42884670
Eulerian path methods for multiple sequence alignment
With the rapid increase in the size of genome sequence databases, the multiple sequence alignment problem is increasingly important and often requires the alignment of a large number of sequences. Beginning in 1975, many heuristic algorithms have been created to improve the speed of computation and the quality of alignment. We introduce a novel approach that is fundamentally distinct from all currently available methods. Our motivation comes from the Eulerian method for fragment assembly in DNA sequence determination, that transforms all the DNA sequencing fragments into a de Bruijn graph and then reduces sequence assembly to a Eulerian path problem.
12263549
Tree-guided Bayesian inference of population structures
MOTIVATION
Inferring population structures using genetic data sampled from a group of individuals is a challenging task. Many methods either consider a fixed population number or ignore the correlation between populations. As a result, they can lose sensitivity and specificity in detecting subtle stratifications. In addition, when a large number of genetic markers are used, many existing algorithms perform rather inefficiently.


RESULT
We propose a new Bayesian method to infer population structures using multiple unlinked single nucleotide polymorphisms (SNPs). Our approach explicitly considers the population correlation through a tree hierarchy, and treat the population number as a random variable. Using both simulated and real datasets of worldwide samples, we demonstrate that an incorporated tree can consistently improve the power in detecting subtle population stratifications. A tree-based model often involves a large number of unknown parameters, and the corresponding estimation procedure can be highly inefficient. We further implement a partition method to analytically integrate out all nuisance parameters in the tree. As a result, our method can analyze large SNP datasets with significantly improved convergence rate.


AVAILABILITY
http://www.stat.psu.edu/~yuzhang/tips.tar.
29942985
Poisson approximation for significance in genome-wide ChIP-chip tiling arrays
MOTIVATION
A genome-wide ChIP-chip tiling array study requires millions of simultaneous comparisons of hybridization for significance. Controlling the false positive rate in genome-wide tiling array studies is very important, because the number of computationally identified regions can easily go beyond the capability of experimental verification. No accurate and efficient method exists for evaluating statistical significance in tiling arrays. The Bonferroni method is overly conservative and the permutation test is time consuming for genome-wide studies.


RESULT
Motivated by the Poisson clumping heuristic, we propose an accurate and efficient method for evaluating statistical significance in genome-wide ChIP-chip tiling arrays. The method works accurately for any large number of multiple comparisons, and the computational cost for evaluating P-values does not increase with the total number of tests. Based on a moving window approach, we demonstrate how to combine results using various window sizes to increase the detection power while maintaining a specified type I error rate. We further introduce a new false discovery rate control that is more appropriate in measuring the false proportion of binding intervals in tiling array analysis. Our method is general and can be applied to many large-scale genomic and genetic studies.


