Why This Matters
DNA sequencing technologies form the backbone of modern genomics, and you'll be tested on understanding not just what each technology does, but how it works and when to use it. The AP exam expects you to distinguish between sequencing approaches based on their underlying mechanisms—chain termination vs. synthesis-based detection vs. physical measurement—and to evaluate which technology best fits a given research scenario. These distinctions matter because choosing the wrong sequencing approach can mean the difference between successfully assembling a complex genome and generating unusable data.
The technologies covered here demonstrate core principles of molecular biology, signal detection, and data analysis trade-offs. You'll see questions asking you to compare read lengths, throughput, and accuracy—and more importantly, to explain why those characteristics make a technology suited for specific applications like variant detection, transcriptome analysis, or metagenomic studies. Don't just memorize platform names—know what biochemical principle each technology exploits and what experimental question it answers best.
First-Generation Sequencing: The Foundation
Before high-throughput methods existed, scientists needed a reliable way to read DNA one fragment at a time. Chain-termination chemistry made this possible by creating a ladder of fragments that could be separated by size.
Sanger Sequencing
- Chain-terminating dideoxynucleotides (ddNTPs)—these lack the 3'-OH group needed for DNA elongation, stopping synthesis at specific bases
- Read length up to 1,000 base pairs with extremely high accuracy (99.99%), making it the gold standard for validation
- Primary uses today include confirming NGS results, sequencing individual genes, and clinical diagnostics requiring maximum accuracy
Next-Generation Sequencing revolutionized genomics by running millions of reactions simultaneously. Massively parallel sequencing trades individual read length for unprecedented data volume, making whole-genome studies economically feasible.
Illumina Sequencing
- Sequencing by synthesis (SBS)—reversible dye terminators allow one nucleotide to be added and detected per cycle before the blocking group is removed
- Short reads (50–300 bp) with accuracy exceeding 99.9%, generating the highest throughput of any current platform
- Dominant platform for whole genome sequencing, RNA-seq, and targeted resequencing due to cost-per-base advantages
Ion Torrent Sequencing
- pH-based detection—measures hydrogen ion release when nucleotides incorporate, eliminating the need for optical detection
- Medium-length reads (up to 400 bp) with faster run times than optical-based methods
- Cost-effective for smaller projects including targeted panels, microbial genomes, and clinical applications requiring rapid turnaround
SOLiD Sequencing
- Sequencing by ligation—uses fluorescently labeled oligonucleotide probes that ligate to the template, with each base read twice for error correction
- Short reads (up to 75 bp) but exceptional accuracy for SNP detection due to two-base encoding
- Largely superseded by Illumina platforms but historically important for understanding alternative sequencing chemistries
Compare: Illumina vs. Ion Torrent—both are short-read NGS platforms, but Illumina uses optical detection of fluorescent dyes while Ion Torrent measures electrical signals from pH changes. If an FRQ asks about sequencing without specialized optics, Ion Torrent is your answer.
Long-Read Technologies: Solving Complex Genomes
Short reads struggle with repetitive regions and structural variants because fragments can't span the repeat. Long-read sequencing addresses this by reading continuous stretches of DNA thousands of bases long, enabling de novo assembly of complex genomes.
Pacific Biosciences (PacBio) SMRT Sequencing
- Single Molecule Real-Time (SMRT) sequencing—watches a polymerase incorporate fluorescent nucleotides in real time within zero-mode waveguides
- Read lengths up to 30,000 bp (with HiFi mode achieving 99.9% accuracy), ideal for resolving repetitive regions and structural variants
- Detects base modifications directly during sequencing, valuable for epigenetic studies without additional sample preparation
Nanopore Sequencing
- Ionic current measurement—DNA passes through a protein nanopore, and each base creates a characteristic disruption in electrical current
- Ultra-long reads (megabases possible) with portable devices like the MinION enabling field-based sequencing
- Real-time data generation allows adaptive sampling and direct RNA sequencing without reverse transcription
454 Pyrosequencing
- Pyrosequencing chemistry—detects light emitted when pyrophosphate is released during nucleotide incorporation
- Longer reads (up to 1,000 bp) made it valuable for early metagenomics and de novo assembly before being discontinued
- Historical significance as the first commercially successful NGS platform, demonstrating the viability of massively parallel sequencing
Compare: PacBio vs. Nanopore—both produce long reads for complex genome assembly, but PacBio achieves higher raw accuracy through circular consensus sequencing while Nanopore offers portability and ultra-long reads. Choose PacBio for accuracy-critical applications; choose Nanopore for field work or maximum contiguity.
