What Is Next-Generation Sequencing (NGS)

Next-generation sequencing (NGS) is a high-throughput approach that analyzes large numbers of DNA or RNA-derived library molecules in parallel. Compared with Sanger sequencing, which typically examines a small number of defined targets in separate reactions, NGS can generate large, multiplexed datasets across many samples, genes, transcripts, microorganisms, or genomic regions.

The value of NGS extends beyond simply generating large amounts of sequence data. Its key advantage is scalability: experimental design can be tailored to the biological question, target size, number of samples, required sequencing depth or coverage, and downstream analysis needs. As a result, a targeted amplicon study, a microbial community survey, an RNA-seq experiment, and a whole-genome sequencing project may all use NGS, but each requires a different study design and sequencing strategy.

At Quintara Biosciences, NGS refers to our high-throughput, Illumina-based short-read sequencing services. These services support a broad range of applications, including targeted amplicon sequencing, 16S/ITS microbiome profiling, RNA sequencing, single-cell sequencing, spatial transcriptomics, and short-read whole-genome sequencing. Nanopore sequencing is offered separately as a long-read sequencing service and is categorized independently from Quintara Biosciences' NGS services.

What Does Next-Generation Sequencing Mean?

The term “next-generation” reflects the shift from sequencing one DNA template at a time to sequencing large numbers of library fragments in parallel. This parallelization makes it possible to study substantially more sequence information in a single experiment.

Traditional Sanger sequencing remains highly effective for focused questions such as confirming a plasmid, checking a defined mutation, or sequencing a small number of PCR products. It becomes less practical when a study requires broad genomic coverage, quantitative profiling of mixed populations, transcriptome-wide expression measurements, or large numbers of targets and samples.

NGS addresses these needs through parallel sequencing and multiplexing. Different samples can receive unique index sequences, allowing libraries to be pooled for sequencing and separated computationally during data processing.

Key advantages include:

• Scalable analysis of many targets, samples, or genomic regions in one project.

• Flexible sequencing depth that can be matched to the required sensitivity.

• Read-count-based quantification of sequence variants, barcodes, transcripts, or taxa.

• Improved ability to study rare or low-abundance signals when sufficient depth is used.

• Efficient expansion from targeted assays to transcriptome- or genome-scale studies.

What Research Questions Can NGS Answer?

The right NGS application depends on what you need to measure. Selecting a service only by platform name can produce unnecessary data, insufficient sensitivity, or an analysis plan that does not answer the original research question.

Do You Need to Analyze Specific DNA Targets?

Targeted amplicon sequencing is appropriate when the regions of interest are already known and the goal is to measure sequence variation or abundance at those loci.

Typical questions include:

• Did CRISPR editing occur at the intended target?

• What SNPs or small indels are present in a selected region?

• How common is a particular sequence variant?

• What is the relative abundance of different barcodes or engineered constructs?

• How diverse is a targeted T-cell receptor (TCR) or B-cell receptor (BCR) repertoire?

Quintara's AmpExpress NGS service supports short, predefined genomic regions and applications such as CRISPR/Cas9 editing validation, targeted SNP/indel analysis, barcode or variant counting, and targeted TCR/BCR repertoire profiling.

Do You Need to Profile a Microbial Community?

Marker-gene sequencing is commonly used to compare microbial community composition across samples. The 16S rRNA gene is widely used to profile bacterial and archaeal communities, while internal transcribed spacer (ITS) regions are commonly used for fungal profiling.

This approach may be appropriate for:

• Soil, water, and environmental microbiome studies.

• Host-associated microbiome research.

• Microbial culture or community comparisons.

• Treatment-versus-control microbiome studies.

• Taxonomic composition, relative abundance, and diversity analysis.

Study design should consider sample type, extraction method, target region, positive and negative controls, and the taxonomic resolution required. Marker-gene sequencing profiles selected taxonomic markers rather than complete microbial genomes, and achievable resolution depends on the assay and reference database.

Quintara supports 16S/ITS sequencing together with microbiome data-analysis options for research samples and extracted DNA.

Do You Need to Measure Gene Expression?

RNA sequencing (RNA-seq) is used to measure transcript abundance and compare gene-expression patterns across biological conditions.

Researchers may want to determine:

• Which genes respond to a treatment or perturbation?

• How does a disease model differ from a control?

• Which pathways are activated or suppressed?

• Are non-coding RNAs relevant to the study?

• How does transcript expression vary among tissues, time points, or conditions?

Bulk mRNA-seq generally uses poly(A) enrichment to focus on polyadenylated transcripts and is well suited to protein-coding gene-expression studies. Total RNA or broader transcriptome workflows commonly use rRNA depletion, allowing a wider range of coding and non-coding RNA species to be retained for library preparation.

Quintara offers both poly(A)-enriched bulk mRNA-seq and rRNA-depleted total RNA/whole-transcriptome workflows so that the enrichment strategy can be matched to the biological objective and sample quality.

Do You Need Cell-Level or Spatial Resolution?

