Genexpression data from a breast cancer study published by van't Veer et al. in 2002 and van de Vijver et al. in 2002, provided as an eSet.

Banksy is an R package that incorporates spatial information to cluster cells in a feature space (e.g. gene expression). To incorporate spatial information, BANKSY computes the mean neighborhood expression and azimuthal Gabor filters that capture gene expression gradients. These features are combined with the cell's own expression to embed cells in a neighbor-augmented product space which can then be clustered, allowing for accurate and spatially-aware cell typing and tissue domain segmentation.

Full genome sequences for Gallus gallus (Chicken) as provided by UCSC (galGal4, Nov. 2011) and stored in Biostrings objects.

Full genome sequences for Macaca mulatta (Rhesus) as provided by UCSC (rheMac8, Nov. 2015) and stored in Biostrings objects.

Full genome sequences for Mus musculus (Mouse) as provided by UCSC (mm8, Feb. 2006) and stored in Biostrings objects.

Full genome sequences for Drosophila melanogaster (Fly) as provided by UCSC (dm2, Apr. 2004) and stored in Biostrings objects. The sequences are the same as in BSgenome.Dmelanogaster.UCSC.dm2, 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.

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.

The basecallQC package provides tools to work with Illumina bcl2Fastq (versions >= 2.1.7) software.Prior to basecalling and demultiplexing using the bcl2Fastq software, basecallQC functions allow the user to update Illumina sample sheets from versions <= 1.8.9 to >= 2.1.7 standards, clean sample sheets of common problems such as invalid sample names and IDs, create read and index basemasks and the bcl2Fastq command. Following the generation of basecalled and demultiplexed data, the basecallQC packages allows the user to generate HTML tables, plots and a self contained report of summary metrics from Illumina XML output files.

Full genome sequences for Sus scrofa (Pig) as provided by UCSC (susScr11, Feb. 2017) and stored in Biostrings objects.

Full genome sequences for Homo sapiens (Human) as provided by UCSC (hg38, based on GRCh38.p12) with major allele injected from dbSNP151, and stored in Biostrings objects. Only single nucleotide variants (SNVs) were considered. At each SNV, the most frequent allele was chosen at the major allele to be injected into the reference genome.

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

BEER implements a Bayesian model for analyzing phage-immunoprecipitation sequencing (PhIP-seq) data. Given a PhIPData object, BEER returns posterior probabilities of enriched antibody responses, point estimates for the relative fold-change in comparison to negative control samples, and more. Additionally, BEER provides a convenient implementation for using edgeR to identify enriched antibody responses.

Full genome sequences for Rattus norvegicus (Rat) as provided by UCSC (genome rn7) and stored in Biostrings objects.

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.

Full genome sequences for Arabidopsis thaliana as provided by TAIR (snapshot from April 23, 2008) and stored in Biostrings objects.

Full genome sequences for Gallus gallus (Chicken) as provided by UCSC (galGal3, May 2006) and stored in Biostrings objects. The sequences are the same as in BSgenome.Ggallus.UCSC.galGal3, 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 Rattus norvegicus (Rat) as provided by UCSC (rn5, Mar. 2012) and stored in Biostrings objects.

Parse GFF and GTF files using C++ classes. The package also provides utilities to read and write GFF3 files. The GFF (General Feature Format) format is a tab-delimited file format for describing genes and other features of DNA, RNA, and protein sequences. GFF files are often used to describe the features of genomes.

Full genome sequences for Apis mellifera as provided by NCBI (assembly Amel_HAv3.1, assembly accession GCF_003254395.2) and stored in Biostrings objects.

High-throughput experimental data are accumulating exponentially in public databases. However, mining valid scientific discoveries from these abundant resources is hampered by technical artifacts and inherent biological heterogeneity. The former are usually termed "batch effects," and the latter is often modelled by "subtypes." The R package BUScorrect fits a Bayesian hierarchical model, the Batch-effects-correction-with-Unknown-Subtypes model (BUS), to correct batch effects in the presence of unknown subtypes. BUS is capable of (a) correcting batch effects explicitly, (b) grouping samples that share similar characteristics into subtypes, (c) identifying features that distinguish subtypes, and (d) enjoying a linear-order computation complexity.

The biodbChebi library provides access to the ChEBI Database, using biodb package framework. It allows to retrieve entries by their accession number. Web services can be accessed for searching the database by name, mass or other fields.

Full genome sequences for Cryptococcus neoformans var. grubii KN99 (assembly ASM221672v1 assembly accession GCA_002216725.1).

Full genome sequences for Homo sapiens (Human) as provided by UCSC (hg18, Mar. 2006) and stored in Biostrings objects.

Full genome sequences for Caenorhabditis elegans (Worm) as provided by UCSC (ce11, Feb. 2013) and stored in Biostrings objects.