Inter- and intra-combinatorial regulation by transcription factors and microRNAs-4

<p><b>Copyright information:</b></p><p>Taken from "Inter- and intra-combinatorial regulation by transcription factors and microRNAs"</p><p>http://www.biomedcentral.com/1471-2164/8/396</p><p>BMC Genomics 2007;8():396-396.</p><p>Published online 30 Oct 2007</p><p>PMCID:PMC2206040.</p><p></p>gulators. Figure 1a measures this association by a Fisher's Exact Test p-value (dark pixels represent lower p-values or alternatively a higher value of -log). Figure 1b measures the association by the Bayesian probability Pr{logOR>0.6} (Here a dark pixel means a high probability). The TFs and miRNAs are ordered so that the number of targets of each regulator increases as one moves across the Figure from left to right on the horizontal axis, and also up the vertical axis. Both Figures 1a and 1b illustrate that while TF-TF and miRNA-miRNA associations are common, TF-miRNA interactions are less so. The TF-miRNA rectangles of Fig 1a demonstrate that the most significant associations (as found by Fisher's Exact Test) tend to involve TF-miRNA pairs with the TF having a large number of targets. In the corresponding areas of Figure 1b, we see a more uniform sprinkling of dark points, indicating that the Bayesian approach is less sensitive to sample size effects. The stripes on the TF-miRNA rectangles of both figures demonstrate that certain TFs are associated with almost all the miRNAs – while, surprisingly, many TFs with a similar number of targets seem to not be significantly associated with any miRNA

Inter- and intra-combinatorial regulation by transcription factors and microRNAs-2

<p><b>Copyright information:</b></p><p>Taken from "Inter- and intra-combinatorial regulation by transcription factors and microRNAs"</p><p>http://www.biomedcentral.com/1471-2164/8/396</p><p>BMC Genomics 2007;8():396-396.</p><p>Published online 30 Oct 2007</p><p>PMCID:PMC2206040.</p><p></p>other regulator denoted by B, and both regulators A and B regulate a common target C

Inter- and intra-combinatorial regulation by transcription factors and microRNAs-5

<p><b>Copyright information:</b></p><p>Taken from "Inter- and intra-combinatorial regulation by transcription factors and microRNAs"</p><p>http://www.biomedcentral.com/1471-2164/8/396</p><p>BMC Genomics 2007;8():396-396.</p><p>Published online 30 Oct 2007</p><p>PMCID:PMC2206040.</p><p></p>n score. The 2D distributions demonstrate how the relationship between Fisher's Exact Test and our Bayesian score depends on the logOR threshold we use. Each sub-plot represents a different threshold value ranging from 0 to 1 – as indicated by each subtitle. For a particular threshold value, a pixel on the plot represents the local density of miRNA-TF pairs having the corresponding p-value (from the y-axis) and Bayesian Probability (from the x-axis). Here darker shaded regions indicate higher densities. For a 0 threshold, the Bayesian Test and Fisher's Test agree exactly. As we increase the threshold, we see fewer and fewer TF-miRNA pairs that are highly associated as measured by both ranking criteria (pairs whose measures approach 1 at the x-axis and 0 at the y-axis). The higher the threshold, the more emphasis we are placing on the size of the TF-miRNA association (as measured by a log Odds Ratio) and the less emphasis on sample size. Note that a very high Bayesian probability implies that the associated p-value will be small, no matter what threshold we use

Inter- and intra-combinatorial regulation by transcription factors and microRNAs-3

<p><b>Copyright information:</b></p><p>Taken from "Inter- and intra-combinatorial regulation by transcription factors and microRNAs"</p><p>http://www.biomedcentral.com/1471-2164/8/396</p><p>BMC Genomics 2007;8():396-396.</p><p>Published online 30 Oct 2007</p><p>PMCID:PMC2206040.</p><p></p> association. The histogram displays the fraction of FFLs that result in each bin, when grouping miRNA/TF/GO triplets according to their log p-value of joint-association. To generate this histogram, we used a slightly restricted set of biological-process GO terms, such that each group includes at least one gene that is a predicted target of a TF and a miRNA. The plot suggests that when a miRNA/TF/GO triplet is significantly associated, the corresponding miRNA and TF are more likely to form a feed forward loop

Inter- and intra-combinatorial regulation by transcription factors and microRNAs-0

