Inferring functional information from domain co-evolution

MOTIVATION: Co-evolution is a powerful mechanism for understanding protein function. Prior work in this area has shown that co-evolving proteins are more likely to share the same function than those that do not because of functional constraints. Many of the efforts founded on this observation, however, are at the level of entire sequences, implicitly assuming that the complete protein sequence follows a single evolutionary trajectory. Since it is well known that a domain can exist in various contexts, this assumption is not valid for numerous multi-domain proteins. Motivated by these observations, we introduce a novel technique called Coevolutionary-Matrix that captures co-evolution between regions of two proteins. Instead of using existing domain information, the method exploits residue-level conservation to identify co-evolving regions that might correspond to domains. RESULTS: We show that the Coevolutionary-Matrix method can detect greater number of known functional associations for the Escherichia coli proteins when compared with earlier implementations of phylogenetic profiles. Furthermore, co-evolving regions of proteins detected by our method enable us to make hypotheses about their specific functions, many of which are supported by existing biochemical studies.     

Gene mapping and marker clustering using Shannon's mutual information

Finding the causal genetic regions underlying complex traits is one of the main aims in human genetics. In the context of complex diseases, which are believed to be controlled by multiple contributing loci of largely unknown effect and position, it is especially important to develop general yet sensitive methods for gene mapping. We discuss the use of Shannon's information theory for population-based gene mapping of discrete and quantitative traits and for marker clustering. Various measures of mutual information were employed in order to develop a comprehensive framework for gene mapping analyses. An algorithm aimed at finding so-called relevance chains of causal markers is proposed. Moreover, entropy measures are used in conjunction with multidimensional scaling to visualize clusters of genetic markers. The relevance chain algorithm successfully detected the two causal regions in a simulated scenario. The approach has also been applied to a published clinical study on autoimmune (Graves') disease. Results were consistent with those of standard statistical methods, but identified an additional locus of interest in the promoter region of the associated gene CTLA4. The developed software is freely available at http://www.lnt.ei.tum.de/download/InfoGeneMap/.     

The information transmitted at final position in visually triggered forearm movements

Visually triggered forearm movements were analyzed by an Information Theory approach. Human subjects made smooth movements which were characterized by moderate speeds, ranging about 100 degrees per second, by continuity in the position and velocity traces, and attainment of final average EMG levels before completion of the movement. We calculated the information transmitted by final position, biceps EMG, triceps EMG, and the ratio of the EMGs. The results were: (1) The information transmitted by final joint angle increased with number of targets but gradually levelled off. The maximum value was slightly over 3 bits, corresponding to an equivalent number of less than nine independent arm positions for a single movement. (2) The information transmitted by the ratio of the EMGs exceeds that transmitted by the biceps or triceps alone. (3) A previous theoretical prediction based on a spring model (Sakitt, 1980a) gives a moderately good fit to the experimental EMG ratio as a function of final position over a large range of angles. Our results lend consistency to two ideas about the nature of visually triggered forearm movements. First, our finding about the EMG ratio suggests that the basic motor program for final position is probably in terms of relative allocation of innervations, rather than looking up individual values. Second, single movements of this kind transmit surprisingly little information. If this is the case, it suggests that very fine accuracy is not achieved by a single program but requires feedback in order to program and execute additional movement.Laboratoire de Physiologie Neurosensorielle, CNRS, Paris, France     

Developing and analyzing idealized models for molecular recognition

We study equilibrium aspects of molecular recognition of two biomolecules using idealized model systems and methods from statistical physics. Starting from the basic experimental findings we demonstrate exemplarily how an idealized coarse-grained model for the investigation of molecular recognition of two biomolecules can be developed. In addition we provide details regarding two model systems for the recognition of a flexible and a rigid biomolecule respectively, the latter taking into account conformational changes. We focus particularly on the interplay and influence of the correlations of the residue distributions of the biomolecules on the recognition process.     

Gene clustering based on clusterwide mutual information.

Cluster analysis of gene-wide expression data from DNA microarray hybridization studies has proved to be a useful tool for identifying biologically relevant groupings of genes and constructing gene regulatory networks. The motivation for considering mutual information is its capacity to measure a general dependence among gene random variables. We propose a novel clustering strategy based on minimizing mutual information among gene clusters. Simulated annealing is employed to solve the optimization problem. Bootstrap techniques are employed to get more accurate estimates of mutual information when the data sample size is small. Moreover, we propose to combine the mutual information criterion and traditional distance criteria such as the Euclidean distance and the fuzzy membership metric in designing the clustering algorithm. The performances of the new clustering methods are compared with those of some existing methods, using both synthesized data and experimental data. It is seen that the clustering algorithm based on a combined metric of mutual information and fuzzy membership achieves the best performance. The supplemental material is available at www.gspsnap.tamu.edu/gspweb/zxb/glioma_zxb.     

Interocular yoking in human saccades examined by mutual information analysis

Background

Saccadic eye movements align the two eyes precisely to foveate a target. Trial-by-trial variance of eye movement is always observed within an identical experimental condition. This has often been treated as experimental error without addressing its significance. The present study examined statistical linkages between the two eyes’ movements, namely interocular yoking, for the variance of eye position and velocity.

