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Tommaso Mazza

Publications and source records attributed to Tommaso Mazza.

2 recordsLinked to original sources

Global prevalence of hereditary hemorrhagic telangiectasia-associated variants estimated by analysis of large-scale genomic databases.

BACKGROUND: Hereditary hemorrhagic telangiectasia (HHT) is an autosomal dominant disorder with an overwhelming hemorrhagic phenotype. It is mainly caused by variants in the ENG and ACVRL1 genes. HHT prevalence is currently estimated to be 1 in 5000 individuals, but the disease is likely underdiagnosed due to variable clinical presentation, misdiagnosis, and delayed recognition. OBJECTIVES: To estimate the global genetic prevalence of HHT-associated variants in ENG and ACVRL1. METHODS: We analyzed 3 large population-scale genomic databases: gnomAD, All of Us, and Regeneron Genetics Center-Million Exome. We considered known pathogenic and likely pathogenic variants of ENG and ACVRL1 and extended the analysis to potentially pathogenic variants passing the pathogenic criteria established by the guidelines for HHT of the American College of Medical Genetics and Genomics/Association for Molecular Pathology. RESULTS: The genetic prevalence of HHT ranged from 1.753 to 2.555 in 5000 individuals, when considering only pathogenic and likely pathogenic variants, and from 2.874 to 4.327 in 5000 individuals, when also potentially pathogenic variants were considered. CONCLUSION: This study assesses the prevalence of HHT-associated variants in the general population. Our unbiased approach demonstrates that the genetic prevalence of the disease is substantially higher than currently estimated.

Humans↗

Using ontologies in PROTEUS for modeling proteomics data mining applications.

Bioinformatics applications are often characterized by a combination of (pre) processing of raw data representing biological elements, (e.g. sequence alignment, structure prediction), and an high level data mining analysis. Developing such applications needs knowledge of both data mining and bioinformatics domains, that can be effectively achieved by combining ontology about the application domain and ontology about the approaches and processes to solve the given problem. In this paper we talk about using ontologies to model proteomics in silico experiments. In particular data mining of mass spectrometry proteomics data is considered.

Computational Biology↗