Correlations between CRM length and GC content (column 1) and degree of sequence conservation with seven species

Values given are the Spearman correlation coefficients. Black bars indicate CRM sequences, gray bars indicate size-matched randomly drawn non-coding sequence. Asterisks signify that the correlation is statistically significant (Bonferroni-adjusted < 0.05). , ; , ; , ; , ; , ; , ; , .<p><b>Copyright information:</b></p><p>Taken from "Large-scale analysis of transcriptional -regulatory modules reveals both common features and distinct subclasses"</p><p>http://genomebiology.com/2007/8/6/R101</p><p>Genome Biology 2007;8(6):R101-R101.</p><p>Published online 5 Jun 2007</p><p>PMCID:PMC2394749.</p><p></p

GC content of the REDfly analysis CRMs as well as coding, intronic, and intergenic sequences

<p><b>Copyright information:</b></p><p>Taken from "Large-scale analysis of transcriptional -regulatory modules reveals both common features and distinct subclasses"</p><p>http://genomebiology.com/2007/8/6/R101</p><p>Genome Biology 2007;8(6):R101-R101.</p><p>Published online 5 Jun 2007</p><p>PMCID:PMC2394749.</p><p></p

[(C₂H₅)₄N][U₂O₄(HCOO)₅], an Ammonium Uranyl Formate Framework Showing Para- to Ferro-Electric Transition: Synthesis, Structures, and Properties

We report an ammonium uranyl formate framework of formula [(C₂H₅)₄N]­[U₂O₄(HCOO)₅], prepared by using components of tetraethylammonium, uranyl, and formate. The compound possesses a layered structure of anionic uranyl–formate wavy sheets and intercalated (C₂H₅)₄N⁺ cations. The sheet consists of pentagonal bipyramidal uranyl cations connected by equatorial <i>anti</i>–<i>anti</i> and <i>anti</i>–<i>syn</i> HCOO^– bridges, and it has a topology of 3³·4³·5⁴ made of edge-sharing square and triangle grids. The high-temperature (HT) phase belongs to the chiral but nonpolar tetragonal space group <i>P</i>4̅2₁<i>m</i>. In the structure, one HCOO^– is 2-fold disordered, showing a flip motion between the two <i>anti</i>–<i>syn</i> orientations. On cooling, this flip motion slowed and finally froze, leading to a phase transition at ∼200 K. The low-temperature (LT) structure is monoclinic and polar in space group <i>P</i>2₁; the cations shift, and the layers slide. Especially, the concerted and net shifts of the ammonium cations toward the −<i>b</i> direction, with respect to the anionic sheets, result in an estimated spontaneous polarization of 0.86 μC cm^–2 in LT. The phase transition is thus para- to ferro-electric, in Aizu notation 4̅2<i>mF</i>2, accompanied by significant, anisotropic dielectric anomalies, with a quite significant thermal hysteresis. Variable-temperature luminescent spectroscopy and differential scanning calorimetry confirmed the transition and provided further information. The structure–property relationship is established

Leveraging Prior Information to Detect Causal Variants via Multi-Variant Regression

<div><p>Although many methods are available to test sequence variants for association with complex diseases and traits, methods that specifically seek to identify causal variants are less developed. Here we develop and evaluate a Bayesian hierarchical regression method that incorporates prior information on the likelihood of variant causality through weighting of variant effects. By simulation studies using both simulated and real sequence variants, we compared a standard single variant test for analyzing variant-disease association with the proposed method using different weighting schemes. We found that by leveraging linkage disequilibrium of variants with known GWAS signals and sequence conservation (phastCons), the proposed method provides a powerful approach for detecting causal variants while controlling false positives.</p></div

Causal variant detection in the exome sequencing data analysis.

<p>(A): <i>NOD2</i> data; (B): <i>ITPA</i> data. The two top panels are from one replicate of the simulation. For single variant test, SNP effect size was represented by −log10 of <i>p</i> value from logistic regression model; for Bayesian liability model, it was represented by the standardized effect estimated at each SNP. Red dots indicate two causal variants (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003093#pcbi-1003093-t001" target="_blank">Table 1</a> for more information). Blue vertical bars show values of SNP weights (<i>r</i> × phastCons). The horizontal dashed line indicates effect size at the significance threshold (permutation <i>p</i> value = 0.01). The bottom panel shows proportion of simulations where a variant was detected (i.e., significant at permutation <i>p</i> = 0.01 level). Causal variants are marked in red color.</p

Workflow of the simulation study.

<p>Before carrying out these steps, a large pool of haplotypes (<i>n</i> = 15,000) was simulated. Given GRR and MAF of causal variants, cases and controls were simulated by randomly choosing pairs of haplotypes and calculating the risk of each individual to probabilistically assign phenotype.</p

Supplementary document for The effects of biomass burning on CO, C2H6, C2H2, HCN and H2CO during long-term FTIR measurements in Hefei, China - 6847918.pdf

The typical spectral fitting results of the target specie

Power of different methods in the simulation analysis.

<p>Results were based on 200 replicates. In each replicate, 500 cases and 500 controls were used to identify three causal variants from a total of ∼1000 variants, with each method being evaluated. We assumed causal variants have a constant GRR of 3 and render disease susceptibility under a dominant model.</p>1<p>The proportion of replicate simulations in which at least one causal variant was detected.</p>2<p>The proportion of replicate simulations in which at least two causal variants were detected.</p>3<p>The proportion of replicate simulations in which all three causal variants were detected.</p>4<p>The average power of three causal variants.</p

<i>NOD2</i> and <i>ITPA</i> causal variants in the exome sequencing data.

<p>The composite phastCons is weighted sum of vertebrate cons, mammal cons and primate cons (shown in parenthesis), with weight 1/2, 1/3 and 1/6, respectively.</p

Distributions of three informative weights (<i>r</i>, phastCons and <i>r</i>×phastCons) for causal variants and non-causal variants on the causal and null chromosomes in the simulation study.

<p>In each MAF range, weights were collected from 200 replicates, and weights in each replicate were scaled by dividing each by the maximal value so as to bound final weight between 0 and 1.</p