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

doi:10.1016/j.dib.2016.03.004

Prediction and visualization data for the interpretation of sarcomeric and non-sarcomeric DNA variants found in patients with hypertrophic cardiomyopathy

Data Brief. 2016 Mar 10:7:607-13. doi: 10.1016/j.dib.2016.03.004. eCollection 2016 Jun.

Affiliations

¹ Medical Genetics, Department of Molecular Medicine, Sapienza University, San Camillo-Forlanini Hospital, Rome, Italy.
² Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy.
³ Department of Biochemical Sciences, Sapienza University of Rome, Rome, Italy.
⁴ Cardiomyopathies Unit, Division of Cardiology and Cardiac Arrhythmias, San Camillo-Forlanini Hospital, Rome, Italy.

Abstract

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.