Detailed Information

Cited 8 time in webofscience Cited 9 time in scopus
Metadata Downloads

Impact of mouse contamination in genomic profiling of patient-derived models and best practice for robust analysis

Full metadata record
DC FieldValueLanguage
dc.contributor.authorJo, Se-Young-
dc.contributor.authorKim, Eunyoung-
dc.contributor.authorKim, Sangwoo-
dc.date.accessioned2021-12-01T09:40:22Z-
dc.date.available2021-12-01T09:40:22Z-
dc.date.issued2019-12-
dc.identifier.issn1474-7596-
dc.identifier.urihttps://yscholarhub.yonsei.ac.kr/handle/2021.sw.yonsei/5389-
dc.description.abstractBackground 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.-
dc.publisherBioMed Central-
dc.titleImpact of mouse contamination in genomic profiling of patient-derived models and best practice for robust analysis-
dc.typeArticle-
dc.publisher.location영국-
dc.identifier.doi10.1186/s13059-019-1849-2-
dc.identifier.scopusid2-s2.0-85074742418-
dc.identifier.wosid000495585900001-
dc.identifier.bibliographicCitationGenome Biology, v.20, no.1, pp 231-
dc.citation.titleGenome Biology-
dc.citation.volume20-
dc.citation.number1-
dc.citation.startPage231-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClasssci-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
Files in This Item
Appears in
Collections
ETC > 1. Journal Articles

qrcode

Items in Scholar Hub are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher KIM, EUNYOUNG photo

KIM, EUNYOUNG
College of Medicine
Read more

Altmetrics

Total Views & Downloads

BROWSE