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Cited 6 time in webofscience Cited 7 time in scopus
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Impact of mouse contamination in genomic profiling of patient-derived models and best practice for robust analysis

Authors
Jo, Se-YoungKim, EunyoungKim, Sangwoo
Issue Date
Dec-2019
Publisher
BioMed Central
Citation
Genome Biology, v.20, no.1, pp.231
Journal Title
Genome Biology
Volume
20
Number
1
Start Page
231
URI
https://yscholarhub.yonsei.ac.kr/handle/2021.sw.yonsei/5389
DOI
10.1186/s13059-019-1849-2
ISSN
1474-7596
Abstract
Background Patient-derived xenograft and cell line models are popular models for clinical cancer research. However, the inevitable inclusion of a mouse genome in a patient-derived model is a remaining concern in the analysis. Although multiple tools and filtering strategies have been developed to account for this, research has yet to demonstrate the exact impact of the mouse genome and the optimal use of these tools and filtering strategies in an analysis pipeline. Results We construct a benchmark dataset of 5 liver tissues from 3 mouse strains using human whole-exome sequencing kit. Next-generation sequencing reads from mouse tissues are mappable to 49% of the human genome and 409 cancer genes. In total, 1,207,556 mouse-specific alleles are aligned to the human genome reference, including 467,232 (38.7%) alleles with high sensitivity to contamination, which are pervasive causes of false cancer mutations in public databases and are signatures for predicting global contamination. Next, we assess the performance of 8 filtering methods in terms of mouse read filtration and reduction of mouse-specific alleles. All filtering tools generally perform well, although differences in algorithm strictness and efficiency of mouse allele removal are observed. Therefore, we develop a best practice pipeline that contains the estimation of contamination level, mouse read filtration, and variant filtration. Conclusions The inclusion of mouse cells in patient-derived models hinders genomic analysis and should be addressed carefully. Our suggested guidelines improve the robustness and maximize the utility of genomic analysis of these models.
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