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

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 w...

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Main Authors:	Irene Bottillo, Daniela D’Angelantonio, Viviana Caputo, Alessandro Paiardini, Martina Lipari, Carmelilia De Bernardo, Silvia Majore, Marco Castori, Elisabetta Zachara, Federica Re, Paola Grammatico
Format:	Article
Language:	English
Published:	Elsevier 2016-06-01
Series:	Data in Brief
Online Access:	http://www.sciencedirect.com/science/article/pii/S2352340916301123

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spelling	doaj-d58b4d23c74d4869a07aad78f33829592020-11-24T22:00:22ZengElsevierData in Brief2352-34092016-06-017607613Prediction and visualization data for the interpretation of sarcomeric and non-sarcomeric DNA variants found in patients with hypertrophic cardiomyopathyIrene Bottillo0Daniela D’Angelantonio1Viviana Caputo2Alessandro Paiardini3Martina Lipari4Carmelilia De Bernardo5Silvia Majore6Marco Castori7Elisabetta Zachara8Federica Re9Paola Grammatico10Medical 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.Medical Genetics, Department of Molecular Medicine, Sapienza University, San Camillo-Forlanini Hospital, Rome, ItalyDepartment of Experimental Medicine, Sapienza University of Rome, Rome, ItalyDepartment of Biochemical Sciences, Sapienza University of Rome, Rome, ItalyMedical Genetics, Department of Molecular Medicine, Sapienza University, San Camillo-Forlanini Hospital, Rome, ItalyMedical Genetics, Department of Molecular Medicine, Sapienza University, San Camillo-Forlanini Hospital, Rome, ItalyMedical Genetics, Department of Molecular Medicine, Sapienza University, San Camillo-Forlanini Hospital, Rome, ItalyMedical Genetics, Department of Molecular Medicine, Sapienza University, San Camillo-Forlanini Hospital, Rome, ItalyCardiomyopathies Unit, Division of Cardiology and Cardiac Arrhythmias, San Camillo-Forlanini Hospital, Rome, ItalyCardiomyopathies Unit, Division of Cardiology and Cardiac Arrhythmias, San Camillo-Forlanini Hospital, Rome, ItalyMedical Genetics, Department of Molecular Medicine, Sapienza University, San Camillo-Forlanini Hospital, Rome, ItalyGenomic 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.http://www.sciencedirect.com/science/article/pii/S2352340916301123
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Irene Bottillo Daniela D’Angelantonio Viviana Caputo Alessandro Paiardini Martina Lipari Carmelilia De Bernardo Silvia Majore Marco Castori Elisabetta Zachara Federica Re Paola Grammatico
spellingShingle	Irene Bottillo Daniela D’Angelantonio Viviana Caputo Alessandro Paiardini Martina Lipari Carmelilia De Bernardo Silvia Majore Marco Castori Elisabetta Zachara Federica Re Paola Grammatico Prediction and visualization data for the interpretation of sarcomeric and non-sarcomeric DNA variants found in patients with hypertrophic cardiomyopathy Data in Brief
author_facet	Irene Bottillo Daniela D’Angelantonio Viviana Caputo Alessandro Paiardini Martina Lipari Carmelilia De Bernardo Silvia Majore Marco Castori Elisabetta Zachara Federica Re Paola Grammatico
author_sort	Irene Bottillo
title	Prediction and visualization data for the interpretation of sarcomeric and non-sarcomeric DNA variants found in patients with hypertrophic cardiomyopathy
title_short	Prediction and visualization data for the interpretation of sarcomeric and non-sarcomeric DNA variants found in patients with hypertrophic cardiomyopathy
title_full	Prediction and visualization data for the interpretation of sarcomeric and non-sarcomeric DNA variants found in patients with hypertrophic cardiomyopathy
title_fullStr	Prediction and visualization data for the interpretation of sarcomeric and non-sarcomeric DNA variants found in patients with hypertrophic cardiomyopathy
title_full_unstemmed	Prediction and visualization data for the interpretation of sarcomeric and non-sarcomeric DNA variants found in patients with hypertrophic cardiomyopathy
title_sort	prediction and visualization data for the interpretation of sarcomeric and non-sarcomeric dna variants found in patients with hypertrophic cardiomyopathy
publisher	Elsevier
series	Data in Brief
issn	2352-3409
publishDate	2016-06-01
description	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.
url	http://www.sciencedirect.com/science/article/pii/S2352340916301123
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Prediction and visualization data for the interpretation of sarcomeric and non-sarcomeric DNA variants found in patients with hypertrophic cardiomyopathy

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