<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.     

COMPOSITION OF EXTRACELLULAR POLYMERIC SUBSTANCES FROM PERIPHYTON ASSEMBLAGES IN THE FLORIDA EVERGLADES1

In wetland habitats, periphyton is a common component of open‐water areas with species assemblage determined by local water quality. Extracellular polymeric substances (EPS) secreted by algae and bacteria give structure to periphyton, and differences in EPS chemistry affect the functional roles of these polymers. The Florida Everglades provide a unique opportunity to study compositional differences of EPS from distinctive algal assemblages that characterize areas of differing water chemistry. Water conservation area (WCA)‐1 is a soft‐water impoundment; periphyton was loosely associated with Utricularia stems and amorphous in structure, with a diverse desmid and diatom assemblage, and varying cyanobacterial abundance. Extracellular polymers were abundant and were loosely cell‐associated sheaths and slime layers in addition to tightly cell‐associated capsules. The EPS were complex heteropolysaccharides with significant saccharide residues of glucose, xylose, arabinose, and fucose. Carboxylic acids were also prominent, while ester sulfates and proteins were small components. Structured, cohesive cyanobacteria‐dominated periphyton was observed in WCA‐2A, a minerotrophic impoundment, and filaments were heavily encrusted with calcium carbonate and detrital matter. EPS were primarily cell‐associated sheaths, and polymer residues were dominated by glucose, xylose, fucose, and galactose, with uronic acids also a significant component of the polymers. Principal components analysis revealed that periphyton community assemblage determined the monosaccharide composition of EPS, which ultimately determines a range of biogeochemical processes within the periphyton.     

Detrital Floc and Surface Soil Microbial Biomarker Responses to Active Management of the Nutrient Impacted Florida Everglades

Alterations in microbial community composition, biomass, and function in the Florida Everglades impacted by cultural eutrophication reflect a new physicochemical environment associated with monotypic stands of Typha domingensis. Phospholipid fatty acid (PLFA) biomarkers were used to quantify microbial responses in detritus and surface soils in an active management experiment in the eutrophic Everglades. Creation of open plots through removal of Typha altered the physical and chemical characteristics of the region. Mass of PLFA biomarkers increased in open plots, but magnitude of changes differed among microbial groups. Biomarkers indicative of Gram-negative bacteria and fungi were significantly greater in open plots, reflective of the improved oxic environment. Reduction in the proportion of cyclopropyl lipids and the ratio of Gram-positive to Gram-negative bacteria in open plots further suggested an altered oxygen environment and conditions for the rapid growth of Gram-negative bacteria. Changes in the PLFA composition were greater in floc relative to soils, reflective of rapid inputs of new organic matter and direct interaction with the new physicochemical environment. Created open plot microbial mass and composition were significantly different from the oligotrophic Everglades due to differences in phosphorus availability, plant community structure, and a shift to organic peat from marl-peat soils. PLFA analysis also captured the dynamic inter-annual hydrologic variability, notably in PLFA concentrations, but to a lesser degree content. Recently, use of concentration has been advocated over content in studies of soil biogeochemistry, and our results highlight the differential response of these two quantitative measures to similar pressures.