Methods

Horizontal saccadic movements were recorded from twelve right-eye-dominant subjects while they decided on saccade direction in Go-Only sessions and on both saccade execution and direction in Go/NoGo sessions. We used infrared corneal reflection to record simultaneously and independently the movement of each eye. Quantitative measures of yoking were provided by mutual information analysis of eye position or velocity, which is sensitive to both linear and non-linear relationships between the eyes’ movements. Our mutual information analysis relied on the variance of the eyes movements in each experimental condition. The range of movements for each eye varies for different conditions so yoking was further studied by comparing GO-Only vs. Go/NoGo sessions, leftward vs. rightward saccades.

Results

Mutual information analysis showed that velocity yoking preceded positional yoking. Cognitive load increased trial variances of velocity with no increase in velocity yoking, suggesting that cognitive load may alter neural processes in areas to which oculomotor control is not tightly linked. The comparison between experimental conditions showed that interocular linkage in velocity variance of the right eye lagged that of the left eye during saccades.

Conclusions

We conclude quantitative measure of interocular yoking based on trial-to-trial variance within a condition, as well as variance between conditions, provides a powerful tool for studying the binocular movement mechanism.

     

The mutual information: detecting and evaluating dependencies between variables

MOTIVATION: Clustering co-expressed genes usually requires the definition of 'distance' or 'similarity' between measured datasets, the most common choices being Pearson correlation or Euclidean distance. With the size of available datasets steadily increasing, it has become feasible to consider other, more general, definitions as well. One alternative, based on information theory, is the mutual information, providing a general measure of dependencies between variables. While the use of mutual information in cluster analysis and visualization of large-scale gene expression data has been suggested previously, the earlier studies did not focus on comparing different algorithms to estimate the mutual information from finite data. RESULTS: Here we describe and review several approaches to estimate the mutual information from finite datasets. Our findings show that the algorithms used so far may be quite substantially improved upon. In particular when dealing with small datasets, finite sample effects and other sources of potentially misleading results have to be taken into account.     

In silico comparison of bacterial strains using mutual information

Fast-sequencing throughput methods have increased the number of completely sequenced bacterial genomes to about 400 by December 2006, with the number increasing rapidly. These include several strains. In silico methods of comparative genomics are of use in categorizing and phylogenetically sorting these bacteria. Various word-based tools have been used for quantifying the similarities and differences between entire genomes. The simple di-nucleotide frequency comparison, codon specificity and k-mer repeat detection are among some of the well-known methods. In this paper, we show that the Mutual Information function, which is a measure of correlations and a concept from Information Theory, is very effective in determining the similarities and differences among genome sequences of various strains of bacteria such as the plant pathogen Xylella fastidiosa, marine Cyanobacteria Prochlorococcus marinus or animal and human pathogens such as species of Ehrlichia and Legionella. The short-range three-base periodicity, small sequence repeats and long-range correlations taken together constitute a genome signature that can be used as a technique for identifying new bacterial strains with the help of strains already catalogued in the database. There have been several applications of using the Mutual Information function as a measure of correlations in genomics but this is the first whole genome analysis done to detect strain similarities and differences.     

Evaluation of gene-expression clustering via mutual information distance measure

Background  

The definition of a distance measure plays a key role in the evaluation of different clustering solutions of gene expression profiles. In this empirical study we compare different clustering solutions when using the Mutual Information (MI) measure versus the use of the well known Euclidean distance and Pearson correlation coefficient.     

A statistical approach for analyzing structural and regulative information in prokaryotic genomes

Although DNA is iconized as a straight double helix, it does not exist in this canonical form in biological systems. Instead, it is characterized by sequence dependent structural and dynamic deviations from the monotonous regularity of the canonical B-DNA. Despite the complexity of the system, we showed that DNA structural and dynamics large-scale properties can be predicted starting from the simple knowledge of nucleotide sequence by adopting a statistical approach. The paper reports the statistical analysis of large pools of different prokaryotic genes in terms of the sequence-dependent curvature and flexibility. Conserved features characterize the regions close to the Start Translation Site, which are related to their function in the regulation system. In addition, regular patterns with three-fold periodicity were found in the coding regions. They were reproduced in terms of the nucleotide frequency expected on the basis of the genetic code and the pertinent occurrence of the aminoacid residues.     

Second order dimensionality reduction using minimum and maximum mutual information models

Conventional methods used to characterize multidimensional neural feature selectivity, such as spike-triggered covariance (STC) or maximally informative dimensions (MID), are limited to Gaussian stimuli or are only able to identify a small number of features due to the curse of dimensionality. To overcome these issues, we propose two new dimensionality reduction methods that use minimum and maximum information models. These methods are information theoretic extensions of STC that can be used with non-Gaussian stimulus distributions to find relevant linear subspaces of arbitrary dimensionality. We compare these new methods to the conventional methods in two ways: with biologically-inspired simulated neurons responding to natural images and with recordings from macaque retinal and thalamic cells responding to naturalistic time-varying stimuli. With non-Gaussian stimuli, the minimum and maximum information methods significantly outperform STC in all cases, whereas MID performs best in the regime of low dimensional feature spaces.     

WinMGM: A fast CPK molecular graphics program for analyzing molecular structure

A molecular modeling program is presented which has been written for Microsoft windows 3.1 and Windows NT operating systems. The program permits interactive molecular manipulation and also provides analytical tools such as energy computations and solvent accessible surfaces. An extremely fast algorithm is used which generates realistic space-filling CPK images in addition to wire frame, ribbons, MIDAS, labels, and points. An important feature of this algorithm is a highly optimized Z-buffer, which is described.     

Frozen in time: analyzing molecular dynamics with time-resolved cryo-EM

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