It is specifically designed for the Bazzini Lab at Stowers Institute for Medical Research utilizing the outputs from gRNADesign.
You can install the released version from GitHub with:
# install.packages("devtools")
devtools::install_github("GabPescador/gRNASelect")
This is a basic example which shows how the functions select gRNAs per gene based on ATGC% and RNA Fold scores.
library(gRNASelect)
head(RNAFoldOutput, n=2)
#> gRNA_name seq_23nt
<char> <char>
1: ENSDART00000136386_ENSDARG00000092856_1107_1129 TTTTTTTTTTTTTTTCGTCAGTC
2: ENSDART00000136386_ENSDARG00000092856_126_148 CAAATTCAGCCACATTCAAAACC
Gene|Longest_transcript Seq_start Seq_end Average_score G_Percentage C_Percentage
<char> <int> <int> <num> <num> <num>
1: ENSDART00000136386_ENSDARG00000092856 1107 1129 0.453 8.696 13.043
2: ENSDART00000136386_ENSDARG00000092856 126 148 0.425 4.348 34.783
A_Percentage T_Percentage Transcript_ID CDS_UTR
<num> <num> <char> <char>
1: 4.348 73.913 ENSDART00000136386 3UTR3
2: 43.478 17.391 ENSDART00000136386 5UTR1
You can filter gRNAs based on a range of ATGC percentages.
atgc_filtered <- gRNAatgcFilter(RNAFoldOutput)
summary(atgc_filtered)
#> gRNA_name seq_23nt Gene|Longest_transcript Seq_start Seq_end
Length:5261 Length:5261 Length:5261 Min. : 1 Min. : 23
Class :character Class :character Class :character 1st Qu.: 611 1st Qu.: 633
Mode :character Mode :character Mode :character Median : 1394 Median : 1416
Mean : 2104 Mean : 2126
3rd Qu.: 2915 3rd Qu.: 2937
Max. :10809 Max. :10831
Average_score G_Percentage C_Percentage A_Percentage T_Percentage
Min. :0.02400 Min. :17.39 Min. :17.39 Min. :17.39 Min. :17.39
1st Qu.:0.06200 1st Qu.:21.74 1st Qu.:21.74 1st Qu.:21.74 1st Qu.:21.74
Median :0.07700 Median :26.09 Median :26.09 Median :26.09 Median :26.09
Mean :0.08383 Mean :24.50 Mean :24.56 Mean :25.89 Mean :25.05
3rd Qu.:0.09700 3rd Qu.:30.43 3rd Qu.:30.43 3rd Qu.:30.43 3rd Qu.:30.43
Max. :0.46800 Max. :34.78 Max. :34.78 Max. :34.78 Max. :34.78
Transcript_ID CDS_UTR type
Length:5261 Length:5261 Length:5261
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
Combined to the ATGC filter, or not, you can also select gRNAs based on their RNA Fold scores. The function will select the top and bottom scores, then select random scores for the leftover gRNAs. Since the default for us is 10 gRNAs per gene, if any gene has less than 10 predicted gRNAs it will select all of them.
filteredgRNAs <- gRNASelection(y, minN = 10, randomN = 8)
head(filteredgRNAs, n=2)
#> gRNA_name seq_23nt
<char> <char>
1: ENSDART00000183252_ENSDARG00000091905_56_78 GATCTGCGTCTAAGCTCAAGAAA
2: ENSDART00000183252_ENSDARG00000091905_85_107 CCAAATGTCCGCGACCTTATCGT
Gene|Longest_transcript Seq_start Seq_end Average_score G_Percentage C_Percentage
<char> <int> <int> <num> <num> <num>
1: ENSDART00000183252_ENSDARG00000091905 56 78 0.094 21.739 21.739
2: ENSDART00000183252_ENSDARG00000091905 85 107 0.085 17.391 34.783
A_Percentage T_Percentage Transcript_ID CDS_UTR type type2
<num> <num> <char> <char> <char> <char>
1: 34.783 21.739 ENSDART00000183252 CDS1 CDS Less than 10
2: 21.739 26.087 ENSDART00000183252 CDS1 CDS Less than 10
This new table can be passed to the mismatch module from gRNADesign to look for gRNAs that can match different trancripts. The output from the mismatch module can be then used to filter out gRNas with <4 mismatches.
finalgRNAs <- gRNAmismatchFilter(path = system.file("extdata",
"",
package = "gRNASelect"),
data = newgRNAs)
It can also generate 96-well plate excel tables to order the gRNAs from IDT.
gRNA96well(finalgRNAs, outputpath = "./")
There are also functions to visualize your filtering and how many gRNAs you are getting per gene and transcript region they target to.
gRNAatgcPlot(newgRNAs)
gRNAScorePlot(newgRNAs)
gRNAutrperGene(newgRNAs)
To see a more detailed version of what can be done with the package see the example Rmarkdown vignette in
vignettes/Example_Run.Rmd