Since the first reports of the relation between copy number variations (CNVs) and human diseases, CNV analysis has become an integral part of clinical genetics. NGS, which is routinely used in small variant detection, promises detection of CNVs as well, though with some caveats.
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With the release 6.0, SEQ Platform now auto-classifies variants detected in a sample, based on ACMG’s Standards and Guidelines for the Interpretation of Sequence Variants.
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SEQ platform lists variant annotations of the variants detected in a sample on analysis details page. Therefore, sometimes there may be more than one annotation for the same variant. Figure 1 shows two annotations of the same variant. The variants located on overlapping genes may have more than one annotation.
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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 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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