本文へ移動
cccskills
無料GitHub で公開

bioconductor-crisprviz

Provides functionalities to visualize and contextualize CRISPR guide RNAs (gRNAs) on genomic tracks across nucleases and applications. Works in conjunction with the crisprBase and crisprDesign Bioconductor packages. Plots are produced using the Gviz framework.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md5.1 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

crisprViz

Workflows

Standard Workflow

library(crisprViz)
library(BSgenome.Hsapiens.UCSC.hg38)
data("krasGuideSet", package="crisprViz")
data("krasGeneModel", package="crisprViz")

plotGuideSet(krasGuideSet[1:4], geneModel=krasGeneModel, targetGene="KRAS")

Inputs: A GuideSet object (krasGuideSet) and a gene model GRangesList (krasGeneModel). Outputs: A genomic track plot visualizing the gRNAs against the gene model.

Compare Multiple Guidesets

library(crisprViz)
library(BSgenome.Hsapiens.UCSC.hg38)
data("cas9GuideSet", package="crisprViz")
data("cas12aGuideSet", package="crisprViz")
data("ltn1GeneModel", package="crisprViz")

plotMultipleGuideSets(list(SpCas9=cas9GuideSet, AsCas12a=cas12aGuideSet),
                      geneModel=ltn1GeneModel,
                      targetGene="LTN1",
                      bsgenome=BSgenome.Hsapiens.UCSC.hg38,
                      margin=0.2,
                      gcWindow=10)

Inputs: A list of GuideSet objects for different nucleases, a gene model, and a reference BSgenome object. Outputs: A multi-track genomic plot comparing target distributions and GC content.

When to Use

  • gRNA Track Visualization: Visualizing gRNA cutting locations against target genes or genomic regions using plotGuideSet.
  • Nuclease Comparison: Comparing multiple GuideSet objects targeting the same region side-by-side using plotMultipleGuideSets.
  • Genomic Annotations: Adding genomic annotations (e.g., repeat elements, CAGE peaks, DNase I hypersensitivity sites) to gRNA plots using the annotations argument.
  • Score-Based Color Coding: Coloring gRNAs based on on-target efficiency scores (e.g., DeepHF) using the onTargetScore argument.

When NOT to Use

  • Unsupported R/Bioconductor Versions: Do not use if R version is less than 4.2.0 or Bioconductor version is less than 3.16.
  • gRNA Design or Scoring: Do not use for designing gRNAs or calculating scores directly (use crisprDesign and crisprScore instead).

Data Requirements

  • GuideSet: A GuideSet object containing candidate gRNAs (e.g., krasGuideSet).
  • Gene Model: A gene model represented as a GRangesList object (e.g., krasGeneModel).
  • Reference Genome: A BSgenome object for genomic sequence visualization (e.g., BSgenome.Hsapiens.UCSC.hg38).

Key Parameters

  • geneModel (no default): A GRangesList object describing the gene structure.
  • targetGene (no default): Character string specifying the name of the target gene.
  • from (NULL): Numeric coordinate specifying the start of the plot window.
  • to (NULL): Numeric coordinate specifying the end of the plot window.
  • extend.left (0): Numeric value to extend the plot window to the left.
  • extend.right (0): Numeric value to extend the plot window to the right.
  • showGuideLabels (TRUE): Logical indicating whether to display gRNA labels.
  • pamSiteOnly (FALSE): Logical indicating whether to plot only the PAM site instead of the full protospacer.
  • onTargetScore (NULL): Character string specifying the metadata column name containing on-target scores.
  • annotations (NULL): Named list of GRanges objects representing genomic annotations.
  • gcWindow (NULL): Integer specifying the window size for calculating percent GC content.

Best Practices

  • Window Adjustment: Adjust the plot window manually using from, to, extend.left, and extend.right to show the entire gene or focus on a specific exon.
  • Crowding Prevention: Set showGuideLabels = FALSE when plotting a large number of candidate gRNAs to avoid crowding the plot space.
  • PAM Site Only: Set pamSiteOnly = TRUE when visualizing many overlapping gRNAs in a small window to simplify the plot.
  • Pre-filtering: Filter out gRNAs overlapping repeat elements or SNPs using crisprDesign::removeRepeats before plotting to ensure high-quality selections.

Common Pitfalls

  • Rendering Errors: Plotting too many gene isoforms or gRNAs in a small graphical device, resulting in rendering errors. Fix: Increase the height/width of the plot space using grDevices::quartz or similar device settings.

Alternatives

  • Gviz: For general genomic track visualization (which crisprViz is built upon).
  • biovizBase: For nucleotide color schemes and basic genomic plotting utilities.

Citations

  • No specific primary publication is cited in the vignette text, but the package is part of the crisprVerse ecosystem.

References

  • Homepage: bioconductor.org/packages/crisprViz
  • Vignette: bioconductor.org/packages/release/bioc/vignettes/crisprViz/inst/doc/introduction.html

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.

日本語の概要は準備中です。原文の説明を表示しています。

bioMate-AI/biomate-bioconductor-kb8042026年6月21日 更新

This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte

日本語の概要は準備中です。原文の説明を表示しています。

bioMate-AI/biomate-bioconductor-kb8042026年6月21日 更新

This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, En

日本語の概要は準備中です。原文の説明を表示しています。

bioMate-AI/biomate-bioconductor-kb8042026年6月21日 更新

apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.

日本語の概要は準備中です。原文の説明を表示しています。

bioMate-AI/biomate-bioconductor-kb8042026年6月21日 更新

ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).

日本語の概要は準備中です。原文の説明を表示しています。

bioMate-AI/biomate-bioconductor-kb8042026年6月21日 更新

AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched with

日本語の概要は準備中です。原文の説明を表示しています。

bioMate-AI/biomate-bioconductor-kb8042026年6月21日 更新

bioMate-AI のスキルをすべて見る

このスキルの問題を報告する