Compositional features of eukaryotic genomes for checking predicted genes

Gene prediction relies on the identification of characteristic features of coding sequences that distinguish them from non-coding DNA. The recent large-scale sequencing of entire genomes from higher eukaryotes, in conjunction with currently used gene prediction algorithms, has provided an abundance of putative genes that can now be analysed for their compositional properties. Strong, systematic differences still exist, in several species, between the compositional properties of sets of ex novo predicted genes and genes that have been experimentally detected and/or verified. This is particularly evident in the estimated gene set (>45,000 genes) of the recently sequenced rice genome, where roughly half the predicted genes are compositionally unusual and have no known orthologues in the dicot Arabidopsis. In a few cases such differences might suggest a bias in experimental gene-finding protocols, but the quasi-random nature of the compositionally aberrant predicted genes is a strong indication that many, if not most, of them are false positives. It therefore appears that some important features of coding regions have not yet been taken into account in existing gene prediction programs. Statistical base compositional properties of curated gene data sets from vertebrates, which we briefly review here, should therefore provide a useful benchmark for fine-tuning probabilistic gene models and model parameters that are currently in use.     

Long-range correlations between DNA bending sites: relation to the structure and dynamics of nucleosomes

It has been established that the precise positioning of nucleosomes on genomic DNA can be achieved, at least for a minority of them, through sequence-dependent processes. However, to what extent DNA sequences play a role in the positioning of the major part of nucleosomes is still debated. The aim of the present study is to examine to what extent long-range correlations (LRC) are related to the presence of nucleosomes. Using the wavelet transform technique, we perform a comparative analysis of the DNA text and of the corresponding bending profiles generated with curvature tables based on nucleosome positioning data. The exploration of a number of eukaryotic and bacterial genomes through the optics of the so-called "wavelet transform microscope" reveals a characteristic scale of 100-200 bp that separates two regimes of different LRC. Here, we focus on the existence of LRC in the small-scale regime (10-200 bp) which are actually observed in eukaryotic genomes, in contrast to their absence in eubacterial genomes. Analysis of viral DNA genomes shows that, like their host's genomes, eukaryotic viruses present LRC but eubacterial viruses do not. There is one exception for genomes of poxviruses (Vaccinia and Melamoplus sanguinipes) which do not replicate in the cell nucleus and do not exhibit LRC. No small-scale LRC are detected in the genomes of all examined RNA viruses, with the exception of retroviruses. These results together with the observation of LRC between particular sequence motifs known to participate in the formation of nucleosomes (e.g. AA dinucleotides) strongly suggest that the 10-200 bp LRC are a signature of the sequence-dependence of nucleosome positioning. Finally, we discuss possible interpretations of these LRC in terms of the physical mechanisms that might govern the positioning and the dynamics of the nucleosomes along the DNA chain through cooperative processes.     

The UAA/GAN internal loop motif: a new RNA structural element that forms a cross-strand AAA stack and long-range tertiary interactions

Analysis of aligned RNA sequences and high-resolution crystal structures has revealed a new RNA structural element, termed the UAA/GAN motif. Found in internal loops of the 23 S rRNA, as well as in RNase P RNA and group I and II introns, this six-nucleotide motif adopts a distinctive local structure that includes two base-pairs with non-canonical conformations and three conserved adenine bases, which form a cross-strand AAA stack in the minor groove. Most importantly, the motif invariably forms long-range tertiary contacts, as the AAA stack typically forms A-minor interactions and the flipped-out N nucleotide forms additional contacts that are specific to the structural context of each loop. The widespread presence of this motif and its propensity to form long-range contacts suggest that it plays a critical role in defining the architectures of structured RNAs.     

Robust clines and robust sampling: a reply to Kyriacou et al

Kyriacou et al. (2007) have questioned a number of issues with our recent paper on a lack of clinal variation in the period and clock timing genes in Drosophila melanogaster from eastern Australia. Here we show why their arguments are not valid and reiterate that clinal variation in genes and molecular markers need to be assessed on field flies collected over a brief period of time.     

The evidence that the tree of life is not rooted within the Archaea is unreliable: a reply to Skophammer et al

Skophammer et al. [Skophammer, RG, Herbold, CW, Rivera, MC, Servin, JA, Lake, JA. 2006. Evidence that the root of the tree of life is not within the Archaea. Mol Biol Evol, 23, 1648-1651] report evidence suggesting that the tree of life cannot be rooted within the Archaea domain. I have observed that an alignment used in their analysis is not reliable and that, therefore, their conclusions are unjustified.     

How to reach linguistic consensus: a proof of convergence for the naming game

In this paper we introduce a mathematical model of naming games. Naming games have been widely used within research on the origins and evolution of language. Despite the many interesting empirical results these studies have produced, most of this research lacks a formal elucidating theory. In this paper we show how a population of agents can reach linguistic consensus, i.e. learn to use one common language to communicate with one another. Our approach differs from existing formal work in two important ways: one, we relax the too strong assumption that an agent samples infinitely often during each time interval. This assumption is usually made to guarantee convergence of an empirical learning process to a deterministic dynamical system. Two, we provide a proof that under these new realistic conditions, our model converges to a common language for the entire population of agents. Finally the model is experimentally validated.