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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: May 12, 2026

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)
09:06

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

Published on: October 5, 2018

QC-Chain: fast and holistic quality control method for next-generation sequencing data.

Qian Zhou1, Xiaoquan Su, Anhui Wang

  • 1CAS Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong, China.

Plos One
|April 9, 2013
PubMed
Summary

QC-Chain is a novel method for next-generation sequencing (NGS) data quality control. It rapidly identifies and removes low-quality and contaminating reads, improving downstream analysis accuracy and speed.

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Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
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Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Related Experiment Videos

Last Updated: May 12, 2026

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)
09:06

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

Published on: October 5, 2018

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

Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) is crucial in life sciences but raw data often contains artifacts like low-quality and contaminating reads.
  • Existing quality control (QC) tools struggle with large data volumes and cannot identify unknown contamination de novo.

Purpose of the Study:

  • To introduce QC-Chain, a fast, accurate, and comprehensive method for NGS data quality control.
  • To address limitations of existing tools in processing speed and de novo contamination identification.

Main Methods:

  • QC-Chain integrates quality assessment, trimming using Parallel-QC, and de novo identification, quantification, and filtration of unknown contamination.
  • The method is optimized using parallel computation for significantly enhanced processing speed.

Main Results:

  • QC-Chain effectively identifies and filters low-quality reads and unknown contaminants.
  • Demonstrated significant improvements in downstream analyses such as genome assembly and gene prediction.
  • Achieved a 7-8 fold speed-up compared to traditional QC methods.

Conclusions:

  • QC-Chain provides a fast and effective solution for NGS read quality processing and de novo contamination filtration.
  • The tool significantly facilitates downstream genomic analyses by ensuring high-quality clean reads.