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Related Experiment Video

Updated: Nov 20, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

SequencErr: measuring and suppressing sequencer errors in next-generation sequencing data.

Eric M Davis¹, Yu Sun^1,2, Yanling Liu¹

¹Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA.

|January 25, 2021

Summary

This summary is machine-generated.

A new computational method, SequencErr, precisely measures DNA sequencing errors, revealing that many sequencers and flow cells exceed acceptable error rates. This tool can assess, calibrate, and improve sequencer accuracy for next-generation sequencing applications.

Keywords:

DNA sequencing Error suppression Sequencer/instrument error

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Targeted DNA Methylation Analysis by Next-generation Sequencing

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Accurate measurement of DNA sequencing errors is critical for next-generation sequencing (NGS) applications, especially for analyzing heterogeneous cellular populations.
Current methods lack the precision to quantify errors occurring within sequencing instruments (sequencers).

Purpose of the Study:

To introduce SequencErr, a novel computational method for precisely measuring DNA sequencing errors by analyzing base correspondence in overlapping read regions.
To assess the accuracy and identify error-prone components within a large number of publicly available sequencing datasets.

Main Methods:

Developed SequencErr, a computational tool that measures sequencing errors by comparing overlapping forward and reverse reads.
Analyzed 3777 public datasets from 75 institutions across 18 countries to evaluate sequencer error rates.
Compared SequencErr's performance against FastQC and error correction tools like Lighter and Musket.

Main Results:

Identified an average sequencer error rate of approximately 10 errors per million (pm).
Found that 1.4% of sequencers and 2.7% of flow cells exhibit error rates greater than 100 pm.
Observed elevated error rates in specific areas of flow cells, with over 90% of HiSeq and NovaSeq flow cells containing outlier error-prone tiles.
Demonstrated that sequencers with higher error rates lead to reduced overall sequencing accuracy, and removing outlier tiles improves accuracy.
SequencErr provided novel insights compared to FastQC and achieved a 10-fold lower error rate than existing correction methods.

Conclusions:

The study provides novel insights into the characteristics of DNA sequencing errors.
SequencErr can be utilized to assess, calibrate, and monitor the accuracy of DNA sequencers.
The method enables computational suppression of sequencer errors in existing sequencing data, enhancing its reliability.