Assessment of gene order computing methods for Alzheimer's disease

PubMed Central

2013-01-01

Background Computational genomics of Alzheimer disease (AD), the most common form of senile dementia, is a nascent field in AD research. The field includes AD gene clustering by computing gene order which generates higher quality gene clustering patterns than most other clustering methods. However, there are few available gene order computing methods such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Further, their performance in gene order computation using AD microarray data is not known. We thus set forth to evaluate the performances of current gene order computing methods with different distance formulas, and to identify additional features associated with gene order computation. Methods Using different distance formulas- Pearson distance and Euclidean distance, the squared Euclidean distance, and other conditions, gene orders were calculated by ACO and GA (including standard GA and improved GA) methods, respectively. The qualities of the gene orders were compared, and new features from the calculated gene orders were identified. Results Compared to the GA methods tested in this study, ACO fits the AD microarray data the best when calculating gene order. In addition, the following features were revealed: different distance formulas generated a different quality of gene order, and the commonly used Pearson distance was not the best distance formula when used with both GA and ACO methods for AD microarray data. Conclusion Compared with Pearson distance and Euclidean distance, the squared Euclidean distance generated the best quality gene order computed by GA and ACO methods. PMID:23369541

Creation of Anatomically Accurate Computer-Aided Design (CAD) Solid Models from Medical Images

NASA Technical Reports Server (NTRS)

Stewart, John E.; Graham, R. Scott; Samareh, Jamshid A.; Oberlander, Eric J.; Broaddus, William C.

1999-01-01

Most surgical instrumentation and implants used in the world today are designed with sophisticated Computer-Aided Design (CAD)/Computer-Aided Manufacturing (CAM) software. This software automates the mechanical development of a product from its conceptual design through manufacturing. CAD software also provides a means of manipulating solid models prior to Finite Element Modeling (FEM). Few surgical products are designed in conjunction with accurate CAD models of human anatomy because of the difficulty with which these models are created. We have developed a novel technique that creates anatomically accurate, patient specific CAD solids from medical images in a matter of minutes.

Improved patient size estimates for accurate dose calculations in abdomen computed tomography

NASA Astrophysics Data System (ADS)

Lee, Chang-Lae

2017-07-01

The radiation dose of CT (computed tomography) is generally represented by the CTDI (CT dose index). CTDI, however, does not accurately predict the actual patient doses for different human body sizes because it relies on a cylinder-shaped head (diameter : 16 cm) and body (diameter : 32 cm) phantom. The purpose of this study was to eliminate the drawbacks of the conventional CTDI and to provide more accurate radiation dose information. Projection radiographs were obtained from water cylinder phantoms of various sizes, and the sizes of the water cylinder phantoms were calculated and verified using attenuation profiles. The effective diameter was also calculated using the attenuation of the abdominal projection radiographs of 10 patients. When the results of the attenuation-based method and the geometry-based method shown were compared with the results of the reconstructed-axial-CT-image-based method, the effective diameter of the attenuation-based method was found to be similar to the effective diameter of the reconstructed-axial-CT-image-based method, with a difference of less than 3.8%, but the geometry-based method showed a difference of less than 11.4%. This paper proposes a new method of accurately computing the radiation dose of CT based on the patient sizes. This method computes and provides the exact patient dose before the CT scan, and can therefore be effectively used for imaging and dose control.

Accurate computation of survival statistics in genome-wide studies.

PubMed

Vandin, Fabio; Papoutsaki, Alexandra; Raphael, Benjamin J; Upfal, Eli

2015-05-01

A key challenge in genomics is to identify genetic variants that distinguish patients with different survival time following diagnosis or treatment. While the log-rank test is widely used for this purpose, nearly all implementations of the log-rank test rely on an asymptotic approximation that is not appropriate in many genomics applications. This is because: the two populations determined by a genetic variant may have very different sizes; and the evaluation of many possible variants demands highly accurate computation of very small p-values. We demonstrate this problem for cancer genomics data where the standard log-rank test leads to many false positive associations between somatic mutations and survival time. We develop and analyze a novel algorithm, Exact Log-rank Test (ExaLT), that accurately computes the p-value of the log-rank statistic under an exact distribution that is appropriate for any size populations. We demonstrate the advantages of ExaLT on data from published cancer genomics studies, finding significant differences from the reported p-values. We analyze somatic mutations in six cancer types from The Cancer Genome Atlas (TCGA), finding mutations with known association to survival as well as several novel associations. In contrast, standard implementations of the log-rank test report dozens-hundreds of likely false positive associations as more significant than these known associations.

Accurate Computation of Survival Statistics in Genome-Wide Studies

PubMed Central

Vandin, Fabio; Papoutsaki, Alexandra; Raphael, Benjamin J.; Upfal, Eli

2015-01-01

A key challenge in genomics is to identify genetic variants that distinguish patients with different survival time following diagnosis or treatment. While the log-rank test is widely used for this purpose, nearly all implementations of the log-rank test rely on an asymptotic approximation that is not appropriate in many genomics applications. This is because: the two populations determined by a genetic variant may have very different sizes; and the evaluation of many possible variants demands highly accurate computation of very small p-values. We demonstrate this problem for cancer genomics data where the standard log-rank test leads to many false positive associations between somatic mutations and survival time. We develop and analyze a novel algorithm, Exact Log-rank Test (ExaLT), that accurately computes the p-value of the log-rank statistic under an exact distribution that is appropriate for any size populations. We demonstrate the advantages of ExaLT on data from published cancer genomics studies, finding significant differences from the reported p-values. We analyze somatic mutations in six cancer types from The Cancer Genome Atlas (TCGA), finding mutations with known association to survival as well as several novel associations. In contrast, standard implementations of the log-rank test report dozens-hundreds of likely false positive associations as more significant than these known associations. PMID:25950620

An Accurate and Dynamic Computer Graphics Muscle Model

NASA Technical Reports Server (NTRS)

Levine, David Asher

1997-01-01

A computer based musculo-skeletal model was developed at the University in the departments of Mechanical and Biomedical Engineering. This model accurately represents human shoulder kinematics. The result of this model is the graphical display of bones moving through an appropriate range of motion based on inputs of EMGs and external forces. The need existed to incorporate a geometric muscle model in the larger musculo-skeletal model. Previous muscle models did not accurately represent muscle geometries, nor did they account for the kinematics of tendons. This thesis covers the creation of a new muscle model for use in the above musculo-skeletal model. This muscle model was based on anatomical data from the Visible Human Project (VHP) cadaver study. Two-dimensional digital images from the VHP were analyzed and reconstructed to recreate the three-dimensional muscle geometries. The recreated geometries were smoothed, reduced, and sliced to form data files defining the surfaces of each muscle. The muscle modeling function opened these files during run-time and recreated the muscle surface. The modeling function applied constant volume limitations to the muscle and constant geometry limitations to the tendons.

A Unified Methodology for Computing Accurate Quaternion Color Moments and Moment Invariants.

