Using R enviroments for annotation.

S4 generic functions needed by many Bioconductor packages.

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 SummarizedExperiment container contains one or more assays, each represented by a matrix-like object of numeric or other mode. The rows typically represent genomic ranges of interest and the columns represent samples.

Some basic functions for filtering genes

Genome wide annotation for Human, primarily based on mapping using Entrez Gene identifiers.

In recent years a wealth of biological data has become available in public data repositories. Easy access to these valuable data resources and firm integration with data analysis is needed for comprehensive bioinformatics data analysis.

This package provides classes and methods to support Gene Set Enrichment Analysis (GSEA).

Genome wide annotation for Mouse, primarily based on mapping using Entrez Gene identifiers.

Genome wide annotation for Yeast, primarily based on mapping using ORF identifiers from SGD.

Functions for plotting genomic data