genomize-Seq’s Pathogenicity Classification For Variants

June 12, 2017 in White Papers
With the advancements in high-throughput next generation sequencing (NGS), sequencing has become more affordable and faster. The applicability of NGS at gene panel, exome and genome levels makes it a very versatile and robust option in clinical testing. However, this method comes with some challenges. The large amount of data produced by NGS is not always straightforward to analyze, and proper analysis and interpretation of clinically significant variants is the key for an accurate clinical diagnosis. In this blog letter, we are going to explain the standards and guidelines adopted by genomize-Seq for sequence variant interpretation.

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genomize-Seq’s Confidence Classification for Variants

May 9, 2017 in White Papers
genomize-Seq is a next generation sequencing (NGS) data analysis, management and sharing platform. In this blog letter, we will explain the parameters genomize-Seq employs for variant confidence classification.
genomize-Seq uses a three-level confidence classification scheme, with the classes High, Low and Failed. (Figure 1) A parameter optimization is necessary to classify the real variants into high confidence class. As a variety of algorithms and dozens of parameters are used in Next Generation Sequencing, different analyses of the same raw data may cause different results (Chapman, O’Rawe et al.). No matter how complex the parameter optimization and machine learning algorithms are, the clinician and/or the patient will want to know the real result.

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