Understanding and Improving Identification of Somatic Variants.
Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2016, Virginia Tech
It is important to understand the entire spectrum of somatic variants to gain more insight into mutations that occur in different cancers for development of better diagnostic, prognostic and therapeutic tools. This thesis outlines our work in understanding somatic variant calling, improving the identification of somatic variants from whole genome and whole exome platforms and identification of biomarkers for lung cancer.
Integrating somatic variants from whole genome and whole exome platforms poses a challenge as variants identified in the exonic regions of the whole genome platform may not be identified on the whole exome platform and vice-versa. Taking a simple union or intersection of the somatic variants from both platforms would lead to inclusion of many false positives (through union) and exclusion of many true variants (through intersection). We develop the first framework to improve the identification of somatic variants on whole genome and exome platforms using a machine learning approach by combining the results from two popular somatic variant callers. Testing on simulated and real data sets shows that our framework identifies variants more accurately than using only one somatic variant caller or using variants from only one platform.
Short tandem repeats (STRs) are repetitive units of 2-6 nucleotides. STRs make up approximately 1% of the human genome and have been traditionally used as genetic markers in population studies. We conduct a series of in silico analyses using the exome data of 32 individuals with lung cancer to identify 103 STRs that could potentially serve as cancer diagnostic markers and 624 STRs that could potentially serve as cancer predisposition markers.
Overall these studies improve the accuracy in identification of somatic variants and highlight the association of STRs to lung cancer.
Advisors/Committee Members: Zhang, Liqing (committeechair), Wu, Xiaowei (committee member), Heath, Lenwood S. (committee member), Franck, Christopher T. (committee member).
Subjects/Keywords: Somatic variants; Somatic variant callers; Somatic point mutations; Short tandem repeat variation; Lung squamous cell carcinoma
somatic point mutations in exomes
Figure 2.2: Sensitivity, precision and… …F1 score of different pipelines detecting somatic
point mutations in genomes
Figure 2.3… …Factors affecting the detection of somatic point mutations.
Figure 2.4: Sensitivity of… …different pipelines in detecting somatic point mutations
using a high quality exome data set
35… …pipelines while detecting somatic point mutations in exomes 36
Figure 2.6: Sensitivity as a…
to Zotero / EndNote / Reference
APA (6th Edition):
Vijayan, V. (2016). Understanding and Improving Identification of Somatic Variants. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/72969
Chicago Manual of Style (16th Edition):
Vijayan, Vinaya. “Understanding and Improving Identification of Somatic Variants.” 2016. Doctoral Dissertation, Virginia Tech. Accessed April 18, 2021.
MLA Handbook (7th Edition):
Vijayan, Vinaya. “Understanding and Improving Identification of Somatic Variants.” 2016. Web. 18 Apr 2021.
Vijayan V. Understanding and Improving Identification of Somatic Variants. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/10919/72969.
Council of Science Editors:
Vijayan V. Understanding and Improving Identification of Somatic Variants. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/72969