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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.
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Although all next-generation methods use different technologies, they all share a set of standard features....

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Related Experiment Video

Updated: Aug 14, 2025

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

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

Published on: October 5, 2018

SAMStat 2: quality control for next generation sequencing data.

Timo Lassmann¹

¹Precision Health, Telethon Kids Institute, University of Western Australia, Perth, WA 6009, Australia.

Bioinformatics (Oxford, England)

|January 13, 2023

Summary

This summary is machine-generated.

SAMStat is an efficient tool for extracting quality control metrics from sequencing data. This updated version enhances analysis for paired-end and long-read data, improving genomic data quality assessment.

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Published on: February 24, 2015

Area of Science:

Bioinformatics
Genomic Data Analysis
Computational Biology

Background:

Accurate quality control of sequencing data is crucial for reliable downstream analysis.
Existing tools may not adequately address the complexities of modern sequencing data, such as paired-end and long reads.

Purpose of the Study:

To present a significant update to SAMStat, an efficient program for extracting quality control metrics.
To enhance SAMStat's capabilities to support paired-end and long-read sequencing data.
To improve the identification of potential issues affecting read mapping quality.

Main Methods:

Utilizes SAM/BAM and fastq files for quality control metric extraction.
Implements detailed profiling of sequence composition, base quality, and mapping errors.
Incorporates support for paired-end and long-read data.
Employs the plotly javascript library for generating quality control plots.

Main Results:

SAMStat efficiently extracts key quality control metrics from various sequencing file formats.
The updated version provides enhanced support for paired-end and long-read data.
Detailed mapping quality-split profiles aid in rapid identification of poor mapping reasons, such as adapter contamination or low base quality.

Conclusions:

The updated SAMStat offers a robust and efficient solution for comprehensive sequencing data quality control.
Its expanded support for diverse data types and detailed error profiling facilitates more accurate genomic analyses.
SAMStat is a valuable tool for researchers seeking to ensure the quality and reliability of their sequencing data.