The dada2 package infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, an
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Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
BiocManager::install("dada2")dada algorithm.filterAndTrim.learnErrors.removeBimeraDenovo.phyloseq instead).truncLen: Truncate reads after this many bases. Reads shorter than this are discarded. Crucial for removing low-quality tails.maxEE: After truncation, reads with higher than this number of expected errors are discarded (e.g., maxEE=2).trimLeft: The number of nucleotides to remove from the start of each read (useful for removing primers).maxN: Maximum number of ambiguous nucleotides allowed. DADA2 requires maxN=0.multithread: If TRUE, enables parallel processing to speed up computation.err: The error model generated by learnErrors, passed into the dada function.plotQualityProfile before setting truncLen parameters.learnErrors on at least a subset of your data to ensure accurate sample inference.mergePairs succeeds.removeBimeraDenovo on the combined sequence table created by makeSequenceTable.mergePairs to fail. Fix this by ensuring truncLen leaves enough sequence for a >20nt overlap.trimLeft in filterAndTrim or an external tool to remove primers.maxN=0 is set in filterAndTrim.This skill is the knowledge layer — when, why, and how to use dada2. 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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