patient cohorts. It calculates multiple statistics to give a general overview of the cohort status in terms of the depth and breadth of phenotyping, allowing us to detect less well-phenotyped patients for re-examining or excluding from further analyses. In addition, it performs clustering analysis to find subgroups of patients that share similar phenotypic profiles. We used it to analyse three cohorts of genetic diseases patients with very different properties. We found that cohorts with the most specific and complete phenotypic characterization give more potential insights into the disease than those that were less deeply characterised by forming more informative clusters. For two of the cohorts, we also analysed genomic data related to the patients, and linked the genomic data to the patient-subgroups by mapping shared variants to genes and functions. The work highlights the need for improved phenotyping in this era of personalized medicine. The tool itself is freely available alongside a workflow to allow the analyses shown in this work to be applied to other datasets. eng Genetic diseases Cohort analyzer Human phenotype ontology Cluster analysis Phenotype quality assessment Enfermedades genéticas congénitas Ontología de genes Análisis por grupos Fenotipo Garantía de la calidad de atención de salud Evaluating, Filtering and Clustering Genetic Disease Cohorts Based on Human Phenotype Ontology Data with Cohort Analyzer research article