000007682 001__ 7682
000007682 005__ 20251022154457.0
000007682 0247_ $$2DOI$$a10.6083/m4z31xrz
000007682 037__ $$aETD
000007682 245__ $$aEvaluation of background prediction for variant detection in a clinical context : towards improved NGS monitoring of minimal residual disease in hematological malignancies
000007682 260__ $$bOregon Health and Science University
000007682 269__ $$a2017
000007682 336__ $$aThesis
000007682 502__ $$bM.S.
000007682 520__ $$aWith the growing value of next generation sequencing (NGS) assays for the determination of minimal residual disease (MRD) in the clinic, the confident and sensitive detection of low frequency variants is crucial to the treatment of cancer. Current in silico pipelines often lack the sensitivity to detect low frequency variants, whose variant allele frequencies (VAFs) covary with sample purity (i.e. tumor-normal and/or normal-tumor contamination), sample clonality, and copy number variations. Sensitivity is also confounded by the background error inherent to sequencing data, which may be introduced by systematic platform error, library amplification, and errors in sample preparation. Attempting to mitigate background error in sequencing data, researchers have developed many software error correction programs that model sources of error to mitigate its impact on downstream processing. While these models have been developed for de novo assembly, metagenomics research, and viral haplotype reconstruction, their application to the use case of low frequency variant detection has yet to be explored in-depth. For this research, we sought to develop a software framework for the evaluation of background error models in the low frequency variant use case, with a specific focus on their potential value to MRD monitoring.
000007682 542__ $$fIn copyright - single owner
000007682 650__ $$aLeukemia$$021430
000007682 650__ $$aPrecision Medicine$$038927
000007682 650__ $$aGenomics$$033016
000007682 650__ $$aNeoplasm, Residual$$030747
000007682 691__ $$aSchool of Medicine$$041369
000007682 692__ $$aDepartment of Medical Informatics and Clinical Epidemiology$$041422
000007682 7001_ $$aCordier, Benjamin$$uOregon Health and Science University$$041354$$10000-0001-5122-7879
000007682 7201_ $$aMcWeeney, Shannon$$uOregon Health and Science University$$041354$$7Personal$$eAdvisor$$eMentor$$eCommittee chair
000007682 8564_ $$945862531-6c9b-4a0f-be21-3e3309f32544$$s2798621$$uhttps://digitalcollections.ohsu.edu/record/7682/files/Cordier.Benjamin.2017.pdf$$ePublic$$2336085553ef9c9d90480aac0fff3f969$$31
000007682 905__ $$a/rest/prod/7p/88/cg/96/7p88cg965
000007682 909CO $$ooai:digitalcollections.ohsu.edu:7682$$pstudent-work
000007682 980__ $$aTheses and Dissertations