Full genome sequences for Cicer arietinum (Chickpea) as provided by NCBI (ASM33114v1, Jan. 2013) and stored in Biostrings objects.

Full genome sequences for Macaca fascicularis (long-tailed macaque) as provided by NCBI (Macaca_fascicularis_5.0, 2013-06-12) and stored in Biostrings objects.

Full genome sequences for Macaca mulatta (Rhesus) as provided by UCSC (rheMac3, Oct. 2010) and stored in Biostrings objects. The sequences are the same as in BSgenome.Mmulatta.UCSC.rheMac3, except that each of them has the 4 following masks on top: (1) the mask of assembly gaps (AGAPS mask), (2) the mask of intra-contig ambiguities (AMB mask), (3) the mask of repeats from RepeatMasker (RM mask), and (4) the mask of repeats from Tandem Repeats Finder (TRF mask). Only the AGAPS and AMB masks are "active" by default.

Full genome sequences for Callithrix jacchus (Marmoset) as provided by UCSC (calJac4, May 2020) and wrapped in a BSgenome object.

bettr provides a set of interactive visualization methods to explore the results of a benchmarking study, where typically more than a single performance measures are computed. The user can weight the performance measures according to their preferences. Performance measures can also be grouped and aggregated according to additional annotations.

Full genome sequences for Apis mellifera (Honey Bee) as provided by UCSC (apiMel2, Jan. 2005) and stored in Biostrings objects. The sequences are the same as in BSgenome.Amellifera.UCSC.apiMel2, except that each of them has the 3 following masks on top: (1) the mask of assembly gaps (AGAPS mask), (2) the mask of intra-contig ambiguities (AMB mask), and (3) the mask of repeats from RepeatMasker (RM mask). Only the AGAPS and AMB masks are "active" by default.

Data from 6 gpr files aims to identify differential expressed genes between the beta 7+ and beta 7- memory T helper cells.

Full genome sequences for Danio rerio (Zebrafish) as provided by UCSC (danRer6, Dec. 2008) and stored in Biostrings objects.

Full genome sequences for Danio rerio (Zebrafish) as provided by UCSC (danRer7, Jul. 2010) and stored in Biostrings objects. The sequences are the same as in BSgenome.Drerio.UCSC.danRer7, except that each of them has the 4 following masks on top: (1) the mask of assembly gaps (AGAPS mask), (2) the mask of intra-contig ambiguities (AMB mask), (3) the mask of repeats from RepeatMasker (RM mask), and (4) the mask of repeats from Tandem Repeats Finder (TRF mask). Only the AGAPS and AMB masks are "active" by default.

Full reference nuclear genome sequences for Vitis vinifera subsp. vinifera PN40024 (derived from Pinot Noir and close to homozygosity after 6-9 rounds of selfing) as assembled by the IGGP (version 8X) and available at the URGI (INRA). More details in Jaillon et al (Nature, 2007).

Represents the OpenAPI v2 Azul API as an R object for performing requests. The infrastructure uses the AnVIL and rapiclient packages. Users can connect to either the AnVIL or Human Cell Atlas Data Explorers.

The barbieQ package provides a series of robust statistical tools for analysing barcode count data generated from cell clonal tracking (i.e., lineage tracing) experiments. In these experiments, an initial cell and its offspring collectively form a clone (i.e., lineage). A unique barcode sequence, incorporated into the DNA of the inital cell, is inherited within the clone. This one-to-one mapping of barcodes to clones enables clonal tracking of their behaviors. By counting barcodes, researchers can quantify the population abundance of individual clones under specific experimental perturbations. barbieQ supports barcode count data preprocessing, statistical testing, and visualization.

Full genome sequences for Bos taurus (Cow) as provided by UCSC (bosTau3, Aug. 2006) and stored in Biostrings objects.

From the perspective of metabolites as the continuation of the central dogma of biology, metabolomics provides the closest link to many phenotypes of interest. This makes metabolomics research promising in teasing apart the complexities of living systems. However, due to experimental reasons, the data includes non-biological variation which limits quality and reproducibility, especially if the data is obtained from several batches. The batchCorr package reduces unwanted variation by way of between-batch alignment, within-batch drift correction and between-batch normalization using batch-specific quality control samples and long-term reference QC samples. Please see the associated article for more thorough descriptions of algorithms.