PubMed

Karakasis, Evangelos G; Papakostas, George A; Koulouriotis, Dimitrios E; Tourassis, Vassilios D

2014-02-01

In this paper, a general framework for computing accurate quaternion color moments and their corresponding invariants is proposed. The proposed unified scheme arose by studying the characteristics of different orthogonal polynomials. These polynomials are used as kernels in order to form moments, the invariants of which can easily be derived. The resulted scheme permits the usage of any polynomial-like kernel in a unified and consistent way. The resulted moments and moment invariants demonstrate robustness to noisy conditions and high discriminative power. Additionally, in the case of continuous moments, accurate computations take place to avoid approximation errors. Based on this general methodology, the quaternion Tchebichef, Krawtchouk, Dual Hahn, Legendre, orthogonal Fourier-Mellin, pseudo Zernike and Zernike color moments, and their corresponding invariants are introduced. A selected paradigm presents the reconstruction capability of each moment family, whereas proper classification scenarios evaluate the performance of color moment invariants.

Methods for Efficiently and Accurately Computing Quantum Mechanical Free Energies for Enzyme Catalysis.

PubMed

Kearns, F L; Hudson, P S; Boresch, S; Woodcock, H L

2016-01-01

Enzyme activity is inherently linked to free energies of transition states, ligand binding, protonation/deprotonation, etc.; these free energies, and thus enzyme function, can be affected by residue mutations, allosterically induced conformational changes, and much more. Therefore, being able to predict free energies associated with enzymatic processes is critical to understanding and predicting their function. Free energy simulation (FES) has historically been a computational challenge as it requires both the accurate description of inter- and intramolecular interactions and adequate sampling of all relevant conformational degrees of freedom. The hybrid quantum mechanical molecular mechanical (QM/MM) framework is the current tool of choice when accurate computations of macromolecular systems are essential. Unfortunately, robust and efficient approaches that employ the high levels of computational theory needed to accurately describe many reactive processes (ie, ab initio, DFT), while also including explicit solvation effects and accounting for extensive conformational sampling are essentially nonexistent. In this chapter, we will give a brief overview of two recently developed methods that mitigate several major challenges associated with QM/MM FES: the QM non-Boltzmann Bennett's acceptance ratio method and the QM nonequilibrium work method. We will also describe usage of these methods to calculate free energies associated with (1) relative properties and (2) along reaction paths, using simple test cases with relevance to enzymes examples. © 2016 Elsevier Inc. All rights reserved.

Accurate computation of gravitational field of a tesseroid

NASA Astrophysics Data System (ADS)

Fukushima, Toshio

2018-02-01

We developed an accurate method to compute the gravitational field of a tesseroid. The method numerically integrates a surface integral representation of the gravitational potential of the tesseroid by conditionally splitting its line integration intervals and by using the double exponential quadrature rule. Then, it evaluates the gravitational acceleration vector and the gravity gradient tensor by numerically differentiating the numerically integrated potential. The numerical differentiation is conducted by appropriately switching the central and the single-sided second-order difference formulas with a suitable choice of the test argument displacement. If necessary, the new method is extended to the case of a general tesseroid with the variable density profile, the variable surface height functions, and/or the variable intervals in longitude or in latitude. The new method is capable of computing the gravitational field of the tesseroid independently on the location of the evaluation point, namely whether outside, near the surface of, on the surface of, or inside the tesseroid. The achievable precision is 14-15 digits for the potential, 9-11 digits for the acceleration vector, and 6-8 digits for the gradient tensor in the double precision environment. The correct digits are roughly doubled if employing the quadruple precision computation. The new method provides a reliable procedure to compute the topographic gravitational field, especially that near, on, and below the surface. Also, it could potentially serve as a sure reference to complement and elaborate the existing approaches using the Gauss-Legendre quadrature or other standard methods of numerical integration.

An Accurate and Computationally Efficient Model for Membrane-Type Circular-Symmetric Micro-Hotplates

PubMed Central

Khan, Usman; Falconi, Christian

2014-01-01

Ideally, the design of high-performance micro-hotplates would require a large number of simulations because of the existence of many important design parameters as well as the possibly crucial effects of both spread and drift. However, the computational cost of FEM simulations, which are the only available tool for accurately predicting the temperature in micro-hotplates, is very high. As a result, micro-hotplate designers generally have no effective simulation-tools for the optimization. In order to circumvent these issues, here, we propose a model for practical circular-symmetric micro-hot-plates which takes advantage of modified Bessel functions, computationally efficient matrix-approach for considering the relevant boundary conditions, Taylor linearization for modeling the Joule heating and radiation losses, and external-region-segmentation strategy in order to accurately take into account radiation losses in the entire micro-hotplate. The proposed model is almost as accurate as FEM simulations and two to three orders of magnitude more computationally efficient (e.g., 45 s versus more than 8 h). The residual errors, which are mainly associated to the undesired heating in the electrical contacts, are small (e.g., few degrees Celsius for an 800 °C operating temperature) and, for important analyses, almost constant. Therefore, we also introduce a computationally-easy single-FEM-compensation strategy in order to reduce the residual errors to about 1 °C. As illustrative examples of the power of our approach, we report the systematic investigation of a spread in the membrane thermal conductivity and of combined variations of both ambient and bulk temperatures. Our model enables a much faster characterization of micro-hotplates and, thus, a much more effective optimization prior to fabrication. PMID:24763214

Accurate HLA type inference using a weighted similarity graph.

PubMed

Xie, Minzhu; Li, Jing; Jiang, Tao

2010-12-14

The human leukocyte antigen system (HLA) contains many highly variable genes. HLA genes play an important role in the human immune system, and HLA gene matching is crucial for the success of human organ transplantations. Numerous studies have demonstrated that variation in HLA genes is associated with many autoimmune, inflammatory and infectious diseases. However, typing HLA genes by serology or PCR is time consuming and expensive, which limits large-scale studies involving HLA genes. Since it is much easier and cheaper to obtain single nucleotide polymorphism (SNP) genotype data, accurate computational algorithms to infer HLA gene types from SNP genotype data are in need. To infer HLA types from SNP genotypes, the first step is to infer SNP haplotypes from genotypes. However, for the same SNP genotype data set, the haplotype configurations inferred by different methods are usually inconsistent, and it is often difficult to decide which one is true. In this paper, we design an accurate HLA gene type inference algorithm by utilizing SNP genotype data from pedigrees, known HLA gene types of some individuals and the relationship between inferred SNP haplotypes and HLA gene types. Given a set of haplotypes inferred from the genotypes of a population consisting of many pedigrees, the algorithm first constructs a weighted similarity graph based on a new haplotype similarity measure and derives constraint edges from known HLA gene types. Based on the principle that different HLA gene alleles should have different background haplotypes, the algorithm searches for an optimal labeling of all the haplotypes with unknown HLA gene types such that the total weight among the same HLA gene types is maximized. To deal with ambiguous haplotype solutions, we use a genetic algorithm to select haplotype configurations that tend to maximize the same optimization criterion. Our experiments on a previously typed subset of the HapMap data show that the algorithm is highly accurate

