Background: In the post-genomic era, correct gene prediction has become one of the biggest challenges in genome annotation. Improved promoter prediction methods can be one step towards developing more reliable ab initio gene prediction methods. This work presents a novel prokaryotic promoter prediction method based on DNA stability. Results: The promoter region is less stable and hence more prone to melting as compared to other genomic regions. Our analysis shows that a method of promoter prediction based on the differences in the stability of DNA sequences in the promoter and non-promoter region works much better compared to existing prokaryotic promoter prediction programs, which are based on sequence motif searches. At present the method works optimally for genomes such as that of Escherichia coli, which have near 50 % G+C composition and also performs satisfactorily in case of other prokaryotic promoters. Conclusions: Our analysis clearly shows that the change in stability of DNA seems to provide a much better clue than usual sequence motifs, such as Pribnow box and -35 sequence, for differentiating promoter region from non-promoter regions. To a certain extent, it is more general and is likely to be applicable across organisms. Hence incorporation of such features in addition to the signature motifs can greatly improve the presently available promoter prediction programs.

T-bet and GATA3 regulate the CD4+ T cell Th1/Th2 cell fate decision but little is known about the interplay between these factors outside of the murine Ifng and Il4/Il5/Il13 loci. Here we show that T-bet and GATA3 bind to multiple distal sites at immune regulatory genes in human effector T cells. These sites display markers of functional elements, act as enhancers in reporter assays and are associated with a requirement for T-bet and GATA3. Furthermore, we demonstrate that both factors bind distal sites at Tbx21 and that T-bet directly activates its own expression. We also show that in Th1 cells, GATA3 is distributed away from Th2 genes, instead occupying T-bet binding sites at Th1 genes, and that T-bet is sufficient to induce GATA3 binding at these sites. We propose these aspects of T-bet and GATA3 function are important for Th1/Th2 differentiation and for understanding transcription factor interactions in other T cell lineage decisions.

Motivation: Theoretical efforts to understand the regulation of gene expression are traditionally centered around the identification of transcription factor binding sites at specific DNA positions. More recently these efforts have been supplemented by experimental data for relative binding affinities of proteins to longer intergenic sequences. The question arises to what extent these two approaches converge. In this paper, we adopt a physical binding model to predict the relative binding affinity of a transcription factor for a given sequence. Results: We find that a significant fraction of genome-wide binding data in yeast can be accounted for by simple count matrices and a physical model with only two parameters. We demonstrate that our approach is both conceptually and practically more powerful than traditional methods, which require selection of a cutoff. Our analysis yields biologically meaningful parameters, suitable for predicting relative binding affinities in the absence of experimental binding data. Availability: The C source code for our TRAP program is freely available for non-commercial use at Contact:vingron@molgen.mpg.de

Long noncoding RNAs (lncRNAs) constitute the majority of transcripts in the mammalian genomes, and yet, their functions remain largely unknown. As part of the FANTOM6 project, we systematically knocked down the expression of 285 lncRNAs in human dermal fibroblasts and quantified cellular growth, morphological changes, and transcriptomic responses using Capped Analysis of Gene Expression (CAGE). Antisense oligonucleotides targeting the same lncRNAs exhibited global concordance, and the molecular phenotype, measured by CAGE, recapitulated the observed cellular phenotypes while providing additional insights on the affected genes and pathways. Here, we disseminate the largest-to-date lncRNA knockdown data set with molecular phenotyping (over 1000 CAGE deep-sequencing libraries) for further exploration and highlight functional roles for ZNF213-AS1 and lnc-KHDC3L-2.

During the process of transcription, RNA polymerase can exactly locate a promoter sequence in the complex maze of a genome. Several experimental studies and computational analyses have shown that the promoter sequences apparently possess some special properties, such as unusual DNA structures and low stability, which make them distinct from the rest of the genome. But most of these studies have been carried out on a particular set of promoter sequences or on promoter sequences from similar organisms. To examine whether the promoters from a wide variety of organisms share these special properties, we have carried out an analysis of sets of promoters from bacteria, vertebrates and plants. These promoters were analyzed with respect to the prediction of three different properties, such as DNA curvature, bendability and stability, which are relevant to transcription. All the promoter sequences are predicted to share certain features, such as stability and bendability profiles, but there are significant differences in DNA curvature profiles and nucleotide composition between the different organisms. These similarities and differences are correlated with some of the known facts about transcription process in the promoters from the three groups of organisms.