The vast amount of data generated by whole-exome sequencing (WES) introduces new opportunities for cancer research, but simultaneously poses challenges that require novel computational and theoretical approaches in big data analysis. While workflows for raw data processing and variant calling have improved, filtering tens of thousands of candidate genes and variants to identify a subset of relevant ones is still complex.
The most challenging part of using WES is analyzing, interpreting, and filtering the large number of detected variants.
A robust, methodical WES analysis pipeline is essential to help researchers decipher which of the detected variants have functional significance and which are irrelevant to the phenotype in question. In order to pinpoint the most relevant variants, comprehensive annotation of all detected variants is necessary. With large data sets coming from WES, prioritization is crucial to reduce the list of relevant variants to a manageable set requiring further validation.
This application note from Qiagen presents a protocol for whole-exome variant annotation using QCI Interpret Translational, a next-generation sequencing variant assessment software solution that enables evidence-powered variant annotation, filtering, and triage for human exome, genome, and large cohort sequencing data.
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