Strand asymmetry in the repair of replication dependent double-strand breaks.

Single-strand breaks (SSBs) are one of the most common endogenous lesions and have the potential to give rise to cytotoxic double-strand breaks (DSBs) during DNA replication. To investigate the mechanism of replication fork collapse at SSBs and subsequent repair, we employed Cas9 nickase (nCas9) to generate site and strand-specific nicks in the budding yeast genome. We show that nCas9-induced nicks are converted to mostly double-ended DSBs during S-phase. We find that repair of replication-dependent DSBs requires homologous recombination (HR) and is independent of canonical non-homologous end joining. Consistent with a strong bias to repair these lesions using a sister chromatid template, we observe minimal induction of inter-chromosomal HR by nCas9. Using nCas9 and a gRNA to nick either the leading or lagging strand template, we carried out a genome-wide screen to identify factors necessary for the repair of replication-dependent DSBs. All the core HR genes were recovered in the screen with both gRNAs, but we recovered components of the replication-coupled nucleosome assembly (RCNA) pathway with only the gRNA targeting the leading strand template. By use of additional gRNAs, we find that the RCNA pathway is especially important to repair a leading strand fork collapse.

ScreenMill: a freely available software suite for growth measurement, analysis and visualization of high-throughput screen data.

BACKGROUND: Many high-throughput genomic experiments, such as Synthetic Genetic Array and yeast two-hybrid, use colony growth on solid media as a screen metric. These experiments routinely generate over 100,000 data points, making data analysis a time consuming and painstaking process. Here we describe ScreenMill, a new software suite that automates image analysis and simplifies data review and analysis for high-throughput biological experiments. RESULTS: The ScreenMill, software suite includes three software tools or "engines": an open source Colony Measurement Engine (CM Engine) to quantitate colony growth data from plate images, a web-based Data Review Engine (DR Engine) to validate and analyze quantitative screen data, and a web-based Statistics Visualization Engine (SV Engine) to visualize screen data with statistical information overlaid. The methods and software described here can be applied to any screen in which growth is measured by colony size. In addition, the DR Engine and SV Engine can be used to visualize and analyze other types of quantitative high-throughput data. CONCLUSIONS: ScreenMill automates quantification, analysis and visualization of high-throughput screen data. The algorithms implemented in ScreenMill are transparent allowing users to be confident about the results ScreenMill produces. Taken together, the tools of ScreenMill offer biologists a simple and flexible way of analyzing their data, without requiring programming skills.