Aubergene - a sensitive genome alignment tool.

Motivation: The accumulation of genome sequences will only accelerate in the coming years. We aim to use this abundance of data to improve the quality of genomic alignments and devise a method which is capable of detecting regions evolving under weak or no evolutionary constraints. Results: We describe a genome alignment program AuberGene, which explores the idea of transitivity of local alignments. Assessment of the program was done based on a 2 Mbp genomic region containing the CFTR gene of 13 species. In this region, we can identify 53% of human sequence sharing common ancestry with mouse, as compared with 44% found using the usual pairwise alignment. Between human and tetraodon 93 orthologous exons are found, as compared with 77 detected by the pairwise human-tetraodon comparison. AuberGene allows the user to (1) identify distant, previously undetected, conserved orthogonal regions such as ORFs or regulatory regions; (2) identify neutrally evolving regions in related species which are often overlooked by other alignment programs; (3) recognize false orthologous genomic regions. The increased sensitivity of the method is not obtained at the cost of reduced specificity. Our results suggest that, over the CFTR region, human shares 10% more sequence with mouse than previously thought (∼50%, instead of 40% found with the pairwise alignment). © 2006 Oxford University Press

Distance Matrix-Based Approach to Protein Structure Prediction

Much structural information is encoded in the internal distances; a distance matrix-based approach can be used to predict protein structure and dynamics, and for structural refinement. Our approach is based on the square distance matrix D = [rij2] containing all square distances between residues in proteins. This distance matrix contains more information than the contact matrix C, that has elements of either 0 or 1 depending on whether the distance rij is greater or less than a cutoff value rcutoff .We have performed spectral decomposition of the distance matrices D=∑λkVkVTk , in terms of eigenvalues λk and the corresponding eigenvectors vk and found that it contains at most 5 nonzero terms. A dominant eigenvector is proportional to r2 - the square distance of points from the center of mass, with the next three being the principal components of the system of points. By knowing r2 we can approximate a distance matrix of a protein with an expected RMSD value of about 4.5Å. We can also explain the role of hydrophobic interactions for the protein structure, because r is highly correlated with the hydrophobic profile of the sequence. Moreover, r is highly correlated with several sequence profiles which are useful in protein structure prediction, such as contact number, the residue-wise contact order (RWCO) or mean square fluctuations (i.e. crystallographic temperature factors). We have also shown that the next three components are related to spatial directionality of the secondary structure elements, and they may be also predicted from the sequence, improving overall structure prediction. We have also shown that the large number of available HIV-1 protease structures provides a remarkable sampling of conformations, which can be viewed as direct structural information about the dynamics. After structure matching, we apply principal component analysis (PCA) to obtain the important apparent motions for both bound and unbound structures. There are significant similarities between the first few key motions and the first few low-frequency normal modes calculated from a static representative structure with an elastic network model (ENM) that is based on the contact matrix C (related to D), strongly suggesting that the variations among the observed structures and the corresponding conformational changes are facilitated by the low-frequency, global motions intrinsic to the structure. Similarities are also found when the approach is applied to an NMR ensemble, as well as to atomic molecular dynamics (MD) trajectories. Thus, a sufficiently large number of experimental structures can directly provide important information about protein dynamics, but ENM can also provide a similar sampling of conformations. Finally, we use distance constraints from databases of known protein structures for structure refinement. We use the distributions of distances of various types in known protein structures to obtain the most probable ranges or the mean-force potentials for the distances. We then impose these constraints on structures to be refined or include the mean-force potentials directly in the energy minimization so that more plausible structural models can be built. This approach has been successfully used by us in 2006 in the CASPR structure refinement http://predictioncenter.org/caspR)

Improving the quality of multiple sequence alignment

Multiple sequence alignment is an important bioinformatics problem, with applications in diverse types of biological analysis, such as structure prediction, phylogenetic analysis and critical sites identification. In recent years, the quality of multiple sequence alignment was improved a lot by newly developed methods, although it remains a difficult task for constructing accurate alignments, especially for divergent sequences. In this dissertation, we propose three new methods (PSAlign, ISPAlign, and NRAlign) for further improving the quality of multiple sequences alignment. In PSAlign, we propose an alternative formulation of multiple sequence alignment based on the idea of finding a multiple alignment which preserves all the pairwise alignments specified by edges of a given tree. In contrast with traditional NP-hard formulations, our preserving alignment formulation can be solved in polynomial time without using a heuristic, while still retaining very good performance when compared to traditional heuristics. In ISPAlign, by using additional hits from database search of the input sequences, a few strategies have been proposed to significantly improve alignment accuracy, including the construction of profiles from the hits while performing profile alignment, the inclusion of high scoring hits into the input sequences, the use of intermediate sequence search to link distant homologs, and the use of secondary structure information. In NRAlign, we observe that it is possible to further improve alignment accuracy by taking into account alignment of neighboring residues when aligning two residues, thus making better use of horizontal information. By modifying existing multiple alignment algorithms to make use of horizontal information, we show that this strategy is able to consistently improve over existing algorithms on all the benchmarks that are commonly used to measure alignment accuracy

Estimating evolutionary dynamics of cleavage site peptides among H5HA avian influenza employing mathematical information theory approaches

Estimating evolutionary conservation of cleavage site peptides among HA protein of all strains facilitates vaccine development against pandemic influenza. Conserved epitopes may be useful for diagnosis of animals infected with the influenza virus, and preventing their spread in other regions [ 1]. In the preliminary stage of this study, in silico analysis of hemagglutinin was applied to predict potential cleavage sites of each strain employing SigCleave [2] and SignalP 3.0 server [3]. The second stage of the study focused on analyzing the structure of connecting peptides of hemagglutinin cleavage sites based on the availability of the existing experimental data. Our result divulges higher frequency of base amino acids, essential for processing by the cellular protease, among pathogenic strains compared with non/low pathogenic strains. In addition, two complementary methods for identifying conserved amino acids were applied: statistical entropy based method, possibly the most sensitive tool to estimate the diversity of peptides [5], and relative entropy estimation. Analysis of both methods demonstrates that the connecting peptide of HA cleavage site of AIV in the United States were highly conserved over long periods of time. Entropy values aid to select those sequences that have the highest potential for mutation in a broad spectrum of avian population. Position 340 among our group of strains with the entropy value of 0.877928 has the highest bit of information value where highly conserved positions are those with

Front Matter - Soft Computing for Data Mining Applications

Efficient tools and algorithms for knowledge discovery in large data sets have been devised during the recent years. These methods exploit the capability of computers to search huge amounts of data in a fast and effective manner. However, the data to be analyzed is imprecise and afflicted with uncertainty. In the case of heterogeneous data sources such as text, audio and video, the data might moreover be ambiguous and partly conflicting. Besides, patterns and relationships of interest are usually vague and approximate. Thus, in order to make the information mining process more robust or say, human-like methods for searching and learning it requires tolerance towards imprecision, uncertainty and exceptions. Thus, they have approximate reasoning capabilities and are capable of handling partial truth. Properties of the aforementioned kind are typical soft computing. Soft computing techniques like Genetic