@@ -12,7 +12,7 @@ options("knitr.graphics.auto_pdf"=TRUE)

 Authors: Jean-Philippe Fortin, Aaron Lun, Luke Hoberecht, Pirunthan Perampalan

-Date: July 1, 2022

+Date: July 25, 2024

 # Overview

@@ -26,8 +26,8 @@ package also provides a Lindel-derived score to predict the probability

 of a gRNA to produce indels inducing a frameshift for the Cas9 nuclease.

 Note that DeepHF, DeepCpf1 and enPAM+GB are not available on Windows machines. 

-Our work is described in a recent bioRxiv preprint:

-["The crisprVerse: A comprehensive Bioconductor ecosystem for the design of CRISPR guide RNAs across nucleases and technologies"](https://www.biorxiv.org/content/10.1101/2022.04.21.488824v3)

+Our work is described in our recent papera recent bioRxiv preprint:

+["A comprehensive Bioconductor ecosystem for the design of CRISPR guide RNAs across nucleases and technologies"](https://www.nature.com/articles/s41467-022-34320-7)

 Our main gRNA design package [crisprDesign](https://github.com/crisprVerse/crisprDesign) utilizes the `crisprScore` package to add on- and off-target scores to user-designed gRNAs; check out our [Cas9 gRNA tutorial page](https://github.com/crisprVerse/Tutorials/tree/master/Design_CRISPRko_Cas9) to learn how to use `crisprScore` via `crisprDesign`. 

@@ -40,13 +40,12 @@ Our main gRNA design package [crisprDesign](https://github.com/crisprVerse/crisp

 This package is supported for macOS, Linux and Windows machines.

 Some functionalities are not supported for Windows machines.

-Packages were developed and tested on R version 4.2.1.

+Packages require R version 4.4 or higher. 

-## Installation from Bioconductor

+## Installation 

-`crisprScore` can be installed from from the Bioconductor devel branch

-using the following commands in a fresh R session:

+`crisprScore` can be installed from Bioconductor using the following commands in a fresh R session:

 ```{r, eval=FALSE}

 if (!require("BiocManager", quietly = TRUE))

@@ -56,26 +55,25 @@ BiocManager::install(version="devel")

 BiocManager::install("crisprScore")

 ```

-The complete documentation for the package can be found [here](https://bioconductor.org/packages/devel/bioc/manuals/crisprScore/man/crisprScore.pdf).

+To install the release version, use this instead:

-## Installation from GitHub

-

-Alternatively, the development version of `crisprScore` and its dependencies can be installed by typing the following commands inside of an R session:

-

-

-```r

-install.packages("devtools")

-library(devtools)

-install_github("crisprVerse/crisprScoreData")

-install_github("crisprVerse/crisprScore")

