Chromosomal distribution of predicted target genes is influenced by length and GC content of 3' UTRs

The line chart of the z-scores of human chromosomal 3' UTR GC and length distribution (A) and the line chart of the combination of these z-scores compared to the human "Vari" target density z-scores of at least three microRNAs (B). The scattered area in (A) corresponds to a p-value higher than 0.05. The values in (B) are normalized by their standard deviation to focus on the relationship and not the values. Additionally, the Pearson correlation coefficient r is given. The plots show that the observed non-uniform distribution of the targets is probably caused by the non-uniform distribution of 3' UTR GC content and length over the chromosomes. For predicted murine targets a similar effect is observed, the corresponding Pearson correlation coefficient is 0.950. If we restrict the chromosomes included in the calculation to the chromosomes with target densities significantly different to a random distribution (p-value smaller than 0.01), we obtain even higher values (0.982 and 0.968 respectively).<p><b>Copyright information:</b></p><p>Taken from "Structural conservation versus functional divergence of maternally expressed microRNAs in the imprinting region"</p><p>http://www.biomedcentral.com/1471-2164/9/346</p><p>BMC Genomics 2008;9():346-346.</p><p>Published online 23 Jul 2008</p><p>PMCID:PMC2500034.</p><p></p

Correlation map of predicted human "Vari" target transcripts (A) and antitarget transcripts (B) with annotated expression data

The figure shows the correlation map (squared Pearson correlation) of the expression values in each tissue. The expression values of the targets (A) show a clear separation of the three brain tissues amygdala, cerebellum and hypothalamus, from the heterogeneous group of other tissues. This separation is not observed for the antitargets.<p><b>Copyright information:</b></p><p>Taken from "Structural conservation versus functional divergence of maternally expressed microRNAs in the imprinting region"</p><p>http://www.biomedcentral.com/1471-2164/9/346</p><p>BMC Genomics 2008;9():346-346.</p><p>Published online 23 Jul 2008</p><p>PMCID:PMC2500034.</p><p></p

Gene expression of predicted target genes compared to all transcripts with annotated expression data

The figure shows a box plot of the expression values of predicted human "Vari" targets of at least three microRNAs within the region and randomly selected microRNAs of the reference set compared to the background (the expression values of all transcripts with annotated expression data).<p><b>Copyright information:</b></p><p>Taken from "Structural conservation versus functional divergence of maternally expressed microRNAs in the imprinting region"</p><p>http://www.biomedcentral.com/1471-2164/9/346</p><p>BMC Genomics 2008;9():346-346.</p><p>Published online 23 Jul 2008</p><p>PMCID:PMC2500034.</p><p></p

ChIPmentation: fast, robust, low-input ChIP-seq for histones and transcription factors

<p>ChIP-seq combines chromatin immunoprecipitation with sequencing in order to<br>measure genome-wide distributions of chromatin proteins. Despite recent efforts to<br>optimize and improve ChIP-seq, current protocols are still quite tedious, time-<br>consuming, and costly. The development of a hyperactive Tn5 transposase that<br>enables simultaneous DNA fragmentation and adapter tagging (“tagmentation”)<br>presents an opportunity for faster and cheaper library preparation (Ref. 1). Here, we<br>demonstrate tagmentation of immunoprecipitated chromatin in a robust one-step<br>reaction performed directly on bead-bound chromatin (Schmidl et al. 2015 Nature<br>Methods, Ref. 2). This method – which we call ChIPmentation – provides a fast,<br>cheap, low-input ChIP-seq workflow and yields excellent results for both histone<br>marks and transcription factors. Using ChIPmentation we observed footprints that<br>resemble those in ATAC-seq data. Moreover, histone ChIPmentation data shows a<br>striking periodicity indicative of nucleosome stability, and an ordered signal around<br>nucleosome dyads that might be used for accurate nucleosome positioning.</p> <p> </p

Predicted DNA Structure Differs in the Neighborhood of Methylated CpG Islands Compared with Their Unmethylated Counterparts

<p>The diagram on the left shows boxplots of the predicted DNA rise distribution over the CpG island and the ten sequence windows from −20 kb to 20 kb surrounding the CpG island (averaged over all 132 CpG islands in the Chromosome 21 dataset). Green bars (left) correspond to methylated CpG islands, red bars (right) to unmethylated CpG islands. The diagram on the right shows similar information for the predicted DNA twist.</p

Large-scale chromatin profiling uncovers heterogeneity of molecular phenotypes and gene regulatory networks of chronic lymphocytic leukemia

