From 33e6dcf5ea2e1a807f89fa2f096e36ac25e64c6c Mon Sep 17 00:00:00 2001 From: David Bikard <david.bikard@pasteur.fr> Date: Wed, 31 Jan 2018 19:24:59 +0100 Subject: [PATCH] initial commit --- README.md | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 README.md diff --git a/README.md b/README.md new file mode 100644 index 0000000..7de83e4 --- /dev/null +++ b/README.md @@ -0,0 +1,29 @@ +This repository contains the code used in the paper: + +**A CRISPRi screen in E. coli reveals an unexpected sequence-specific toxicity of dCas9** +Lun Cui, Antoine Vigouroux, Francois Rousset, Hugo Varet, Varun Khanna & David Bikard + + +In this study We designed a library of 84215 unique guide RNAs targeting random positions +along the genome of E. coli MG1655, with the simple requirement of a “NGG†PAM. The library +contains an average of 19 targets per gene. A pool of guide RNAs obtained through on-chip +oligo synthesis was cloned under the control of a constitutive promoter on plasmid psgRNA +and electroporated in strain LC-E18 carrying the dCas9 gene under the control of a Ptet +promoter in the chromosome. The pooled library of cells was then grown in rich medium +over 17 generations with anhydrotetracycline (aTc). The effect of each guide on the cell fitness can be measured as +the fold change in abundance (log2FC) of the guide RNA in the library +during the course of the experiment, as measured through deep sequencing of the library. + +The log2FC data for all guides in the screen can be found in the file Supplementary_table_6-screen_data.csv + +The analysis is divded in three jupyter notebooks: +* Effect of dCas9 binding position and orientation.ipynb +> In this notebook we look at the effect of dCas9 binding position and orientation. In particular we perform an analysis of polar and reverse-polar effects. + +* off-target analysis.ipynb +> In this notebook we investigate whether the unexpected fitness defect produced by some guides in the screen can be explained by off-target positions blocking the expression of essential or fitness genes. + +* bad-seed analysis.ipynb +> In this notebook we use a machine learning approach to reveal how dCas9 can kill _E. coli_ when guided by some specific 5nt PAM-proximal sequences + + -- GitLab