This package offers tools to create DNA barcode sets capable of correcting insertion, deletion, and substitution errors. Existing barcodes can be analyzed regarding their minimal, maximal and average distances between barcodes. Finally, reads that start with a (possibly mutated) barcode can be demultiplexed, i.e. assigned to their original reference barcode.

This package exposes an annotation databases generated from UCSC by exposing these as TxDb objects.

BadRegionFinder is a package for identifying regions with a bad, acceptable and good coverage in sequence alignment data available as bam files. The whole genome may be considered as well as a set of target regions. Various visual and textual types of output are available.

This is a representation of public golub data with some covariate data of provenance unknown to the maintainer at present; it now employs ExpressionSet format.

The QFeatures infrastructure enables the management and processing of quantitative features for high-throughput mass spectrometry assays. It provides a familiar Bioconductor user experience to manages quantitative data across different assay levels (such as peptide spectrum matches, peptides and proteins) in a coherent and tractable format.

This is a package for detection of differentially bound regions in ChIP-seq data with sliding windows, with methods for normalization and proper FDR control.

This package provides a method for combining single-cell cytometry datasets, which increases the analytical flexibility and the statistical power of the analyses while minimizing technical noise.

ATAC-seq, an assay for Transposase-Accessible Chromatin using sequencing, is a rapid and sensitive method for chromatin accessibility analysis. It was developed as an alternative method to MNase-seq, FAIRE-seq and DNAse-seq. The ATACseqQC package was developed to help users to quickly assess whether their ATAC-seq experiment is successful. It includes diagnostic plots of fragment size distribution, proportion of mitochondria reads, nucleosome positioning pattern, and CTCF or other Transcript Factor footprints.

BiFET identifies transcription factors (TFs) whose footprints are over-represented in target regions compared to background regions after correcting for the bias arising from the imbalance in read counts and GC contents between the target and background regions. For a given TF k, BiFET tests the null hypothesis that the target regions have the same probability of having footprints for the TF k as the background regions while correcting for the read count and GC content bias.

The package is usable with Affymetrix GeneChip short oligonucleotide arrays, and it can be adapted or extended to other platforms. It is able to modify or replace the grouping of probes in the probe sets. Also, the package contains simple functions to read R connections in the FASTA format and it can create an alternative mapping from sequences.

The package provides two frameworks. One for the differential transcript usage analysis between different conditions and one for the tuQTL analysis. Both are based on modeling the counts of genomic features (i.e., transcripts) with the Dirichlet-multinomial distribution. The package also makes available functions for visualization and exploration of the data and results.

This package provides HDF5 storage based methods and functions for manipulation of flow cytometry data.

HDCytoData contains a set of high-dimensional cytometry benchmark datasets. These datasets are formatted into SummarizedExperiment and flowSet Bioconductor object formats, including all required metadata. Row metadata includes sample IDs, group IDs, patient IDs, reference cell population or cluster labels and labels identifying spiked in cells. Column metadata includes channel names, protein marker names, and protein marker classes.

This package provides methods to infer clonal tree configuration for a population of cells using single-cell RNA-seq data (scRNA-seq), and possibly other data modalities. Methods are also provided to assign cells to inferred clones and explore differences in gene expression between clones. These methods can flexibly integrate information from imperfect clonal trees inferred based on bulk exome-seq data, and sparse variant alleles expressed in scRNA-seq data. A flexible beta-binomial error model that accounts for stochastic dropout events as well as systematic allelic imbalance is used.

This package interfaces R with the graphviz library for plotting R graph objects from the graph package.

This package aims to make NMR spectroscopy data analysis as easy as possible. It only requires a small set of functions to perform an entire analysis. Speaq offers the possibility of raw spectra alignment and quantitation but also an analysis based on features whereby the spectra are converted to peaks which are then grouped and turned into features. These features can be processed with any number of statistical tools either included in speaq or available elsewhere on CRAN.

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

This package provides a library of core pre-processing and normalization routines.

ChIPComp implements a statistical method for quantitative comparison of multiple ChIP-seq datasets. It detects differentially bound sharp binding sites across multiple conditions considering matching control in ChIP-seq datasets.

This package installs a self-contained Conda instance that is managed by the R/Bioconductor installation machinery. This aims to provide a consistent Python environment that can be used reliably by Bioconductor packages. Functions are also provided to enable smooth interoperability of multiple Python environments in a single R session.

This package provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results.

The epigenomics road map describes locations of epigenetic marks in DNA from a variety of cell types. Of interest are locations of histone modifications, sites of DNA methylation, and regions of accessible chromatin. This package presents a selection of elements of the road map including metadata and outputs of the ChromImpute procedure applied to ENCODE cell lines by Ernst and Kellis.

This package contains the helper files that are required to run the Bioconductor package CopywriteR. It contains pre-assembled 1kb bin GC-content and mappability files for the reference genomes hg18, hg19, hg38, mm9 and mm10. In addition, it contains a blacklist filter to remove regions that display copy number variation. Files are stored as GRanges objects from the GenomicRanges Bioconductor package.

This package provides functions and classes for de novo prediction of transcription factor binding consensus by heuristic search.