HDX-analyzer: a novel package for statistical analysis of protein structure dynamics

BACKGROUND: HDX mass spectrometry is a powerful platform to probe protein structure dynamics during ligand binding, protein folding, enzyme catalysis, and such. HDX mass spectrometry analysis derives the protein structure dynamics based on the mass increase of a protein of which the backbone protons exchanged with solvent deuterium. Coupled with enzyme digestion and MS/MS analysis, HDX mass spectrometry can be used to study the regional dynamics of protein based on the m/z value or percentage of deuterium incorporation for the digested peptides in the HDX experiments. Various software packages have been developed to analyze HDX mass spectrometry data. Despite the progresses, proper and explicit statistical treatment is still lacking in most of the current HDX mass spectrometry software. In order to address this issue, we have developed the HDXanalyzer for the statistical analysis of HDX mass spectrometry data using R, Python, and RPY2. IMPLEMENTATION AND RESULTS: HDXanalyzer package contains three major modules, the data processing module, the statistical analysis module, and the user interface. RPY2 is employed to enable the connection of these three components, where the data processing module is implemented using Python and the statistical analysis module is implemented with R. RPY2 creates a low-level interface for R and allows the effective integration of statistical module for data processing. The data processing module generates the centroid for the peptides in form of m/z value, and the differences of centroids between the peptides derived from apo and ligand-bound protein allow us to evaluate whether the regions have significant changes in structure dynamics or not. Another option of the software is to calculate the deuterium incorporation rate for the comparison. The two types of statistical analyses are Paired Student's t-test and the linear combination of the intercept for multiple regression and ANCOVA model. The user interface is implemented with wxpython to facilitate the data visualization in graphs and the statistical analysis output presentation. In order to evaluate the software, a previously published xylanase HDX mass spectrometry analysis dataset is processed and presented. The results from the different statistical analysis methods are compared and shown to be similar. The statistical analysis results are overlaid with the three dimensional structure of the protein to highlight the regional structure dynamics changes in the xylanase enzyme. CONCLUSION: Statistical analysis provides crucial evaluation of whether a protein region is significantly protected or unprotected during the HDX mass spectrometry studies. Although there are several other available software programs to process HDX experimental data, HDXanalyzer is the first software program to offer multiple statistical methods to evaluate the changes in protein structure dynamics based on HDX mass spectrometry analysis. Moreover, the statistical analysis can be carried out for both m/z value and deuterium incorporation rate. In addition, the software package can be used for the data generated from a wide range of mass spectrometry instruments.     

The statistical analysis of survival in animal populations

Estimating, comparing and modelling survival rates are central to population biology. However, there are many difficulties in measuring these rates in animal populations in the wild. The most relevant information is based on samples of marked individuals, i.e. capture-recapture data. In recent years, a number of new statistical approaches to the analysis of such data have been developed, permitting more sophisticated and precise measurement of survival rates.     

Automated statistical analysis of microbial enumeration by dilution series

Equations are formulated for the standard error and confidence interval for the MPN estimate of microbial density from a general dilution series. A statistical test of homogeneity is presented. This tests whether a handling error in the dilution series may have occurred which would invalidate the density estimate. The analysis may be automated using a Basic computer program which contains a fast algorithm for the solution of the general MPN equation. This allows the calculation of the MPN, standard error, 95% confidence interval and test statistic for any dilution series, with any degree of replication at each dilution level, with variable sample volumes at each dilution level, with variable dilution ratio between levels, and with any number of levels.     

A statistical analysis of the relationships among viable microbial populations,vegetation, and environment in a subantarctic tundra

Parametric and nonparametric analyses were used to investigate the relationships between the populations of viable microbes and 4 edaphic variables — soil moisture, rainfall, temperature, and pH. Microbial populations were sampled over a 2-year period in contrasting grass and moss stands on the subantarctic island of South Georgia. Moisture was found to be the most significant edaphic variable, but there were highly significant correlations between bacterial and fungal populations at both sites. Individual plant species showed clear correlations with both bacterial and fungal populations.     