Bulk RNA-seq measures an average signal across all cells in a sample. That average can mask biologically important differences between cell populations.

Single-cell RNA sequencing is more appropriate when researchers need to:

• Identify distinct cell populations or states.

• Characterize cellular heterogeneity.

• Detect rare cell populations.

• Compare cell-type-specific expression patterns.

• Study immune, developmental, or disease-associated cell states.

Spatial transcriptomics adds positional information, helping researchers determine where gene-expression patterns occur within a tissue section rather than analyzing dissociated cells alone.

Quintara provides customizable single-cell sequencing services using 10x Genomics workflows, with options ranging from sample processing and GEM generation through library preparation, sequencing, and preliminary analysis. Spatial profiling options include Visium HD workflows.

Do You Need Genome-Wide Information?

Whole-genome sequencing (WGS) is appropriate when the research question extends beyond predefined targets and requires information across an organism’s genome.

Short-read WGS can support:

• SNP and small-indel discovery.

• Reference-based genome mapping.

• Comparative genomics.

• Microbial genome analysis.

• Viral genome analysis.

• Genomic profiling across larger sample sets.

The appropriate coverage depends on genome size, sample type, ploidy, expected variant frequency, reference quality, and the sensitivity required for downstream interpretation. Long-read sequencing may be preferable when the main challenge involves repetitive regions, large structural variants, haplotype context, or de novo assembly.

Which NGS Application Should You Choose?

The following table provides a practical starting point. Final study design should still be matched to the sample type, target size, read structure, depth, controls, and analysis requirements.

Is NGS the Same as Whole-Genome Sequencing?

No. NGS describes a sequencing approach or service category, while whole-genome sequencing describes the scope of an experiment. A project can use NGS without sequencing an entire genome; amplicon sequencing, RNA-seq, 16S/ITS sequencing, and single-cell sequencing are all examples of high-throughput sequencing applications that are not WGS.

WGS can also be performed with different sequencing technologies. In practical terms:

· Short-read NGS is well suited to high-accuracy, high-depth reference-based analysis, particularly for SNPs and small indels.

· Nanopore long-read sequencing is useful when long-range sequence context, repetitive regions, large structural variants, haplotypes, or de novo assembly are important.

· Long-read platforms can also complement short-read data when a project needs both high-depth base-level measurements and long-range genomic context.

The key questions are therefore: Does the project require whole-genome coverage, and which read type best matches the expected biology and genome complexity?

NGS vs. Sanger Sequencing: Which Should You Use?

Sanger sequencing and NGS are complementary technologies. The better choice depends on the scope of the question, the complexity of the sample, whether quantitative read counts are needed, and the amount of sequence information required.

Sanger sequencing remains a practical choice for plasmid verification, clone confirmation, PCR-product sequencing, and mutation checks involving a limited number of defined targets. NGS becomes more appropriate when researchers need to compare many targets, quantify sequence abundance, profile mixed populations, measure expression, characterize microbial communities, or generate genome-scale data.

The methods can also be combined—for example, NGS for broad screening or discovery followed by Sanger sequencing for focused confirmation of selected short targets.

How Should You Plan an NGS Project?

A successful NGS project begins with a clearly defined research objective. Library type, read configuration, sequencing depth, controls, and analysis cannot be selected reliably until the desired biological outcome is understood.

1. Define the Primary Question

Avoid starting with a vague request such as “I need NGS.” Instead, define the decision the data must support. For example:

• Identify variants within three CRISPR target regions.

• Compare gene expression between treated and untreated cells.

• Characterize bacterial diversity across environmental samples.

• Detect SNPs across a microbial genome.

• Identify transcriptionally distinct cell populations in a tissue.

A precise objective allows the sequencing strategy to be designed around the question and helps avoid unnecessary cost or insufficient data.

2. Evaluate the Starting Material

Sample type and quality directly affect which workflows are feasible. Important details include:

• DNA, RNA, cells, tissue, PCR products, or prepared libraries.

• Sample quantity, concentration, and available volume.

• DNA or RNA purity and integrity.

• Preservation method and storage history.

• Species, strain, genome size, and reference availability.

• Known inhibitors, low-input limitations, or degradation concerns.

Whether extraction, amplification, or other preprocessing is needed.

Quintara Biosciences's NGS services accept a range of starting materials and may include extraction, PCR, indexing, library preparation, sequencing, and analysis depending on the selected application.  

3. Choose the Library Strategy and Read Configuration

The library-preparation method determines what molecules are represented in the final data. Read length and single-end versus paired-end sequencing should then be chosen based on the insert size, target architecture, mapping requirements, and analysis plan. RNA-seq projects may also require decisions about poly(A) enrichment versus rRNA depletion and, when relevant, stranded library preparation.

4. Include Appropriate Replicates and Controls

Sequencing depth cannot compensate for weak experimental design. Biological replicates are essential for many comparative studies, especially RNA-seq and microbiome experiments. Negative controls, positive controls, mock communities, reference samples, or technical controls may also be appropriate depending on the assay.

