1. crisprScore
  2. Commit 652002e808722b1a4a3b46fcb37d18cd2ea404e9
Browse code

Updated vignette

Jean-Philippe Fortin authored on 17/10/2022 19:08:30
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DESCRIPTION
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 Package: crisprScore
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-Version: 1.1.16
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-Date: 2022-10-22
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+Version: 1.1.17
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+Date: 2022-10-17
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 Title: On-Target and Off-Target Scoring Algorithms for CRISPR gRNAs
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 Authors@R: c(
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     person("Jean-Philippe", "Fortin", email = "[email protected]", role = c("aut", "cre", "cph")),
README.Rmd
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@@ -10,24 +10,26 @@ bibliography: vignettes/references.bib
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 options("knitr.graphics.auto_pdf"=TRUE)
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 ```
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-Authors: Jean-Philippe Fortin, Aaron Lun, Luke Hoberecht
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+Authors: Jean-Philippe Fortin, Aaron Lun, Luke Hoberecht, Pirunthan Perampalan
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 Date: July 1, 2022
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 # Overview
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-crisprScore provides R wrappers of several on-target and off-target scoring
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+The `crisprScore` package provides R wrappers of several on-target and off-target scoring
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 methods for CRISPR guide RNAs (gRNAs). The following nucleases are supported:
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 SpCas9, AsCas12a, enAsCas12a, and RfxCas13d (CasRx). The available on-target
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 cutting efficiency scoring methods are RuleSet1, RuleSet3, Azimuth, DeepHF,
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-DeepSpCas9, DeepCpf1, enPAM+GB, CRISPRscan and CRISPRater. Both the CFD and MIT
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+DeepSpCas9, DeepCpf1, enPAM+GB, CRISPRscan and CRISPRater. Both the CFD and MIT
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 scoring methods are available for off-target specificity prediction. The
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 package also provides a Lindel-derived score to predict the probability
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 of a gRNA to produce indels inducing a frameshift for the Cas9 nuclease.
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 Note that DeepHF, DeepCpf1 and enPAM+GB are not available on Windows machines.
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29 29 
 Our work is described in a recent bioRxiv preprint:
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-["A comprehensive Bioconductor ecosystem for the design of CRISPR guide RNAs across nucleases and technologies"](https://www.biorxiv.org/content/10.1101/2022.04.21.488824v2)
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+["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)
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+
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+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`.
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 # Installation and getting started
README.md
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@@ -43,27 +43,35 @@ crisprScore: on-target and off-target scoring for CRISPR gRNAs
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 -   <a href="#reproducibility" id="toc-reproducibility">Reproducibility</a>
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 -   <a href="#references" id="toc-references">References</a>
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-Authors: Jean-Philippe Fortin, Aaron Lun, Luke Hoberecht
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+Authors: Jean-Philippe Fortin, Aaron Lun, Luke Hoberecht, Pirunthan
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+Perampalan
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 Date: July 1, 2022
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50 51 
 # Overview
51 52
52 
-crisprScore provides R wrappers of several on-target and off-target
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-scoring methods for CRISPR guide RNAs (gRNAs). The following nucleases
54 
-are supported: SpCas9, AsCas12a, enAsCas12a, and RfxCas13d (CasRx). The
55 
-available on-target cutting efficiency scoring methods are RuleSet1,
56 
-RuleSet3, Azimuth, DeepHF, DeepSpCas9, DeepCpf1, enPAM+GB, CRISPRscan
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-and CRISPRater. Both the CFD and MIT scoring methods are available for
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-off-target specificity prediction. The package also provides a
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-Lindel-derived score to predict the probability of a gRNA to produce
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-indels inducing a frameshift for the Cas9 nuclease. Note that DeepHF,
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-DeepCpf1 and enPAM+GB are not available on Windows machines.
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-
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-Our work is described in a recent bioRxiv preprint: [“A comprehensive
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-Bioconductor ecosystem for the design of CRISPR guide RNAs across
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-nucleases and
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-technologies”](https://www.biorxiv.org/content/10.1101/2022.04.21.488824v2)
53 
+The `crisprScore` package provides R wrappers of several on-target and
54 
+off-target scoring methods for CRISPR guide RNAs (gRNAs). The following
55 
+nucleases are supported: SpCas9, AsCas12a, enAsCas12a, and RfxCas13d
56 
+(CasRx). The available on-target cutting efficiency scoring methods are
57 
+RuleSet1, RuleSet3, Azimuth, DeepHF, DeepSpCas9, DeepCpf1, enPAM+GB,
58 
+CRISPRscan and CRISPRater. Both the CFD and MIT scoring methods are
59 
+available for off-target specificity prediction. The package also
60 
+provides a Lindel-derived score to predict the probability of a gRNA to
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+produce indels inducing a frameshift for the Cas9 nuclease. Note that
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+DeepHF, DeepCpf1 and enPAM+GB are not available on Windows machines.
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+
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+Our work is described in a recent bioRxiv preprint: [“The crisprVerse: A
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+comprehensive Bioconductor ecosystem for the design of CRISPR guide RNAs
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+across nucleases and
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+technologies”](https://www.biorxiv.org/content/10.1101/2022.04.21.488824v3)
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+
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+Our main gRNA design package
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+[crisprDesign](https://github.com/crisprVerse/crisprDesign) utilizes the
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+`crisprScore` package to add on- and off-target scores to user-designed
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+gRNAs; check out our [Cas9 gRNA tutorial
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+page](https://github.com/crisprVerse/Tutorials/tree/master/Design_CRISPRko_Cas9)
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+to learn how to use `crisprScore` via `crisprDesign`.
