83 research outputs found

    Noncoding RNAs and Intranuclear Positioning in Monoallelic Gene Expression

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    Mammalian X inactivation, imprinting, and allelic exclusion are classic examples of monoallelic gene expression. Two emerging themes are thought to be critical for monoallelic expression: (1) noncoding, often antisense, transcription linked to differential chromatin marks on otherwise homologous alleles and (2) physical segregation of alleles to separate domains within the nucleus. Here, we highlight recent progress in identifying these phenomena as possible key regulatory mechanisms of monoallelic expression

    Quantized correlation coefficient for measuring reproducibility of ChIP-chip data

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    <p>Abstract</p> <p>Background</p> <p>Chromatin immunoprecipitation followed by microarray hybridization (ChIP-chip) is used to study protein-DNA interactions and histone modifications on a genome-scale. To ensure data quality, these experiments are usually performed in replicates, and a correlation coefficient between replicates is used often to assess reproducibility. However, the correlation coefficient can be misleading because it is affected not only by the reproducibility of the signal but also by the amount of binding signal present in the data.</p> <p>Results</p> <p>We develop the Quantized correlation coefficient (QCC) that is much less dependent on the amount of signal. This involves discretization of data into set of quantiles (quantization), a merging procedure to group the background probes, and recalculation of the Pearson correlation coefficient. This procedure reduces the influence of the background noise on the statistic, which then properly focuses more on the reproducibility of the signal. The performance of this procedure is tested in both simulated and real ChIP-chip data. For replicates with different levels of enrichment over background and coverage, we find that QCC reflects reproducibility more accurately and is more robust than the standard Pearson or Spearman correlation coefficients. The quantization and the merging procedure can also suggest a proper quantile threshold for separating signal from background for further analysis.</p> <p>Conclusions</p> <p>To measure reproducibility of ChIP-chip data correctly, a correlation coefficient that is robust to the amount of signal present should be used. QCC is one such measure. The QCC statistic can also be applied in a variety of other contexts for measuring reproducibility, including analysis of array CGH data for DNA copy number and gene expression data.</p

    A Genome-Wide Screen Identifies Genes That Affect Somatic Homolog Pairing in Drosophila

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    In Drosophila and other Dipterans, homologous chromosomes are in close contact in virtually all nuclei, a phenomenon known as somatic homolog pairing. Although homolog pairing has been recognized for over a century, relatively little is known about its regulation. We performed a genome-wide RNAi-based screen that monitored the X-specific localization of the male-specific lethal (MSL) complex, and we identified 59 candidate genes whose knockdown via RNAi causes a change in the pattern of MSL staining that is consistent with a disruption of X-chromosomal homolog pairing. Using DNA fluorescent in situ hybridization (FISH), we confirmed that knockdown of 17 of these genes has a dramatic effect on pairing of the 359 bp repeat at the base of the X. Furthermore, dsRNAs targeting Pr-set7, which encodes an H4K20 methyltransferase, cause a modest disruption in somatic homolog pairing. Consistent with our results in cultured cells, a classical mutation in one of the strongest candidate genes, pebble (pbl), causes a decrease in somatic homolog pairing in developing embryos. Interestingly, many of the genes identified by our screen have known roles in diverse cell-cycle events, suggesting an important link between somatic homolog pairing and the choreography of chromosomes during the cell cycle

    Impact of sequencing depth in ChIP-seq experiments

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    In a chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) experiment, an important consideration in experimental design is the minimum number of sequenced reads required to obtain statistically significant results. We present an extensive evaluation of the impact of sequencing depth on identification of enriched regions for key histone modifications (H3K4me3, H3K36me3, H3K27me3 and H3K9me2/me3) using deep-sequenced datasets in human and fly. We propose to define sufficient sequencing depth as the number of reads at which detected enrichment regions increase <1% for an additional million reads. Although the required depth depends on the nature of the mark and the state of the cell in each experiment, we observe that sufficient depth is often reached at <20 million reads for fly. For human, there are no clear saturation points for the examined datasets, but our analysis suggests 40–50 million reads as a practical minimum for most marks. We also devise a mathematical model to estimate the sufficient depth and total genomic coverage of a mark. Lastly, we find that the five algorithms tested do not agree well for broad enrichment profiles, especially at lower depths. Our findings suggest that sufficient sequencing depth and an appropriate peak-calling algorithm are essential for ensuring robustness of conclusions derived from ChIP-seq data

    The Male-Specific Lethal-One (Msl-1) Gene of Drosophila Melanogaster Encodes a Novel Protein That Associates with the X Chromosome in Males

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    male-specific lethal-one (msl-1) is one of four genes that are required for dosage compensation in Drosophila males. To determine the molecular basis of msl-1 regulation of dosage compensation, we have cloned the gene and characterized its products. The predicted msl-1 protein (MSL-1) has no significant similarity to proteins in the current data bases but contains an acidic N terminus characteristic of proteins involved in transcription and chromatin modeling. We present evidence that the msl-1 protein is associated with hundreds of sites along the length of the X chromosome in male, but not in female, nuclei. Our findings support the hypothesis that msl-1 plays a direct role in increasing the level of X-linked gene transcription in male nuclei

    Comprehensive analysis of the chromatin landscape in Drosophila melanogaster.

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    Chromatin is composed of DNA and a variety of modified histones and non-histone proteins, which have an impact on cell differentiation, gene regulation and other key cellular processes. Here we present a genome-wide chromatin landscape for Drosophila melanogaster based on eighteen histone modifications, summarized by nine prevalent combinatorial patterns. Integrative analysis with other data (non-histone chromatin proteins, DNase I hypersensitivity, GRO-Seq reads produced by engaged polymerase, short/long RNA products) reveals discrete characteristics of chromosomes, genes, regulatory elements and other functional domains. We find that active genes display distinct chromatin signatures that are correlated with disparate gene lengths, exon patterns, regulatory functions and genomic contexts. We also demonstrate a diversity of signatures among Polycomb targets that include a subset with paused polymerase. This systematic profiling and integrative analysis of chromatin signatures provides insights into how genomic elements are regulated, and will serve as a resource for future experimental investigations of genome structure and function
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