International Journal of Bioinformatics Research and Applications最新文献

We develop a general method for computing extreme value distribution (Gumbel, 1958) parameters for gapped alignments. Our approach uses mixture distribution theory to obtain associated BLOSUM matrices for gapped alignments, which in turn are used for determining significance of gapped alignment scores for pairs of biological sequences. We compare our results with parameters already obtained in the literature.

β-lactam antibiotics are utilised to treat bacterial infection. β-lactamase enzymes (EC 3.5.2.6) are produced by several bacteria and are responsible for their resistance to β-lactam antibiotics like penicillin, cephamycins and carbapenems. New Delhi Metallo-β-lactamase (NDM-1) is a gene that makes bacteria resistant to β-lactam antibiotics. Preparing a compound against NDM-1 will require additional investment and development by drug manufacturers as the current antibiotics will not treat patients with NDM-1 resistance. NDM-1 of Kolkata showed convergent-type evolution with other NDM-1 producing strains. The modelled structure exhibited α-β-α barrel-type domain along with Zn metallo-β-lactamase N-terminal domain. Compounds belonging to cephalosporins (relatively resistant to β-lactamase) and other antibiotics ceftaroline, ceftobiprole, piperacillin, penamecillin, azidocillin, cefonicid, tigecycline and colistin have exhibited better binding affinity with the modelled NDM-1.

Content-based image retrieval has gained considerable attention in today's scenario as a useful tool in many applications; texture is one of them. In this paper, we focus on texture-based image retrieval in compressed domain using compressive sensing with the help of DC coefficients. Medical imaging is one of the fields which have been affected most, as there had been huge size of image database and getting out the concerned image had been a daunting task. Considering this, in this paper we propose a new model of image retrieval process using compressive sampling, since it allows accurate recovery of image from far fewer samples of unknowns and it does not require a close relation of matching between sampling pattern and characteristic image structure with increase acquisition speed and enhanced image quality.

Identification of closely related, ecologically distinct populations of bacteria would benefit microbiologists working in many fields including systematics, epidemiology and biotechnology. Several laboratories have recently developed algorithms aimed at demarcating such 'ecotypes'. We examine the ability of four of these algorithms to correctly identify ecotypes from sequence data. We tested the algorithms on synthetic sequences, with known history and habitat associations, generated under the stable ecotype model and on data from Bacillus strains isolated from Death Valley where previous work has confirmed the existence of multiple ecotypes. We found that one of the algorithms (ecotype simulation) performs significantly better than the others (AdaptML, GMYC, BAPS) in both instances. Unfortunately, it was also shown to be the least efficient of the four. While ecotype simulation is the most accurate, it is by a large margin the slowest of the algorithms tested. Attempts at improving its efficiency are underway.

We propose a model for simultaneous Orientation Distribution Function (ODF) reconstruction and regularisation. The ODFs are represented by real spherical harmonic functions, and we propose to solve the spherical harmonic coefficients of the ODFs, with spatial regularisation by minimising the Vectorial Total Variation (VTV) of the coefficients. The proposed model also incorporates angular regularisation of the ODFs using Laplace-Beltrami operator on the unit sphere. A modified primal-dual hybrid gradient algorithm is applied to solve the model efficiently. The experimental results indicate better directional structures of reconstructed ODFs.

Using of DNA molecules for solving of NP-complete problems is discussed. Properties of DNA allow one to reduce the number of operations from exponential to polynomial. DNA-algorithm for solving of the timetable problem is suggested. The starting point is a set of classes, teachers and hours with some limitations. It is necessary to determine whether there is a timetable satisfying all limitations. The sets of classes, teachers and hours are coded by chains of nucleotides. After preparing of the input multi-set containing all possible timetables the filtering procedure should be made. It allows to exclude all illegal timetables. The filtering algorithm is suggested. An example is described. The analysis of the algorithm is made.

Users of phylogenetic methods require rooted trees, because the direction of time depends on the placement of the root. While phylogenetic trees are typically rooted by using an out-group, this mechanism is inappropriate when the addition of an out-group changes the in-group topology. We perform a formal analysis of phylogenetic algorithms under the inclusion of distant out-groups. It turns out that linkage-based algorithms (including UPGMA) and a class of bisecting methods do not modify the topology of the in-group when an out-group is included. By contrast, the popular neighbour joining algorithm fails this property in a strong sense: every data set can have its structure destroyed by some arbitrarily distant outlier. Furthermore, including multiple outliers can lead to an arbitrary topology on the in-group. The standard rooting approach that uses out-groups may be fundamentally unsuited for neighbour joining.

Many non-coding RNAs (ncRNAs) can fold into alternate native structures and perform different biological functions. The computational prediction of an ncRNA's alternate native structures can be conducted by analysing the ncRNA's energy landscape. Previously, we have developed a computational approach, RNASLOpt, to predict alternate native structures for a single RNA. In this paper, in order to improve the accuracy of the prediction, we incorporate structural conservation information among a family of related ncRNA sequences to the prediction. We propose a comparative approach, RNAConSLOpt, to produce all possible consensus SLOpt stack configurations that are conserved on the consensus energy landscape of a family of related ncRNAs. Benchmarking tests show that RNAConSLOpt can reduce the number of candidate structures compared with RNASLOpt, and can predict ncRNAs' alternate native structures accurately. Moreover, an application of the proposed pipeline to bacteria in Bacillus genus has discovered several novel riboswitch candidates.

Stochastic Differential Equation (SDE) models are used to describe the dynamics of complex systems with inherent randomness. The primary purpose of these models is to study rare but interesting or important behaviours, such as the formation of a tumour. Stochastic simulations are the most common means for estimating (or bounding) the probability of rare behaviours, but the cost of simulations increases with the rarity of events. To address this problem, we introduce a new algorithm specifically designed to quantify the likelihood of rare behaviours in SDE models. Our approach relies on temporal logics for specifying rare behaviours of interest, and on the ability of bit-vector decision procedures to reason exhaustively about fixed-precision arithmetic. We apply our algorithm to a minimal parameterised model of the cell cycle, and take Brownian noise into account while investigating the likelihood of irregularities in cell size and time between cell divisions.

A comparative study of the various motif search algorithms is very important for several reasons. For example, we could identify the strengths and weaknesses of each. As a result, we might be able to devise hybrids that will perform better than the individual components. In this paper, we (either directly or indirectly) compare the performance of PMSprune (an algorithm based on the (l, d)-motif model) and several other algorithms in terms of seven measures and using well-established benchmarks. We have employed several benchmark datasets including the one used by Tompa et al. It is observed that both PMSprune and DME (an algorithm based on position-specific score matrices), in general, perform better than the 13 algorithms reported in Tompa et al. Subsequently, we have compared PMSprune and DME on other benchmark datasets including ChIP-Chip, ChIP-Seq and ABS. Between PMSprune and DME, PMSprune performs better than DME on six measures. DME performs better than PMSprune on one measure (namely, specificity).