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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...

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Updated: May 5, 2026

Targeted DNA Methylation Analysis by Next-generation Sequencing

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

QPLOT: a quality assessment tool for next generation sequencing data.

Bingshan Li¹, Xiaowei Zhan, Mary-Kate Wing

¹Department of Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA.

Biomed Research International

|December 10, 2013

Summary

This summary is machine-generated.

Next generation sequencing (NGS) data quality is crucial for disease research. QPLOT is a new automated tool that assesses sequencing run performance, providing graphical summaries for efficient quality control.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Next generation sequencing (NGS) is vital for identifying genetic variants linked to human diseases.
NGS data quality is susceptible to errors due to rapidly evolving technologies and protocols.
Routine quality assessment of sequencing and alignment data is essential before downstream analyses.

Purpose of the Study:

To introduce QPLOT, an automated tool for assessing next generation sequencing run performance.
To provide researchers with a method for efficient quality control of sequencing data.

Main Methods:

QPLOT takes standard sequence alignments as input.
It generates diagnostic metrics and graphical summaries of sequencing run quality.
The tool is computationally efficient and produces interactive webpages for detailed results.

Main Results:

QPLOT facilitates automated quality assessment of sequencing run performance.
It generates convenient graphical summaries of key diagnostic metrics.
The tool can handle joint output from multiple sequencing runs.

Conclusions:

QPLOT is an automated tool that simplifies the assessment of sequence run quality.
It enables rapid detection of sequencing run problems.
QPLOT is expected to be a valuable resource for the research community.