Multislice Computed Tomography Accurately Detects Stenosis in Coronary Artery Bypass Conduits

PubMed Central

Duran, Cihan; Sagbas, Ertan; Caynak, Baris; Sanisoglu, Ilhan; Akpinar, Belhhan; Gulbaran, Murat

2007-01-01

The aim of this study was to evaluate the accuracy of multislice computed tomography in detecting graft stenosis or occlusion after coronary artery bypass grafting, using coronary angiography as the standard. From January 2005 through May 2006, 25 patients (19 men and 6 women; mean age, 54 ± 11.3 years) underwent diagnostic investigation of their bypass grafts by multislice computed tomography within 1 month of coronary angiography. The mean time elapsed after coronary artery bypass grafting was 6.2 years. In these 25 patients, we examined 65 bypass conduits (24 arterial and 41 venous) and 171 graft segments (the shaft, proximal anastomosis, and distal anastomosis). Compared with coronary angiography, the segment-based sensitivity, specificity, and positive and negative predictive values of multislice computed tomography in the evaluation of stenosis were 89%, 100%, 100%, and 99%, respectively. The patency rate for multislice compu-ted tomography was 85% (55/65: 3 arterial and 7 venous grafts were occluded), with 100% sensitivity and specificity. From these data, we conclude that multislice computed tomography can accurately evaluate the patency and stenosis of bypass grafts during outpatient follow-up. PMID:17948078

Genome-Wide Comparative Gene Family Classification

PubMed Central

Frech, Christian; Chen, Nansheng

2010-01-01

Correct classification of genes into gene families is important for understanding gene function and evolution. Although gene families of many species have been resolved both computationally and experimentally with high accuracy, gene family classification in most newly sequenced genomes has not been done with the same high standard. This project has been designed to develop a strategy to effectively and accurately classify gene families across genomes. We first examine and compare the performance of computer programs developed for automated gene family classification. We demonstrate that some programs, including the hierarchical average-linkage clustering algorithm MC-UPGMA and the popular Markov clustering algorithm TRIBE-MCL, can reconstruct manual curation of gene families accurately. However, their performance is highly sensitive to parameter setting, i.e. different gene families require different program parameters for correct resolution. To circumvent the problem of parameterization, we have developed a comparative strategy for gene family classification. This strategy takes advantage of existing curated gene families of reference species to find suitable parameters for classifying genes in related genomes. To demonstrate the effectiveness of this novel strategy, we use TRIBE-MCL to classify chemosensory and ABC transporter gene families in C. elegans and its four sister species. We conclude that fully automated programs can establish biologically accurate gene families if parameterized accordingly. Comparative gene family classification finds optimal parameters automatically, thus allowing rapid insights into gene families of newly sequenced species. PMID:20976221

Evaluating Computational Gene Ontology Annotations.

PubMed

Škunca, Nives; Roberts, Richard J; Steffen, Martin

2017-01-01

Two avenues to understanding gene function are complementary and often overlapping: experimental work and computational prediction. While experimental annotation generally produces high-quality annotations, it is low throughput. Conversely, computational annotations have broad coverage, but the quality of annotations may be variable, and therefore evaluating the quality of computational annotations is a critical concern.In this chapter, we provide an overview of strategies to evaluate the quality of computational annotations. First, we discuss why evaluating quality in this setting is not trivial. We highlight the various issues that threaten to bias the evaluation of computational annotations, most of which stem from the incompleteness of biological databases. Second, we discuss solutions that address these issues, for example, targeted selection of new experimental annotations and leveraging the existing experimental annotations.

Validation of reference genes aiming accurate normalization of qRT-PCR data in Dendrocalamus latiflorus Munro.

PubMed

Liu, Mingying; Jiang, Jing; Han, Xiaojiao; Qiao, Guirong; Zhuo, Renying

2014-01-01

Dendrocalamus latiflorus Munro distributes widely in subtropical areas and plays vital roles as valuable natural resources. The transcriptome sequencing for D. latiflorus Munro has been performed and numerous genes especially those predicted to be unique to D. latiflorus Munro were revealed. qRT-PCR has become a feasible approach to uncover gene expression profiling, and the accuracy and reliability of the results obtained depends upon the proper selection of stable reference genes for accurate normalization. Therefore, a set of suitable internal controls should be validated for D. latiflorus Munro. In this report, twelve candidate reference genes were selected and the assessment of gene expression stability was performed in ten tissue samples and four leaf samples from seedlings and anther-regenerated plants of different ploidy. The PCR amplification efficiency was estimated, and the candidate genes were ranked according to their expression stability using three software packages: geNorm, NormFinder and Bestkeeper. GAPDH and EF1α were characterized to be the most stable genes among different tissues or in all the sample pools, while CYP showed low expression stability. RPL3 had the optimal performance among four leaf samples. The application of verified reference genes was illustrated by analyzing ferritin and laccase expression profiles among different experimental sets. The analysis revealed the biological variation in ferritin and laccase transcript expression among the tissues studied and the individual plants. geNorm, NormFinder, and BestKeeper analyses recommended different suitable reference gene(s) for normalization according to the experimental sets. GAPDH and EF1α had the highest expression stability across different tissues and RPL3 for the other sample set. This study emphasizes the importance of validating superior reference genes for qRT-PCR analysis to accurately normalize gene expression of D. latiflorus Munro.

Time accurate application of the MacCormack 2-4 scheme on massively parallel computers

NASA Technical Reports Server (NTRS)

Hudson, Dale A.; Long, Lyle N.

1995-01-01

Many recent computational efforts in turbulence and acoustics research have used higher order numerical algorithms. One popular method has been the explicit MacCormack 2-4 scheme. The MacCormack 2-4 scheme is second order accurate in time and fourth order accurate in space, and is stable for CFL's below 2/3. Current research has shown that the method can give accurate results but does exhibit significant Gibbs phenomena at sharp discontinuities. The impact of adding Jameson type second, third, and fourth order artificial viscosity was examined here. Category 2 problems, the nonlinear traveling wave and the Riemann problem, were computed using a CFL number of 0.25. This research has found that dispersion errors can be significantly reduced or nearly eliminated by using a combination of second and third order terms in the damping. Use of second and fourth order terms reduced the magnitude of dispersion errors but not as effectively as the second and third order combination. The program was coded using Thinking Machine's CM Fortran, a variant of Fortran 90/High Performance Fortran, and was executed on a 2K CM-200. Simple extrapolation boundary conditions were used for both problems.

A comparative analysis of soft computing techniques for gene prediction.

