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Next-generation Sequencing

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.

Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

Maxam-Gilbert Sequencing

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...

Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

Sanger Sequencing

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...

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Related Experiment Video

Updated: May 24, 2026

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

GemSIM: general, error-model based simulator of next-generation sequencing data.

Kerensa E McElroy¹, Fabio Luciani, Torsten Thomas

¹Centre for Marine Bio-Innovation and School of Biotechnology and Biomolecular Sciences, UNSW, Sydney, NSW, Australia.

|February 17, 2012

Summary

GemSIM (General Error-Model based SIMulator) realistically simulates next-generation sequencing data using empirical error models. This tool helps researchers understand how sequencing run variations impact downstream analysis, like SNP-calling accuracy.

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Last Updated: May 24, 2026

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

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Targeted DNA Methylation Analysis by Next-generation Sequencing

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Next-generation sequencing (NGS) technologies generate vast amounts of data for genetic diversity studies.
Sequencing run variability and technology-specific error profiles complicate data analysis.
Accurate simulation of sequencing errors is crucial for robust downstream analysis.

Purpose of the Study:

To introduce GemSIM, a novel simulator for generating realistic NGS reads.
To create and utilize sequence-context based error models for accurate simulation.
To assess the impact of varying error profiles on SNP-calling accuracy.

Main Methods:

GemSIM derives empirical error models from sequencing run data (e.g., Illumina, Roche/454).
It simulates single or paired-end reads using fragment length and quality score distributions.
Simulated data from mixed bacterial haplotypes were analyzed for SNP-calling accuracy.

Main Results:

Error rates varied significantly between sequencing runs, read pairs, and technologies (Illumina vs. Roche/454).
Roche/454 showed more indels, while both technologies had increased errors towards read ends.
SNP-calling accuracy with VarScan was limited to >3% frequency and sensitive to sequencing run error profiles.

Conclusions:

GemSIM provides insights into individual sequencing run error profiles.
Simulation with GemSIM aids researchers in understanding and mitigating the effects of sequencing errors.
This tool is valuable for deep sequencing, metagenomic, and resequencing projects.