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

// Jian Carrot-Zhang 1,2 and Jacek Majewski 3,4 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 Abstract 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 .