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Related Concept Videos

Next-generation Sequencing03:00

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-seq03:21

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...
Sanger Sequencing01:57

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: Jun 4, 2026

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
13:24

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

Efficient study design for next generation sequencing.

Joshua Sampson1, Kevin Jacobs, Meredith Yeager

  • 1Biostatistics Branch, DCEG, National Cancer Institute, Rockville, MD 20852, USA. joshua.sampson@nih.gov

Genetic Epidemiology
|March 4, 2011
PubMed
Summary
This summary is machine-generated.

Optimizing Next Generation Sequencing studies is key for genetic research. Efficient designs balancing subject numbers and coverage depth improve detection of disease-associated variants, especially in pilot studies.

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Last Updated: Jun 4, 2026

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Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Published on: August 29, 2014

Area of Science:

  • Genomics
  • Genetic Epidemiology
  • Bioinformatics

Background:

  • Next Generation Sequencing (NGS) is crucial for identifying genetic variations linked to human diseases.
  • High NGS costs necessitate efficient study designs balancing subject numbers (n) and coverage depth (µ).
  • Resource allocation between n and µ significantly impacts study success, particularly for pilot investigations.

Purpose of the Study:

  • To propose an optimal strategy for selecting n and µ combinations in NGS studies.
  • To guide the design of studies targeting rare variant detection and association analyses.
  • To inform resource allocation for maximizing the success of genetic variation studies.

Main Methods:

  • Analysis of coverage depth distribution across the genome.
  • Determination of the depth required for minor allele identification.
  • Application of the likelihood ratio test for rare variant detection.
  • Comparative analysis of sequencing strategies for association studies.

Main Results:

  • Optimal coverage depth for rare variant detection using the likelihood ratio test is 2–8 reads.
  • For association studies, sequencing all available subjects is the preferred strategy.
  • The optimal coverage depth is contingent upon the specific study objectives.

Conclusions:

  • Study design in NGS, particularly the n-µ trade-off, is critical for successful genetic variation detection.
  • Tailoring coverage depth to study aims (rare variant detection vs. association) is essential.
  • Efficient NGS study designs enhance the power to identify genetic variants associated with human disease.