PubMed

Goel, Neelam; Singh, Shailendra; Aseri, Trilok Chand

2013-07-01

The rapid growth of genomic sequence data for both human and nonhuman species has made analyzing these sequences, especially predicting genes in them, very important and is currently the focus of many research efforts. Beside its scientific interest in the molecular biology and genomics community, gene prediction is of considerable importance in human health and medicine. A variety of gene prediction techniques have been developed for eukaryotes over the past few years. This article reviews and analyzes the application of certain soft computing techniques in gene prediction. First, the problem of gene prediction and its challenges are described. These are followed by different soft computing techniques along with their application to gene prediction. In addition, a comparative analysis of different soft computing techniques for gene prediction is given. Finally some limitations of the current research activities and future research directions are provided. Copyright © 2013 Elsevier Inc. All rights reserved.

Reference Genes for Accurate Transcript Normalization in Citrus Genotypes under Different Experimental Conditions

PubMed Central

Mafra, Valéria; Kubo, Karen S.; Alves-Ferreira, Marcio; Ribeiro-Alves, Marcelo; Stuart, Rodrigo M.; Boava, Leonardo P.; Rodrigues, Carolina M.; Machado, Marcos A.

2012-01-01

Real-time reverse transcription PCR (RT-qPCR) has emerged as an accurate and widely used technique for expression profiling of selected genes. However, obtaining reliable measurements depends on the selection of appropriate reference genes for gene expression normalization. The aim of this work was to assess the expression stability of 15 candidate genes to determine which set of reference genes is best suited for transcript normalization in citrus in different tissues and organs and leaves challenged with five pathogens (Alternaria alternata, Phytophthora parasitica, Xylella fastidiosa and Candidatus Liberibacter asiaticus). We tested traditional genes used for transcript normalization in citrus and orthologs of Arabidopsis thaliana genes described as superior reference genes based on transcriptome data. geNorm and NormFinder algorithms were used to find the best reference genes to normalize all samples and conditions tested. Additionally, each biotic stress was individually analyzed by geNorm. In general, FBOX (encoding a member of the F-box family) and GAPC2 (GAPDH) was the most stable candidate gene set assessed under the different conditions and subsets tested, while CYP (cyclophilin), TUB (tubulin) and CtP (cathepsin) were the least stably expressed genes found. Validation of the best suitable reference genes for normalizing the expression level of the WRKY70 transcription factor in leaves infected with Candidatus Liberibacter asiaticus showed that arbitrary use of reference genes without previous testing could lead to misinterpretation of data. Our results revealed FBOX, SAND (a SAND family protein), GAPC2 and UPL7 (ubiquitin protein ligase 7) to be superior reference genes, and we recommend their use in studies of gene expression in citrus species and relatives. This work constitutes the first systematic analysis for the selection of superior reference genes for transcript normalization in different citrus organs and under biotic stress. PMID:22347455

Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets

PubMed Central

2014-01-01

Background Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism. Results S. cerevisiae cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug’s transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions – exposure time and concentration and (ii) Network training conditions – training compendium modifications. Two analyses of SSEM-Lasso output – gene set and single gene – were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets. Conclusions This study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved

Higher-order accurate space-time schemes for computational astrophysics—Part I: finite volume methods

NASA Astrophysics Data System (ADS)

Balsara, Dinshaw S.

2017-12-01

As computational astrophysics comes under pressure to become a precision science, there is an increasing need to move to high accuracy schemes for computational astrophysics. The algorithmic needs of computational astrophysics are indeed very special. The methods need to be robust and preserve the positivity of density and pressure. Relativistic flows should remain sub-luminal. These requirements place additional pressures on a computational astrophysics code, which are usually not felt by a traditional fluid dynamics code. Hence the need for a specialized review. The focus here is on weighted essentially non-oscillatory (WENO) schemes, discontinuous Galerkin (DG) schemes and PNPM schemes. WENO schemes are higher order extensions of traditional second order finite volume schemes. At third order, they are most similar to piecewise parabolic method schemes, which are also included. DG schemes evolve all the moments of the solution, with the result that they are more accurate than WENO schemes. PNPM schemes occupy a compromise position between WENO and DG schemes. They evolve an Nth order spatial polynomial, while reconstructing higher order terms up to Mth order. As a result, the timestep can be larger. Time-dependent astrophysical codes need to be accurate in space and time with the result that the spatial and temporal accuracies must be matched. This is realized with the help of strong stability preserving Runge-Kutta schemes and ADER (Arbitrary DERivative in space and time) schemes, both of which are also described. The emphasis of this review is on computer-implementable ideas, not necessarily on the underlying theory.

Computational challenges in modeling gene regulatory events.

PubMed

Pataskar, Abhijeet; Tiwari, Vijay K

2016-10-19

Cellular transcriptional programs driven by genetic and epigenetic mechanisms could be better understood by integrating "omics" data and subsequently modeling the gene-regulatory events. Toward this end, computational biology should keep pace with evolving experimental procedures and data availability. This article gives an exemplified account of the current computational challenges in molecular biology.

Accurate optimization of amino acid form factors for computing small-angle X-ray scattering intensity of atomistic protein structures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tong, Dudu; Yang, Sichun; Lu, Lanyuan

2016-06-20

Structure modellingviasmall-angle X-ray scattering (SAXS) data generally requires intensive computations of scattering intensity from any given biomolecular structure, where the accurate evaluation of SAXS profiles using coarse-grained (CG) methods is vital to improve computational efficiency. To date, most CG SAXS computing methods have been based on a single-bead-per-residue approximation but have neglected structural correlations between amino acids. To improve the accuracy of scattering calculations, accurate CG form factors of amino acids are now derived using a rigorous optimization strategy, termed electron-density matching (EDM), to best fit electron-density distributions of protein structures. This EDM method is compared with and tested againstmore » other CG SAXS computing methods, and the resulting CG SAXS profiles from EDM agree better with all-atom theoretical SAXS data. By including the protein hydration shell represented by explicit CG water molecules and the correction of protein excluded volume, the developed CG form factors also reproduce the selected experimental SAXS profiles with very small deviations. Taken together, these EDM-derived CG form factors present an accurate and efficient computational approach for SAXS computing, especially when higher molecular details (represented by theqrange of the SAXS data) become necessary for effective structure modelling.« less

Fast and accurate computation of system matrix for area integral model-based algebraic reconstruction technique

NASA Astrophysics Data System (ADS)

Zhang, Shunli; Zhang, Dinghua; Gong, Hao; Ghasemalizadeh, Omid; Wang, Ge; Cao, Guohua

2014-11-01

Iterative algorithms, such as the algebraic reconstruction technique (ART), are popular for image reconstruction. For iterative reconstruction, the area integral model (AIM) is more accurate for better reconstruction quality than the line integral model (LIM). However, the computation of the system matrix for AIM is more complex and time-consuming than that for LIM. Here, we propose a fast and accurate method to compute the system matrix for AIM. First, we calculate the intersection of each boundary line of a narrow fan-beam with pixels in a recursive and efficient manner. Then, by grouping the beam-pixel intersection area into six types according to the slopes of the two boundary lines, we analytically compute the intersection area of the narrow fan-beam with the pixels in a simple algebraic fashion. Overall, experimental results show that our method is about three times faster than the Siddon algorithm and about two times faster than the distance-driven model (DDM) in computation of the system matrix. The reconstruction speed of our AIM-based ART is also faster than the LIM-based ART that uses the Siddon algorithm and DDM-based ART, for one iteration. The fast reconstruction speed of our method was accomplished without compromising the image quality.

