IsogenicZ is an adapted version of the DrugZ software from the Hart Lab. For paired analysis such as a
CRISPR drug screen, DrugZ analyses two endpoint samples per replicate as input. IsogenicZ uses two
endpoint samples (wildtype vs. isogenic mutant) and the two corresponding t0 samples per replicate as
input. All samples are normalized to an equal total read number, and per replicate (endpoint/t0) fold
changes for each sgRNA are calculated for wildtype and isogenic mutant, multiplied by the combined
wildtype and isogenic mutant t0 count, and normalized to equal read numbers again to yield t0-corrected
endpoint samples. These samples are then analyzed using the standard DrugZ analysis.
This normalization method was used for analysis of pooled full genome CRISPR screens in multiple
research papers:
ELOF1 is a transcription-coupled DNA repair factor that directs RNA polymerase II ubiquitylation
MMS22L-TONSL functions in sister chromatid cohesion in a pathway parallel to DSCC1-RFC
Below the DrugZ readme.md edited where appropriate:
IsogenicZ analyzes parallel isogenic CRISPR screens for synthetic lethal interactions.
usage: isogenicz.py [-h] [-i sgRNA_count.txt] [-o isogenicz-output.txt]
[-f drugz-foldchange.txt] -c wildtype samples t0 -d wildtype samples endpoint
-x mutant samples t0 -y mutant samples endpoint
[-r remove genes] [-p pseudocount] [-I INDEX_COLUMN]
[--minobs minObs] [--half_window_size half_window_size] [-q]
-i Readcount file, tab-delimited text (input)
-o IsogenicZ results file, tab-delimited text (output)
-f DrugZ Z-transformed fold change file (optional)
-c Wildtype samples t0: comma-delimited list of column headers in readcount file
-d Wildtype samples endpoint: comma-delimited list of column headers in readcount file
-x Mutant samples t0: comma-delimited list of column headers in readcount file
-y Mutant samples endpoint: comma-delimited list of column headers in readcount file
-r Comma-delimited list of genes to remove before analysis
-p Pseudocount to add to all readcounts; prevents log(0) problems (default=5)
-I Index column (default=0)
--minobs Ignore genes with fewer observations ( gRNA/gene x replicates) (default=1)
--half_window_size Size of the first bin and half the size of the inital sample
(window) to estimate std (default=500)
-unpaired Unpaired approach: compares mean(treated samples) to mean(control samples) (default=False)
The input file should be a tab-delimited file with the following format (order of sample columns or unused columns have no effect):
sgRNA Gene WTt0_1 WTt0_2 WTt0_3 WTt12_1 WTt12_2 WTt12_3 MUTt0_1 MUTt0_2 MUTt0_3 MUTt12_1 MUTt12_2 MUTt12_3
ACTGGCGCCATCGAGAGCCA A1BG 150 148 161 130 95 247 154 184 128 124 213 169
CAAGAGAAAGACCACGAGCA A1BG 352 367 344 355 320 445 520 539 484 679 627 532
GCTCAGCTGGGTCCATCCTG A1BG 673 839 826 624 885 724 857 841 704 711 982 854
GTCGAGCTGATTCTGAGCGA A1BG 305 264 360 444 193 379 343 361 295 319 394 288
AGTTATGTTAGGTATACCCG A1CF 380 326 371 510 257 312 351 363 343 458 467 332
ATGACTCTCATACTCCACGA A1CF 371 457 445 444 472 508 604 590 547 642 667 545
GGTGCAGCATCCCAACCAGG A1CF 407 468 401 618 449 306 464 545 492 508 494 501
TGCGCTGGACCAGTGCGCGG A1CF 213 186 251 272 159 285 167 206 138 190 206 253
AACAGCCACACTCACAGCGA A2M 475 664 642 547 552 501 632 644 622 758 701 529
(etc)
Usage from command line, assuming python 3 with numpy and pandas is installed:
python isogenicz.py -i elof1_in.txt -o elof1_out.txt -c WTt0_1,WTt0_2,WTt0_3 -d WTt12_1,WTt12_2,WTt12_3 -x MUTt0_1,MUTt0_2,MUTt0_3 -y MUTt12_1,MUTt12_2,MUTt12_3
File elof1_in.txt contains the t0 and t12 counts from the screens of the RPE-1 iCas9 cell line ("WT") and the derived ELOF1 knockout line ("MUT"), see ELOF1 publication
Performing analysis using these parameters as input will create elof1_out.txt as an output file, with results as in elof1_out_example.txt