ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.
We use first-principles density functional theory within the local density approximation to ascertain the ground state structure of real and theoretical compounds with the formula Rb, Cs, Ca, Sr, Ba, Tl, Sn, Pb, and Bi; and B = Sc, Y, Ti, Zr, V, and Nb) under the constraint that B must have a d 0 electronic configuration. Our findings indicate that none of these AB combinations prefer a perovskite ground state with corner-sharing BS 6 octahedra, but that they prefer phases with either edge-or face-sharing motifs. Further, a simple two-dimensional structure field map created from A and B ionic radii provides a neat demarcation between combinations preferring face-sharing versus edge-sharing phases for most of these combinations. We then show that by modifying the common Goldschmidt tolerance factor with a multiplicative term based on the electronegativity difference between A and S, the demarcation between predicted edge-sharing and face-sharing ground state phases is enhanced. We also demonstrate that, by calculating the free energy contribution of phonons, some of these compounds may assume multiple phases as synthesis temperatures are altered, or as ambient temperatures rise or fall.
We investigate the effect of ferroelectric polarization direction on the geometric properties of Pd deposited on the positive and negative surfaces of LiNbO(3) (0001). We predict preferred geometries and diffusion properties of small Pd clusters using density functional theory, and use these calculations as the basis for kinetic Monte Carlo simulations of Pd deposition on a larger scale. Our results show that on the positive surface, Pd atoms favor a clustered configuration, while on the negative surface, Pd atoms are adsorbed in a more dispersed pattern due to suppression of diffusion and agglomeration. This suggests that the effect of LiNbO(3) polarization direction on the catalytic activity of Pd [J. Phys. Chem. 88, 1148 (1984)] is due, at least in part, to differences in adsorption geometry. Further investigations using these methods can aid the search for catalysts whose activities switch reversibly with the polarization of their ferroelectric substrates.
Using density functional theory (DFT) within the local density approximation (LDA), we calculate the physical and electronic properties of PbTiO 3 (PTO) and a series of hypothetical compounds PbTiO 3-x S x x = 0.2, 0.25, 0.33, 0.5, 1, 2, and 3 arranged in the corner-sharing cubic perovskite structure. We determine that replacing the apical oxygen atom in the PTO tetragonal unit cell with a sulfur atom reduces the x = 0 LDA calculated band gap of 1.47 eV to 0.43 -0.67 eV for x = 0.2 -1 and increases the polarization. PBE0 and GW methods predict that the compositions x = 0.2-2 will have band gaps in the visible range. For all values of x < 2, the oxysulfide perovskite retains the tetragonal phase of PbTiO 3 , and the a lattice parameter remains within 2.5% of the oxide. Thermodynamic analysis indicates that chemical routes using high temperature gas, such as H 2 S and CS 2 , can be used to substitute O for S in PTO for the compositions x = 0.2 -0.5.
ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNAbinding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a wellknown oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.
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