seqLogo takes the position weight matrix of a DNA sequence motif and plots the corresponding sequence logo as introduced by Schneider and Stephens (1990).
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seqLogo().makePWM().ggseqlogo instead because seqLogo uses the base grid package for rendering.ggseqlogo instead because seqLogo is currently implemented primarily for DNA/RNA sequences.matrix or data.frame where column probabilities add up to 1.0.pwm class created by makePWM().pwm object, matrix, or data.frame).pwm object from a matrix or data frame using makePWM() to automatically check that column probabilities sum to 1.0.ic.scale=FALSE if you want all columns to have the same uniform height rather than being scaled by information content.alphabet="RNA" in makePWM() and provide fill colors for either c("A", "C", "G", "U") or c("A", "C", "G", "T").matrix or data.frame is properly normalized before passing it to makePWM().fill argument in seqLogo() with specific colors for A, C, G, and T/U.seqLogo is designed for DNA/RNA alignments.This skill is the knowledge layer — when, why, and how to use seqlogo. To run this analysis on your own data with managed compute, automated QC, and reproducible outputs, use BioMate — free to start.
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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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