Next Generation Sequencing technologies have completely changed the way to study molecular bases underlying Rare Genetic Diseases (RGDs). Currently, sequencing of the exonic portion of the human genome – the exome (1%) – performed through Whole Exome Sequencing (WES) experiments represents the most used approach to discover molecular mechanisms underlying RGDs. To date, several tools have been developed to analyse and interpret data generated from WES. However, due to both technical and experimental limitations, its diagnostic rate is ~20-30%. In this context, we evaluated whether WES data contain information on non-coding sequences, focusing on microRNAs (miRNAs). Comparative analysis of capture design and experimental coverage allowed to disclose that in WES data reside information related to miRNA sequences that are efficiently captured by most exome enrichment kits. We therefore analysed WES of a cohort of 259 individuals, including patients affected by several genetic diseases and their unaffected relatives, searching for variants in miRNAs and performing functional annotation. Sanger sequence validation confirms the reliable call of variants mapping in miRNA sequences. To date, no dedicated tool is available to properly retrieve and analyse miRNAs from WES and WGS data. We therefore developed a tool, “AnnomiR”, that allows to systematically analyse miRNA variants and miRNAs, providing functional annotation retrieved from several databases. This tool can be integrated in a standard workflow of analysis for WES and WGS data. WES data contain a great amount of information that is generally discarded by commonly used workflow of analysis and that should be considered, as it could help in the comprehension of molecular mechanisms underlying RGDs. In this context, systematic study of miRNAs could help elucidating their role as disease-causative and phenotypic modifiers in a wide spectrum of human diseases, allowing to achieve a better characterisation of variability of the human genome related to these non-coding sequences.
Analysis of non-coding DNA from whole exome sequencing data / Giovannetti, Agnese. - (2019 Feb 13).
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|Titolo:||Analysis of non-coding DNA from whole exome sequencing data|
|Data di discussione:||13-feb-2019|
|Appartiene alla tipologia:||07a Tesi di Dottorato|