Correlating overrepresented upstream motifs to gene expression: a computational approach to regulatory element discovery in eukaryotes

Gene regulation in eukaryotes is mainly effected through transcription factors binding to rather short recognition motifs generally located upstream of the coding region. We present a novel computational method to identify regulatory elements in the upstream region of eukaryotic genes. The genes are grouped in sets sharing an overrepresented short motif in their upstream sequence. For each set, the average expression level from a microarray experiment is determined: If this level is significantly higher or lower than the average taken over the whole genome, then the overerpresented motif shared by the genes in the set is likely to play a role in their regulation. The method was tested by applying it to the genome of Saccharomyces cerevisiae, using the publicly available results of a DNA microarray experiment, in which expression levels for virtually all the genes were measured during the diauxic shift from fermentation to respiration. Several known motifs were correctly identified, and a new candidate regulatory sequence was determined.Comment: Published version available from http://www.biomedcentral.com/1471-2105/3/

Modelling element distributions in the atmospheres of magnetic Ap stars

In recent papers convincing evidence has been presented for chemical stratification in Ap star atmospheres, and surface abundance maps have been shown to correlate with the magnetic field direction. Radiatively driven diffusion in magnetic fields is among the processes responsible for these inhomogeneities. Here we explore the hypothesis that equilibrium stratifications can, in a number of cases, explain the observed abundance maps and vertical distributions of the various elements. The investigation of equilibrium stratifications in stellar atmospheres with temperatures from 8500K to 12000K and fields up to 10 kG reveals considerable variations in the vertical distribution of the 5 elements studied (Mg, Si, Ca, Ti, Fe), often with zones of large over- or under-abundances and with indications of other competing processes (such as mass loss). Horizontal magnetic fields can be very efficient in helping the accumulation of elements in higher layers. A comparison between our calculations and the vertical abundance profiles and surface maps derived by magnetic Doppler imaging reveals that equilibrium stratifications are in a number of cases consistent with the main trends inferred from observed spectra. However, it is not clear whether such equilibrium solutions will ever be reached during the evolution of an Ap star.Comment: 7 pages, 6 figures, the paper will be published in Astronomy & Astrophysics, on November 200

Theoretical He I Emissivities in the Case B Approximation

We calculate the He I case B recombination cascade spectrum using improved radiative and collisional data. We present new emissivities over a range of electron temperatures and densities. The differences between our results and the current standard are large enough to have a significant effect not only on the interpretation of observed spectra of a wide variety of objects but also on determinations of the primordial helium abundance.Comment: Accepted to ApJ

Radiative recombination data for modeling dynamic finite-density plasmas

We have calculated partial final-state resolved radiative recombination (RR) rate coefficients from the initial ground and metastable levels of all elements up to and including Zn, plus Kr, Mo, and Xe, for all isoelectronic sequences up to Na-like forming Mg-like. The data are archived according to the Atomic Data and Analysis Structure (ADAS) data class adf48, which spans a temperature range of z2(101-107) K, where z is the initial ion charge. Fits to total rate coefficients have been determined, for both the ground and metastable levels, and those for the ground are presented here. Comparison is made both with previous RR rate coefficients and with (background) R-matrix photoionization cross sections. This RR database complements a dielectronic recombination (DR) database already produced, and both are being used to produce updated ionization balances for both (electron) collisionally ionized and photoionized plasmas

Disease-gene discovery by integration of 3D gene expression and transcription factor binding affinities

Abstract Motivation: The computational evaluation of candidate genes for hereditary disorders is a non-trivial task. Several excellent methods for disease-gene prediction have been developed in the past 2 decades, exploiting widely differing data sources to infer disease-relevant functional relationships between candidate genes and disorders. We have shown recently that spatially mapped, i.e. 3D, gene expression data from the mouse brain can be successfully used to prioritize candidate genes for human Mendelian disorders of the central nervous system. Results: We improved our previous work 2-fold: (i) we demonstrate that condition-independent transcription factor binding affinities of the candidate genes' promoters are relevant for disease-gene prediction and can be integrated with our previous approach to significantly enhance its predictive power; and (ii) we define a novel similarity measure—termed Relative Intensity Overlap—for both 3D gene expression patterns and binding affinity profiles that better exploits their disease-relevant information content. Finally, we present novel disease-gene predictions for eight loci associated with different syndromes of unknown molecular basis that are characterized by mental retardation. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online