High-throughput sequencing and genomes

Jelmer Poelstra

CFAES Bioinformatics Core, Ohio State University

2026-01-29

Overview

• Introduction to sequencing technologies
• High-throughput sequencing (HTS)
• Short-read HTS
• Reference genomes
• Looking forward
• Bonus: More on HTS applications

Introduction to sequencing technologies

What do we mean by sequencing?

• Sequencing generally refers to determining the sequence of DNA, RNA, or protein fragments

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• Most commonly, especially in the context of “high-throughput” sequencing,
  it specifically refers to DNA sequencing

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• Here, we’ll only focus on DNA sequencing, keeping in mind that:

  • Protein sequencing involves completely different technologies

  • RNA can be, and usually is, sequenced as DNA as well

    How is that done and why?  

    RNA is usually reverse transcribed to DNA (cDNA) prior to sequencing.

    While it is becoming more feasible to directly sequence RNA molecules,
    RNA is an unstable molecule that is easily degraded and harder to sequence.

Overview of sequencing technologies

• Sanger sequencing (since ~1985)
  Sequences a single, typically PCR-amplified, DNA fragment at a time

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• High-throughput sequencing (HTS)
  Sequences 10⁵-10⁹, often randomly selected, DNA fragments at a time — two types:

  • Short-read HTS: More accurate, shorter reads (since 2005)

  • Long-read HTS: Less accurate, longer reads (since 2011)

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These sequenced fragments of DNA are usually called reads

Sanger sequencing

• Sanger sequencing almost always starts with PCR amplification of the target DNA region —
  as illustrated by Dr. Popp last week:

• Therefore, to design primers, you must know something about the target sequence in advance — this can be highly limiting

• The sequenced fragment can be up to about 800-1,000 bp

Sanger sequencing

• Sequencing itself is performed by synthesizing a new DNA strand with fluorescently-labeled nucleotides, using a different color for each base (A, C, G, T)

• The final result is a chromatogram that can be “base-called”:

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The entire human genome was sequenced with Sanger technology!

How many basepairs is that? Want to guess how much it cost to do this?

Sequencing cost through time

https://www.genome.gov/about-genomics/fact-sheets/Sequencing-Human-Genome-cost

Present-day Sanger applications

• Because HTS has much higher throughput and is much cheaper per base,
  Sanger sequencing is now less widely used

• But it is not obsolete, in part because high throughput isn’t always needed

Image generated by Adobe Firefly

Present-day Sanger applications

Some present-day uses of Sanger sequencing include…

• Taxonomic identification of samples

• Examining genetic variation among individuals/populations

…where using one or few marker loci or candidate genes can be sufficient

High-throughput sequencing (HTS)

Omics

Let’s start with the big picture – HTS data underlies several of these main “omics” approaches:

Copyright ThermoFisher

The main omics data types

Omics type	Molecule type
Genomics	DNA
Epigenomics	DNA modifications	High-throughput sequencing (HTS)
Transcriptomics	RNA
Proteomics	Proteins
Metabolomics	Metabolites

What does the -omics suffix mean?  

The “omics” suffix indicates the involvement of large-scale datasets —
for example, “genomics” data typically spans much or all of the genome

The main omics data types

Omics type	Molecule type	Data mainly produced by
Genomics	DNA	High-throughput sequencing (HTS)
Epigenomics	DNA modifications	High-throughput sequencing (HTS)
Transcriptomics	RNA	High-throughput sequencing (HTS)
Proteomics	Proteins	Mass Spectrometry
Metabolomics	Metabolites	Mass Spectrometry

Examples of HTS applications

• Whole-genome assembly — for producing reference genomes

• Typing of SNPs and other sequence variants — for population genetics, GWAS, etc.

• Metabarcoding — for microbial community characterization

• RNA-Seq — for large-scale gene expression analysis

Does any of you work on, or is planning to work on, projects like these?  

