Prediction and visualization data for the interpretation of sarcomeric and non-sarcomeric DNA variants found in patients with hypertrophic cardiomyopathy
Irene Bottillo,
Daniela D’Angelantonio,
Viviana Caputo,
Alessandro Paiardini,
Martina Lipari,
Carmelilia De Bernardo,
Silvia Majore,
Marco Castori,
Elisabetta Zachara,
Federica Re,
Paola Grammatico
Affiliations
Irene Bottillo
Medical Genetics, Department of Molecular Medicine, Sapienza University, San Camillo-Forlanini Hospital, Rome, Italy; Correspondence to: Medical Genetics, Department of Molecular Medicine, Sapienza University, San Camillo-Forlanini Hospital Circonvallazione Gianicolense, 87 - 00152 Rome, Italy. Tel.: +39 06 58704622; fax: +39 06 5870 4657.
Daniela D’Angelantonio
Medical Genetics, Department of Molecular Medicine, Sapienza University, San Camillo-Forlanini Hospital, Rome, Italy
Viviana Caputo
Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
Alessandro Paiardini
Department of Biochemical Sciences, Sapienza University of Rome, Rome, Italy
Martina Lipari
Medical Genetics, Department of Molecular Medicine, Sapienza University, San Camillo-Forlanini Hospital, Rome, Italy
Carmelilia De Bernardo
Medical Genetics, Department of Molecular Medicine, Sapienza University, San Camillo-Forlanini Hospital, Rome, Italy
Silvia Majore
Medical Genetics, Department of Molecular Medicine, Sapienza University, San Camillo-Forlanini Hospital, Rome, Italy
Marco Castori
Medical Genetics, Department of Molecular Medicine, Sapienza University, San Camillo-Forlanini Hospital, Rome, Italy
Elisabetta Zachara
Cardiomyopathies Unit, Division of Cardiology and Cardiac Arrhythmias, San Camillo-Forlanini Hospital, Rome, Italy
Federica Re
Cardiomyopathies Unit, Division of Cardiology and Cardiac Arrhythmias, San Camillo-Forlanini Hospital, Rome, Italy
Paola Grammatico
Medical Genetics, Department of Molecular Medicine, Sapienza University, San Camillo-Forlanini Hospital, Rome, Italy
Genomic technologies are redefining the understanding of genotype–phenotype relationships and over the past decade, many bioinformatics algorithms have been developed to predict functional consequences of single nucleotide variants. This article presents the data from a comprehensive computational workflow adopted to assess the biomedical impact of the DNA variants resulting from the experimental study “Molecular analysis of sarcomeric and non-sarcomeric genes in patients with hypertrophic cardiomyopathy” (Bottillo et al., 2016) [1]. Several different independently methods were employed to predict the functional consequences of alleles that result in amino acid substitutions, to study the effect of some DNA variants over the splicing process and to investigate the impact of a sequence variant with respect to the evolutionary conservation.
