However, it remains a challenging task to interpret sequencing data in terms of highlighting the disease-causing gene from thousands of mutated genes in one’s VCF file. For instance, BWA + GATK is frequently recommended and employed. The software pipeline for a typical workflow from raw sequencing data (FASTQ format) to a variant call format (VCF) file is relatively mature. Traditional interpretationĮmerging genome and exome sequencing technologies and platforms are producing massive amounts of sequencing data globally. Recently, some gene prioritization and literature evidence search tools are also provided to save manually search time. To interpret a VCF file and determine the disease-cause gene, traditional interpretation tools are required at first. It can facilitate the application of genomic analytics in clinical research and practices. provides an intelligent web portal for genomics data interpretation via the integration of bioinformatics tools, distributed parallel computing, biomedical text mining. Evaluation on the DDD project dataset demonstrates an accuracy of 77% (235 out of 305 cases) for top-50 genes and an accuracy of 41.6% (127 out of 305 cases) for top-5 genes. The system is freely available as a web portal at for academic research. Its phenotype-driven ranking and biological data mining approach significantly speed up the whole interpretation process. Therefore, we developed, an online DNA sequencing interpretation system, which prioritizes genes and variants for novel disease-gene relation discovery and integrates text mining results to provide literature evidence for the discovery. Those steps are time-consuming and error-prone in the absence of systematic support. A typical interpretation workflow includes annotation, filtration, manual inspection and literature review. It is still challenging despite the recent rapid development of genomics and bioinformatics. An important task in the interpretation of sequencing data is to highlight pathogenic genes (or detrimental variants) in the field of Mendelian diseases.
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