![]() ![]() Whole-genome sequencing remains the superior strategy for the comprehensive detection of all types of sequence variants. Copy number variants (CNVs) spanning multiple exons can be called with reasonable sensitivity using panel and exome data. All three strategies generally offer excellent sensitivity for detecting SNVs/indels using tools such as GATK HaplotypeCaller and Platypus. These differences in depth and breadth of sequencing coverage have implications on variant calling. Other considerations, such as cost and turnaround time, also influence the choice of sequencing strategy but are beyond the scope of this review. Whole-genome sequencing offers the most comprehensive approach and typically yields ~ 30–60× average sequence depth across the entire genome. Exome sequencing, which targets virtually all ~ 20,000 protein-coding genes, typically achieves > 100× average depth across the target regions. Numerous gene panels are commercially available, ranging in size from a single gene to hundreds of genes. For example, the OtoSCOPE hearing loss panel targets 89 genes and microRNAs associated with hearing loss (1574 total exons) across a cohort of 711 sequenced patients, the average sequence depth achieved was 716× per patient. Single- or multi-gene panels are increasingly cost-effective means of testing for subsets of genes associated with specific clinical phenotypes. ![]() The choice of sequencing strategy for a clinical sample has important ramifications for variant calling (Table 1). I also include guidance on benchmarking NGS analysis pipeline performance using “gold standard” reference datasets to achieve the optimum balance of sensitivity and specificity. This includes recommendations for the choice of sequencing strategy, NGS read alignment/preprocessing, combination of multiple variant calling tools, and rigorous filtering to remove false positives. In this review, I discuss the current “best practices” for variant calling in clinical sequencing for both germline analysis in family trios and somatic analysis of tumor-normal pairs. Ten years and thousands of samples later, we now have a much deeper understanding of the capabilities and limitations of NGS for detecting and characterizing sequence variation. As NGS technologies have matured, so too have the software tools for key analytical tasks, such as variant calling. The characteristics and sheer volume of NGS reads necessitated the development of a new generation of computational algorithms and analysis pipelines equipped to handle such data. Targeted panels which leverage this approach to interrogate medically relevant subsets of genes have become core components of precision oncology. Whole-exome sequencing, which selectively targets the protein-coding regions of known genes, has become a frontline diagnostic tool for inherited disorders. They have also been widely adopted for clinical genetic testing. NGS technologies enabled ambitious large-scale genomic sequencing efforts that have transformed our understanding of human health and disease, such The Cancer Genome Atlas, the Centers for Mendelian Genomics, and the UK10K Project. The emergence of next-generation sequencing more than a decade ago represented a major technological advance over traditional sequencing methods. Although NGS technologies are continually evolving, and new capabilities (such as long-read single-molecule sequencing) are emerging, the “best practice” principles in this review should be relevant to clinical variant calling in the long term. Recommended tools and strategies for calling variants of different classes are also provided, along with guidance on variant review, validation, and benchmarking to ensure optimal performance. I describe the relative strengths and weaknesses of panel, exome, and whole-genome sequencing for variant detection. In this review, I discuss the current best practices for variant calling in clinical sequencing studies, with a particular emphasis on trio sequencing for inherited disorders and somatic mutation detection in cancer patients. Just as NGS technologies have evolved considerably over the past 10 years, so too have the software tools and approaches for detecting sequence variants in clinical samples. Accurate variant calling in NGS data is a critical step upon which virtually all downstream analysis and interpretation processes rely. Next-generation sequencing technologies have enabled a dramatic expansion of clinical genetic testing both for inherited conditions and diseases such as cancer. ![]()
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