Simplified homology-assisted CRISPR for gene editing in Drosophila.

In vivo genome editing with clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 generates powerful tools to study gene regulation and function. We revised the homology-assisted CRISPR knock-in method to convert Drosophila GAL4 lines to LexA lines using a new universal knock-in donor strain. A balancer chromosome-linked donor strain with both body color (yellow) and eye red fluorescent protein (RFP) expression markers simplified the identification of LexA knock-in using light or fluorescence microscopy. A second balancer chromosome-linked donor strain readily converted the second chromosome-linked GAL4 lines regardless of target location in the cis-chromosome but showed limited success for the third chromosome-linked GAL4 lines. We observed a consistent and robust expression of the yellow transgene in progeny harboring a LexA knock-in at diverse genomic locations. Unexpectedly, the expression of the 3xP3-RFP transgene in the "dual transgene" cassette was significantly increased compared with that of the original single 3xP3-RFP transgene cassette in all tested genomic locations. Using this improved screening approach, we generated 16 novel LexA lines; tissue expression by the derived LexA and originating GAL4 lines was similar or indistinguishable. In collaboration with 2 secondary school classes, we also established a systematic workflow to generate a collection of LexA lines from frequently used GAL4 lines.

A comprehensive benchmarking of WGS-based deletion structural variant callers.

Advances in whole-genome sequencing (WGS) promise to enable the accurate and comprehensive structural variant (SV) discovery. Dissecting SVs from WGS data presents a substantial number of challenges and a plethora of SV detection methods have been developed. Currently, evidence that investigators can use to select appropriate SV detection tools is lacking. In this article, we have evaluated the performance of SV detection tools on mouse and human WGS data using a comprehensive polymerase chain reaction-confirmed gold standard set of SVs and the genome-in-a-bottle variant set, respectively. 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 the 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 ultralow-pass sequencing data as well as for different deletion length categories.