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A Whole Germline BRCA2 Gene Deletion: How to Learn from CNV In Silico Analysis.

A Whole Germline BRCA2 Gene Deletion: How to Learn from CNV In Silico Analysis.
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Scaglione GL, Concolino P, De Bonis M, De Paolis E, Minucci A, Ferrandina G, Scambia G, Capoluongo E,


Scaglione GL, Concolino P, De Bonis M, De Paolis E, Minucci A, Ferrandina G, Scambia G, Capoluongo E, (click to view)

Scaglione GL, Concolino P, De Bonis M, De Paolis E, Minucci A, Ferrandina G, Scambia G, Capoluongo E,

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International journal of molecular sciences 2018 03 2319(4) pii 10.3390/ijms19040961

Abstract

screening in Hereditary Breast and Ovarian Syndrome (HBOC) is an essential step for effective patients’ management. Next-Generation Sequencing (NGS) can rapidly provide high throughput and reliable information about the qualitative and quantitative status of tumor-associated genes. Straightforwardly, bioinformatics methods play a key role in molecular diagnostics pipelines.genes were evaluated with our NGS workflow, coupled with Multiplex Amplicon Quantification (MAQ) and Multiplex Ligation-dependent Probe Amplification (MLPA) assays. Variant calling was performed on, while Copy Number Variant (CNV) prediction by in house and commercial CNV tools, before confirmatory MAQ/MLPA testing. The germline profile ofgenes revealed a unique HBOC pattern. Although variant calling analysis pinpointed heterozygote and homozygote polymorphisms onand, respectively, the CNV predicted by our script suggested two conflicting interpretations:duplication and/ordeletion. Our commercial software reported aduplication, in contrast with variant calling results. Finally, the MAQ/MLPA assays assessed a wholecopy loss. In silico CNV analysis is a time and cost-saving procedure to powerfully identify possible Large Rearrangements using robust and efficient NGS pipelines. Our layout shows as bioinformatics algorithms alone cannot completely and correctly identify wholedeletions/duplications. In particular, the complete deletion of an entire gene, like in our case, cannot be solved without alternative strategies as MLPA/MAQ. These findings support the crucial role of bioinformatics in deciphering pitfalls within NGS data analysis.

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