Computational challenges in modeling gene regulatory events

PubMed Central

Pataskar, Abhijeet; Tiwari, Vijay K.

2016-01-01

ABSTRACT Cellular transcriptional programs driven by genetic and epigenetic mechanisms could be better understood by integrating “omics” data and subsequently modeling the gene-regulatory events. Toward this end, computational biology should keep pace with evolving experimental procedures and data availability. This article gives an exemplified account of the current computational challenges in molecular biology. PMID:27390891

A simplified approach to characterizing a kilovoltage source spectrum for accurate dose computation.

PubMed

Poirier, Yannick; Kouznetsov, Alexei; Tambasco, Mauro

2012-06-01

%. The HVL and kVp are sufficient for characterizing a kV x-ray source spectrum for accurate dose computation. As these parameters can be easily and accurately measured, they provide for a clinically feasible approach to characterizing a kV energy spectrum to be used for patient specific x-ray dose computations. Furthermore, these results provide experimental validation of our novel hybrid dose computation algorithm. © 2012 American Association of Physicists in Medicine.

Rapid functional analysis of computationally complex rare human IRF6 gene variants using a novel zebrafish model.

PubMed

Li, Edward B; Truong, Dawn; Hallett, Shawn A; Mukherjee, Kusumika; Schutte, Brian C; Liao, Eric C

2017-09-01

Large-scale sequencing efforts have captured a rapidly growing catalogue of genetic variations. However, the accurate establishment of gene variant pathogenicity remains a central challenge in translating personal genomics information to clinical decisions. Interferon Regulatory Factor 6 (IRF6) gene variants are significant genetic contributors to orofacial clefts. Although approximately three hundred IRF6 gene variants have been documented, their effects on protein functions remain difficult to interpret. Here, we demonstrate the protein functions of human IRF6 missense gene variants could be rapidly assessed in detail by their abilities to rescue the irf6 -/- phenotype in zebrafish through variant mRNA microinjections at the one-cell stage. The results revealed many missense variants previously predicted by traditional statistical and computational tools to be loss-of-function and pathogenic retained partial or full protein function and rescued the zebrafish irf6 -/- periderm rupture phenotype. Through mRNA dosage titration and analysis of the Exome Aggregation Consortium (ExAC) database, IRF6 missense variants were grouped by their abilities to rescue at various dosages into three functional categories: wild type function, reduced function, and complete loss-of-function. This sensitive and specific biological assay was able to address the nuanced functional significances of IRF6 missense gene variants and overcome many limitations faced by current statistical and computational tools in assigning variant protein function and pathogenicity. Furthermore, it unlocked the possibility for characterizing yet undiscovered human IRF6 missense gene variants from orofacial cleft patients, and illustrated a generalizable functional genomics paradigm in personalized medicine.

Identification of Importin 8 (IPO8) as the most accurate reference gene for the clinicopathological analysis of lung specimens

PubMed Central

Nguewa, Paul A; Agorreta, Jackeline; Blanco, David; Lozano, Maria Dolores; Gomez-Roman, Javier; Sanchez, Blas A; Valles, Iñaki; Pajares, Maria J; Pio, Ruben; Rodriguez, Maria Jose; Montuenga, Luis M; Calvo, Alfonso

2008-01-01

Background The accurate normalization of differentially expressed genes in lung cancer is essential for the identification of novel therapeutic targets and biomarkers by real time RT-PCR and microarrays. Although classical "housekeeping" genes, such as GAPDH, HPRT1, and beta-actin have been widely used in the past, their accuracy as reference genes for lung tissues has not been proven. Results We have conducted a thorough analysis of a panel of 16 candidate reference genes for lung specimens and lung cell lines. Gene expression was measured by quantitative real time RT-PCR and expression stability was analyzed with the softwares GeNorm and NormFinder, mean of |ΔCt| (= |Ct Normal-Ct tumor|) ± SEM, and correlation coefficients among genes. Systematic comparison between candidates led us to the identification of a subset of suitable reference genes for clinical samples: IPO8, ACTB, POLR2A, 18S, and PPIA. Further analysis showed that IPO8 had a very low mean of |ΔCt| (0.70 ± 0.09), with no statistically significant differences between normal and malignant samples and with excellent expression stability. Conclusion Our data show that IPO8 is the most accurate reference gene for clinical lung specimens. In addition, we demonstrate that the commonly used genes GAPDH and HPRT1 are inappropriate to normalize data derived from lung biopsies, although they are suitable as reference genes for lung cell lines. We thus propose IPO8 as a novel reference gene for lung cancer samples. PMID:19014639

Discovering and understanding oncogenic gene fusions through data intensive computational approaches

PubMed Central

Latysheva, Natasha S.; Babu, M. Madan

2016-01-01

Abstract Although gene fusions have been recognized as important drivers of cancer for decades, our understanding of the prevalence and function of gene fusions has been revolutionized by the rise of next-generation sequencing, advances in bioinformatics theory and an increasing capacity for large-scale computational biology. The computational work on gene fusions has been vastly diverse, and the present state of the literature is fragmented. It will be fruitful to merge three camps of gene fusion bioinformatics that appear to rarely cross over: (i) data-intensive computational work characterizing the molecular biology of gene fusions; (ii) development research on fusion detection tools, candidate fusion prioritization algorithms and dedicated fusion databases and (iii) clinical research that seeks to either therapeutically target fusion transcripts and proteins or leverages advances in detection tools to perform large-scale surveys of gene fusion landscapes in specific cancer types. In this review, we unify these different—yet highly complementary and symbiotic—approaches with the view that increased synergy will catalyze advancements in gene fusion identification, characterization and significance evaluation. PMID:27105842

Reverse engineering and analysis of large genome-scale gene networks

PubMed Central

Aluru, Maneesha; Zola, Jaroslaw; Nettleton, Dan; Aluru, Srinivas

2013-01-01

Reverse engineering the whole-genome networks of complex multicellular organisms continues to remain a challenge. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute intensive limiting their scale of applicability. To enable fast and accurate reconstruction of large networks, we developed Tool for Inferring Network of Genes (TINGe), a parallel mutual information (MI)-based program. The novel features of our approach include: (i) B-spline-based formulation for linear-time computation of MI, (ii) a novel algorithm for direct permutation testing and (iii) development of parallel algorithms to reduce run-time and facilitate construction of large networks. We assess the quality of our method by comparison with ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks) and GeneNet and demonstrate its unique capability by reverse engineering the whole-genome network of Arabidopsis thaliana from 3137 Affymetrix ATH1 GeneChips in just 9 min on a 1024-core cluster. We further report on the development of a new software Gene Network Analyzer (GeNA) for extracting context-specific subnetworks from a given set of seed genes. Using TINGe and GeNA, we performed analysis of 241 Arabidopsis AraCyc 8.0 pathways, and the results are made available through the web. PMID:23042249

Sign: large-scale gene network estimation environment for high performance computing.

