In May 2020, Angela officially joins Mangul Lab at USC as a PhD student co-advised by Serghei Mangul and Stan Louie. During her first year in the Pharmaceutical & Translational Sciences (PHTS) Program, Angela completed one rotation in Mangul Lab.
Angela’s long-term career goal is to work in the biotechnology and pharmaceutical industry as a clinical pharmacologist. Angela is very excited to be co-advised by Serghei—her rotational experience in Mangul Lab inspired her to emphasize development of both traditional “wet” laboratory and computational skills during her doctoral education.
For her PhD project, Angela will be investigating therapeutics for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the novel coronavirus disease. Angela will be leveraging machine learning and other bioinformatics-based approaches to identify clinical biomarkers of interest critical in viral induced respiratory distress. As her doctoral research project shifts and progresses, Angela will incorporate useful bioinformatics tools and studies.
Angela is a co-author for one of our recent preprints: Sarwal, Varuni; Niehus, Sebastian; Ayyala, Ram; Chang, Sei; Lu, Angela; Darci-Maher, Nicholas; Littman, Russell Jared; Wesel, Emily; Castellanos, Jacqueline; Chikka, Rahul; Distler, Margaret G; Eskin, Eleazar; Flint, Jonathan; Mangul, Serghei A comprehensive benchmarking of WGS-based structural variant callers Journal Article bioRxiv, 2020. Abstract | Links | BibTeX @article{Sarwal2020,
title = {A comprehensive benchmarking of WGS-based structural variant callers},
author = {Varuni Sarwal and Sebastian Niehus and Ram Ayyala and Sei Chang and Angela Lu and Nicholas Darci-Maher and Russell Jared Littman and Emily Wesel and Jacqueline Castellanos and Rahul Chikka and Margaret G Distler and Eleazar Eskin and Jonathan Flint and Serghei Mangul},
url = {https://www.biorxiv.org/content/10.1101/2020.04.16.045120v1},
doi = {10.1101/2020.04.16.045120},
year = {2020},
date = {2020-04-18},
journal = {bioRxiv},
abstract = {Advances in whole genome sequencing promise to enable the accurate and comprehensive structural variant (SV) discovery. Dissecting SVs from whole genome sequencing (WGS) data presents a substantial number of challenges and a plethora of SV-detection methods have been developed. Currently, there is a paucity of evidence which investigators can use to select appropriate SV-detection tools. In this paper, we evaluated the performance of SV-detection tools using a comprehensive PCR-confirmed gold standard set of SVs. In contrast to the previous benchmarking studies, our gold standard dataset included a complete set of SVs allowing us to report both precision and sensitivity rates of SV-detection methods. Our study investigates the ability of the methods to detect deletions, thus providing an optimistic estimate of SV detection performance, as the SV-detection methods that fail to detect deletions are likely to miss more complex SVs. We found that SV-detection tools varied widely in their performance, with several methods providing a good balance between sensitivity and precision. Additionally, we have determined the SV callers best suited for low and ultra-low pass sequencing data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Advances in whole genome sequencing promise to enable the accurate and comprehensive structural variant (SV) discovery. Dissecting SVs from whole genome sequencing (WGS) data presents a substantial number of challenges and a plethora of SV-detection methods have been developed. Currently, there is a paucity of evidence which investigators can use to select appropriate SV-detection tools. In this paper, we evaluated the performance of SV-detection tools using a comprehensive PCR-confirmed gold standard set of SVs. In contrast to the previous benchmarking studies, our gold standard dataset included a complete set of SVs allowing us to report both precision and sensitivity rates of SV-detection methods. Our study investigates the ability of the methods to detect deletions, thus providing an optimistic estimate of SV detection performance, as the SV-detection methods that fail to detect deletions are likely to miss more complex SVs. We found that SV-detection tools varied widely in their performance, with several methods providing a good balance between sensitivity and precision. Additionally, we have determined the SV callers best suited for low and ultra-low pass sequencing data. |
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