+```{r, eval=FALSE}

+BiocManager::install(version="release")

 ```

-

-When calling one of the scoring methods for the first time after package

-installation, the underlying python module and conda environment will be 

+Under the hood, `crisprScore` uses the `basilisk` package to install Python

+environments needed to run some of the prediction algorithms. When calling

+one of the scoring methods for the first time after package

+installation, the underlying python module and conda environments will be 

 automatically downloaded and installed without the need for user intervention.

 This may take several minutes, but this is a one-time installation. 

-the first time after package installation.

+

+We made recent changes to install Python packages only from the public 

+Bioconda and conda-forge channels to be compliant with the latest

+Anaconda licensing policies. Setting the environment variable `BASILISK_USE_MINIFORGE=1` will ensure that crisprScore will use miniforge

+to install the Python packages. See the documentation of the `basilisk` package

+[(link here)](https://www.bioconductor.org/packages/release/bioc/html/basilisk.html) for more information re. different settings for the installation of the

+underlying Python packages

 Note that RStudio users will need to add the following line to their `.Rprofile`

 file in order for `crisprScore` to work properly:

@@ -8,11 +8,7 @@ crisprScore: on-target and off-target scoring for CRISPR gRNAs

     -   <a href="#software-requirements" id="toc-software-requirements">Software

         requirements</a>

         -   <a href="#os-requirements" id="toc-os-requirements">OS Requirements</a>

-    -   <a href="#installation-from-bioconductor"

-        id="toc-installation-from-bioconductor">Installation from

-        Bioconductor</a>

-    -   <a href="#installation-from-github"

-        id="toc-installation-from-github">Installation from GitHub</a>

+    -   <a href="#installation" id="toc-installation">Installation</a>

 -   <a href="#getting-started" id="toc-getting-started">Getting started</a>

 -   <a href="#on-targeting-efficiency-scores"

     id="toc-on-targeting-efficiency-scores">On-targeting efficiency

@@ -46,7 +42,7 @@ crisprScore: on-target and off-target scoring for CRISPR gRNAs

 Authors: Jean-Philippe Fortin, Aaron Lun, Luke Hoberecht, Pirunthan

 Perampalan

-Date: July 1, 2022

+Date: July 25, 2024

 # Overview

@@ -61,10 +57,10 @@ provides a Lindel-derived score to predict the probability of a gRNA to

 produce indels inducing a frameshift for the Cas9 nuclease. Note that

 DeepHF, DeepCpf1 and enPAM+GB are not available on Windows machines.

-Our work is described in a recent bioRxiv preprint: [“The crisprVerse: A

+Our work is described in our recent papera recent bioRxiv preprint: [“A

 comprehensive Bioconductor ecosystem for the design of CRISPR guide RNAs

 across nucleases and

-technologies”](https://www.biorxiv.org/content/10.1101/2022.04.21.488824v3)

+technologies”](https://www.nature.com/articles/s41467-022-34320-7)

 Our main gRNA design package

 [crisprDesign](https://github.com/crisprVerse/crisprDesign) utilizes the

@@ -80,13 +76,13 @@ to learn how to use `crisprScore` via `crisprDesign`.

 ### OS Requirements

 This package is supported for macOS, Linux and Windows machines. Some

-functionalities are not supported for Windows machines. Packages were

-developed and tested on R version 4.2.1.

+functionalities are not supported for Windows machines. Packages require

+R version 4.4 or higher.

-## Installation from Bioconductor

+## Installation

-`crisprScore` can be installed from from the Bioconductor devel branch

-using the following commands in a fresh R session:

+`crisprScore` can be installed from Bioconductor using the following

+commands in a fresh R session:

 ``` r

 if (!require("BiocManager", quietly = TRUE))

@@ -96,27 +92,29 @@ BiocManager::install(version="devel")

 BiocManager::install("crisprScore")

 ```

-The complete documentation for the package can be found

-[here](https://bioconductor.org/packages/devel/bioc/manuals/crisprScore/man/crisprScore.pdf).

-

-## Installation from GitHub

-

-Alternatively, the development version of `crisprScore` and its

-dependencies can be installed by typing the following commands inside of

-an R session:

+To install the release version, use this instead:

 ``` r

-install.packages("devtools")

-library(devtools)

-install_github("crisprVerse/crisprScoreData")

-install_github("crisprVerse/crisprScore")

+BiocManager::install(version="release")

 ```

+Under the hood, `crisprScore` uses the `basilisk` package to install

+Python environments needed to run some of the prediction algorithms.

 When calling one of the scoring methods for the first time after package

-installation, the underlying python module and conda environment will be

-automatically downloaded and installed without the need for user

+installation, the underlying python module and conda environments will

+be automatically downloaded and installed without the need for user

 intervention. This may take several minutes, but this is a one-time

-installation. the first time after package installation.

+installation.

+

+We made recent changes to install Python packages only from the public

+Bioconda and conda-forge channels to be compliant with the latest

+Anaconda licensing policies. Setting the environment variable

+`BASILISK_USE_MINIFORGE=1` will ensure that crisprScore will use

+miniforge to install the Python packages. See the documentation of the

+`basilisk` package [(link

+here)](https://www.bioconductor.org/packages/release/bioc/html/basilisk.html)

+for more information re. different settings for the installation of the

+underlying Python packages

 Note that RStudio users will need to add the following line to their

 `.Rprofile` file in order for `crisprScore` to work properly:

@@ -547,8 +545,8 @@ getMITScores(spacers=spacer,

