Abstract

Background

Detection of DNA mutations at very low allele fractions with high accuracy will significantly improve the effectiveness of precision medicine for cancer patients. To achieve this goal through next generation sequencing, researchers need a detection method that 1) captures rare mutation-containing DNA fragments efficiently in the mix of abundant wild-type DNA; 2) sequences the DNA library extensively to deep coverage; and 3) distinguishes low level true variants from amplification and sequencing errors with high accuracy. Targeted enrichment using PCR primers provides researchers with a convenient way to achieve deep sequencing for a small, yet most relevant region using benchtop sequencers. Molecular barcoding (or indexing) provides a unique solution for reducing sequencing artifacts analytically. Although different molecular barcoding schemes have been reported in recent literature, most variant calling has been done on limited targets, using simple custom scripts. The analytical performance of barcode-aware variant calling can be significantly improved by incorporating advanced statistical models.

Results

We present here a highly efficient, simple and scalable enrichment protocol that integrates molecular barcodes in multiplex PCR amplification. In addition, we developed smCounter, an open source, generic, barcode-aware variant caller based on a Bayesian probabilistic model. smCounter was optimized and benchmarked on two independent read sets with SNVs and indels at 5 and 1% allele fractions. Variants were called with very good sensitivity and specificity within coding regions.

Conclusions

We demonstrated that we can accurately detect somatic mutations with allele fractions as low as 1% in coding regions using our enrichment protocol and variant caller.

Details

Title
Detecting very low allele fraction variants using targeted DNA sequencing and a novel molecular barcode-aware variant caller
Author
Chang, Xu; Nezami Ranjbar, Mohammad R; Wu, Zhong; DiCarlo, John; Wang, Yexun
First page
1
Section
Methodology Article
Publication year
2017
Publication date
2017
Publisher
BioMed Central
e-ISSN
14712164
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2348266818
Copyright
© 2017. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.