BACKGROUND: Functional genomic studies involving high-throughput sequencing and tiling array applications, such as ChIP-seq and ChIP-chip, generate large numbers of experimentally-derived signal peaks across the genome under study. In analyzing these loci to determine their potential regulatory functions, areas of signal enrichment must be considered relative to proximal genes and regulatory elements annotated throughout the target genome Regions of chromatin association by transcriptional regulators should be distinguished as individual binding sites in order to enhance downstream analyses, such as the identification of known and novel consensus motifs. RESULTS: PeakAnalyzer is a set of high-performance utilities for the automated processing of experimentally-derived peak regions and annotation of genomic loci. The programs can accurately subdivide multimodal regions of signal enrichment into distinct subpeaks corresponding to binding sites or chromatin modifications, retrieve genomic sequences encompassing the computed subpeak summits, and identify positional features of interest such as intersection with exon/intron gene components, proximity to up- or downstream transcriptional start sites and cis-regulatory elements. The software can be configured to run either as a pipeline component for high-throughput analyses, or as a cross-platform desktop application with an intuitive user interface. CONCLUSIONS: PeakAnalyzer comprises a number of utilities essential for ChIP-seq and ChIP-chip data analysis. High-performance implementations are provided for Unix pipeline integration along with a GUI version for interactive use. Source code in C++ and Java is provided, as are native binaries for Linux, Mac OS X and Windows systems.

MOTIVATION: Quantitative real-time polymerase chain reaction (qPCR) is routinely used for RNA expression profiling, validation of microarray hybridization data and clinical diagnostic assays. Although numerous statistical tools are available in the public domain for the analysis of microarray experiments, this is not the case for qPCR. Proprietary software is typically provided by instrument manufacturers, but these solutions are not amenable to the tandem analysis of multiple assays. This is problematic when an experiment involves more than a simple comparison between a control and treatment sample, or when many qPCR datasets are to be analyzed in a high-throughput facility. RESULTS: We have developed HTqPCR, a package for the R statistical computing environment, to enable the processing and analysis of qPCR data across multiple conditions and replicates. AVAILABILITY: HTqPCR and user documentation can be obtained through Bioconductor or at http://www.ebi.ac.uk/bertone/software. CONTACT: [email protected]