This package can be used to compute associations among genes (gene-networks) or between genes and some external traits (i.e. clinical).

Full genome sequences for Danio rerio (Zebrafish) as provided by UCSC (danRer5, Jul. 2007) and stored in Biostrings objects. The sequences are the same as in BSgenome.Drerio.UCSC.danRer5, except that each of them has the 4 following masks on top: (1) the mask of assembly gaps (AGAPS mask), (2) the mask of intra-contig ambiguities (AMB mask), (3) the mask of repeats from RepeatMasker (RM mask), and (4) the mask of repeats from Tandem Repeats Finder (TRF mask). Only the AGAPS and AMB masks are "active" by default.

Full genome sequences for Taeniopygia guttata (Zebra finch) as provided by UCSC (taeGut1, Jul. 2008) and stored in Biostrings objects.

The BioPlex package implements access to the BioPlex protein-protein interaction networks and related resources from within R. Besides protein-protein interaction networks for HEK293 and HCT116 cells, this includes access to CORUM protein complex data, and transcriptome and proteome data for the two cell lines. Functionality focuses on importing the various data resources and storing them in dedicated Bioconductor data structures, as a foundation for integrative downstream analysis of the data.

Full genome sequences for Gallus gallus (Chicken) as provided by UCSC (galGal5, Dec. 2015) and stored in Biostrings objects.

Full genome sequences for Gallus gallus (Chicken) as provided by UCSC (galGal4, Nov. 2011) and stored in Biostrings objects. The sequences are the same as in BSgenome.Ggallus.UCSC.galGal4, except that each of them has the 4 following masks on top: (1) the mask of assembly gaps (AGAPS mask), (2) the mask of intra-contig ambiguities (AMB mask), (3) the mask of repeats from RepeatMasker (RM mask), and (4) the mask of repeats from Tandem Repeats Finder (TRF mask). Only the AGAPS and AMB masks are "active" by default.

Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. BatchQC is a software tool that streamlines batch preprocessing and evaluation by providing interactive diagnostics, visualizations, and statistical analyses to explore the extent to which batch variation impacts the data. BatchQC diagnostics help determine whether batch adjustment needs to be done, and how correction should be applied before proceeding with a downstream analysis. Moreover, BatchQC interactively applies multiple common batch effect approaches to the data and the user can quickly see the benefits of each method. BatchQC is developed as a Shiny App. The output is organized into multiple tabs and each tab features an important part of the batch effect analysis and visualization of the data. The BatchQC interface has the following analysis groups: Summary, Differential Expression, Median Correlations, Heatmaps, Circular Dendrogram, PCA Analysis, Shape, ComBat and SVA.

This package provides an interactive Shiny dashboard for Bioconductor package maintainers. It visualizes various package statuses, metadata, and development metrics, offering insights into package health and activity. This tool aims to support maintainers of multiple packages by filtering packages via maintainer email.

This is a probabilistic modelling pipeline for computing per- nucleotide posterior probabilities of modification from the data collected in structure probing experiments. The model supports multiple experimental replicates and empirically corrects coverage- and sequence-dependent biases. The model utilises the measure of a "drop-off rate" for each nucleotide, which is compared between replicates through a log-ratio (LDR). The LDRs between control replicates define a null distribution of variability in drop-off rate observed by chance and LDRs between treatment and control replicates gets compared to this distribution. Resulting empirical p-values (probability of being "drawn" from the null distribution) are used as observations in a Hidden Markov Model with a Beta-Uniform Mixture model used as an emission model. The resulting posterior probabilities indicate the probability of a nucleotide of having being modified in a structure probing experiment.

This package implements a variety of methods for batch correction in single-cell RNA sequencing (scRNA-seq) data. It incorporates quantitative metrics (e.g. Wasserstein distance, Adjusted Rand Index) to evaluate their performance. Furthermore, the package assists users in identifying and applying the optimal method for specific datasets.