PubMed

Tamada, Yoshinori; Shimamura, Teppei; Yamaguchi, Rui; Imoto, Seiya; Nagasaki, Masao; Miyano, Satoru

2011-01-01

Our research group is currently developing software for estimating large-scale gene networks from gene expression data. The software, called SiGN, is specifically designed for the Japanese flagship supercomputer "K computer" which is planned to achieve 10 petaflops in 2012, and other high performance computing environments including Human Genome Center (HGC) supercomputer system. SiGN is a collection of gene network estimation software with three different sub-programs: SiGN-BN, SiGN-SSM and SiGN-L1. In these three programs, five different models are available: static and dynamic nonparametric Bayesian networks, state space models, graphical Gaussian models, and vector autoregressive models. All these models require a huge amount of computational resources for estimating large-scale gene networks and therefore are designed to be able to exploit the speed of 10 petaflops. The software will be available freely for "K computer" and HGC supercomputer system users. The estimated networks can be viewed and analyzed by Cell Illustrator Online and SBiP (Systems Biology integrative Pipeline). The software project web site is available at http://sign.hgc.jp/ .

Identification and evaluation of new reference genes in Gossypium hirsutum for accurate normalization of real-time quantitative RT-PCR data.

PubMed

Artico, Sinara; Nardeli, Sarah M; Brilhante, Osmundo; Grossi-de-Sa, Maria Fátima; Alves-Ferreira, Marcio

2010-03-21

Normalizing through reference genes, or housekeeping genes, can make more accurate and reliable results from reverse transcription real-time quantitative polymerase chain reaction (qPCR). Recent studies have shown that no single housekeeping gene is universal for all experiments. Thus, suitable reference genes should be the first step of any qPCR analysis. Only a few studies on the identification of housekeeping gene have been carried on plants. Therefore qPCR studies on important crops such as cotton has been hampered by the lack of suitable reference genes. By the use of two distinct algorithms, implemented by geNorm and NormFinder, we have assessed the gene expression of nine candidate reference genes in cotton: GhACT4, GhEF1alpha5, GhFBX6, GhPP2A1, GhMZA, GhPTB, GhGAPC2, GhbetaTUB3 and GhUBQ14. The candidate reference genes were evaluated in 23 experimental samples consisting of six distinct plant organs, eight stages of flower development, four stages of fruit development and in flower verticils. The expression of GhPP2A1 and GhUBQ14 genes were the most stable across all samples and also when distinct plants organs are examined. GhACT4 and GhUBQ14 present more stable expression during flower development, GhACT4 and GhFBX6 in the floral verticils and GhMZA and GhPTB during fruit development. Our analysis provided the most suitable combination of reference genes for each experimental set tested as internal control for reliable qPCR data normalization. In addition, to illustrate the use of cotton reference genes we checked the expression of two cotton MADS-box genes in distinct plant and floral organs and also during flower development. We have tested the expression stabilities of nine candidate genes in a set of 23 tissue samples from cotton plants divided into five different experimental sets. As a result of this evaluation, we recommend the use of GhUBQ14 and GhPP2A1 housekeeping genes as superior references for normalization of gene expression measures in

Identification and evaluation of new reference genes in Gossypium hirsutum for accurate normalization of real-time quantitative RT-PCR data

PubMed Central

2010-01-01

Background Normalizing through reference genes, or housekeeping genes, can make more accurate and reliable results from reverse transcription real-time quantitative polymerase chain reaction (qPCR). Recent studies have shown that no single housekeeping gene is universal for all experiments. Thus, suitable reference genes should be the first step of any qPCR analysis. Only a few studies on the identification of housekeeping gene have been carried on plants. Therefore qPCR studies on important crops such as cotton has been hampered by the lack of suitable reference genes. Results By the use of two distinct algorithms, implemented by geNorm and NormFinder, we have assessed the gene expression of nine candidate reference genes in cotton: GhACT4, GhEF1α5, GhFBX6, GhPP2A1, GhMZA, GhPTB, GhGAPC2, GhβTUB3 and GhUBQ14. The candidate reference genes were evaluated in 23 experimental samples consisting of six distinct plant organs, eight stages of flower development, four stages of fruit development and in flower verticils. The expression of GhPP2A1 and GhUBQ14 genes were the most stable across all samples and also when distinct plants organs are examined. GhACT4 and GhUBQ14 present more stable expression during flower development, GhACT4 and GhFBX6 in the floral verticils and GhMZA and GhPTB during fruit development. Our analysis provided the most suitable combination of reference genes for each experimental set tested as internal control for reliable qPCR data normalization. In addition, to illustrate the use of cotton reference genes we checked the expression of two cotton MADS-box genes in distinct plant and floral organs and also during flower development. Conclusion We have tested the expression stabilities of nine candidate genes in a set of 23 tissue samples from cotton plants divided into five different experimental sets. As a result of this evaluation, we recommend the use of GhUBQ14 and GhPP2A1 housekeeping genes as superior references for normalization of gene

Gene identification for risk of relapse in stage I lung adenocarcinoma patients: a combined methodology of gene expression profiling and computational gene network analysis.

PubMed

Ludovini, Vienna; Bianconi, Fortunato; Siggillino, Annamaria; Piobbico, Danilo; Vannucci, Jacopo; Metro, Giulio; Chiari, Rita; Bellezza, Guido; Puma, Francesco; Della Fazia, Maria Agnese; Servillo, Giuseppe; Crinò, Lucio

2016-05-24

Risk assessment and treatment choice remains a challenge in early non-small-cell lung cancer (NSCLC). The aim of this study was to identify novel genes involved in the risk of early relapse (ER) compared to no relapse (NR) in resected lung adenocarcinoma (AD) patients using a combination of high throughput technology and computational analysis. We identified 18 patients (n.13 NR and n.5 ER) with stage I AD. Frozen samples of patients in ER, NR and corresponding normal lung (NL) were subjected to Microarray technology and quantitative-PCR (Q-PCR). A gene network computational analysis was performed to select predictive genes. An independent set of 79 ADs stage I samples was used to validate selected genes by Q-PCR.From microarray analysis we selected 50 genes, using the fold change ratio of ER versus NR. They were validated both in pool and individually in patient samples (ER and NR) by Q-PCR. Fourteen increased and 25 decreased genes showed a concordance between two methods. They were used to perform a computational gene network analysis that identified 4 increased (HOXA10, CLCA2, AKR1B10, FABP3) and 6 decreased (SCGB1A1, PGC, TFF1, PSCA, SPRR1B and PRSS1) genes. Moreover, in an independent dataset of ADs samples, we showed that both high FABP3 expression and low SCGB1A1 expression was associated with a worse disease-free survival (DFS).Our results indicate that it is possible to define, through gene expression and computational analysis, a characteristic gene profiling of patients with an increased risk of relapse that may become a tool for patient selection for adjuvant therapy.

