Short Read (Next-Generation) Sequencing

Abstract

Rapid advances in DNA sequencing technologies have made it increasingly cost-effective to obtain accurate and timely large-scale genomic sequence data on individuals (short read massively parallel or next generation [next-gen]). A next-gen molecular diagnostic approach that has seen rapid deployment in the clinic over the last year is exome sequencing. Whole exome sequencing covers all protein-coding genes in the genome (≈1.1% of genome), and an exome test for a single patient generates ≈6 gigabases (109 bp) of DNA sequence data. A key challenge facing routine use of next-gen data in patient diagnosis and management is data interpretation. What sequence variant findings are relevant to diagnosis (pathogenic mutations)? What sequence variant findings are relevant to clinical care but not necessarily to patient diagnosis (clinically actionable incidental data)? What sequence information should be stored, and where can it be stored? This review provides a tutorial on current approaches to answering these questions. A recent landmark study showed that application of next-gen sequencing to a large cohort of idiopathic dilated cardiomyopathy patients found ≈27% of patients to show mutations of the titin gene, the most complex gene in the genome (363 exons). We use titin in cardiomyopathy as an exemplar for explaining next-gen sequencing approaches and data interpretation. Decreasing sequencing costs and broad dissemination of next-generation (next-gen) equipment and expertise are increasing availability of massively parallel sequencing of patient DNA samples (short read massively parallel or next-gen sequencing).1,2 Most rapidly expanding is exome sequencing, where all protein-coding sequences (exons) are selected from total genomic DNA and selectively sequenced.3 Alternative approaches to next-gen sequencing include targeted sequencing (TS) and whole genome (complete genome) sequencing. Currently, marketed targeted Sanger sequencing panels using traditional individual exon-by-exon sequencing remain expensive and time consuming, and massively parallel next-gen approaches are beginning to supplant …