 ```

     ##                 spacer          protospacer      score

-    ## 1 ATCGATGCTGATGCTAGATA ACCGATGCTGATGCTAGATA 0.31500000

-    ## 2 ATCGATGCTGATGCTAGATA ATCGATGCTGATGCTAGATT 1.00000000

+    ## 1 ATCGATGCTGATGCTAGATA ACCGATGCTGATGCTAGATA 1.00000000

+    ## 2 ATCGATGCTGATGCTAGATA ATCGATGCTGATGCTAGATT 0.41700000

     ## 3 ATCGATGCTGATGCTAGATA ATCGATGCTGATGCTAGATA 0.06944444

 ## CFD score

@@ -636,65 +634,52 @@ code.

 sessionInfo()

 ```

-    ## R version 4.2.1 (2022-06-23)

-    ## Platform: x86_64-apple-darwin17.0 (64-bit)

-    ## Running under: macOS Catalina 10.15.7

+    ## R version 4.4.1 (2024-06-14)

+    ## Platform: x86_64-apple-darwin20

+    ## Running under: macOS Ventura 13.6.7

     ## 

     ## Matrix products: default

-    ## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib

-    ## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

+    ## BLAS:   /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib 

+    ## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

     ## 

     ## locale:

     ## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

     ## 

+    ## time zone: America/Los_Angeles

+    ## tzcode source: internal

+    ## 

     ## attached base packages:

     ## [1] stats     graphics  grDevices utils     datasets  methods   base     

     ## 

     ## other attached packages:

-    ## [1] crisprScore_1.3.2     crisprScoreData_1.3.0 ExperimentHub_2.5.0  

-    ## [4] AnnotationHub_3.5.1   BiocFileCache_2.5.0   dbplyr_2.2.1         

-    ## [7] BiocGenerics_0.43.4  

+    ## [1] crisprScore_1.9.1     crisprScoreData_1.9.0 ExperimentHub_2.13.0 

+    ## [4] AnnotationHub_3.13.0  BiocFileCache_2.13.0  dbplyr_2.5.0         

+    ## [7] BiocGenerics_0.51.0  

     ## 

     ## loaded via a namespace (and not attached):

-    ##  [1] Rcpp_1.0.9                    lattice_0.20-45              

-    ##  [3] dir.expiry_1.5.1              png_0.1-7                    

-    ##  [5] Biostrings_2.65.3             assertthat_0.2.1             

-    ##  [7] digest_0.6.29                 utf8_1.2.2                   

-    ##  [9] mime_0.12                     R6_2.5.1                     

-    ## [11] GenomeInfoDb_1.33.7           stats4_4.2.1                 

-    ## [13] RSQLite_2.2.16                evaluate_0.16                

-    ## [15] highr_0.9                     httr_1.4.4                   

-    ## [17] pillar_1.8.1                  basilisk_1.9.6               

-    ## [19] zlibbioc_1.43.0               rlang_1.0.6                  

-    ## [21] curl_4.3.2                    rstudioapi_0.14              

-    ## [23] blob_1.2.3                    S4Vectors_0.35.3             

-    ## [25] Matrix_1.5-3                  reticulate_1.26              

-    ## [27] rmarkdown_2.16                stringr_1.4.1                

-    ## [29] RCurl_1.98-1.8                bit_4.0.4                    

-    ## [31] shiny_1.7.2                   compiler_4.2.1               

-    ## [33] httpuv_1.6.5                  xfun_0.32                    

-    ## [35] pkgconfig_2.0.3               htmltools_0.5.3              

-    ## [37] tidyselect_1.1.2              KEGGREST_1.37.3              

-    ## [39] tibble_3.1.8                  GenomeInfoDbData_1.2.8       