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 # Installation and getting started
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     ## [1] stats     graphics  grDevices utils     datasets  methods   base
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     ##
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     ## other attached packages:
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-    ## [1] crisprScore_1.1.14    crisprScoreData_1.1.3 ExperimentHub_2.5.0
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-    ## [4] AnnotationHub_3.5.0   BiocFileCache_2.5.0   dbplyr_2.2.1
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-    ## [7] BiocGenerics_0.43.1
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+    ## [1] crisprScore_1.1.15    crisprScoreData_1.1.3 ExperimentHub_2.5.0
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+    ## [4] AnnotationHub_3.5.1   BiocFileCache_2.5.0   dbplyr_2.2.1
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+    ## [7] BiocGenerics_0.43.4
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     ##
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     ## loaded via a namespace (and not attached):
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     ##  [1] Rcpp_1.0.9                    lattice_0.20-45
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-    ##  [3] dir.expiry_1.5.0              png_0.1-7
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-    ##  [5] Biostrings_2.65.2             assertthat_0.2.1
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+    ##  [3] dir.expiry_1.5.1              png_0.1-7
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+    ##  [5] Biostrings_2.65.3             assertthat_0.2.1
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     ##  [7] digest_0.6.29                 utf8_1.2.2
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     ##  [9] mime_0.12                     R6_2.5.1
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-    ## [11] GenomeInfoDb_1.33.5           stats4_4.2.1
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+    ## [11] GenomeInfoDb_1.33.7           stats4_4.2.1
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     ## [13] RSQLite_2.2.16                evaluate_0.16
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     ## [15] highr_0.9                     httr_1.4.4
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-    ## [17] pillar_1.8.1                  basilisk_1.9.3
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-    ## [19] zlibbioc_1.43.0               rlang_1.0.4
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+    ## [17] pillar_1.8.1                  basilisk_1.9.6
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+    ## [19] zlibbioc_1.43.0               rlang_1.0.5
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     ## [21] curl_4.3.2                    rstudioapi_0.14
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-    ## [23] blob_1.2.3                    S4Vectors_0.35.1
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-    ## [25] Matrix_1.4-1                  reticulate_1.25
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-    ## [27] rmarkdown_2.15.2              stringr_1.4.1
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+    ## [23] blob_1.2.3                    S4Vectors_0.35.3
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+    ## [25] Matrix_1.4-1                  reticulate_1.26
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+    ## [27] rmarkdown_2.16                stringr_1.4.1
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     ## [29] RCurl_1.98-1.8                bit_4.0.4
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     ## [31] shiny_1.7.2                   compiler_4.2.1
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     ## [33] httpuv_1.6.5                  xfun_0.32
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     ## [39] tibble_3.1.8                  GenomeInfoDbData_1.2.8
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     ## [41] interactiveDisplayBase_1.35.0 IRanges_2.31.2
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     ## [43] randomForest_4.7-1.1          fansi_1.0.3
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-    ## [45] crayon_1.5.1                  dplyr_1.0.9
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-    ## [47] later_1.3.0                   basilisk.utils_1.9.1
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+    ## [45] crayon_1.5.1                  dplyr_1.0.10
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+    ## [47] later_1.3.0                   basilisk.utils_1.9.3
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     ## [49] bitops_1.0-7                  rappdirs_0.3.3
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     ## [51] grid_4.2.1                    jsonlite_1.8.0
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     ## [53] xtable_1.8-4                  lifecycle_1.0.1
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     ## [55] DBI_1.1.3                     magrittr_2.0.3
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-    ## [57] cli_3.3.0                     stringi_1.7.8
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-    ## [59] cachem_1.0.6                  XVector_0.37.0
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+    ## [57] cli_3.4.0                     stringi_1.7.8
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+    ## [59] cachem_1.0.6                  XVector_0.37.1
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     ## [61] promises_1.2.0.1              ellipsis_0.3.2
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     ## [63] filelock_1.0.2                generics_0.1.3
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     ## [65] vctrs_0.4.1                   tools_4.2.1
vignettes/crisprScore.Rmd
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@@ -29,19 +29,20 @@ options("knitr.graphics.auto_pdf"=TRUE)
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30 30 
 # Overview
31 31
32 
-crisprScore provides R wrappers of several on-target and off-target scoring
32 
+The `crisprScore` package provides R wrappers of several on-target and off-target scoring
33 33 
 methods for CRISPR guide RNAs (gRNAs). The following nucleases are supported:
34 34 
 SpCas9, AsCas12a, enAsCas12a, and RfxCas13d (CasRx). The available on-target
35 35 
 cutting efficiency scoring methods are RuleSet1, RuleSet3, Azimuth, DeepHF,
36 
-DeepSpCas9, DeepCpf1, enPAM+GB, CRISPRscan and CRISPRater. Both the CFD and MIT
36 
+DeepSpCas9, DeepCpf1, enPAM+GB, CRISPRscan and CRISPRater. Both the CFD and MIT
37 37 
 scoring methods are available for off-target specificity prediction. The
38 38 
 package also provides a Lindel-derived score to predict the probability
39 39 
 of a gRNA to produce indels inducing a frameshift for the Cas9 nuclease.
40 40 
 Note that DeepHF, DeepCpf1 and enPAM+GB are not available on Windows machines.
41 41
42 42 
 Our work is described in a recent bioRxiv preprint:
43 
-["A comprehensive Bioconductor ecosystem for the design of CRISPR guide RNAs across nucleases and technologies"](https://www.biorxiv.org/content/10.1101/2022.04.21.488824v2)
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+["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)
44 44
45 
+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`.
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 # Installation and getting started
47 48