Characterization of the microbial community of lotic organic aggregates ('river snow') in the Elbe River of Germany by cultivation and molecular methods

Aerobic and anaerobic cultivation techniques, 16S rDNA-based phylogeny, and fluorescent in situ hybridization were used to describe the phylogenetic diversity and physiological versatility of lotic microbial aggregates ('river snow') obtained from the river Elbe. In the course of the year the 'river snow' community changed. It was characterized by a great bacterial diversity in spring, the predominant occurrence of algae in summer and reduction of the total bacterial cell count in autumn and winter. In all 'river snow' samples, more than 70% of the bacteria counted with the general DNA stain DAPI also hybridized with the Bacteria-specific probe EUB338. In situ analysis of the bacterial 'river snow' community with a comprehensive suite of specific rRNA-targeted probes revealed population dynamics to be governed by seasonal factors. During all seasons, beta-Proteobacteria constituted the numerically most important bacterial group forming up to 54% of the total cell counts. In contrast to this, the relative abundance of other major bacterial lineages ranged from 2% for the order Planctomycetales to 36% for Cytophaga-Flavobacteria. Cultivation of 'river snow' under aerobic and anaerobic conditions with a variety of different media resulted in the isolation of 40 new bacterial strains. Phenotypic and phylogenetic analyses revealed these new strains to be mostly unknown organisms affiliated to different bacterial phyla. Application of newly developed specific oligonucleotide probes proved the cultivated bacteria, including clostridia and the numerically abundant beta-Proteobacteria, as relevant in situ members of the 'river snow' community.     

<Emphasis Type="Italic">Scoredist</Emphasis>: A simple and robust protein sequence distance estimator

Background  

Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used.     

Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER

Background  

Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. The programs' abilities to build models and score sequences were compared on a SCOP/Pfam based test set. The comparison was done separately for local and global HMM scoring.