Biology subjects

Wesel, E.

Publications and source records attributed to Wesel, E..

2 recordsLinked to original sources

bioRxiv2023

CRISPR/Cas9 gene editing to generate Drosophila LexA lines in secondary school classes

Genome editing in vivo with CRISPR/Cas9 generates powerful tools to study gene regulation and function. We developed CRISPR-based methods that permitted secondary school student scientists to convert Drosophila GAL4 lines to LexA lines. Our novel curricula implement a new donor strain optimizing Homology-assisted CRISPR knock-in (HACK) that simplifies screening using light microscopy. Successful curricula adoption by a consortium of schools led to the generation and characterization of 16 novel LexA lines. This includes extensive comparative tissue expression analysis between the parental Gal4 and derived LexA lines. From this collaboration, we established a workflow to systematically generate LexA lines from frequently-used GAL4 lines. Modular courses developed from this effort can be tailored to specific secondary school scheduling needs, and serve as a template for science educators to innovate courses and instructional goals. Our unique collaborations highlight that resources and expertise harnessed by university-based research laboratories can transform experiential science instruction in secondary schools while addressing research needs for the community of science.

genetics↗

bioRxiv2020

A comprehensive benchmarking of WGS-based structural variant callers

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.

bioinformatics↗