Numerical algorithm comparison for the accurate and efficient computation of high-incidence vortical flow

NASA Technical Reports Server (NTRS)

Chaderjian, Neal M.

1991-01-01

Computations from two Navier-Stokes codes, NSS and F3D, are presented for a tangent-ogive-cylinder body at high angle of attack. Features of this steady flow include a pair of primary vortices on the leeward side of the body as well as secondary vortices. The topological and physical plausibility of this vortical structure is discussed. The accuracy of these codes are assessed by comparison of the numerical solutions with experimental data. The effects of turbulence model, numerical dissipation, and grid refinement are presented. The overall efficiency of these codes are also assessed by examining their convergence rates, computational time per time step, and maximum allowable time step for time-accurate computations. Overall, the numerical results from both codes compared equally well with experimental data, however, the NSS code was found to be significantly more efficient than the F3D code.

Microarray-based cancer prediction using soft computing approach.

PubMed

Wang, Xiaosheng; Gotoh, Osamu

2009-05-26

One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably.

An accurate, compact and computationally efficient representation of orbitals for quantum Monte Carlo calculations

NASA Astrophysics Data System (ADS)

Luo, Ye; Esler, Kenneth; Kent, Paul; Shulenburger, Luke

Quantum Monte Carlo (QMC) calculations of giant molecules, surface and defect properties of solids have been feasible recently due to drastically expanding computational resources. However, with the most computationally efficient basis set, B-splines, these calculations are severely restricted by the memory capacity of compute nodes. The B-spline coefficients are shared on a node but not distributed among nodes, to ensure fast evaluation. A hybrid representation which incorporates atomic orbitals near the ions and B-spline ones in the interstitial regions offers a more accurate and less memory demanding description of the orbitals because they are naturally more atomic like near ions and much smoother in between, thus allowing coarser B-spline grids. We will demonstrate the advantage of hybrid representation over pure B-spline and Gaussian basis sets and also show significant speed-up like computing the non-local pseudopotentials with our new scheme. Moreover, we discuss a new algorithm for atomic orbital initialization which used to require an extra workflow step taking a few days. With this work, the highly efficient hybrid representation paves the way to simulate large size even in-homogeneous systems using QMC. This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Computational Materials Sciences Program.

An autonomous molecular computer for logical control of gene expression.

PubMed

Benenson, Yaakov; Gil, Binyamin; Ben-Dor, Uri; Adar, Rivka; Shapiro, Ehud

2004-05-27

Early biomolecular computer research focused on laboratory-scale, human-operated computers for complex computational problems. Recently, simple molecular-scale autonomous programmable computers were demonstrated allowing both input and output information to be in molecular form. Such computers, using biological molecules as input data and biologically active molecules as outputs, could produce a system for 'logical' control of biological processes. Here we describe an autonomous biomolecular computer that, at least in vitro, logically analyses the levels of messenger RNA species, and in response produces a molecule capable of affecting levels of gene expression. The computer operates at a concentration of close to a trillion computers per microlitre and consists of three programmable modules: a computation module, that is, a stochastic molecular automaton; an input module, by which specific mRNA levels or point mutations regulate software molecule concentrations, and hence automaton transition probabilities; and an output module, capable of controlled release of a short single-stranded DNA molecule. This approach might be applied in vivo to biochemical sensing, genetic engineering and even medical diagnosis and treatment. As a proof of principle we programmed the computer to identify and analyse mRNA of disease-related genes associated with models of small-cell lung cancer and prostate cancer, and to produce a single-stranded DNA molecule modelled after an anticancer drug.

Integrative structural annotation of de novo RNA-Seq provides an accurate reference gene set of the enormous genome of the onion (Allium cepa L.)

PubMed Central

Kim, Seungill; Kim, Myung-Shin; Kim, Yong-Min; Yeom, Seon-In; Cheong, Kyeongchae; Kim, Ki-Tae; Jeon, Jongbum; Kim, Sunggil; Kim, Do-Sun; Sohn, Seong-Han; Lee, Yong-Hwan; Choi, Doil

2015-01-01

The onion (Allium cepa L.) is one of the most widely cultivated and consumed vegetable crops in the world. Although a considerable amount of onion transcriptome data has been deposited into public databases, the sequences of the protein-coding genes are not accurate enough to be used, owing to non-coding sequences intermixed with the coding sequences. We generated a high-quality, annotated onion transcriptome from de novo sequence assembly and intensive structural annotation using the integrated structural gene annotation pipeline (ISGAP), which identified 54,165 protein-coding genes among 165,179 assembled transcripts totalling 203.0 Mb by eliminating the intron sequences. ISGAP performed reliable annotation, recognizing accurate gene structures based on reference proteins, and ab initio gene models of the assembled transcripts. Integrative functional annotation and gene-based SNP analysis revealed a whole biological repertoire of genes and transcriptomic variation in the onion. The method developed in this study provides a powerful tool for the construction of reference gene sets for organisms based solely on de novo transcriptome data. Furthermore, the reference genes and their variation described here for the onion represent essential tools for molecular breeding and gene cloning in Allium spp. PMID:25362073

Spatial adaption procedures on unstructured meshes for accurate unsteady aerodynamic flow computation

NASA Technical Reports Server (NTRS)

Rausch, Russ D.; Batina, John T.; Yang, Henry T. Y.

1991-01-01

Spatial adaption procedures for the accurate and efficient solution of steady and unsteady inviscid flow problems are described. The adaption procedures were developed and implemented within a two-dimensional unstructured-grid upwind-type Euler code. These procedures involve mesh enrichment and mesh coarsening to either add points in a high gradient region or the flow or remove points where they are not needed, respectively, to produce solutions of high spatial accuracy at minimal computational costs. A detailed description is given of the enrichment and coarsening procedures and comparisons with alternative results and experimental data are presented to provide an assessment of the accuracy and efficiency of the capability. Steady and unsteady transonic results, obtained using spatial adaption for the NACA 0012 airfoil, are shown to be of high spatial accuracy, primarily in that the shock waves are very sharply captured. The results were obtained with a computational savings of a factor of approximately fifty-three for a steady case and as much as twenty-five for the unsteady cases.

Spatial adaption procedures on unstructured meshes for accurate unsteady aerodynamic flow computation

NASA Technical Reports Server (NTRS)

Rausch, Russ D.; Yang, Henry T. Y.; Batina, John T.

