NuRD-mediated deacetylation of H3K27 facilitates recruitment of Polycomb Repressive Complex 2 to direct gene repression

The NURD and Polycomb complexes PRC1 and PRC2 have been implicated in stem cell differentiation although their molecular roles are unclear. This study identifies a common set of genes that are sequentially regulated by these complexes, the NURD complex deacetylates H3K27 as a prerequisite for subsequent methylation by PRC2

HTqPCR- high-throughput qPCR analysis in R and

The package HTqPCR is designed for the analysis of cycle threshold (Ct) values from quantitative real-time PCR data. The main areas of functionality comprise data import, quality assessment, normalisation, data visualisation, and testing for statistical significance in Ct values between different features (genes, miRNAs)

PeakAnalyzer: Genome-wide annotation of chromatin binding and modification loci

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <it>cis</it>-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.</p> <p>Conclusions</p> <p>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.</p

Sample processing obscures cancer-specific alterations in leukemic transcriptomes

Substantial effort is currently devoted to identifying cancer-associated alterations using genomics. Here, we show that standard blood collection procedures rapidly change the transcriptional and posttranscriptional landscapes of hematopoietic cells, resulting in biased activation of specific biological pathways; up-regulation of pseudogenes, antisense RNAs, and unannotated coding isoforms; and RNA surveillance inhibition. Affected genes include common mutational targets and thousands of other genes participating in processes such as chromatin modification, RNA splicing, T-and B-cell activation, and NF-κB signaling. The majority of published leukemic transcriptomes exhibit signals of this incubation-induced dysregulation, explaining up to 40% of differences in gene expression and alternative splicing between leukemias and reference normal transcriptomes. The effects of sample processing are particularly evident in pan-cancer analyses. We provide biomarkers that detect prolonged incubation of individual samples and show that keeping blood on ice markedly reduces changes to the transcriptome. In addition to highlighting the potentially confounding effects of technical artifacts in cancer genomics data, our study emphasizes the need to survey the diversity of normal as well as neoplastic cells when characterizing tumors. leukemia | RNA splicing | nonsense-mediated decay | batch effect