CB2(CRISPRBetaBinomial) is a new algorithm for analyzing CRISPR data based on beta-binomial distribution. We provide CB2 as a R package, and the interal algorithms of CB2 are also implemented in CRISPRCloud.


May 26, 2020

April 14, 2020

December 16, 2019

July 2, 2019

There are several updates.

How to install

Currently CB2 is now on CRAN, and you can install it using install.package function.


Installation Github version of CB2 can be done using the following lines of code in your R terminal.


Alternatively, here is a one-liner command line for the installation.

Rscript -e "install.packages('devtools'); devtools::install_github('LiuzLab/CB2')"

A simple example how to use CB2 in R

FASTA <- system.file("extdata", "toydata",
                     package = "CB2")
df_design <- data.frame()
for(g in c("Low", "High", "Base")) {
  for(i in 1:2) {
    FASTQ <- system.file("extdata", "toydata",
                         sprintf("%s%d.fastq", g, i), 
                         package = "CB2")
    df_design <- rbind(df_design, 
        group = g, 
        sample_name = sprintf("%s%d", g, i),
        fastq_path = FASTQ, 
        stringsAsFactors = F)

MAP_FILE <- system.file("extdata", "toydata", "sg2gene.csv", package="CB2")
sgrna_count <- run_sgrna_quant(FASTA, df_design, MAP_FILE)
sgrna_stat <- measure_sgrna_stats(sgrna_count$count, df_design, 
                                  "Base", "Low", 
                                  ge_id = "gene",
                                  sg_id = "id")
gene_stat <- measure_gene_stats(sgrna_stat)

Or you could run the example with the following commented code.

sgrna_count <- run_sgrna_quant(FASTA, df_design)
sgrna_stat <- measure_sgrna_stats(sgrna_count$count, df_design, "Base", "Low")
gene_stat <- measure_gene_stats(sgrna_stat)

More detailed tutorial is available here!