The 'msa' package provides a unified R/Bioconductor interface to the multiple sequence alignment algorithms ClustalW, ClustalOmega, and Muscle. All three algorithms are integrated in the package, therefore, they do not depend on any externa
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Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
BiocManager::install("msa")msa().msaPrettyPrint().msaConsensusSequence() and msaConservationScore().minimap2 because msa is designed for gene- or protein-scale alignments.pwalign because it is specifically built for pairwise comparisons.AAStringSet (or similar XStringSet) using readAAStringSet().unmasked() to retrieve the original alignment if a colmask() has been applied to hide specific columns.msaConvert() for downstream phylogenetic analysis (e.g., with seqinr or ape).output="asis" in msaPrettyPrint() when integrating alignments into Sweave or knitr documents.msaPrettyPrint() failing to compile PDFs; fix this by ensuring a working LaTeX system with the texshade.sty package is installed on your system path.subseq() and printing them separately.DECIPHER: A powerful Bioconductor package for sequence alignment, database management, and oligonucleotide design.ape: For phylogenetic analysis and basic sequence manipulation.seqinr: For biological sequence retrieval and analysis.This skill is the knowledge layer — when, why, and how to use msa. 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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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
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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
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。