No abstract

Motivation: High-throughput screens (HTS) by RNAi or small molecules are among the most promising tools in functional genomics. They enable researchers to observe detailed reactions to experimental perturbations on a genome-wide scale. While there is a core set of computational approaches used in many publications to analyze these data, a specialized software combining them and making them easily accessible has so far been missing.Results: Here we describe , a flexible software to build integrated analysis pipelines for HTS data that contains over-representation analysis, gene set enrichment analysis, comparative gene set analysis and rich sub-network identification. interfaces with commonly used pre-processing packages for HTS data and presents its results as HTML pages and network plots.Availability: Our software is written in the R language and freely available via the Bioconductor project at http://www.bioconductor.org.Contact: florian.markowetz@cancer.org.uk

To investigate the kinetics of Cas9-mediated double strand break generation and repair in vivo, we developed two new tools. The first, chemically inducible Cas9 (ciCas9), is a rapidly-activated, single-component Cas9 variant engineered using a novel domain replacement strategy. ciCas9 can be activated in a matter of minutes, and the level of ciCas9 specificity and activity can be tuned. The second tool, DSB-ddPCR, is a droplet digital PCR-based assay for double strand breaks. DSB-ddPCR is the first assay to demonstrate time-resolved, highly quantitative and targeted measurement of DSBs. Combining these tools facilitated an unprecedented exploration of the kinetics of Cas9-mediated DNA cleavage and repair. We find that sgRNAs targeting different sites generally produce cleavage within minutes and repair within an hour or two. However, we observe distinct kinetic profiles, even for proximal sites, suggesting that target sequence and chromatin state modulate cleavage and repair kinetics.

Oncogene amplification on extrachromosomal DNA (ecDNA) is a common event, driving aggressive tumor growth, drug resistance and shorter survival. Currently, the impact of nonchromosomal oncogene inheritance—random identity by descent—is poorly understood. Also unclear is the impact of ecDNA on somatic variation and selection. Here integrating theoretical models of random segregation, unbiased image analysis, CRISPR-based ecDNA tagging with live-cell imaging and CRISPR-C, we demonstrate that random ecDNA inheritance results in extensive intratumoral ecDNA copy number heterogeneity and rapid adaptation to metabolic stress and targeted treatment. Observed ecDNAs benefit host cell survival or growth and can change within a single cell cycle. ecDNA inheritance can predict, a priori, some of the aggressive features of ecDNA-containing cancers. These properties are facilitated by the ability of ecDNA to rapidly adapt genomes in a way that is not possible through chromosomal oncogene amplification. These results show how the nonchromosomal random inheritance pattern of ecDNA contributes to poor outcomes for patients with cancer.