LoLoPicker: detecting low allelic-fraction variants from low-quality cancer samples.

Jian Carrot-Zhang,Jacek Majewski

doi:10.18632/oncotarget.16144

Jian Carrot-Zhang, Jacek Majewski

Open Access

https://doi.org/10.18632/oncotarget.16144

Copy DOI

Journal: Oncotarget	Publication Date: Mar 12, 2017
Citations: 23	License type: cc-by

Affiliation: Dana-Farber Cancer Institute, McGill University

Abstract

IntroductionAlthough several programs are designed to identify variants with low allelic-fraction, further improvement is needed, especially to push the detection limit of low allelic-faction variants in low-quality, ”noisy” tumor samples.ResultsWe developed LoLoPicker, an efficient tool dedicated to calling somatic variants from next-generation sequencing (NGS) data of tumor sample against the matched normal sample plus a user-defined control panel of additional normal samples. The control panel allows accurately estimating background error rate and therefore ensures high-accuracy mutation detection.ConclusionsCompared to other methods, we showed a superior performance of LoLoPicker with significantly improved specificity. The algorithm of LoLoPicker is particularly useful for calling low allelic-fraction variants from low-quality cancer samples such as formalin-fixed and paraffin-embedded (FFPE) samples.Implementation and Availability: The main scripts are implemented in Python-2.7 and the package is released athttps://github.com/jcarrotzhang/LoLoPicker.

Highlights

Several programs are designed to identify variants with low allelic-fraction, further improvement is needed, especially to push the detection limit of low allelic-faction variants in low-quality, ”noisy” tumor samples
Compared to other methods, we showed a superior performance of LoLoPicker with significantly improved specificity
The algorithm of LoLoPicker is useful for calling low allelic-fraction variants from low-quality cancer samples such as formalin-fixed and paraffin-embedded (FFPE) samples

Summary

Introduction

Several programs are designed to identify variants with low allelic-fraction, further improvement is needed, especially to push the detection limit of low allelic-faction variants in low-quality, ”noisy” tumor samples. One of the major problems is that variants with low allelic-fractions that are commonly observed in tumor samples owing to normal tissue contaminations or cancer heterogeneity, are difficult to distinguish from systematic errors inherent to most sequencing technologies. Technical artifacts, such as C to T and G to A transitions can arise from the formalin-fixation process, which is widely used to preserve tissues in hospitals worldwide [1,2]. A panel of control samples provides an opportunity to precisely estimate background error rates, which can be used to increase the sensitivity of calling single nucleotide variants (SNVs) for sites with low error rate, and to reduce false positives for sites with high error rate [4]

Methods

Results

Conclusion