Collectively, rare genetic disorders affect a substantial portion of the world’s population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.

Resources and tools for rare disease variant interpretation / Licata, Luana; Via, Allegra; Turina, Paola; Babbi, Giulia; Benevenuta, Silvia; Carta, Claudio; Casadio, Rita; Cicconardi, Andrea; Facchiano, Angelo; Fariselli, Piero; Giordano, Deborah; Isidori, Federica; Marabotti, Anna; Martelli, Pier Luigi; Pascarella, Stefano; Pinelli, Michele; Pippucci, Tommaso; Russo, Roberta; Savojardo, Castrense; Scafuri, Bernardina; Valeriani, Lucrezia; Capriotti, Emidio. - In: FRONTIERS IN MOLECULAR BIOSCIENCES. - ISSN 2296-889X. - 10:(2023). [10.3389/fmolb.2023.1169109]

Resources and tools for rare disease variant interpretation

Via, Allegra;Pascarella, Stefano;
2023

Abstract

Collectively, rare genetic disorders affect a substantial portion of the world’s population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.
2023
rare disease; variant calling; variant interpretation; pathogenic variants; gene prioritization; data sharing
01 Pubblicazione su rivista::01a Articolo in rivista
Resources and tools for rare disease variant interpretation / Licata, Luana; Via, Allegra; Turina, Paola; Babbi, Giulia; Benevenuta, Silvia; Carta, Claudio; Casadio, Rita; Cicconardi, Andrea; Facchiano, Angelo; Fariselli, Piero; Giordano, Deborah; Isidori, Federica; Marabotti, Anna; Martelli, Pier Luigi; Pascarella, Stefano; Pinelli, Michele; Pippucci, Tommaso; Russo, Roberta; Savojardo, Castrense; Scafuri, Bernardina; Valeriani, Lucrezia; Capriotti, Emidio. - In: FRONTIERS IN MOLECULAR BIOSCIENCES. - ISSN 2296-889X. - 10:(2023). [10.3389/fmolb.2023.1169109]
File allegati a questo prodotto
File Dimensione Formato  
Licata_Resources_2023.pdf

accesso aperto

Tipologia: Documento in Post-print (versione successiva alla peer review e accettata per la pubblicazione)
Licenza: Creative commons
Dimensione 1.83 MB
Formato Adobe PDF
1.83 MB Adobe PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1679508
Citazioni
  • ???jsp.display-item.citation.pmc??? 3
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 5
social impact