Browsing by Issue Date, starting with "2019-10-28"
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- The Technology and Innovation Unit of the National Institute of Health: A sequencing and bioinformatics core facility specializing in public health genomicsPublication . Silva, Catarina; Ataíde Sampaio, Daniel; Mendonça, Joana; Carpinteiro, Dina; Duarte, Sílvia; Barreiro, Paula; Isidro, Joana; Machado, Miguel; Vieira, LuísThe National Institute of Health (INSA) has a long tradition in investigating the molecular etiology of genetic and complex diseases. These activities greatly benefit from centralized sequencing services provided by the Technology and Innovation Unit (UTI). Its mission is to perform sequencing and genotyping assays in the framework of research, diagnosis and epidemiological surveillance, as well as to implement data analysis pipelines for the study of human gene variants. The equipment portfolio includes a NextSeq 550, a MiSeq, two 3500 AB Genetic Analyzers, a Fragment Analyzer and a 7500 Real-time PCR system. UTI provides results for average of 36.000 sequencing/fragment samples per year. The team has already performed >300 small genome, amplicon, gene panel (including clinical exome), 16S rRNA gene and RNA/microRNA next-generation sequencing assays for INSA and for several Universities in the scope of scientific collaborations. Technical procedures are conducted under a quality control system that includes external quality assessment for next-generation sequencing/Sanger sequencing and ISO 15189 accreditation for Sanger sequencing. UTI plays a key role in public health genomics, providing state-of-the-art equipment, centralized resources, technical expertise and short response times.
- NGS Panels applied to Hereditary Cancer SyndromesPublication . Rodrigues, Pedro; Theisen, Patrícia; Silva, Catarina; Vieira, Luís; Gonçalves, JoãoCancer is among the leading causes of morbidity and mortality worldwide (Okur et al, 2017). Germline pathogenic variants for monogenic, highly penetrant cancer susceptibility genes are observed in 5%–10% of all cancers (Lu et al, 2014). Hereditary cancers due to monogenic causes are characterized by earlier age of onset, other associated cancers, and often a family history of specific cancers. From the clinical perspective, it is important to recognize the affected individuals to provide them the best clinical management (Hennessy et al, 2010; Ledermann et al, 2014; Pennington et al, 2014) and to identify at-risk family members who will benefit from predictive genetic testing and enhanced surveillance, including early detection and/or risk reduction measures (Kurian et al, 2010; Okur et al, 2017). Germline variants identified in major cancer susceptibility genes associated with hereditary breast or ovarian cancer (HBOC) or hereditary colorectal cancer (HCRC), also account for 5-10% of the patients with these cancers. In the last years, new susceptibility genes, with different penetrance degrees, have been identified. Variants in any of those genes are rare and classical methodologies (e.g. Sanger sequencing - SS) are time consuming and expensive. Next-generation sequencing (NGS) has several advantages compared to SS, including the simultaneous analysis of many samples and sequencing of a large set of genes, higher sensitivity (down to 1% vs 15-20% in SS), lower cost and faster turnaround time, reasons that make NGS the best approach for molecular diagnosis. It is possible nowadays to choose between whole-genome sequencing (WGS), whole-exome sequencing (WES) and NGS limited to a set of genes (NGS-Panel). In cases where a suspected genetic disease or condition has been identified, targeted sequencing of specific genes or genomic regions is preferred (Grada et al, 2013). For that reason, we use NGS-Panel approach using TruSight Cancer (Illumina) to sequence DNA extracted from blood samples of patients with personal and/or familiar history of cancer. This hereditary cancer gene panel sequences 94 genes associated with both common (e.g., breast, colorectal) and rare hereditary cancers and allows the creation of virtual gene panels according to each phenotype or disease under study. NGS workflow analysis (Figure 1) includes five steps: quality assessment of raw data, read alignment to a reference genome, variant identification/calling, variant annotation and data visualization (Pabinger et al, 2013). The establishment of the most appropriate bioinformatics pipeline is crucial in order to achieve the best results. NGS data allows the identification of several types of variants like single nucleotide variants (SNVs), small insertions/deletions, inversions and also copy number variants (CNVs).
- Environmental and Genetic Determinants of Human Disease: an overview of current research and the way forwardPublication . Silva, Maria João; Louro, Henriqueta; Ventura, Célia; Gonçalves, JoãoAn overview of current research and the way forward on the environmental and genetic determinants of human disease.