Next-generation sequencing (NGS) platforms differ from Sanger sequencing primarily in their throughput, read length, and applications, with NGS enabling high-throughput sequencing of multiple fragments simultaneously, while Sanger sequencing is limited to single-fragment processing.
Why it matters
- Throughput: NGS can sequence millions of fragments in parallel, significantly increasing the amount of data generated in a single run compared to Sanger sequencing, which processes one fragment at a time.
- Read Length: Sanger sequencing provides longer reads (700–900 bp), which are beneficial for resolving complex regions, whereas NGS typically generates shorter reads (75–300 bp) but offers long-read options for specific applications.
- Cost Efficiency: NGS reduces the cost per base sequenced, making it more suitable for large-scale projects such as whole-genome sequencing or population studies.
- Applications: NGS can be applied to a wide range of genomic analyses, including whole-genome sequencing, targeted panels, RNA sequencing, and metagenomics, whereas Sanger is mainly used for targeted sequencing and validation.
- Data Complexity: NGS generates vast amounts of data that require advanced bioinformatics tools for analysis, making it essential for practitioners to understand data management and interpretation.
How to apply
- Select the Appropriate Platform: Choose an NGS platform based on the project requirements (e.g., Illumina for short reads, PacBio or Oxford Nanopore for long reads).
- Sample Preparation: Follow specific protocols for library preparation, ensuring quality control of the DNA or RNA samples.
- Sequencing: Run the samples on the chosen NGS platform, adhering to manufacturer guidelines for optimal performance.
- Data Analysis: Utilize bioinformatics tools to process raw sequencing data, including quality control, alignment, variant calling, and functional annotation.
- Interpret Results: Collaborate with bioinformaticians and domain experts to interpret the data in the context of the biological question being addressed.
- Validation: Consider validating key findings using Sanger sequencing or other methods to confirm results, especially for clinical applications.
Metrics to track
- Coverage Depth: Measure the average number of times a nucleotide is read; higher coverage increases confidence in variant calls.
- Error Rate: Track the percentage of incorrect base calls to assess the quality of the sequencing run.
- Read Length Distribution: Monitor the distribution of read lengths to ensure they meet project specifications.
- Library Complexity: Evaluate the diversity of the sequenced library to ensure adequate representation of the target population.
- Turnaround Time: Measure the time from sample preparation to data delivery to assess workflow efficiency.
Pitfalls
- Sample Quality: Poor-quality samples can lead to low yield and biased results; ensure samples are well-preserved and free from contaminants.
- Bioinformatics Challenges: The complexity of NGS data analysis requires skilled personnel; inadequate bioinformatics support can lead to misinterpretation of results.
- Overlooked Variants: Low-frequency variants may be missed in low-coverage sequencing; ensure adequate depth for variant discovery.
- Cost Overruns: While NGS is cost-effective, unexpected expenses can arise from additional bioinformatics needs or sample reprocessing.
- Data Management: The large volume of data generated can overwhelm storage and analysis capabilities; establish robust data management practices.
Key takeaway: NGS offers significant advantages over Sanger sequencing in throughput and application scope, but it requires careful planning and expertise in data analysis.