Identifying functional relationships within sets of co-expressed genes by combining upstream regulatory motif analysis and gene expression information

Existing clustering approaches for microarray data do not adequately differentiate between subsets of co-expressed genes. We devised a novel approach that integrates expression and sequence data in order to generate functionally coherent and biologically meaningful subclusters of genes. Specifically, the approach clusters co-expressed genes on the basis of similar content and distributions of predicted statistically significant sequence motifs in their upstream regions

Electronic Structures of Group 9 Metallocorroles with Axial Ammines

The electronic structures of metallocorroles (tpfc)M(NH_3)_2 and (tfc)M(NH_3)_2 (tpfc is the trianion of 5,10,15-(tris)pentafluorophenylcorrole, tfc is the trianion of 5,10,15-trifluorocorrole, and M = Co, Rh, Ir) have been computed using first principles quantum mechanics [B3LYP flavor of Density Functional Theory (DFT) with Poisson−Boltzmann continuum solvation]. The geometry was optimized for both the neutral systems (formal M^(III) oxidation state) and the one-electron oxidized systems (formally M^(IV)). As expected, the M^(III) systems have a closed shell d^6 configuration; for all three metals, the one-electron oxidation was calculated to occur from a ligand-based orbital (highest occupied molecular orbital (HOMO) of B_1 symmetry). The ground state of the formal M^(IV) system has M^(III)-Cπ character, indicating that the metal remains d^6, with the hole in the corrole π system. As a result the calculated M^(IV/III) reduction potentials are quite similar (0.64, 0.67, and 0.56 V vs SCE for M = Ir, Rh and Co, respectively), whereas the differences would have been large for purely metal-based oxidations. Vertically excited states with substantial metal character are well separated from the ground state in one-electron-oxidized cobalt (0.27 eV) and rhodium (0.24 eV) corroles, but become closer in energy in the iridium (0.15 eV) analogues. The exact splittings depend on the chosen functional and basis set combination and vary by ~0.1 eV

The Isolation of Ecdysterone Inducible Genes by Hybridization Subtraction Chromatography

We have developed a procedure for selectively enriching a mRNA population for inducible sequences. Other than the induced mRNA species, the population of mRNA in control cells is approximately the same as the mRNA population in induced cells. Cytoplasmic mRNA from control cells is bound to oligo (dT)-cellulose and used as a template for reverse transcriptase, the oligo (dT) serving as a primer. After removing the template mRNAs, the cDNA-cellulose column is used to hybridize a population of mRNAs from induced cells. The non-hybridized poly A + RNAs are greatly enriched in the inducible sequences. We have used this technique of hybridization subtraction chromotography to select a mRNA population enriched for the mRNAs inducible by ecdysterone in Schneider\u27s Line 2 Drosophila cells. This population of RNAs was used to screen a recombinant library. Preliminary results indicate that approximately 10% of the RNA in the probe population represents ecdysterone inducible sequences. Methods are described for optimizing the cDNA synthesis reaction (we obtain ≥ 30% efficiency) and hybridizing RNA to the cDNA-cellulose resin. This method can be used to select induced mRNAs regardless of the way in which the induction is brought about

A Rigorous Derivation of the Gross-Pitaevskii Energy Functional for a Two-Dimensional Bose Gas

We consider the ground state properties of an inhomogeneous two-dimensional Bose gas with a repulsive, short range pair interaction and an external confining potential. In the limit when the particle number $N$ is large but $\bar\rho a^2$ is small, where $\bar\rho$ is the average particle density and $a$ the scattering length, the ground state energy and density are rigorously shown to be given to leading order by a Gross-Pitaevskii (GP) energy functional with a coupling constant $g\sim 1/|\ln(\bar\rho a^2)|$. In contrast to the 3D case the coupling constant depends on $N$ through the mean density. The GP energy per particle depends only on $Ng$. In 2D this parameter is typically so large that the gradient term in the GP energy functional is negligible and the simpler description by a Thomas-Fermi type functional is adequate.Comment: 14 pages, no figures, latex 2e. References, some clarifications and an appendix added. To appear in Commun. Math. Phy

A Novel Ensemble Learning Method for de Novo Computational Identification of DNA Binding Sites

Despite the diversity of motif representations and search algorithms, the de novo computational identification of transcription factor binding sites remains constrained by the limited accuracy of existing algorithms and the need for user-specified input parameters that describe the motif being sought.ResultsWe present a novel ensemble learning method, SCOPE, that is based on the assumption that transcription factor binding sites belong to one of three broad classes of motifs: non-degenerate, degenerate and gapped motifs. SCOPE employs a unified scoring metric to combine the results from three motif finding algorithms each aimed at the discovery of one of these classes of motifs. We found that SCOPE\u27s performance on 78 experimentally characterized regulons from four species was a substantial and statistically significant improvement over that of its component algorithms. SCOPE outperformed a broad range of existing motif discovery algorithms on the same dataset by a statistically significant margin