A set of tools for working with miRNA affinity models (KdModels), efficiently scanning for miRNA binding sites, and predicting target repression. It supports scanning using miRNA seeds, full miRNA sequences (enabling 3' alignment) and KdModels, and includes the prediction of slicing and TDMD sites. Finally, it includes utility and plotting functions (e.g. for the visual representation of miRNA-target alignment).
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A set of tools for working with miRNA affinity models (KdModels), efficiently scanning for miRNA binding sites, and predicting target repression. It supports scanning using miRNA seeds, full miRNA sequences (enabling 3' alignment) and KdModels, and includes the prediction of slicing and TDMD sites. Finally, it includes utility and plotting functions (e.g. for the visual representation of miRNA-target alignment).
library(scanMiR)
data("SampleTranscript")
data("SampleKdModel")
# Scan for matches using a KdModel
matches <- findSeedMatches(SampleTranscript, SampleKdModel, verbose = FALSE)
# View target alignment
viewTargetAlignment(matches[1], SampleKdModel, SampleTranscript)
# Aggregate matches
agg_matches <- aggregateMatches(matches)
Input: A transcript sequence (DNAStringSet or character) and a miRNA KdModel; Output: A GRanges object of matches and a data.frame of aggregated repression values.
DNAStringSet) for miRNA binding sites using a seed sequence, full miRNA sequence, or a KdModel using findSeedMatches.assignKdType.viewTargetAlignment or plot KdModel affinities using plotKdModel.aggregateMatches.DESeq2 or edgeR).KdModel / KdModelList objects.DNAStringSet (optionally with ORF.length metadata column).TRUE/FALSE): Controls progress reporting during scanning in findSeedMatches.FALSE): Restricts the scan to canonical miRNA binding sites when using a KdModel."GRanges"): Specifies the return format of findSeedMatches (e.g., "GRanges", "data.frame", or "aggregated").0): Treats matches within the first shadow positions of the UTR as if they were in the ORF.7): Minimum distance between matches of the same miRNA; only the highest affinity match is kept."seeds"): Specifies what to plot in plotKdModel (e.g., "seeds").ORF.length as a metadata column in the input DNAStringSet to distinguish between matches in the ORF and 3'UTR regions.onlyCanonical = TRUE in findSeedMatches if you want to exclude low-affinity non-canonical binding sites.aggregateMatches to compute the predicted repression of transcripts based on the biochemical model of occupancy.BiocParallel parameter (e.g., BP = MulticoreParam()) to findSeedMatches.n_seeds parameter or set useTmpFiles = TRUE.targetscan: For standard TargetScan-based miRNA target predictions.mirbase: For retrieving miRNA sequences and annotations.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。
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.
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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
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。
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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。
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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。
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。
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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。