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The Structstrings package implements the widely used dot bracket annotation for storing base pairing information in structured RNA. Structstrings uses the infrastructure provided by the Biostrings package and derives the DotBracketString and related classes from the BString class. From these, base pair tables can be produced for in depth analysis. In addition, the loop indices of the base pairs can be retrieved as well. For better efficiency, information conversion is implemented in C, inspired to a large extend by the ViennaRNA package.

Annotate variants, compute amino acid coding changes, predict coding outcomes.

Imports transcript-level abundance, estimated counts and transcript lengths, and summarizes into matrices for use with downstream gene-level analysis packages. Average transcript length, weighted by sample-specific transcript abundance estimates, is provided as a matrix which can be used as an offset for different expression of gene-level counts.

svaRetro contains functions for detecting retrotransposed transcripts (RTs) from structural variant calls. It takes structural variant calls in GRanges of breakend notation and identifies RTs by exon-exon junctions and insertion sites. The candidate RTs are reported by events and annotated with information of the inserted transcripts.

svaNUMT contains functions for detecting NUMT events from structural variant calls. It takes structural variant calls in GRanges of breakend notation and identifies NUMTs by nuclear-mitochondrial breakend junctions. The main function reports candidate NUMTs if there is a pair of valid insertion sites found on the nuclear genome within a certain distance threshold. The candidate NUMTs are reported by events.

StructuralVariantAnnotation provides a framework for analysis of structural variants within the Bioconductor ecosystem. This package contains contains useful helper functions for dealing with structural variants in VCF format. The packages contains functions for parsing VCFs from a number of popular callers as well as functions for dealing with breakpoints involving two separate genomic loci encoded as GRanges objects.

SpotClean is a computational method to adjust for spot swapping in spatial transcriptomics data. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind mRNA. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case due to bleed from nearby spots, an artifact we refer to as spot swapping. SpotClean is able to estimate the contamination rate in observed data and decontaminate the spot swapping effect, thus increase the sensitivity and precision of downstream analyses.

This package implements sampling, iteration, and input of FASTQ files. The package includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, and easily manipulated for a diversity of purposes. The package also contains legacy support for early single-end, ungapped alignment formats.

scCB2 is an R package implementing CB2 for distinguishing real cells from empty droplets in droplet-based single cell RNA-seq experiments (especially for 10x Chromium). It is based on clustering similar barcodes and calculating Monte-Carlo p-value for each cluster to test against background distribution. This cluster-level test outperforms single-barcode-level tests in dealing with low count barcodes and homogeneous sequencing library, while keeping FDR well controlled.

This package provides an interface to the 'samtools', 'bcftools', and 'tabix' utilities for manipulating SAM (Sequence Alignment / Map), FASTA, binary variant call (BCF) and compressed indexed tab-delimited (tabix) files.

This package provides version 1.18 of the 'HTSlib' C library for high-throughput sequence analysis. The package is primarily useful to developers of other R packages who wish to make use of HTSlib. Motivation and instructions for use of this package are in the vignette, vignette(package="Rhtslib", "Rhtslib").

This package provides functionality for reading data from HDF Scalable Data Service from within R. The HSDSArray function bridges from HSDS to the user via the DelayedArray interface. Bioconductor manages an open HSDS instance graciously provided by John Readey of the HDF Group.

The package provides functions to read raw RT-qPCR data of different platforms.

An R package which interfaces the OME Bio-Formats Java library to allow reading of proprietary microscopy image data and metadata.

The Zarr specification defines a format for chunked, compressed, N-dimensional arrays. It's design allows efficient access to subsets of the stored array, and supports both local and cloud storage systems. Rarr aims to implement this specification in R with minimal reliance on an external tools or libraries.

'OSTA.data' is a companion package for the "Orchestrating Spatial Transcriptomics Analysis" (OSTA) with Bioconductor online book. Throughout OSTA, we rely on a set of publicly available datasets that cover different sequencing- and imaging-based platforms, such as Visium, Visium HD, Xenium (10x Genomics) and CosMx (NanoString). In addition, we rely on scRNA-seq (Chromium) data for tasks, e.g., spot deconvolution and label transfer (i.e., supervised clustering). These data been deposited in an Open Storage Framework (OSF) repository, and can be queried and downloaded using functions from the 'osfr' package. For convenience, we have implemented 'OSTA.data' to query and retrieve data from our OSF node, and cache retrieved Zip archives using 'BiocFileCache'.

Save MultiAssayExperiments to h5mu files supported by muon and mudata. Muon is a Python framework for multimodal omics data analysis. It uses an HDF5-based format for data storage.