<p><b>Copyright information:</b></p><p>Taken from "Inter- and intra-combinatorial regulation by transcription factors and microRNAs"</p><p>http://www.biomedcentral.com/1471-2164/8/396</p><p>BMC Genomics 2007;8():396-396.</p><p>Published online 30 Oct 2007</p><p>PMCID:PMC2206040.</p><p></p>gulators. Figure 1a measures this association by a Fisher's Exact Test p-value (dark pixels represent lower p-values or alternatively a higher value of -log). Figure 1b measures the association by the Bayesian probability Pr{logOR>0.6} (Here a dark pixel means a high probability). The TFs and miRNAs are ordered so that the number of targets of each regulator increases as one moves across the Figure from left to right on the horizontal axis, and also up the vertical axis. Both Figures 1a and 1b illustrate that while TF-TF and miRNA-miRNA associations are common, TF-miRNA interactions are less so. The TF-miRNA rectangles of Fig 1a demonstrate that the most significant associations (as found by Fisher's Exact Test) tend to involve TF-miRNA pairs with the TF having a large number of targets. In the corresponding areas of Figure 1b, we see a more uniform sprinkling of dark points, indicating that the Bayesian approach is less sensitive to sample size effects. The stripes on the TF-miRNA rectangles of both figures demonstrate that certain TFs are associated with almost all the miRNAs – while, surprisingly, many TFs with a similar number of targets seem to not be significantly associated with any miRNA

Additional file 1: Table S1. of Risk factors for relapse or persistence of bacteraemia caused by Enterobacter spp.: a case–control study

Clinical details of relapsed Enterobacter bacteraemia cases with emergence of antibiotic resistance. (DOCX 17 kb

Additional file 5: Figure S4a-c. of Differential impact of smoking on mortality and kidney transplantation among adult Men and Women undergoing dialysis

Age-specific transplantation rates for Coronary disease, Peripheral Arterial Disease and Stroke for Women by smoking status. P–value for differences between smokers and non-smokers **P < 0.01, *P < 0.05. (ZIP 582 kb

Additional file 1: Table S1. of Differential impact of smoking on mortality and kidney transplantation among adult Men and Women undergoing dialysis

Factors associated with the presence of Smoking at dialysis onset among new Dialysis Patients1. Multivariable analyses of factors associated with smoking at dialysis initiation. Model included demographic, clinical, biochemical, lifestyle, employment and functional status indicators measured at dialysis onset. C-statistic 78 %, 2 95 % confidence intervals. (DOC 59 kb

Abundantly expressed, novel <i>IL23R</i> isoform.

<p>A. Sequence reads were mapped to the <i>IL23R</i> gene region in Th17-enriched and in vitro differentiated Th1 cells using Tophat v1.3.3. The intron 6 region was highly covered in both the Th17-enriched and Th1 cell subsets. The scale on the y-axis represents coverage (average number of reads that cover a particular base). B. Zoom in picture of extended coverage on exon 6 and intron 6. Blue and red bars correspond to sense- and anti-sense reads, respectively. The expanded inset demonstrates sequence reads mapping to an extended exon 6 resulting in a stop signal 9 codons downstream with contiguous 3′UTR sequence; in addition an independent transcript maps immediately centromeric to this. C. 3′ RACE (rapid amplification of cDNA ends) was performed, and 2000 and 700 base pair fragments were sequenced, confirming the exon contents designated. The 700 base pair transcript terminating in the intron 6 region would encode for a transcript terminating prior to the transmembrane domain in exon 9.</p

Differential gene expression between CD4+ T cell subsets results from distinct molecular mechanisms.

<p>Shown are the fractions of methylation at conserved CpG promoter sites estimated by mass spectrometry (N = 5) for A. <i>IL23R</i>, B. <i>IL12RB2</i>, C. <i>IL17A</i> and D. <i>CCL20</i> promoters. Paired t-tests were used to test for differential methylation fractions between naïve vs. Th1 and Th17-negative vs. Th17-enriched were estimated by paired t-test; *P<0.05, **P<0.01, ***P<0.001. E. Average expression estimates from four individulas in both RNASeq and microarray show that Th17-specific transcripts <i>IL17A</i> and <i>CCL20</i> have nearly zero expression in Th17-negative cells, corresponding to high promoter methylation levels (C–D). Gene expression was measured by FPKM (fragments of RNA per Kilobase of exon per Million fragments mapped) for RNASeq and log2 RMA normalized intensity for microarray.</p