GenomeInfoDb

The ability to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose containers for storing and manipulating genomic intervals and variables defined along a genome. More specialized containers for representing and manipulating short alignments against a reference genome, or a matrix-like summarization of an experiment, are defined in the GenomicAlignments and SummarizedExperiment packages, respectively. Both packages build on top of the GenomicRanges infrastructure.

A set of tools for making TxDb objects from genomic annotations from various sources (e.g. UCSC, Ensembl, and GFF files). These tools allow the user to download the genomic locations of transcripts, exons, and CDS, for a given assembly, and to import them in a TxDb object. TxDb objects are implemented in the GenomicFeatures package, together with flexible methods for extracting the desired features in convenient formats.

Ularcirc reads in STAR aligned splice junction files and provides visualisation and analysis tools for splicing analysis. Users can assess backsplice junctions and forward canonical junctions.

VariantExperiment is a Bioconductor package for saving data in VCF/GDS format into RangedSummarizedExperiment object. The high-throughput genetic/genomic data are saved in GDSArray objects. The annotation data for features/samples are saved in DelayedDataFrame format with mono-dimensional GDSArray in each column. The on-disk representation of both assay data and annotation data achieves on-disk reading and processing and saves memory space significantly. The interface of RangedSummarizedExperiment data format enables easy and common manipulations for high-throughput genetic/genomic data with common SummarizedExperiment metaphor in R and Bioconductor.

Wrapping an array-like object (typically an on-disk object) in a DelayedArray object allows one to perform common array operations on it without loading the object in memory. In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism. Note that this also works on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, ordinary arrays and, data frames.

geneXtendeR optimizes the functional annotation of ChIP-seq peaks by exploring relative differences in annotating ChIP-seq peak sets to variable-length gene bodies. In contrast to prior techniques, geneXtendeR considers peak annotations beyond just the closest gene, allowing users to see peak summary statistics for the first-closest gene, second-closest gene, ..., n-closest gene whilst ranking the output according to biologically relevant events and iteratively comparing the fidelity of peak-to-gene overlap across a user-defined range of upstream and downstream extensions on the original boundaries of each gene's coordinates. Since different ChIP-seq peak callers produce different differentially enriched peaks with a large variance in peak length distribution and total peak count, annotating peak lists with their nearest genes can often be a noisy process. As such, the goal of geneXtendeR is to robustly link differentially enriched peaks with their respective genes, thereby aiding experimental follow-up and validation in designing primers for a set of prospective gene candidates during qPCR.