5. Select the Required Depth or Coverage

More sequencing is not automatically better. Too little data may miss low-frequency signals; excessive depth may increase cost without improving the biological conclusion. “Sequencing depth” generally refers to the amount of read data generated, while “coverage” is often used in genome sequencing to describe how many times, on average, each base is represented.

The appropriate level depends on:

• Target size or genome size.

• Genome or transcriptome complexity.

• Expected variant frequency or abundance.

• Number of samples and planned comparisons.

• Required statistical power.

• Desired taxonomic or expression resolution.

• Whether the study is exploratory or confirmatory.

6. Plan the Bioinformatics Before Sequencing

Define the expected deliverables before samples are processed. Depending on the project, outputs may include:

• Raw FASTQ files and quality-control summaries.

• Reference alignment and mapping statistics.

• SNP/indel analysis or variant calls.

• Barcode or sequence-abundance analysis.

• Gene-count matrices and differential-expression analysis.

• Microbiome taxonomic classification and diversity analysis.

• Genome mapping and variant calling.

• V(D)J or immune-repertoire analysis.

Planning the analysis early helps confirm that the experimental design will generate the data needed for the final comparison.

7. Understand What Affects Cost and Turnaround Time

Project cost and turnaround time vary with sample type and quality, preprocessing requirements, library-preparation method, sample count, read configuration, sequencing depth, genome size, analysis complexity, and the need for repeat processing or custom reporting. A project-specific quotation provides a more reliable estimate and prevents this long-term educational article from becoming outdated.  

What Information Should You Provide for an NGS Project Evaluation?

Before requesting a quotation or technical consultation, prepare the following information:

• Research objective: What question should the sequencing data answer?

• Application: Amplicon, RNA-seq, microbiome, single-cell, spatial, or genome sequencing.

• Organism: Species, strain, genome size, and reference-genome availability.

• Sample type: DNA, RNA, cells, tissue, PCR products, or prepared libraries.

• Sample number and design: Controls, biological replicates, treatment groups, and time points.

• Sample quality: Concentration, purity, integrity, and available volume.

• Target information: Regions, genes, primers, expected amplicon sizes, or construct design.

• Prepared-library details, when applicable: Adapter/index structure, library concentration, and expected insert size.

• Depth or coverage: A known target level or a request for a recommendation.

• Analysis needs: Raw data only or additional bioinformatics.

• Timeline: Experimental, publication, or project deadlines.

Providing these details early makes it easier to identify technical limitations, select the appropriate workflow, define realistic deliverables, and avoid rework after sequencing has begun.

Frequently Asked Questions About NGS

Is Nanopore sequencing considered NGS at Quintara Biosciences?

Sequencing terminology is used differently across the field. For Quintara’s service catalog and website navigation, Illumina NGS and Nanopore sequencing are presented as separate categories. Projects should therefore be evaluated by read type and application rather than by terminology alone.

Is WGS always an NGS service?

No. Whole-genome sequencing describes the scope of the experiment, not a single sequencing technology. WGS may use short-read NGS, long-read Nanopore sequencing, another long-read platform, or a combined strategy depending on the expected variants and genome complexity.

What is a read in NGS?

A read is a nucleotide sequence generated from a library molecule during sequencing. In single-end sequencing, one end of a fragment is read; in paired-end sequencing, sequence is generated from both ends of the same library fragment. Reads are then assigned to samples, quality checked, mapped, counted, assembled, or otherwise analyzed according to the study design.

How much DNA or RNA is required?

There is no universal input requirement for every NGS application. Required quantity, concentration, integrity, and volume vary among amplicon sequencing, RNA-seq, microbiome analysis, single-cell workflows, and genome sequencing. Sample requirements should be confirmed for the selected service before submission.

Do all NGS projects require bioinformatics?

Most NGS projects require at least demultiplexing, quality control, and basic data processing. Additional analysis depends on the objective: variant studies may require alignment and variant calling, RNA-seq may require expression analysis, and microbiome projects may require taxonomic classification and diversity analysis.

Can NGS and Sanger sequencing be used together?

Yes. NGS can provide broad screening, discovery, or quantitative profiling, while Sanger sequencing can be used for focused confirmation of selected short targets. The two methods can be combined when a study benefits from both scale and targeted validation.

Conclusion

Next-generation sequencing is not one fixed test. It is a flexible, high-throughput approach that can be configured for targeted DNA analysis, microbial profiling, RNA expression studies, single-cell research, spatial transcriptomics, and short-read whole-genome sequencing.

The key decision is not simply whether to use NGS, but which application and study design best match the biological question. Sample type, target size, experimental controls, read configuration, sequencing depth, expected variants, and downstream analysis should all be considered before the project begins.

For Quintara projects, providing a concise study objective, complete sample information, and clearly defined analysis needs allows the sequencing workflow to be designed around the scientific goal rather than forcing the project into a standard package.

Planning an NGS project? Share your research objective, sample type, sample count, target or reference information, desired data output, and timeline with Quintara’s NGS team. These details help determine the appropriate library preparation, read configuration, sequencing depth, and analysis strategy.