The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
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Provides a comprehensive suite of functions to design and annotate CRISPR guide RNA (gRNAs) sequences. This includes on- and off-target search, on-target efficiency scoring, off-target scoring, full gene and TSS contextual annotations, and SNP annotation (human only). It currently support five types of CRISPR modalities (modes of perturbations): CRISPR knockout, CRISPR activation, CRISPR inhibition, CRISPR base editing, and CRISPR knockdown. All types of CRISPR nucleases are supported, including
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
library(crisprDesign)
library(BSgenome.Hsapiens.UCSC.hg38)
data(grListExample, package="crisprDesign")
data(BE4max, package="crisprBase")
bsgenome <- BSgenome.Hsapiens.UCSC.hg38
gr <- queryTxObject(txObject=grListExample, featureType="cds", queryColumn="gene_symbol", queryValue="IQSEC3")
gr <- gr[1]
guideSet <- findSpacers(gr, bsgenome=bsgenome, crisprNuclease=BE4max, strict_overlap=FALSE)
Inputs: GRangesList gene coordinates (grListExample), BSgenome object (bsgenome), and a BaseEditor/CrisprNuclease object (BE4max). Output: A GuideSet object containing designed spacer sequences.
findSpacers and predicting edited alleles with addEditedAlleles.queryTxObject.getTxInfoDataFrame.GRangesList object containing gene coordinates (e.g., grListExample).BSgenome object containing reference genome sequences (e.g., BSgenome.Hsapiens.UCSC.hg38).BaseEditor or CrisprNuclease object (e.g., BE4max or SpCas9).GRangesList object containing gene model coordinates used in queryTxObject and getTxInfoDataFrame."cds").CrisprNuclease or BaseEditor object specifying the CRISPR enzyme.TRUE): Logical indicating whether spacer sequences must strictly overlap the target region.0): Minimum editing weight required for an allele to be predicted.0.3): Minimum predicted probability for labeling an allele with a predicted variant.queryTxObject to extract specific genomic features (like "cds") before running spacer design.strict_overlap = FALSE in findSpacers for base editing design to allow the editing window to extend beyond the protospacer sequence region.editingWindow size in addEditedAlleles to avoid exponential increases in computing time.editingWindow to addEditedAlleles exponentially increases computing time. Fix: Use a narrower window like c(-20, -8).strict_overlap = TRUE for base editing might miss spacers whose editing window extends outside the target region. Fix: Set strict_overlap = FALSE.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
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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
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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
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apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.
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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).
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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
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