The main HTS technologies

	Short-read HTS	Long-read HTS
Main companies	Illumina	Oxford Nanopore Technologies (ONT) & Pacific Biosciences (PacBio)

The main HTS technologies

	Short-read HTS	Long-read HTS
Usage	More	Less (but increasing)
Main companies	Illumina	Oxford Nanopore Technologies (ONT) & Pacific Biosciences (PacBio)
Timeline	Since 2005 — technology fairly stable	Since 2011 — still rapid development

The main HTS technologies

	Short-read HTS	Long-read HTS
Usage	More	Less (but increasing)
Main companies	Illumina	Oxford Nanopore Technologies (ONT) & Pacific Biosciences (PacBio)
Timeline	Since 2005 — technology fairly stable	Since 2011 — still rapid development
Read lengths	50-300 bp	10-100+ kbp
Error rates	Mostly <0.1%	1-10% (ONT) / <0.1-10% (PacBio)

The main HTS technologies

	Short-read HTS	Long-read HTS
Usage	More	Less (but increasing)
Main companies	Illumina	Oxford Nanopore Technologies (ONT) & Pacific Biosciences (PacBio)
Timeline	Since 2005 — technology fairly stable	Since 2011 — still rapid development
Read lengths	50-300 bp	10-100+ kbp
Error rates	Mostly <0.1%	1-10% (ONT) / <0.1-10% (PacBio)
Throughput	Higher	Lower
Cost per base	Lower	Higher

Read lengths

Can you think of applications where long reads are useful?  

For example:

• Genome assembly
• Read-based taxonomic identification

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Can you think of applications where read length may not matter much?  

For example:

• (SNP) variant analysis
• “Counting applications” such as RNA-Seq

For these, genomic locations (variant analysis) or gene identities (RNA-Seq) can be reliably inferred from as little as 25 bp

Error rates

A read’s sequence may differ from the actual DNA sequence it originated from:

• The read can have base-calling errors, missing bases, or extra bases

• When the base calling software is not confident, it can also return Ns (= undetermined)

When you receive HTS reads, base calls have typically been made already.
Every base call is accompanied by a quality score, representing the estimated error probability.

Correcting sequencing errors

To overcome sequencing errors, every base can be sequenced multiple times –
i.e., obtaining a “depth of coverage” greater than 1:

Which natural phenomenon might complicate this effort?  

Genetic variation among and (for diploid organisms) within individuals

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Typical depths of coverage: ~50-100x for genome assembly & 10-30x for variant typing (!)

Short-read HTS

Libraries and library prep

• In a HTS context, a “library” is a collection of DNA fragments ready for sequencing

• These fragments can number in the millions or billions and are often randomly generated from input like genomic DNA:

An overview of the library prep procedure. This is typically done for you by a sequencing facility or company.

Libraries and library prep

• After library prep, each DNA fragment is flanked by several types of short sequences that together make up the “adapters”:

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Multiplexing!

Adapters can include “indices” or “barcodes” to identify individual samples, so many samples can be combined (multiplexed) into a single library

Paired-end vs. single-end sequencing

• Fragments can be sequenced from both ends as shown below —
  this is called “paired-end” (PE) sequencing:

• When sequencing is instead single-end (SE), no reverse read is produced:

Fragment size variation

• DNA fragment size varies – by design and because of limited precision in size selection

What happens when a fragment is shorter than the length of a single F or R read?  

“Adapter read-through”: the final bases in the resulting reads will consist of adapter sequence (these should be removed before downstream analysis)

Fragment size variation

• DNA fragment size varies – by design and because of limited precision in size selection

What happens when a fragment is shorter than the combined F + R read length?  

Overlapping reads (this can be useful!):

How Illumina sequencing works

We start with fragments that have been attached to a flow cell —
this image shows just two, but millions are present simultaneously

How Illumina sequencing works

Sequencing is performed by synthesizing a new strand using fluorescently-labeled bases,
and taking a picture each time a new nucleotide is incorporated:

How Illumina sequencing works

Sequencing is performed by synthesizing a new strand using fluorescently-labeled bases,
and taking a picture each time a new nucleotide is incorporated:

How Illumina sequencing works

Sequencing is performed by synthesizing a new strand using fluorescently-labeled bases,
and taking a picture each time a new nucleotide is incorporated:

The scale of Illumina sequencing

The scale of Illumina sequencing

The scale of Illumina sequencing

The scale of Illumina sequencing

Video of Illumina technology

Reference genomes

Reference genomes

Many HTS applications either require a “reference genome” or involve its production:

Reference genomes

Many HTS applications either require a “reference genome” or involve its production:

Reference genomes

What exactly does reference genome refer to? It usually includes:

• An assembly
  A representation of most or all of the genome DNA sequence: the genome assembly

• An annotation
  Provides e.g. locations of genes and other genomic “features” in the corresponding genome assembly, and functional information for these features

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Taxonomic identity

Reference genomes are typically applicable at the species level. For example, if you work with maize, you want a Zea mays reference genome. But:

• If needed, it’s often possible to work with genomes of closely related species
• Conversely, different subspecies/lines may have their own reference genomes

There is enormous variation in genome size

https://en.wikipedia.org/wiki/Genome_size

And enormous growth of genomes in databases

Konkel and Slot (2023)

Genome assemblies

• Besides being produced at higher rates as shown on the previous slide,
  assemblies keep getting better with increasing usage & quality of long-read HTS

• Still, many assemblies instead consist of –often 1000s of– fragments (contigs and scaffolds)

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How is this data stored?