-    ## [41] interactiveDisplayBase_1.35.0 IRanges_2.31.2               

-    ## [43] randomForest_4.7-1.1          fansi_1.0.3                  

-    ## [45] crayon_1.5.1                  dplyr_1.0.10                 

-    ## [47] later_1.3.0                   basilisk.utils_1.9.3         

-    ## [49] bitops_1.0-7                  rappdirs_0.3.3               

-    ## [51] grid_4.2.1                    jsonlite_1.8.0               

-    ## [53] xtable_1.8-4                  lifecycle_1.0.3              

-    ## [55] DBI_1.1.3                     magrittr_2.0.3               

-    ## [57] cli_3.4.0                     stringi_1.7.8                

-    ## [59] cachem_1.0.6                  XVector_0.37.1               

-    ## [61] promises_1.2.0.1              ellipsis_0.3.2               

-    ## [63] filelock_1.0.2                generics_0.1.3               

-    ## [65] vctrs_0.5.1                   tools_4.2.1                  

-    ## [67] bit64_4.0.5                   Biobase_2.57.1               

-    ## [69] glue_1.6.2                    purrr_0.3.4                  

-    ## [71] BiocVersion_3.16.0            parallel_4.2.1               

-    ## [73] fastmap_1.1.0                 yaml_2.3.5                   

-    ## [75] AnnotationDbi_1.59.1          BiocManager_1.30.18          

-    ## [77] memoise_2.0.1                 knitr_1.40

+    ##  [1] KEGGREST_1.45.1         dir.expiry_1.13.0       xfun_0.46              

+    ##  [4] lattice_0.22-6          Biobase_2.65.0          vctrs_0.6.5            

+    ##  [7] tools_4.4.1             generics_0.1.3          stats4_4.4.1           

+    ## [10] curl_5.2.1              parallel_4.4.1          tibble_3.2.1           

+    ## [13] fansi_1.0.6             AnnotationDbi_1.67.0    RSQLite_2.3.7          

+    ## [16] highr_0.11              blob_1.2.4              pkgconfig_2.0.3        

+    ## [19] Matrix_1.7-0            S4Vectors_0.43.2        lifecycle_1.0.4        

+    ## [22] GenomeInfoDbData_1.2.12 stringr_1.5.1           compiler_4.4.1         

+    ## [25] Biostrings_2.73.1       GenomeInfoDb_1.41.1     htmltools_0.5.8.1      

+    ## [28] yaml_2.3.9              pillar_1.9.0            crayon_1.5.3           

+    ## [31] cachem_1.1.0            basilisk_1.17.0         tidyselect_1.2.1       

+    ## [34] digest_0.6.36           stringi_1.8.4           dplyr_1.1.4            

+    ## [37] BiocVersion_3.20.0      grid_4.4.1              fastmap_1.2.0          

+    ## [40] cli_3.6.3               magrittr_2.0.3          randomForest_4.7-1.1   

+    ## [43] utf8_1.2.4              filelock_1.0.3          UCSC.utils_1.1.0       

+    ## [46] rappdirs_0.3.3          bit64_4.0.5             rmarkdown_2.27         

+    ## [49] XVector_0.45.0          httr_1.4.7              bit_4.0.5              

+    ## [52] reticulate_1.38.0       png_0.1-8               memoise_2.0.1          

+    ## [55] evaluate_0.24.0         knitr_1.48              IRanges_2.39.2         

+    ## [58] basilisk.utils_1.17.0   rlang_1.1.4             Rcpp_1.0.13            

+    ## [61] glue_1.7.0              DBI_1.2.3               BiocManager_1.30.23    

+    ## [64] rstudioapi_0.16.0       jsonlite_1.8.8          R6_2.5.1               

+    ## [67] zlibbioc_1.51.1

 # References