PCR-DGGE analysis for the identification of microbial populations from Argentinean dry fermented sausages

Different PCR-DGGE protocols were evaluated to monitor fermentation process and to investigate bacterial communities developed in two artisanal Argentinean fermented sausages. Bacterial universal primers frequently used in PCR-denaturing gradient gel electrophoresis (DGGE) were evaluated. Lactic acid bacteria (LAB) and staphylococci species isolated from Tucumán sausages were used to determine the experimental conditions for PCR amplification and DGGE differentiation. Total microbial DNA extracted directly from both fermented sausages was subjected to DGGE analysis. PCR-DGGE results were different for each set of primers used. Primers Bact-0124f(GC)-Uni-0515r and V1f(GC)-V1r showed to be efficient to differentiate LAB and Staphylococcus cultures while the set V3f(GC)-Uni-0515r allowed to demonstrate the succession of different Lactobacillus and Staphylococcus species during ripening process. An intense band corresponding to Lactobacillus sakei was observed to be present in both samples. Staphylococcus saprophyticus was only observed in Tucumán sausage while a band identified as Brochothrix thermophacta was detected in Córdoba sausage. PCR-DGGE analysis of different 16S rDNA amplicons was able to discriminate between LAB and Gram-positive, coagulase-negative cocci, resulting an effective tool to establish the microbiota developed in artisanal dry sausages.     

Molecular phylogenetic analysis of dominant microbial populations in aged refuse

The phylogenetic analysis of dominant microbial populations in 8-year-old refuse samples was done in terms of the whole Bacterial and Archaeal domains. The results indicated that the Bacterial 16S rRNA genes sequences from the aged refuse were largely affiliated with the genus Bacillus, and that more than 60 % of the Archaeal sequences were closely related to the methanogenic archaeon. Some inferentially identified extremophilic organisms, particularly alkaliphiles and/or halophiles, were noted to be present in the aged refuse. Moreover, molecular evidence for the occurrence of ammonia-oxidizing Archaea in aged refuse was reported, which opens up avenues for elucidating its role in ammonia transformation in landfill systems. It seems reasonable to assume that the highly complex environment within the landfill systems may select for microbial populations with versatile metabolism and strong adaptation. These findings underline the need for further biochemical and ecological study of these organisms in aged refuse.     

A statistical procedure for the analysis of microbial communities based on phenotypic properties of isolates

A novel statistical procedure for the analysis of microbial communities based on phenotypic properties of randomly collected isolates is presented and discussed. The procedure allows the representation of the microbial communities as a set of ellipses in a bidimensional graph. This representation is obtained by the following steps: (a) measurement of a set of binary phenotypic properties for n isolates belonging to k samples, each representing a different community; (b) repeated sampling by bootstrapping of the m samples, thus obtaining, for each community, i subsamples of j isolates; (c) calculation of the frequency of positive results for each test for each subsample; (d) calculation of the matrix of Euclidean distances between the k x i frequency vectors; (e) use of multidimensional scaling (MDS) to obtain a representation in two dimensions of the distance relationships between the frequency vectors; (f) plotting of the 95% confidence ellipses for the i frequency vectors of each of the k communities. By using both simple, synthetic microbial communities, and samples of lactic acid bacteria isolated from natural microbial communities (sourdoughs, compressed yeast, fermented sausages), it was demonstrated that the position and shape of the ellipses are clearly related to the composition of the community, while the relationship between the size of the ellipses and the phenotypical diversity of the community is less straightforward: while communities with very different diversity (measured with the Functional Evenness index and the mean taxonomic distance) had ellipses that were very different in size, there was no strict proportionality between the size of the ellipse and the diversity of the community. Nevertheless, the representation of microbial communities obtained by bootstrapping and multidimensional scaling appears to be superior to the more usual representation based on tabulation of the frequencies of isolates belonging to different clusters.     

Multivariate statistical analysis of quantitative serum protein data in populations of Rwanda

Data on serum protein levels of four populations from Rwanda were analyzed by multivariate statistical methods to assess their utility as an anthropological tool. These populations consisted of two ethnic groups in two different environments. Seven proteins were considered in the analysis. The total concentration of proteins is intermediate between Italian levels and those of the Binga pygmies of the Republic of Central Africa (RCA). Discrimination between the populations was possible with a 25% misclassification. The major principal components can be interpreted from a medical viewpoint, and show significant differences between the populations.     

ADSP-a new package for computational sequence analysis

A new protein sequence analysis package, ADSP, is described,of which the SOMAP Screen–Oriented Multiple AlignmentProcedure forms an integral part. ADSP (Algorithms and DataStructures for Protein sequence analysis) incorporates facilitiesto generate potent pattern-recognition discriminators and offersfour algorithms with which to scan any NBRF format sequencedatabase: the package has been designed, in particular, to interfacewith the OWL composite sequence database, one of the largest,distributed non-redundant sources of sequence data of its kind.The system incorporates a powerful method for compound featureanalysis, which provides the basis for characterizing and predictingthe occurrence of complete protein superfamilies and for pinpointingthe emergence of related subfamilies. Used iteratively, theapproach allows diagnostic performance to be rigorously refinedand its efficacy to be assessed both qualitatively and quantitatively,and results in the generation of refined structural or functionalfeatures suitable for entry into a database: this compilationof characteristic signatures is distinct from, but complementaryto, widely used compendia of pattern templates such as PROSUE.     

