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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Elsevier
2016-06-01
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| Series: | Data in Brief |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340916301123 |
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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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