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Title: Phertilizer: Growing a clonal tree from ultra-low coverage single-cell DNA sequencing of tumors
Emerging ultra-low coverage single-cell DNA sequencing (scDNA-seq) technologies have enabled high resolution evolutionary studies of copy number aberrations (CNAs) within tumors. While these sequencing technologies are well suited for identifying CNAs due to the uniformity of sequencing coverage, the sparsity of coverage poses challenges for the study of single-nucleotide variants (SNVs). In order to maximize the utility of increasingly available ultra-low coverage scDNA-seq data and obtain a comprehensive understanding of tumor evolution, it is important to also analyze the evolution of SNVs from the same set of tumor cells. We presentPhertilizer, a method to infer a clonal tree from ultra-low coverage scDNA-seq data of a tumor. Based on a probabilistic model, our method recursively partitions the data by identifying key evolutionary events in the history of the tumor. We demonstrate the performance ofPhertilizeron simulated data as well as on two real datasets, finding thatPhertilizereffectively utilizes the copy-number signal inherent in the data to more accurately uncover clonal structure and genotypes compared to previous methods.  more » « less
Award ID(s):
2046488
PAR ID:
10500550
Author(s) / Creator(s):
; ; ;
Editor(s):
Przytycka, Teresa M.
Publisher / Repository:
PLOS
Date Published:
Journal Name:
PLOS Computational Biology
Volume:
19
Issue:
10
ISSN:
1553-7358
Page Range / eLocation ID:
e1011544
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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