Biology subjects

Heigwer, F.

Publications and source records attributed to Heigwer, F..

4 recordsLinked to original sources

bioRxiv2018

Time-resolved mapping of genetic interactions to model rewiring of signaling pathways

Context-dependent changes in genetic vulnerabilities are important to understand the wiring of cellular pathways and variations in different environmental conditions. However, methodological frameworks to investigate the plasticity of genetic networks over time or in response to external stresses are lacking. To analyze the plasticity of genetic interactions, we performed an arrayed combinatorial RNAi screen in Drosophila cells at multiple time points and after pharmacological inhibition of Ras signaling activity. Using an image-based morphology assay to capture a broad range of phenotypes, we assessed the effect of 12768 pairwise RNAi perturbations in six different conditions. We found that genetic interactions form in different trajectories and developed an algorithm, termed MODIFI, to analyze how genetic interactions rewire over time. Using this framework, we identified more statistically significant interactions compared to endpoints assays and further observed several examples of context-dependent crosstalk between signaling pathways such as an interaction between Ras and Rel which is dependent on MEK activity.

genomics↗

bioRxiv2017

A Map Of Genetic Interactions In Cancer Cells

Cancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and, as a side effect, create new vulnerabilities for potential therapeutic exploitation. To systematically identify genotype-dependent vulnerabilities and synthetic lethal interactions, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework that integrates CRISPR/Cas9 screens originating from many different libraries and laboratories to build genetic interaction maps. It builds on analytical approaches that were established for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cell lines combining functional data with information on genetic variants to explore the relationships of more than 2.1 million gene-background relationships. In addition to known dependencies, our analysis identified new genotype-specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities associated with aberrant Wnt/{beta}-catenin signaling identified GANAB and PRKCSH as new positive regulators of Wnt/{beta}-catenin signaling. By clustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data is included.

systems biology↗

bioRxiv2017

CRISPRAnalyzeR: Interactive analysis, annotation and documentation of pooled CRISPR screens

Pooled CRISPR/Cas9 screens are a powerful and versatile tool for the systematic investigation of cellular processes in a variety of organisms. Such screens generate large amounts of data that present a new challenge to analyze and interpret. Here, we developed a web application to analyze, document and explore pooled CRISR/Cas9 screens using a unified single workflow. The end-to-end analysis pipeline features eight different hit calling strategies based on state-of-the-art methods, including DESeq2, MAGeCK, edgeR, sgRSEA, Z-Ratio, Mann-Whitney test, ScreenBEAM and BAGEL. Results can be compared with interactive visualizations and data tables. CRISPRAnalyzeR integrates meta-information from 26 external data resources, providing a wide array of options for the annotation and documentation of screens. The application was developed with user experience in mind, requiring no previous knowledge in bioinformatics. All modern operating systems are supported.\n\nAvailability and online documentation: The source code, a pre-configured docker application, sample data and a documentation can be found on our GitHub page (http://www.github.com/boutroslab/CRISPRAnalyzeR). A tutorial video can be found at http://www.crispr-analyzer.org.

bioinformatics↗

bioRxiv2017

HTSvis: A web app for exploratory data analysis and visualization of arrayed high-throughput screens

The analysis and visualization of arrayed high-throughput screens (HTS), such as cell-based RNAi or small-molecule HTS experiments, requires specialized computational methods. Software packages such as the R/Bioconductor package cellHTS have been developed to support the analysis and are broadly used by the high-throughput screening community. However, exploratory data analysis and integration of screening results remains challenging due to the size of produced data tables in multi-channel experiments and the lack of user-friendly tools to integrate and visualize screening results. Here we present HTSvis, an R/Shiny open-source web application for interactive visualization and exploratory analysis of arrayed high-throughput data. Using a light-weight infrastructure suitable for desktop computers, HTSvis can be used to visualize raw data, perform quality control and interactively visualize screening results from single- to multi-channel measurements, such as image-based, screens. Input data can either be a result file obtained upon analysis with cellHTS or a generic table with raw or analyzed data from, e.g. a high-content microscopy screen. HTSvis can be downloaded from http://github.com/boutroslab/HTSvis.

bioinformatics↗