@inbook{e65ea81881a8434987803d2b483c6054,
title = "MuSE: A Novel Approach to Mutation Calling with Sample-Specific Error Modeling",
abstract = "Accurate detection of somatic mutations in genetically heterogeneous tumor cell populations using next-generation sequencing remains challenging. We have developed MuSE, Mutation calling using a Markov Substitution model for Evolution, a novel approach for modeling the evolution of the allelic composition of tumor and normal tissue at each reference base. It adopts a sample-specific error model to depict inter-tumor heterogeneity, which greatly improves the overall accuracy. Here, we describe the method and provide a tutorial on the installation and application of MuSE.",
keywords = "Bayesian model, Cancer evolution, Next-generation DNA sequencing, Single-nucleotide variants, Tumor heterogeneity",
author = "Shuangxi Ji and Montierth, {Matthew D.} and Wenyi Wang",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.",
year = "2022",
doi = "10.1007/978-1-0716-2293-3_2",
language = "English (US)",
series = "Methods in Molecular Biology",
publisher = "Humana Press Inc.",
pages = "21--27",
booktitle = "Methods in Molecular Biology",
}