Function to identify novel sgRNAs in CRISPR organoid screen. This function has been developed for the analysis the screen of the following study:
Title: Genome-scale CRISPR screening in organoids identifies synergistic tumor-suppressor activities that lead to TGFß resistance.
Authors: Till Ringel 1,2, Nina Frey 1,2, Femke Ringnalda 1,2, Sharan Janjuha 1,2, Sarah Cherkaoui 4, Stefan Butz 5, Sumana Srivatsa 6, Martin Pirkl 6, Giancarlo Russo 7, Lukas Villiger 1,2, Gerhard Rogler 8, Hans Clevers 3,9, Niko Beerenwinkel 6, Nicola Zamboni 4, Tuncay Baubec 5 and Gerald Schwank 1,2,10,*
1Institute of Molecular Health Sciences, ETH Zurich, Switzerland.
2Department of Pharmacology and Toxicology, University of Zurich, Switzerland.
3University Medical Center (UMC) Utrecht, Utrecht, Netherlands.
4Institute of Molecular Systems Biology, ETH Zurich, Switzerland.
5Department of Molecular Mechanisms of Disease, University of Zurich, Switzerland.
6Department of Biosystems Science and Engineering, ETH Zurich, Switzerland.
7Functional Genomics Center Zurich, University of Zurich, ETH Zurich, Switzerland.
8Department of Gastroenterology and Hepatology, University Hospital Zurich, Switzerland.
9Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences (KNAW), Utrecht, Netherlands
Because of the nature of our CRISPR screening approach, in which we do not compare treated and non-treated pools, we decided to use the information of abundant positive controls to identify novel functional sgRNAs. We first selected positive control clones with high read counts for sgRNAs targeting known positive regulators. For these control clones, we identified integrated sgRNAs, removed background sgRNAs reads, and applied the learned pattern to analyze integrated sgRNAs in novel clones.
Processing function steps:
# If you do not have devtools installed install.packages("devtools") # Install CRISPRScreenProcessing library(devtools) devtools::install_github("cherkaos/CRISPRScreenProcessing") library("CRISPRScreenProcessing")
Pre-step. Manual identification of positive control organoid clones with high read counts for sgRNAs targeting known positive regulators. In the TGFß screens, the positive regulators were APC, AXIN, TGFBR1/2. An example of such file is provided here. The file should be a txt or csv file (comma-separated) in your working directory.
# Running CRISPRScreenProcessing inputfile="APK-1-and-2-final.txt" controlStart="sample35" controlEnd="sample19-II" resultfile <- "APK-1-and-2-final-result.txt" maxsgRNA=15 minReadCount=100 zscore=TRUE orderOutput=TRUE shortOutput=TRUE screenProcessing(inputfile,controlStart,controlEnd,resultfile,maxsgRNA,minReadCount,zscore,orderOutput,shortOutput)
The output is a file containing control clones and succesfully select novel clones. Each clones has 3 columns, where you can find on the information of the integrated sgRNAs and its read count. An example of such output can be found here.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.