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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.

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...

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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.

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Related Experiment Video

Updated: Jun 20, 2026

Cost-Efficient Transcriptomic-Based Drug Screening

Cost-Efficient Transcriptomic-Based Drug Screening

Published on: February 23, 2024

Benchmarking next-generation transcriptome sequencing for functional and evolutionary genomics.

John G Gibbons¹, Eric M Janson, Chris Todd Hittinger

¹Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA.

Molecular Biology and Evolution

|August 27, 2009

Summary

This summary is machine-generated.

Short-read sequencing of tropical disease vectors provides valuable genomic data for nonmodel organisms. This cost-effective method yields high-quality transcriptomes, aiding evolutionary studies.

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

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Last Updated: Jun 20, 2026

Cost-Efficient Transcriptomic-Based Drug Screening

Cost-Efficient Transcriptomic-Based Drug Screening

Published on: February 23, 2024

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

Genomics
Evolutionary Biology
Bioinformatics

Background:

Next-generation sequencing (NGS) enables genomic analysis of nonmodel organisms.
Long-read sequencing is favored for de novo applications, while short-read utility is debated.

Purpose of the Study:

To evaluate the utility of short-read transcriptome sequencing for genomic analysis in nonmodel organisms.
To assess the quantity, quality, and evolutionary information content of short-read data.

Main Methods:

Short-read sequencing of transcriptomes from Aedes aegypti and Anopheles gambiae.
Comparison of generated data against available reference genomes.
Analysis of contig quantity, quality, error rates, and functional/evolutionary content.

Main Results:

Generated over 0.7 billion nucleotides of data per species, assembling >21,000 contigs (>100 bp).
Achieved a substitution error rate of ~0.25% with minimal assembly errors.
Demonstrated accurate ortholog prediction across evolutionary timescales.

Conclusions:

Short-read transcriptome sequencing is highly valuable for nonmodel organism genomics.
This approach provides significant functional and evolutionary insights.
Offers a method for assessing NGS data utility in evolutionary studies.