bionett / maude

Mean Alterations Using Discrete Expression

MAUDE: Mean Alterations Using Discrete Expression

MAUDE is an R package for finding differences in means of normally distributed (or nearly so) data, via measuring abundances in discrete bins. For example, a pooled CRISPRi screen with expression readout by FACS sorting into discrete bins and sequencing the abundances of the guides in each bin. Most of the documentation and examples are written with a CRISPRi-type sorting screen in mind, but there is no reason why it can't be used for any experiment where normally distributed expression values are read out via abundances in discrete expression bins. For example, MAUDE can also be used for CRISPR base editor screens where the readout is also expression. See 'Usage' below for more information.

Table of contents

• R Installation
• Requirements
• Usage
• Citation

R Installation

If you don't already have devtools, install it:

install.packages("devtools")

Load devtools and install from the GitHub page:

library(devtools)
install_github("de-Boer-Lab/MAUDE")

Requirements

Right now we have three main requirements:

1. Negative control guides are included in the experiment; these are used for calibrating Z-scores and P-values.
2. The abundance of the guides must have been measured somehow (usually by sequencing the guide DNA of unsorted cells)
3. The fractions of cells sorted into each expression bin was quantified

Usage

Tutorials

We provide two tutorials on how to run a MAUDE analysis in R here:

1. Re-analysis of CD69 screen data
2. Analysis of a simulated screen
3. Analysis of a CRISPR base editor non-coding mutation screen

For additional examples, see the script for evaluating and comparing sorting-based CRISPR screen analysis methods.

Quantifying guide DNA abundance

After sequencing, you get fastqs, one per sorting bin and experiment. The first step for a MAUDE analysis is to quantify the number of guides residing in each bin. Here, we provide some guidance as to how to do this.

We have previously used the aligner bowtie2.

To make the bowtie2 reference guide_seq_reference:

bowtie2-build guide_seqs.fa guide_seq_reference

where guide_seqs.fa is a fasta file including the sequences you are mapping against, which will include the guide DNA sequence and any flanking constant regions as well. The amount of constant sequence you include in the reference should be at least as much as what was sequenced.

For example, with 20bp guides with constant flanking GTTTAAGAGCTATGCTGGAAACAGCATAG:

>guide1
GTCGCATATCGCGATAGCGAGTTTAAGAGCTATGCTGGAAACAGCATAG
>guide2
GTCGTGAAAGTGCTGTTGAGGTTTAAGAGCTATGCTGGAAACAGCATAG
...

The following command is an example of how to quantify guide abundance into a format that can easily be input into R for MAUDE analysis:

bowtie2 --no-head -x guide_seq_reference -U $sample.fastq.gz -S $sample.mapped.sam
#here, we include all mapped reads, but by using Samtools, you can filter out reads that map to the wrong strand, have indels, etc.
cat $sample.mapped.sam | awk '{print $3}' | sort | uniq -c | sort > $sample.counts

Here, $sample is the sample name, with $sample.fastq.gz the corresponding fastq file, and guide_seq_reference is the bowtie2 reference. The file $sample.counts will contain guide counts that can be input into R.

To turn this into a format that can easily be used for a MAUDE analysis, you can input the data using something like the following:

#here, allSamples is a data.frame containing one sample per row, with columns including ID, expt, and Bin.  There should be one file for every row in allSamples
allData = data.frame();
for (i in 1:nrow(allSamples)){
  curData = read.table(file=sprintf("%s/%s.counts",inDir,allSamples$ID[i]), quote="", header = F, row.names = NULL, stringsAsFactors = F)
  names(curData) = c("count","guideID");
  curData = curData[curData$gID!="*",] # remove unmapped counts
  curData$ID = allSamples$ID[i];
  curData$expt = allSamples$expt[i];
  curData$Bin = allSamples$Bin[i];
  allData = rbind(allData, curData)
}
#now you have the data in a data.frame that can be reshaped to a MAUDE-compatible format:
library(reshape)
allDataCounts = as.data.frame(cast(allData, expt + guideID ~ Bin, value="count"));
allDataCounts[is.na(allDataCounts)]=0; # fill in 0s for guides not observed at all
#now you just need to label the non-targeting guides and this will be in the correct format

Encountering problems

Should you encounter a problem using MAUDE:

1. Consult the Common Problems
2. Submit an Issue
3. Contact the authors.

Citation

Please cite:

Carl G de Boer*, John P Ray*, Nir Hacohen, Aviv Regev. MAUDE: Inferring Expression Changes in Sorting-Based CRISPR Screens. 2020 Jun 3;21(1):134. doi: 10.1186/s13059-020-02046-8. PMID: 32493396.