Soil microbial populations after wildfire

Abstract Population fluctuations were increased by burning, which also modified the incubation patterns and the densities of several microbial groups, although without changing the order of their population sizes. In the short term, fire produced a sharp increase in microbes but affected the groups studied differently. Aerobic heterotrophic bacteria, including the acidophilic and sporulating ones, were stimulated by fire while cyanobacteria, algae and fungi (propagules as well as hyphae length) were clearly depressed. In the long term, the positive effect of fire on bacteria was nullified except on the sporulating ones; fungal propagules, but not mycelium, reached the unburned soil values, cyanobacteria and algae also increased. Soil incubation both improved the beneficial and diminished the negative fire effect on the microbiota.     

A friendly statistics package for microarray analysis

SUMMARY: The friendly statistics package for microarray analysis (FSPMA) is a tool that aims to fill the gap between simple to use and powerful analysis. FSPMA is a platform-independent R-package that allows efficient exploration of microarray data without the need for computer programming. Analysis is based on a mixed model ANOVA library (YASMA) that was extended to allow more flexible comparisons and other useful operations like k nearest neighbour imputing and spike-based normalization. Processing is controlled by a definition file that specifies all the steps necessary to derive analysis results from quantified microarray data. In addition to providing analysis without programming, the definition file also serves as exact documentation of all the analysis steps. AVAILABILITY: The library is available under GPL 2 license and, together with additional information, provided at http://www.ccbi.cam.ac.uk/software/psyk/software.html#fspma     

On statistical methods for estimating heritability in wild populations

Here we take a look at molecular marker-based heritability estimation suitable for non-model organisms. We address several theoretical issues involved and discuss similarities and differences between our two main approaches: the animal model approach and the shrinkage-estimation based multilocus association approach. Also computational issues and hypothetical example applications for ecologists are considered.     

Evolution of cross-feeding in microbial populations

Although limited by a single resource, microbial populations that grow for long periods in continuous culture (chemostat) frequently evolve stable polymorphisms. These polymorphisms may be maintained by cross-feeding, where one strain partially degrades the primary energy resource and excretes an intermediate that is used as an energy resource by a second strain. It is unclear what selective advantage cross-feeding strains have over a single competitor that completely degrades the primary resource. Here we show that cross-feeding may evolve in microbial populations as a consequence of the following optimization principles: the rate of ATP production is maximized, the concentration of enzymes of the pathway is minimized, and the concentration of intermediates of the pathway is minimized.     

KELVIN: a software package for rigorous measurement of statistical evidence in human genetics

This paper describes the software package KELVIN, which supports the PPL (posterior probability of linkage) framework for the measurement of statistical evidence in human (or more generally, diploid) genetic studies. In terms of scope, KELVIN supports two-point (trait-marker or marker-marker) and multipoint linkage analysis, based on either sex-averaged or sex-specific genetic maps, with an option to allow for imprinting; trait-marker linkage disequilibrium (LD), or association analysis, in case-control data, trio data, and/or multiplex family data, with options for joint linkage and trait-marker LD or conditional LD given linkage; dichotomous trait, quantitative trait and quantitative trait threshold models; and certain types of gene-gene interactions and covariate effects. Features and data (pedigree) structures can be freely mixed and matched within analyses. The statistical framework is specifically tailored to accumulate evidence in a mathematically rigorous way across multiple data sets or data subsets while allowing for multiple sources of heterogeneity, and KELVIN itself utilizes sophisticated software engineering to provide a powerful and robust platform for studying the genetics of complex disorders.     

On the mass distribution model for microbial cell populations

The full implications of a statistical model for growth of a microbial cell population using cell mass as the index of physiological state have been examined by solving the partial differential integral equations resulting from the model. Calculations reveal that a lag phase is predicted during the initial stages of batch growth although no specific cellular mechanism for the phenomenon of lag had been incorporated into the model. The model predicts several situations of batch and continuous growth in which the population density and biomass concentration show opposing trends due to significant variation in the cell mass distribution with time.     

Accessory pigment fluorescence for quantitation of photosynthetic microbial populations.

The in vivo fluorescence of the primary accessory pigments in purple bacteria (carotenoids), green bacteria (bacteriochlorophyll), green algae (chlorophyll), and cyanobacteria (phycocyanin) was found to be a linear function of cell concentration over three of four orders of magnitude. The lowest cell concentrations detectable were 10(4) cells/ml for procaryotes and 10(3) cells/ml for eucaryotes.