MoleculeExperiment contains functions to create and work with objects from the new MoleculeExperiment class. We introduce this class for analysing molecule-based spatial transcriptomics data (e.g., Xenium by 10X, Cosmx SMI by Nanostring, and Merscope by Vizgen). This allows researchers to analyse spatial transcriptomics data at the molecule level, and to have standardised data formats accross vendors.

Representing nucleotide modifications in a nucleotide sequence is usually done via special characters from a number of sources. This represents a challenge to work with in R and the Biostrings package. The Modstrings package implements this functionallity for RNA and DNA sequences containing modified nucleotides by translating the character internally in order to work with the infrastructure of the Biostrings package. For this the ModRNAString and ModDNAString classes and derivates and functions to construct and modify these objects despite the encoding issues are implemenented. In addition the conversion from sequences to list like location information (and the reverse operation) is implemented as well.

A seamless interface to the MEME Suite family of tools for motif analysis. 'memes' provides data aware utilities for using GRanges objects as entrypoints to motif analysis, data structures for examining & editing motif lists, and novel data visualizations. 'memes' functions and data structures are amenable to both base R and tidyverse workflows.

Automatically process the metadata of MACSQuantify FACS sorter. It runs multiple modules: i) imports of raw file and graphical selection of duplicates in well plate, ii) computes statistics on data and iii) can compute combination index.

This package defines a custom landing page for an iSEE app interfacing with the Bioconductor ExperimentHub. The landing page allows users to browse the ExperimentHub, select a data set, download and cache it, and import it directly into a Bioconductor iSEE app.

BED files store ranged genomic data that can be queried even when the files are compressed. iscream can query data from BED files and return them in muliple formats: parsed records or their summary statistics as data frames or GenomicRanges objects, and matrices as matrix, GenomicRanges, or SummarizedExperiment objects. iscream also provides specialized support for importing methylation data.

HubPub provides users with functionality to help with the Bioconductor Hub structures. The package provides the ability to create a skeleton of a Hub style package that the user can then populate with the necessary information. There are also functions to help add resources to the Hub package metadata files as well as publish data to the Bioconductor S3 bucket.

HiCBricks is a library designed for handling large high-resolution Hi-C datasets. Over the years, the Hi-C field has experienced a rapid increase in the size and complexity of datasets. HiCBricks is meant to overcome the challenges related to the analysis of such large datasets within the R environment. HiCBricks offers user-friendly and efficient solutions for handling large high-resolution Hi-C datasets. The package provides an R/Bioconductor framework with the bricks to build more complex data analysis pipelines and algorithms. HiCBricks already incorporates example algorithms for calling domain boundaries and functions for high quality data visualization.

Client for the gypsum REST API (https://gypsum.artifactdb.com), a cloud-based file store in the ArtifactDB ecosystem. This package provides functions for uploads, downloads, and various adminstrative and management tasks. Check out the documentation at https://github.com/ArtifactDB/gypsum-worker for more details.

Programmatically access the NIH / NCI Genomic Data Commons RESTful service.

This package defines classes for representing genomic intervals and provides functions and methods for working with these. Note: The package provides the basic infrastructure for and is enhanced by the package 'girafe'.

The Connectivity Map (CMap) is a massive resource of perturbational gene expression profiles built by researchers at the Broad Institute and funded by the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. Please visit https://clue.io for more information. The cmapR package implements methods to parse, manipulate, and write common CMap data objects, such as annotated matrices and collections of gene sets.

Saves the delayed operations of a DelayedArray to a HDF5 file. This enables efficient recovery of the DelayedArray's contents in other languages and analysis frameworks.

The bugsigdbr package implements convenient access to bugsigdb.org from within R/Bioconductor. The goal of the package is to facilitate import of BugSigDB data into R/Bioconductor, provide utilities for extracting microbe signatures, and enable export of the extracted signatures to plain text files in standard file formats such as GMT.

A test harness for bsseq loading of Biscuit output, summarization of WGBS data over defined regions and in mappable samples, with or without imputation, dropping of mostly-NA rows, age estimates, etc.

This package creates a persistent on-disk cache of files that the user can add, update, and retrieve. It is useful for managing resources (such as custom Txdb objects) that are costly or difficult to create, web resources, and data files used across sessions.

This package allows to efficiently obtain count vectors from indexed bam files. It counts the number of reads in given genomic ranges and it computes reads profiles and coverage profiles. It also handles paired-end data.

These recipes convert a wide variety and a growing number of public bioinformatic data sets into easily-used standard Bioconductor data structures.

Save variant calling SummarizedExperiment to file and load them back as VCF objects. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

Save Biostrings objects to file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

Save SpatialExperiment objects and their images into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

Save SummarizedExperiments into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

Save SingleCellExperiment into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

Save GenomicRanges, IRanges and related data structures into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

Save matrices, arrays and similar objects into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

Save MultiAssayExperiments into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

Save BumpyMatrix objects into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.