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, and outputs the sequence variants and their sample-wise abundances after removing substitution and chimera errors. Taxonomic classification is available via a native implementation of the RDP naive Bayesian classifier, and species-level assignment to 16S rRNA gene fragments by exact matching.

This package provides a suite of helper functions for checking and manipulating TCGA data including data obtained from the curatedTCGAData experiment package. These functions aim to simplify and make working with TCGA data more manageable. Exported functions include those that import data from flat files into Bioconductor objects, convert row annotations, and identifier translation via the GDC API.

This package provides a data-driven test for the assumptions of quantile normalization using raw data such as objects that inherit eSets (e.g. ExpressionSet, MethylSet). Group level information about each sample (such as Tumor / Normal status) must also be provided because the test assesses if there are global differences in the distributions between the user-defined groups.

This package provides data from 6 samples across 2 groups from 450k methylation arrays.

This package implements different performance measures for classification and ranking tasks. Area under curve (AUC), precision at a given recall, F-score for single and multiple classes are available.

SGSeq is a package for analyzing splice events from RNA-seq data. Input data are RNA-seq reads mapped to a reference genome in BAM format. Genes are represented as a splice graph, which can be obtained from existing annotation or predicted from the mapped sequence reads. Splice events are identified from the graph and are quantified locally using structurally compatible reads at the start or end of each splice variant. The software includes functions for splice event prediction, quantification, visualization and interpretation.

This package stores all schemas required by various alabaster.* packages. No computation should be performed by this package, as that is handled by alabaster.base.

Logistic Factor Analysis (LFA) is a method for a PCA analogue on Binomial data via estimation of latent structure in the natural parameter.

Dirichlet-multinomial mixture models can be used to describe variability in microbial metagenomic data. This package is an interface to code originally made available by Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15.

This package provides genome wide annotations for Bovine, primarily based on mapping using Entrez Gene identifiers.

This package contains data from untargeted mass spectrometry (MS) of modifications to oxidized cysteine (Cys) 34 in human serum albumin (HSA).

This package implements the GENIE3 algorithm for inferring gene regulatory networks from expression data.

This package provides genome wide annotation for Yeast, primarily based on mapping using ORF identifiers from SGD.

This package provides functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data. It includes functions for rudimentary data cleaning, construction and summarization of correlation networks, module identification and functions for relating both variables and modules to sample traits. It also includes a number of utility functions for data manipulation and visualization.

This package provides dataset samples (Affymetrix: Expression, Gene, Exon, SNP; NimbleGen: Expression, Tiling) to be used with the oligo package.

This package infers and discriminates RIP peaks from RIP-seq alignments using two-state HMM with negative binomial emission probability. While RIPSeeker is specifically tailored for RIP-seq data analysis, it also provides a suite of bioinformatics tools integrated within this self-contained software package comprehensively addressing issues ranging from post-alignments processing to visualization and annotation.

Genome wide studies of translational control is emerging as a tool to study various biological conditions. The output from such analysis is both the mRNA level (e.g. cytosolic mRNA level) and the level of mRNA actively involved in translation (the actively translating mRNA level) for each mRNA. The standard analysis of such data strives towards identifying differential translational between two or more sample classes - i.e., differences in actively translated mRNA levels that are independent of underlying differences in cytosolic mRNA levels. This package allows for such analysis using partial variances and the random variance model. As 10s of thousands of mRNAs are analyzed in parallel the library performs a number of tests to assure that the data set is suitable for such analysis.

This package provides a database of PROVEAN/SIFT predictions for Homo sapiens dbSNP build 137.

This package provides classes and statistical methods for large single-nucleotide polymorphism (SNP) association studies. This extends the earlier snpMatrix package, allowing for uncertainty in genotypes.

The package performs alignment of the amplicon reads, normalizes gathered data, calculates multiple statistics (e.g. cut rates, frameshifts) and presents the results in the form of aggregated reports. Data and statistics can be broken down by experiments, barcodes, user defined groups, guides and amplicons allowing for quick identification of potential problems.

This package contains classes used in model-view-controller (MVC) design.

Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner.

This package provides ChIP-seq data for demonstration purposes in the chromstaR package.

This package provides basic functions for filtering genes from high-throughput sequencing experiments.