Sequencing Strategies: How You Prepare and Read
Beyond the platform itself, how you prepare and sequence your sample determines what questions you can answer. These strategies can be applied across multiple sequencing technologies.
Paired-End Sequencing
- Sequences both ends of a DNA fragment—provides two reads separated by a known insert size, improving mapping confidence
- Resolves repetitive regions by anchoring reads in unique flanking sequences, critical for structural variant detection
- Platform-agnostic technique applicable to Illumina, Ion Torrent, and other short-read platforms
Whole Genome Sequencing
- Complete genomic coverage—captures coding regions, regulatory elements, and structural variants in a single experiment
- Applications span personalized medicine, population genetics, evolutionary biology, and pathogen surveillance
- Requires significant computational resources for alignment, variant calling, and data storage
Exome Sequencing
- Targets protein-coding exons only—captures ~1-2% of the genome containing ~85% of disease-causing variants
- Cost-effective alternative to whole genome sequencing when focusing on Mendelian disorders or cancer driver mutations
- Misses non-coding variants in regulatory regions, introns, and structural variants outside capture regions
Compare: Whole genome vs. exome sequencing—both identify genetic variants, but exome sequencing is cheaper and focuses on protein-coding regions while whole genome captures everything including regulatory variants. FRQs often ask when each approach is appropriate—exome for suspected coding mutations, whole genome for comprehensive analysis.
Functional Genomics Applications
Sequencing isn't just about reading DNA—it's a tool for understanding what genes do and how they're regulated. These applications combine sequencing with biochemical techniques to answer specific biological questions.
RNA Sequencing (RNA-seq)
- Transcriptome profiling—quantifies gene expression levels, detects alternative splicing, and identifies novel transcripts
- Requires cDNA conversion—RNA is reverse-transcribed before sequencing (except with direct RNA nanopore methods)
- Applications include developmental biology, cancer research, and drug response studies across mRNA, lncRNA, and small RNA
ChIP-seq
- Chromatin immunoprecipitation + sequencing—identifies where proteins bind DNA across the entire genome
- Maps transcription factor binding sites and histone modifications, revealing regulatory networks and epigenetic states
- Essential for understanding gene regulation, chromatin architecture, and disease mechanisms involving transcriptional dysregulation
- Culture-independent analysis—sequences all DNA from environmental or clinical samples to characterize microbial communities
- Reveals biodiversity and function in ecosystems, the human microbiome, and pathogen surveillance
- Computational challenges include assembling genomes from mixed populations and assigning taxonomic identities
Single-Cell Sequencing
- Individual cell resolution—captures genomic, transcriptomic, or epigenomic data from thousands of single cells
- Reveals cellular heterogeneity hidden in bulk sequencing, critical for understanding development, tumor evolution, and rare cell populations
- Technical challenges include amplification bias, dropout events, and computational integration of sparse data
Compare: RNA-seq vs. ChIP-seq—both use NGS to study gene regulation, but RNA-seq measures expression output while ChIP-seq identifies regulatory inputs (protein-DNA binding). Together, they reveal where transcription factors bind and what effect that binding has on gene expression.
Quick Reference Table
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| Chain termination chemistry | Sanger sequencing |
| Sequencing by synthesis | Illumina, PacBio SMRT |
| Electrical/pH detection | Ion Torrent, Nanopore |
| Long-read platforms | PacBio, Nanopore, 454 (historical) |
| Short-read platforms | Illumina, Ion Torrent, SOLiD |
| Transcriptome analysis | RNA-seq, single-cell RNA-seq |
| Protein-DNA interactions | ChIP-seq |
| Complex genome assembly | PacBio, Nanopore, paired-end strategies |
Self-Check Questions
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Which two sequencing technologies both produce long reads but use fundamentally different detection mechanisms? What distinguishes their biochemical approaches?
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A researcher needs to identify SNPs across a patient cohort with maximum accuracy and cost efficiency. Which platform and sequencing strategy would you recommend, and why?
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Compare and contrast whole genome sequencing and exome sequencing: when would each be the preferred choice for studying a suspected genetic disorder?
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If an FRQ describes a field study requiring portable equipment to sequence pathogen DNA in real time, which technology fits this scenario? What trade-off does the researcher accept?
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Explain why paired-end sequencing improves genome assembly compared to single-end reads, particularly in repetitive regions. Which underlying principle makes this possible?