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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...

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Related Experiment Video

Updated: Jun 23, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Overlapping pools for high-throughput targeted resequencing.

Snehit Prabhu¹, Itsik Pe'er

¹Department of Computer Science, Columbia University, New York, New York 10025, USA. snehitp@columbia.edu

Genome Research

|May 19, 2009

Summary

This summary is machine-generated.

This study introduces an overlapping pooling strategy for DNA resequencing, enabling precise identification of individual variant carriers. This method enhances variant detection accuracy and carrier assignment in genetic studies.

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Area of Science:

Genomics
Bioinformatics
Population Genetics

Background:

Pooled DNA resequencing is vital for variant discovery and case-control comparisons.
Current disjoint pooling methods identify allele frequencies but not individual carriers.
Overlapping pooling designs offer a solution for carrier identification.

Purpose of the Study:

To develop a mathematical framework for overlapping pool designs in DNA resequencing.
To address challenges like false positives, false negatives, and ambiguous carrier identification.
To ensure accurate singleton carrier identification while maintaining sensitivity and specificity.

Main Methods:

Developed a novel overlapping pool design framework.
Utilized error-correcting code theory to optimize pool design.
Applied the framework to short-read data from the 1000 Genomes Pilot 3 project.

Main Results:

The proposed overlapping design guarantees high probability of unambiguous singleton carrier identification.
Maintained sensitivity, specificity, and allele frequency estimation comparable to disjoint pools.
Successfully extracted rare variations from real-world sequencing data.

Conclusions:

Overlapping pooling designs significantly improve carrier identification in pooled resequencing studies.
This approach overcomes common pitfalls associated with pooled sequencing.
The framework is robust and applicable to practical genomic research, including rare variant discovery.