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- 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
- Detection of copy number variants in the human genome: Is long-read sequencing an alternative to genomic microarrays?Publication . Silva, Catarina; Ferrão, José; Marques, Barbara; Pedro, Sónia; Correia, Hildeberto; Rodrigues, António Sebastião; Vieira, LuísIntroduction: Copy number variations (CNVs) represent ~13% of the human genome and can harbour important genes and regulatory elements. High-resolution whole genome microarray (MA) analysis is the gold standard tool for detection of CNVs associated with genetic disorders. While short-read sequencing (SRS) can address SV detection, the use of long-read sequencing as proven to overcome SRS mapping inaccuracy in highly repetitive DNA regions and improve genome contiguity. We applied whole genome nanopore sequencing (NS) to call CNVs and compared the results with those obtained by microarray. Methodology: Genomic DNA from 2 cell lines (EOL-1 and 697) were processed using the CytoSan HD Array (Affymetrix) and ChAS software (ThermoFisher). A minimum CNV calling size threshold of 35 Kb was used. DNA was also sequenced on the MinION device (Oxford Nanopore Technologies) following a rapid library preparation method. Sequencing data were basecalled using Guppy, mapped with LRA, and SVs called using both CuteSV and Sniffles2. Sanger sequencing was performed to demonstrate breakpoint positions for 3 CNVs. R packages were used to perform comparisons between MA and NS data. Results: A total of 49 CNVs were confirmed after curated MA analysis in both cell lines, ranging in size from 35 Kb to 79 Mb. From those, 43 CNVs (87.7%) were called in nanopore data by either one (4 CNVs) or both (39 CNVs) callers with a mean whole genome coverage of ~12X. Six of 43 CNVs were called as inversions instead. In 3 CNVs the size of the variant was found to be smaller (ranging from ~5 to 22 Kb) than the threshold of MA analysis. The correlation between CNV sizes obtained with MA and NS was of 0.71 with Sniffles2 and 0.74 with CuteSV, whereas the correlation between callers was of 0.99. The breakpoint precision obtained for NS was much higher (ranging for CuteSV from 2 to 42 bp; and for Sniffles2 from 0 to 87 bp) than the one obtained for MA (ranging from 774 to 7618 bp). Conclusions: NS technology proved to be technically effective in the detection of CNVs of different types and sizes and thus posing itself as an alternative to MA in the detection of pathogenic SVs associated with genetic diseases. However, NS data analysis requires fine-tuning of the analysis conditions as well as the use of different methods, for greater reliability of results in a clinical context.