<p>Assuming you mean CRISPR screens targeting many loci (for example using the GeCKO library) there is an R package <a href="https://git.embl.de/msmith/geckoR" rel="nofollow noreferrer">here</a>.</p>

<p>It uses a linear mixed effect model to compare guide counts before and after a selection step, allowing for multiple guides/gene and multiple replicates. 
It can also do the initial read alignment AFAIK and the author should be quite responsive in case you have questions.</p>

<p>Another option could be the <em>python</em> program <a href="https://bitbucket.org/liulab/mageck-vispr" rel="nofollow noreferrer">MAGeCK</a> </p>

CC BY-SA 4.0
2017-06-06T09:05:07.703Z
2018-07-04T07:16:08.893Z
2018-07-04T07:16:08.893Z
sssascha
<p>From my experience, there is few about CRISPR pooled screenings in R. One of them is <a href="https://github.com/jyyu/ScreenBEAM" rel="nofollow noreferrer">ScreenBEAM</a>. It uses a linear mixture model to determine significance at gene level by considering all the guides together at the same step in the analysis. However, it does not work very well for me. </p>

<p>There are other ways of analyze these screenings outside of R that are easy to use such the suggested MAGeCK or others such HiTSelect, BAGEL or <a href="http://crispr-analyzer.dkfz.de/" rel="nofollow noreferrer">CRISPR-Analyzer</a>.</p>

<p>CRISPR-Analyzer is an online tool that could be useful and that recapitulates some of the mentioned methods in a single analysis. However, if you want to work from command line I would recommend you BAGEL for gene essentiality experiments and MAGeCK for other experimental setup (i.e, control vs treatment)</p>

<p>Sorry for not putting all the links but I can only put two at the moment!</p>

CC BY-SA 3.0
2017-06-07T08:28:15.090Z
2017-06-07T12:08:54.520Z
2017-06-07T12:08:54.520Z
jxx_fa
<p>We are designing a CRISPR/Cas9 experiment and thinking of the down-stream data analyses. </p>

<p>Are there any R packages for analysing raw NGS read count data from pooled genetic screens using CRIPSR/Cas9 to disrupt gene expression in a population of cells? I guess we will need to start with basics, i.e. sequences processing, data exploration, visualisation etc. </p>

CC BY-SA 3.0
2017-06-05T17:26:15.503Z
How is this different from other expression analysis? I.e. why can’t you use one of the standard workflows for differential expression analysis?
Konrad Rudolph
CC BY-SA 3.0
2017-06-06T14:19:51.983Z
I would just like to highlight that using CRISPR/Cas9 for editing and just estimating the expression might not be a great idea per se. If you have really a lot number of samples you might be interested first to check the mutational burden introduced by this editing. Then preferably go with expression analysis.  I would go with the below tools as mentioned as well but will not do anything without even considering if my editing is throwing a lot of SNVs or not. If its not throwing then you should be fine doing the downstream analysis with linear mixed effect.
ivivek_ngs
<r><crispr>
R packages for data analyses of pooled CRISPR screens
# R packages for data analyses of pooled CRISPR screens

(Asked by: username_0 on 2017-06-05)

We are designing a CRISPR/Cas9 experiment and thinking of the down-stream data analyses.

Are there any R packages for analysing raw NGS read count data from pooled genetic screens using CRIPSR/Cas9 to disrupt gene expression in a population of cells? I guess we will need to start with basics, i.e. sequences processing, data exploration, visualisation etc.

**Tags:** r, crispr
> **Comments:**
> **username_1**: How is this different from other expression analysis? I.e. why can’t you use one of the standard workflows for differential expression analysis?  
> **username_2**: I would just like to highlight that using CRISPR/Cas9 for editing and just estimating the expression might not be a great idea per se. If you have really a lot number of samples you might be interested first to check the mutational burden introduced by this editing. Then preferably go with expression analysis.  I would go with the below tools as mentioned as well but will not do anything without even considering if my editing is throwing a lot of SNVs or not. If its not throwing then you should be fine doing the downstream analysis with linear mixed effect.  

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## 2 answers

### Answer by username_3 on 2017-06-06. Score: 5

Assuming you mean CRISPR screens targeting many loci (for example using the GeCKO library) there is an R package [here](https://git.embl.de/msmith/geckoR).

It uses a linear mixed effect model to compare guide counts before and after a selection step, allowing for multiple guides/gene and multiple replicates.
It can also do the initial read alignment AFAIK and the author should be quite responsive in case you have questions.

Another option could be the *python* program [MAGeCK](https://bitbucket.org/liulab/mageck-vispr)


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### Answer by username_4 on 2017-06-07. Score: 1

From my experience, there is few about CRISPR pooled screenings in R. One of them is [ScreenBEAM](https://github.com/jyyu/ScreenBEAM). It uses a linear mixture model to determine significance at gene level by considering all the guides together at the same step in the analysis. However, it does not work very well for me.

There are other ways of analyze these screenings outside of R that are easy to use such the suggested MAGeCK or others such HiTSelect, BAGEL or [CRISPR-Analyzer](http://crispr-analyzer.dkfz.de/).

CRISPR-Analyzer is an online tool that could be useful and that recapitulates some of the mentioned methods in a single analysis. However, if you want to work from command line I would recommend you BAGEL for gene essentiality experiments and MAGeCK for other experimental setup (i.e, control vs treatment)

Sorry for not putting all the links but I can only put two at the moment!


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