Assignment of orthologous relationships among mammalian alpha-globin genes by examining flanking regions reveals a rapid rate of evolution

In order to study the relationships among mammalian alpha-globin genes, we have determined the sequence of the 3' flanking region of the human alpha 1 globin gene and have made pairwise comparisons between sequenced alpha-globin genes. The flanking regions were examined in detail because sequence matches in these regions could be interpreted with the least complication from the gene duplications and conversions that have occurred frequently in mammalian alpha-like globin gene clusters. We found good matches between the flanking regions of human alpha 1 and rabbit alpha 1, human psi alpha 1 and goat I alpha, human alpha 2 and goat II alpha, and horse alpha 1 and goat II alpha. These matches were used to align the alpha-globin genes in gene clusters from different mammals. This alignment shows that genes at equivalent positions in the gene clusters of different mammals can be functional or nonfunctional, depending on whether they corrected against a functional alpha-globin gene in recent evolutionary history. The number of alpha-globin genes (including pseudogenes) appears to differ among species, although highly divergent pseudogenes may not have been detected in all species examined. Although matching sequences could be found in interspecies comparisons of the flanking regions of alpha- globin genes, these matches are not as extensive as those found in the flanking regions of mammalian beta-like globin genes. This observation suggests that the noncoding sequences in the mammalian alpha-globin gene clusters are evolving at a faster rate than those in the beta-like globin gene clusters. The proposed faster rate of evolution fits with the poor conservation of the genetic linkage map around alpha-globin gene clusters when compared to that of the beta-like globin gene clusters. Analysis of the 3' flanking regions of alpha-globin genes has revealed a conserved sequence approximately 100-150 bp 3' to the polyadenylation site; this sequence may be involved in the expression or regulation of alpha-globin genes.      

Ecological mechanisms of population change during outbreaks of the spruce budworm

Abstract.  1. Stage-specific survival and recruitment of spruce budworm were measured by frequent sampling of foliage in four outbreak populations over a 15-year period in Ontario and Quebec, Canada.
2. Patterns of change in population density during the outbreak collapse phase were closely linked to changes in survival of the late immature stages, and were determined largely by the impact of natural enemies.
3. Host-plant feedback also contributed significantly to survival patterns throughout the outbreak: annual defoliation influenced survival of fourth and fifth instars and fecundity while cumulative defoliation influenced survival of the very early larval stages (first and second) via impacts on stand condition.
4. Inclusion of this host-plant feedback reveals spruce budworm population dynamics as a function of density-related trophic interactions that vary in their order and strength of influence over time. This view re-introduces the importance of forest interactions as a component of dynamics of the spruce budworm.     

Integrating structured biological data by Kernel Maximum Mean Discrepancy

MOTIVATION: Many problems in data integration in bioinformatics can be posed as one common question: Are two sets of observations generated by the same distribution? We propose a kernel-based statistical test for this problem, based on the fact that two distributions are different if and only if there exists at least one function having different expectation on the two distributions. Consequently we use the maximum discrepancy between function means as the basis of a test statistic. The Maximum Mean Discrepancy (MMD) can take advantage of the kernel trick, which allows us to apply it not only to vectors, but strings, sequences, graphs, and other common structured data types arising in molecular biology. RESULTS: We study the practical feasibility of an MMD-based test on three central data integration tasks: Testing cross-platform comparability of microarray data, cancer diagnosis, and data-content based schema matching for two different protein function classification schemas. In all of these experiments, including high-dimensional ones, MMD is very accurate in finding samples that were generated from the same distribution, and outperforms its best competitors. Conclusions: We have defined a novel statistical test of whether two samples are from the same distribution, compatible with both multivariate and structured data, that is fast, easy to implement, and works well, as confirmed by our experiments. AVAILABILITY: http://www.dbs.ifi.lmu.de/~borgward/MMD.     

GLIDE: GPU-Based Linear Regression for Detection of Epistasis

Due to recent advances in genotyping technologies, mapping phenotypes to single loci in the genome has become a standard technique in statistical genetics. However, one-locus mapping fails to explain much of the phenotypic variance in complex traits. Here, we present GLIDE, which maps phenotypes to pairs of genetic loci and systematically searches for the epistatic interactions expected to reveal part of this missing heritability. GLIDE makes use of the computational power of consumer-grade graphics cards to detect such interactions via linear regression. This enabled us to conduct a systematic two-locus mapping study on seven disease data sets from the Wellcome Trust Case Control Consortium and on in-house hippocampal volume data in 6 h per data set, while current single CPU-based approaches require more than a year's time to complete the same task.     

Predicting phenotypic effects of gene perturbations in C. elegans using an integrated network model

Predicting the phenotype of an organism from its genotype is a central question in genetics. Most importantly, we would like to find out if the perturbation of a single gene may be the cause of a disease. However, our current ability to predict the phenotypic effects of perturbations of individual genes is limited. Network models of genes are one tool for tackling this problem. In a recent study, (Lee et al.) it has been shown that network models covering the majority of genes of an organism can be used for accurately predicting phenotypic effects of gene perturbations in multicellular organisms. .     

SUMMARY OF GREEN PLANT PHYLOGENY AND CLASSIFICATION

Abstract— A cladogram of green plants involving all major extant groups of green algae, bryophytes, pteridophytes, and seed plants is presented. It is partly based on contributions by B. Mishler and S. Churchill, H. Wagner, and P. Crane. The relationships of green plants to other green organisms ( Prochloron , euglenophytes) are discussed. The characters and subclades of the cladogram are briefly discussed, with an attempt to indicate weak points. The possibility of including some major extinct groups is considered. A cladistic classification consistent with the cladogram is presented. Grades are abandoned as taxa and major clades like the division Chlorophyta (green algae excluding micro-monadophytes and charophytes sensu Mattox and Stewart), the division Streptophyta (charophytes + embryophytes), the subdivision Embryophytina (land plants or embryophytes), the superclass Tracheidatae (tracheophytes), and the class Spermatopsida (seed plants) are recognized.