AVAILABILITY
http://www.stat.psu.edu/~yuzhang/pass.tar
12935102
Whole-Genome Analysis of Gene Conversion Events
Gene conversion events are often overlooked in analyses of genome evolution. In a conversion event, an interval of DNA sequence (not necessarily containing a gene) overwrites a highly similar sequence. The event creates relationships among genomic intervals that can confound attempts to identify orthologs and to transfer functional annotation between genomes. Here we examine 1,112,202 paralogous pairs of human genomic intervals, and detect conversion events in about 13.5% of them. Properties of the putative gene conversions are analyzed, such as the lengths of the paralogous pairs and the spacing between their sources and targets. Our approach is illustrated using conversion events in the beta-globin gene cluster.
32462907
Alignment of Optical Maps
We introduce a new scoring method for calculation of alignments of optical maps. Missing cuts, false cuts, and sizing errors present in optical maps are addressed by our alignment score through calculation of corresponding likelihoods. The size error model is derived through the application of Central Limit Theorem and validated by residual plots collected from real data. Missing cuts and false cuts are modeled as Bernoulli and Poisson events, respectively, as suggested by previous studies. Likelihoods are used to derive an alignment score through calculation of likelihood ratios for a certain hypothesis test. This allows us to achieve maximal descriminative power for the alignment score. Our scoring method is naturally embedded within a well known DP framework for finding optimal alignments.
6114653
Alignment of Optical Maps
An adjustable device that provides a secure anchor in a crevice or other irregularity. The anchor may be used to assist rock climbers, rescue groups, military personnel, construction workers, etc. The device is comprised of a spring loaded sleeve mounted on a first end of a threaded shaft and limited in its movement on the shaft by a pin and slot arrangement. The sleeve provides a means for adjusting the length of the device. A first locking means releasably secures the sleeve in a retracted position, and a second locking means secures the sleeve at the desired extended position. A lever assembly is provided at the second end of the device in such a manner that a force applied to an attachment point of the assembly will be translated along the longitudinal axis of the device.
37630776
SAGE-based estimation of doubly selective channel with an orthogonal polynomial model
A space alternating generalized expectation-maximization (SAGE)-based estimation algorithm for doubly selective channel is proposed, which has very similar performance to the rectangular-windowed least squares methods introduced by Borah and Hart while providing large savings in computational complexity. Theoretical analysis shows that the proposed algorithm is always globally convergent. The convergence rate is also investigated through simulations.
36668943
Parallel Solution in Simulation of Cardiac Excitation Anisotropic Propagation
Solution in simulation of cardiac excitation anisotropic propagation throughout the ventricular myocardium is computationally very expensive that demands the introduction of a high performance computing techniques. In this study, a canine ventricle model was constructed features a realistic anatomical structure, including intramural fiber rotation and a conduction system. By using operator-splitting scheme, adaptive time step and backward differentiation formulation techniques in a parallel implement, we solved mondomain equation successfully. The stimulation produced isochrone's map is close to the clinical record that obtained from the non-contact mapping system of Ensite 3000. The results show that the proposed methods can successfully be used to simulate heart excitation anisotropic propagation in three-dimensional anatomical large tissue size.
4846997
Semi-supervised Discriminant Analysis Via CCCP
Linear discriminant analysis (LDA) is commonly used for dimensionality reduction. In real-world applications where labeled data are scarce, LDA does not work very well. However, unlabeled data are often available in large quantities. We propose a novel semi-supervised discriminant analysis algorithm called SSDA$_{\mathit{CCCP}}$. We utilize unlabeled data to maximize an optimality criterion of LDA and use the constrained concave-convex procedure to solve the optimization problem. The optimization procedure leads to estimation of the class labels for the unlabeled data. We propose a novel confidence measure for selecting those unlabeled data points with high confidence. The selected unlabeled data can then be used to augment the original labeled data set for performing LDA. We also propose a variant of SSDA$_{\mathit{CCCP}}$, called M-SSDA$_{\mathit{CCCP}}$, which adopts the manifold assumption to utilize the unlabeled data. Extensive experiments on many benchmark data sets demonstrate the effectiveness of our proposed methods.
40389264
Application of Efficient Numerical Methods in Solution of Ordinary Differential Equations for Modeling Electrical Activity in Cardiac Cells
There is a large number of ordinary differential equations (ODEs) characterize the electrical behavior generated by ionic movements in human myocardial cell. In this paper, several approaches were investigated in order to improve the efficiency of solving the ODE systems for ten Tusscher et al.’s ionic model of human ventricular tissue. By using non-standard finite difference (NSFD) scheme, the stiffness of the ODEs system will be successfully reduced, so a larger step-size can be used. A popular multi-step method called backward differentiation formulation (BDF) was also incorporated into the computational model for testing the largest possible time steps. The results show that NSFD can be as much as 10 times more efficient than standard forward Euler in single cell model simulation while maintaining an acceptable level of accuracy. The investigation of BDF method shows that a large step size is not recommended for the single cell simulation application. All solvers were coupled to the partial differential equations for the complete simulation of heart tissue, and such computation scheme may be a good calculation technique in heart modeling and simulation.
1781908
An EM-Based Time-Domain Channel Estimation Algorithm Using a priori Information
Based on the expectation-maximization (EM) algorithm, an iterative time-domain channel estimation approach capable of using a priori information is proposed for orthogonal frequency division multiplexing (OFDM) systems in this letter: it outperforms its noniterative counterpart in terms of estimation accuracy as well as bit error rate (BER) performance. Numerical simulations demonstrate that an SNR gain of 1 dB at BER = 10 -4 with only one iteration and estimation mean square error (MSE) which nearly coincides with the Cramer-Rao bound (CRB) in the low SNR region can be obtained, thanks to the efficient use of a priori information.