This presentation provides an overview of Target Enrichment for Next Generation Sequencing (NGS), emphasizing its role in optimizing sequencing efforts for specific genomic regions. The speaker outlines the benefits, including reduced costs, increased sample throughput, and enhanced ability to detect low allele fractions. Three primary target enrichment methodologies are detailed: Hybridization Capture, Amplicon Technologies, and Primer Extension Target Enrichment (Pete), discussing their respective workflows, panel size capabilities, and enrichment performance, alongside their trade-offs in speed and ability to identify novel fusion partners. Key performance metrics—on-target rate and coverage uniformity—are explained, highlighting their impact on sequencing efficiency. The presentation concludes with application data for Pete technology, demonstrating high on-target rates and uniformity with varied FFPE DNA samples.
Target Enrichment for Next Generation Sequencing: Methods, Metrics, and Applications
0:00 Introduction to Target Enrichment: The presentation introduces target enrichment for Next Generation Sequencing (NGS), covering its methods, performance metrics, and application data.
0:19 Why Target Enrichment? Researchers often only need to sequence a small portion of the genome (e.g., the exome, 1% of the genome, or a gene panel, 0.01%), making whole-genome sequencing inefficient.
0:50 Key Benefits of Target Enrichment: This approach enables labs to focus on specific target content, lowers overall sequencing cost per sample, allows for processing more samples per sequencing run, and facilitates deeper sequencing crucial for detecting low (e.g., 5% or even <1%) allele fractions reliably. The primary goals are increased efficiency and reduced cost per sample.
1:56 Overview of Target Enrichment Technologies: Three main technologies are discussed: Hybridization Capture, Amplicon Technologies, and Primer Extension Target Enrichment (Pete).
Workflow: Involves fragmenting DNA, attaching universal adapters, hybridizing biotin-tagged probes to target regions, binding to streptavidin paramagnetic beads, washing away off-target fragments, and PCR amplification of enriched libraries.
Characteristics: This method has a longer workflow (~1.5 days), supports broad panel sizes (tens of kilobases to tens of megabases, suitable for exome and larger targets), and offers best-in-class enrichment performance.
Workflow: Utilizes target-specific primers for limited PCR amplification, followed by universal adapter ligation and further PCR.
Characteristics: Provides the shortest and easiest workflows (<1 full workday), targets small panel sizes (kilobases to hundreds of kilobases), but has good, though not best-in-class, enrichment performance. A significant limitation is its inability to detect novel fusion partners, as both fusion ends must be known for primer design.
Workflow: Similar to hybridization capture in initial library preparation, followed by a single round of primer extension using biotin-tagged capture primers, bead capture, washing, and then a "release primer" extension that specifically pushes on-target molecules off the bead for collection and subsequent PCR amplification.
Characteristics: Offers a fast and easy workflow (~1 day), supports small panel sizes (kilobases to hundreds of kilobases), delivers best-in-class enrichment performance (comparable to hybridization capture), and can detect novel fusion partners.
7:01 Performance Metric: On-Target Rate: This measures the percentage of sequencing reads that successfully overlap the intended target regions. A higher on-target rate directly reduces the overall sequencing required.
7:38 Performance Metric: Coverage Uniformity: This metric assesses how evenly sequencing reads cover the target regions. Better uniformity ensures that both highly and poorly covered regions are closer to the mean depth, thus requiring less total sequencing to meet minimum coverage requirements across all targets. Poor uniformity necessitates significantly more sequencing to adequately cover low-performing regions.
9:02 Example Application Data for Pete: Using 50 nanograms of low, mid, and high-quality FFPE DNA, Pete demonstrated robust performance with an on-target rate exceeding 70% across all sample qualities. Coverage uniformity, measured as the percentage of bases greater than 300X depth, was approximately 99% across most samples, with one low-quality outlier at 92%.
9:36 Key Takeaways: Target enrichment efficiently lowers sequencing costs, increases sample processing capacity, and enables deeper sequencing for low allele fraction detection. Hybridization capture is optimal for broad panels and high performance but requires longer workflows. Amplicon methods offer speed for small panels but at the cost of performance and the inability to detect novel fusions. Pete balances these by providing fast workflows for small panels with best-in-class enrichment performance and the capability to detect novel fusion partners.