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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.
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...
Genomics02:02

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...
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...

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Related Experiment Video

Updated: Jun 6, 2026

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
07:30

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

NGSQC: cross-platform quality analysis pipeline for deep sequencing data.

Manhong Dai1, Robert C Thompson, Christopher Maher

  • 1Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA. daimh@umich.edu

BMC Genomics
|December 15, 2010
PubMed
Summary

Deep sequencing data requires robust quality control. The Next Generation Sequencing Quality Control (NGSQC) pipeline identifies potential issues, ensuring biological discoveries are reliable and not artifacts of sequencing errors.

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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

Related Experiment Videos

Last Updated: Jun 6, 2026

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
07:30

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

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

Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Deep sequencing offers high accuracy but its digital output can create false confidence in data reliability.
  • Earlier hybridization-based technologies had lower precision compared to current deep sequencing methods.

Purpose of the Study:

  • To introduce a novel pipeline for comprehensive quality control of deep sequencing data.
  • To enable researchers to validate if biological findings stem from sequencing quality issues.

Main Methods:

  • Development of the Next Generation Sequencing Quality Control (NGSQC) pipeline.
  • Implementation of novel quality control measures for deep sequencing data.

Main Results:

  • NGSQC detects a wide range of quality issues in deep sequencing data from 2D surfaces.
  • The pipeline is independent of the specific assay technology used.
  • NGSQC helps differentiate true biological signals from sequencing artifacts.

Conclusions:

  • Next-generation sequencing platforms are susceptible to quality issues, including variations between labs, batches, and chips/slides.
  • NGSQC is crucial for ensuring the validity of biological conclusions, especially those based on rare sequence alterations.