Browsing by Author "Dong, Zirui"
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- Chromosome structural variants: Epidemiology, identification and contribution to human diseasesPublication . Dong, Zirui; David, Dezso; Gonzaga-Jauregui, Claudia; Morton, Cynthia C.; Zepeda-Mendoza, Cinthya J.Human chromosome structural variants (SVs) are balanced/unbalanced genomic abnormalities that include translocation, inversion, insertion, and deletion/duplication (also known as copy-number variants, CNVs) events with a size of >50 bp. Currently, the capability of genome sequencing in the research and clinical fields has increased our capacity to detect cryptic SVs and further delineate the complexity of karyotypically/microarray detectable SVs. This has increased our knowledge of pathogenicity mechanisms by considering dysregulation of gene expression through position effects and complex interactions between gene dosage and mutational burden. However, much of the contribution of SVs to human disease is left to explore, as the incidence of SVs is still underestimated owing to limitations of current sequencing technologies and analytical pipelines, and few studies have comprehensively integrated SV information with single nucleotide variants in congenital diseases. Rigorous investigation of SV pathogenicity is warranted for clinical applications. The Research Topic in this issue is divided into three main sections: three articles demonstrate methodologies in SV identification and pathogenicity annotation; five papers discuss the spectrum of SVs in individuals with different indications; and two reports characterize sequence complexity of SVs [...].
- SVInterpreter: A Comprehensive Topologically Associated Domain-Based Clinical Outcome Prediction Tool for Balanced and Unbalanced Structural VariantsPublication . Fino, Joana; Marques, Barbara; Dong, Zirui; David, DezsoWith the advent of genomic sequencing, a number of balanced and unbalanced structural variants (SVs) can be detected per individual. Mainly due to incompleteness and the scattered nature of the available annotation data of the human genome, manual interpretation of the SV’s clinical significance is laborious and cumbersome. Since bioinformatic tools developed for this task are limited, a comprehensive tool to assist clinical outcome prediction of SVs is warranted. Herein, we present SVInterpreter, a free Web application, which analyzes both balanced and unbalanced SVs using topologically associated domains (TADs) as genome units. Among others, gene-associated data (as function and dosage sensitivity), phenotype similarity scores, and copy number variants (CNVs) scoring metrics are retrieved for an informed SV interpretation. For evaluation, we retrospectively applied SVInterpreter to 97 balanced (translocations and inversions) and 125 unbalanced (deletions, duplications, and insertions) previously published SVs, and 145 SVs identified from 20 clinical samples. Our results showed the ability of SVInterpreter to support the evaluation of SVs by (1) confirming more than half of the predictions of the original studies, (2) decreasing 40% of the variants of uncertain significance, and (3) indicating several potential position effect events. To our knowledge, SVInterpreter is the most comprehensive TAD-based tool to identify the possible disease-causing candidate genes and to assist prediction of the clinical outcome of SVs. SVInterpreter is available at http://dgrctools-insa.min-saude.pt/cgi-bin/SVInterpreter.py.
- SVInterpreter: a web-based tool for structural variants inspection and identification of possible disease-causing candidate genesPublication . Fino, Joana; Marques, Barbara; Dong, Zirui; David, DezsoIntroduction: With the advent of genomic sequencing, the identification of structural variants (SVs) is no longer a challenge, being possible to detect an average of 5 K SVs by individual. Contrarily, the annotation of the genome is incomplete, and the data is scattered along different databases, making SV manual evaluation complicated and time-consuming. Also, the available tools are limited on their scope. Thus, to address the need of a comprehensive application to assist evaluation of clinical outcome of SVs, we developed Structural Variant Interpreter (SVInterpreter). Methods: SVInterpreter is a free Python-CGI developed Web application able to analyze SVs using Topologically Associated Domains as genome units, within which genome browsers data, medically actionable genes, virtual gene panels and HPO similarity results, among other information, is retrieved. Results: We started by re-analysing 220 published SVs, of which about 50% were previously classified as VUS. SVInterpreter corroborated the previous classification in about 84% of the SVs. In about 5% of the SVs, SVInterpreter gave indication of possible position effect, through phenotype similarity, disrupted chromatin loops or genome wide association studies. Then, we show the applicability of SVInterpreter on the clinical setting, by inspecting 15 cases analysed by chromosomal microarray or genome sequencing. Conclusions: To our knowledge, SVInterpreter is the most comprehensive TAD based tool to assist prediction of clinical outcome of SVs. Based on gathered information, identification of possible disease-causing candidate genes and SVs is easily achievable. SVInterpreter is available at http://dgrctools-insa.min-saude.pt/cgi-bin/SVInterpreter.py