Chronic lymphocytic leukaemia (CLL) is characterized by substantial clinical heterogeneity, despite relatively few genetic alterations. To provide a basis for studying epigenome deregulation in CLL, here we present genome-wide chromatin accessibility maps for 88 CLL samples from 55 patients measured by the ATAC-seq assay. We also performed ChIPmentation and RNA-seq profiling for ten representative samples. Based on the resulting data set, we devised and applied a bioinformatic method that links chromatin profiles to clinical annotations. Our analysis identified sample-specific variation on top of a shared core of CLL regulatory regions. <i>IGHV</i> mutation status—which distinguishes the two major subtypes of CLL—was accurately predicted by the chromatin profiles and gene regulatory networks inferred for <i>IGHV</i>-mutated versus <i>IGHV</i>-unmutated samples identified characteristic differences between these two disease subtypes. In summary, we discovered widespread heterogeneity in the chromatin landscape of CLL, established a community resource for studying epigenome deregulation in leukaemia and demonstrated the feasibility of large-scale chromatin accessibility mapping in cancer cohorts and clinical research

Correlations of Melting Temperature (Tm) with G + C Content

<p>The correlation coefficients between GC content and Tm are plotted as a function of window sizes. For each chromosome, excluding the segments which contain unknown bases (N's), the correlation coefficient was calculated from all pairs of GC content and average Tm over all nonoverlapping segments of a given window size. Across the chromosomes, the average correlation coefficients and SDs were calculated for each window size. The figure shows the average correlations with SDs (error bars) for window sizes from 10 bp to 1 Mbp for the human chromosomes (red) and the randomized chromosomes (blue).</p

Scatter Plot of Melting Temperature versus GC Content of Flat Melting Segments

<p>Using Chromosome 21, the relationship between local GC content and melting temperature was examined for all flat segments of 50 bps. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030093#pcbi-0030093-g004" target="_blank">Figure 4</a> shows the scatter plot of melting temperature versus GC content. Each data point in this figure represents a 50-bp flat segment. The red dots represent those segments that have higher melting temperatures (Tm) in its neighboring regions at both sides (denoted as category I). The blue dots represent those that have lower Tm in its neighbors (denoted as category III). And, the green dots represent those that have lower Tm in one side neighbor and higher Tm in another (denoted as category II).</p

EpiGRAPH-Generated Diagrams Comparing Genomic Regions with Distinct Melting Profiles

<div><p>Displays boxplots comparing two genomic features between regions of high and low melting temperature (A) and <i>flat</i> and <i>nonflat</i> melting segments (B). Standard boxplots are drawn for the region itself and for ten windows surrounding the region, from −20 Kbp to +20 Kbp <i>(x</i>-axis), in order to capture neighborhood effects. The <i>y</i>-axis shows averages and distribution of the analyzed genomic feature. For each window, two boxplots are drawn, one for each class of melting profiles.</p><p>(A) Regions are characterized by the extreme melting temperatures observed throughout the human genome. “Class 0” comprises 20 regions having low melting temperatures (below 50 °C in all cases), while “class 1” comprises 20 cases having high melting temperature (above 90 °C in all cases). Comparison with the average solvent-accessible surface area of the DNA (as predicted for each base pair using sequence trimers for which solvent accessibility has been established experimentally by the hydroxyl radical method [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030093#pcbi-0030093-b064" target="_blank">64</a>]) shows that regions of high melting temperature exhibit substantially higher values than regions of low melting temperature. This is true not only for the region itself (center boxplot), but to a lesser extent also for its sequence neighborhood.</p><p>(B) Regions are characterized by a <i>flat</i>/<i>nonflat</i> segmentation algorithm of the melting profile. “Class 0” contains 50 flat segments having an end-to-end step height of ±0.11 °C or less, while “class 1” contains 50 nonflat segments defined as having an end-to-end step height of ±6 °C or more. All segments were taken from Chromosome 21, exhibit an equal melting temperature of 68 °C and a segment length of 19 or 20 bps. Comparison with the average length of Alu repeat overlap per 1,000 base pairs (as identified by RepeatMasker) shows that flat regions are typically free of Alu repeats, while nonflat regions frequently exhibit substantial overlap with Alu repeats.</p></div

Loop Entropy Factor Estimation

<p>The exact loop entropy factor for <i>σ</i> = 3.5 · 10⁻⁵, <i>α</i> = 1.75, and <i>d</i> = 0 is plotted (red) as a function of loop size, together with two Fixman–Freire approximations: a 10-exponentials approximation (blue), which is valid up to loop size about 10⁴, and a 21-exponentials approximation (green), which is valid up to loop size about 10⁸.</p