Research Papers:

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

Jian Carrot-Zhang _ and Jacek Majewski

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Oncotarget. 2017; 8:37032-37040. https://doi.org/10.18632/oncotarget.16144

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Jian Carrot-Zhang1,2 and Jacek Majewski3,4

1 Cancer Program, The Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA

2 Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

3 Department of Human Genetics, McGill University, Montreal, Quebec, Canada

4 Genome Quebec Innovation Centre, Montreal, Quebec, Canada

Correspondence to:

Jian Carrot-Zhang, email:

Keywords: somatic mutation detection, low allelic-fraction variants, high specificity, FFPE samples

Received: September 27, 2016 Accepted: December 27, 2016 Published: March 12, 2017


Introduction: Although 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.

Results: We 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.

Conclusions: Compared 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 at https://github.com/jcarrotzhang/LoLoPicker.

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