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

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 dedica...

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Detalles Bibliográficos
Autores principales: Carrot-Zhang, Jian, Majewski, Jacek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals LLC 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5514890/
https://www.ncbi.nlm.nih.gov/pubmed/28416765
http://dx.doi.org/10.18632/oncotarget.16144
Descripción
Sumario: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 athttps://github.com/jcarrotzhang/LoLoPicker.