Both genome assemblies and annotations are typically saved in a single text file each — we’ll explore these in tomorrow’s lab

Recap

You’ve learned…

1. That high-throughput sequencing (HTS) enables DNA sequencing at much larger scales than was previously possible

2. That HTS underlies several major “omics” fields

3. How short-read and long-read HTS have different strengths and weaknesses

4. About libraries and the technology underlying short-read sequencing

4. That reference genomes are essential for many HTS applications

Looking forward

The Garrigós et al. 2025 dataset

The labs this and next week are organized around the data set from Garrigós et al. (2025):

This paper uses paired-end Illumina RNA-Seq data to study gene expression in Culex pipiens mosquitos infected with two different malaria-causing Plasmodium protozoans.

Tomorrow’s lab

• You will first learn about what kind of computing environment is commonly used for analyzing high-throughput sequencing (HTS) data

• Then, you’ll work in this environment to explore an HTS dataset, checking out HTS read and reference genome files, and performing read quality control

Next week’s content

• In the lecture, you will learn how RNA-Seq is used to study gene expression at scale

• In the lab, you will perform differential gene expression analysis on the Culex pipiens RNA-Seq dataset

Bonus: More on HTS applications

Examples of HTS applications

We talked about these HTS applications:

• Whole-genome assembly — for producing reference genomes
• Typing of SNPs and other sequence variants — for population genetics, GWAS, etc.
• Metabarcoding — for microbial community characterization
• RNA-Seq — for large-scale gene expression analysis

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In each of of these applications:

• What part(s) of the genome are you sequencing?
• Who are you sequencing (how many individuals/species)?
• How are the sequences used?

Examples of HTS applications

Appl.	What	Who	How
Whole-genome assembly	The whole genome! 😃	A single individual	“Overlap” reads into larger fragments
Variant typing
16S Metabarcoding
RNA-Seq

Examples of HTS applications

Appl.	What	Who	How
Whole-genome assembly	Whole genome 😃	A single individual	“Overlap” reads into larger fragments
Variant typing	Whole genome or specific (e.g. exome) or random (GBS/RAD) regions	Multiple individuals	For each site & individual, determine the variant state (allele)
16S Metabarcoding
RNA-Seq

Examples of HTS applications

Appl.	What	Who	How
Whole-genome assembly	Whole genome 😃	A single individual	“Overlap” reads into larger fragments
Variant typing	Whole genome or specific (e.g. exome) or random (GBS/RAD) regions	Multiple individuals	For each site & individual, determine the variant state (allele)
16S Metabarcoding	A specific locus (e.g. 16S)	Multi-species samples — soil, gut contents, etc.	Assign reads a taxonomic identity and count
RNA-Seq

Examples of HTS applications

Appl.	What	Who	How
Whole-genome assembly	Whole genome 😃	A single individual	“Overlap” reads into larger fragments
Variant typing	Whole genome or specific (e.g. exome) or random (GBS/RAD) regions	Multiple individuals	For each site & individual, determine the variant state (allele)
16S Metabarcoding	A specific locus (e.g. 16S)	Multi-species samples — soil, gut contents, etc.	Assign reads a taxonomic identity and count
RNA-Seq	Whole transcriptome	Multiple indivuals (& multiple conditions)	Assign reads a gene identity and count

References

Garrigós, Marta, Guillem Ylla, Josué Martínez-de la Puente, Jordi Figuerola, and María José Ruiz-López. 2025. “Two Avian Plasmodium Species Trigger Different Transcriptional Responses on Their Vector Culex pipiens.” Molecular Ecology 34 (15): e17240. https://doi.org/10.1111/mec.17240.

Konkel, Zachary, and Jason C. Slot. 2023. “Mycotools: An Automated and Scalable Platform for Comparative Genomics.” BioRxiv. https://doi.org/10.1101/2023.09.08.556886.