Carrot-Zhang Jian, Majewski Jacek
Cancer Program, The Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
Oncotarget. 2017 Jun 6;8(23):37032-37040. doi: 10.18632/oncotarget.16144.
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.
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.
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.
尽管有几个程序旨在识别低等位基因分数的变异，但仍需要进一步改进，特别是要提高在低质量、“有噪声”的肿瘤样本中检测低等位基因分数变异的极限。
我们开发了LoLoPicker，这是一种高效工具，用于根据匹配的正常样本以及用户定义的其他正常样本控制面板，从肿瘤样本的下一代测序（NGS）数据中识别体细胞变异。该控制面板可准确估计背景错误率，从而确保高精度的突变检测。
与其他方法相比，我们展示了LoLoPicker的卓越性能，其特异性显著提高。LoLoPicker算法对于从低质量癌症样本（如福尔马林固定石蜡包埋（FFPE）样本）中识别低等位基因分数变异特别有用。实现与可用性：主要脚本用Python-2.7实现，该软件包可在https://github.com/jcarrotzhang/LoLoPicker上获取。
