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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

In silico error correction improves cfDNA mutation calling

Chang Sik Kim^1,2,3, Sumitra Mohan¹, Mahmood Ayub¹

¹Clinical and Experimental Pharmacology Group, Cancer Research UK Manchester Institute.

Bioinformatics (Oxford, England)

|December 7, 2018

Abstract

Motivation:

Circulating-free DNA (cfDNA) profiling by sequencing is an important minimally invasive protocol for monitoring the mutation profile of solid tumours in cancer patients. Since the concentration of available cfDNA is limited, sample library generation relies on multiple rounds of PCR amplification, during which the accumulation of errors results in reduced sensitivity and lower accuracy.

Results:

We present PCR Error Correction (PEC), an algorithm to identify and correct errors in short read sequencing data. It exploits the redundancy that arises from multiple rounds of PCR amplification. PEC is particularly well suited to applications such as single-cell sequencing and circulating tumour DNA (ctDNA) analysis, in which many cycles of PCR are used to generate sufficient DNA for sequencing from small amounts of starting material. When applied to ctDNA analysis, PEC significantly improves mutation calling accuracy, achieving similar levels of performance to more complex strategies that require additional protocol steps and access to calibration DNA datasets.

Availability And Implementation:

PEC is available under the GPL-v3 Open Source licence, and is freely available from: https://github.com/CRUKMI-ComputationalBiology/PCR_Error_Correction.git.

Supplementary Information:

Supplementary data are available at Bioinformatics online.

Insights

PCR Error Correction (PEC) is a new algorithm that fixes errors in sequencing data. It improves the accuracy of detecting cancer mutations from circulating tumor DNA (ctDNA), even with limited sample amounts.

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Circulating-free DNA (cfDNA) sequencing is crucial for non-invasive cancer mutation monitoring.
Limited cfDNA amounts necessitate multiple PCR amplification cycles, introducing errors that reduce accuracy.

Purpose of the Study:

To develop an algorithm for identifying and correcting PCR-induced errors in sequencing data.
To enhance the accuracy of mutation detection in low-input DNA samples.

Main Methods:

Developed the PCR Error Correction (PEC) algorithm.
Utilized redundancy from multiple PCR amplification rounds to identify and correct errors.
Applied PEC to circulating tumor DNA (ctDNA) analysis.

Main Results:

PEC effectively identifies and corrects errors in short-read sequencing data.
Significantly improved mutation calling accuracy in ctDNA analysis.
Achieved high performance comparable to more complex methods without additional steps.

Conclusions:

PEC offers a robust solution for mitigating PCR errors in low-input sequencing applications.
Enhances the reliability of cfDNA profiling for cancer patient monitoring.
Provides an accessible open-source tool for the research community.

Read full analysis

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