1991-01-01

Spatial adaption procedures for the accurate and efficient solution of steady and unsteady inviscid flow problems are described. The adaption procedures were developed and implemented within a two-dimensional unstructured-grid upwind-type Euler code. These procedures involve mesh enrichment and mesh coarsening to either add points in high gradient regions of the flow or remove points where they are not needed, respectively, to produce solutions of high spatial accuracy at minimal computational cost. The paper gives a detailed description of the enrichment and coarsening procedures and presents comparisons with alternative results and experimental data to provide an assessment of the accuracy and efficiency of the capability. Steady and unsteady transonic results, obtained using spatial adaption for the NACA 0012 airfoil, are shown to be of high spatial accuracy, primarily in that the shock waves are very sharply captured. The results were obtained with a computational savings of a factor of approximately fifty-three for a steady case and as much as twenty-five for the unsteady cases.

Numerically accurate computational techniques for optimal estimator analyses of multi-parameter models

NASA Astrophysics Data System (ADS)

Berger, Lukas; Kleinheinz, Konstantin; Attili, Antonio; Bisetti, Fabrizio; Pitsch, Heinz; Mueller, Michael E.

2018-05-01

Modelling unclosed terms in partial differential equations typically involves two steps: First, a set of known quantities needs to be specified as input parameters for a model, and second, a specific functional form needs to be defined to model the unclosed terms by the input parameters. Both steps involve a certain modelling error, with the former known as the irreducible error and the latter referred to as the functional error. Typically, only the total modelling error, which is the sum of functional and irreducible error, is assessed, but the concept of the optimal estimator enables the separate analysis of the total and the irreducible errors, yielding a systematic modelling error decomposition. In this work, attention is paid to the techniques themselves required for the practical computation of irreducible errors. Typically, histograms are used for optimal estimator analyses, but this technique is found to add a non-negligible spurious contribution to the irreducible error if models with multiple input parameters are assessed. Thus, the error decomposition of an optimal estimator analysis becomes inaccurate, and misleading conclusions concerning modelling errors may be drawn. In this work, numerically accurate techniques for optimal estimator analyses are identified and a suitable evaluation of irreducible errors is presented. Four different computational techniques are considered: a histogram technique, artificial neural networks, multivariate adaptive regression splines, and an additive model based on a kernel method. For multiple input parameter models, only artificial neural networks and multivariate adaptive regression splines are found to yield satisfactorily accurate results. Beyond a certain number of input parameters, the assessment of models in an optimal estimator analysis even becomes practically infeasible if histograms are used. The optimal estimator analysis in this paper is applied to modelling the filtered soot intermittency in large eddy

A highly sensitive and accurate gene expression analysis by sequencing ("bead-seq") for a single cell.

PubMed

Matsunaga, Hiroko; Goto, Mari; Arikawa, Koji; Shirai, Masataka; Tsunoda, Hiroyuki; Huang, Huan; Kambara, Hideki

2015-02-15

Analyses of gene expressions in single cells are important for understanding detailed biological phenomena. Here, a highly sensitive and accurate method by sequencing (called "bead-seq") to obtain a whole gene expression profile for a single cell is proposed. A key feature of the method is to use a complementary DNA (cDNA) library on magnetic beads, which enables adding washing steps to remove residual reagents in a sample preparation process. By adding the washing steps, the next steps can be carried out under the optimal conditions without losing cDNAs. Error sources were carefully evaluated to conclude that the first several steps were the key steps. It is demonstrated that bead-seq is superior to the conventional methods for single-cell gene expression analyses in terms of reproducibility, quantitative accuracy, and biases caused during sample preparation and sequencing processes. Copyright © 2014 Elsevier Inc. All rights reserved.

A computational interactome for prioritizing genes associated with complex agronomic traits in rice (Oryza sativa).

PubMed

Liu, Shiwei; Liu, Yihui; Zhao, Jiawei; Cai, Shitao; Qian, Hongmei; Zuo, Kaijing; Zhao, Lingxia; Zhang, Lida

2017-04-01

Rice (Oryza sativa) is one of the most important staple foods for more than half of the global population. Many rice traits are quantitative, complex and controlled by multiple interacting genes. Thus, a full understanding of genetic relationships will be critical to systematically identify genes controlling agronomic traits. We developed a genome-wide rice protein-protein interaction network (RicePPINet, http://netbio.sjtu.edu.cn/riceppinet) using machine learning with structural relationship and functional information. RicePPINet contained 708 819 predicted interactions for 16 895 non-transposable element related proteins. The power of the network for discovering novel protein interactions was demonstrated through comparison with other publicly available protein-protein interaction (PPI) prediction methods, and by experimentally determined PPI data sets. Furthermore, global analysis of domain-mediated interactions revealed RicePPINet accurately reflects PPIs at the domain level. Our studies showed the efficiency of the RicePPINet-based method in prioritizing candidate genes involved in complex agronomic traits, such as disease resistance and drought tolerance, was approximately 2-11 times better than random prediction. RicePPINet provides an expanded landscape of computational interactome for the genetic dissection of agronomically important traits in rice. © 2017 The Authors The Plant Journal © 2017 John Wiley & Sons Ltd.

On Computing Breakpoint Distances for Genomes with Duplicate Genes.

PubMed

Shao, Mingfu; Moret, Bernard M E

2017-06-01

A fundamental problem in comparative genomics is to compute the distance between two genomes in terms of its higher level organization (given by genes or syntenic blocks). For two genomes without duplicate genes, we can easily define (and almost always efficiently compute) a variety of distance measures, but the problem is NP-hard under most models when genomes contain duplicate genes. To tackle duplicate genes, three formulations (exemplar, maximum matching, and any matching) have been proposed, all of which aim to build a matching between homologous genes so as to minimize some distance measure. Of the many distance measures, the breakpoint distance (the number of nonconserved adjacencies) was the first one to be studied and remains of significant interest because of its simplicity and model-free property. The three breakpoint distance problems corresponding to the three formulations have been widely studied. Although we provided last year a solution for the exemplar problem that runs very fast on full genomes, computing optimal solutions for the other two problems has remained challenging. In this article, we describe very fast, exact algorithms for these two problems. Our algorithms rely on a compact integer-linear program that we further simplify by developing an algorithm to remove variables, based on new results on the structure of adjacencies and matchings. Through extensive experiments using both simulations and biological data sets, we show that our algorithms run very fast (in seconds) on mammalian genomes and scale well beyond. We also apply these algorithms (as well as the classic orthology tool MSOAR) to create orthology assignment, then compare their quality in terms of both accuracy and coverage. We find that our algorithm for the "any matching" formulation significantly outperforms other methods in terms of accuracy while achieving nearly maximum coverage.

An autonomous molecular computer for logical control of gene expression

PubMed Central

Benenson, Yaakov; Gil, Binyamin; Ben-Dor, Uri; Adar, Rivka; Shapiro, Ehud

2013-01-01

Early biomolecular computer research focused on laboratory-scale, human-operated computers for complex computational problems1–7. Recently, simple molecular-scale autonomous programmable computers were demonstrated8–15 allowing both input and output information to be in molecular form. Such computers, using biological molecules as input data and biologically active molecules as outputs, could produce a system for ‘logical’ control of biological processes. Here we describe an autonomous biomolecular computer that, at least in vitro, logically analyses the levels of messenger RNA species, and in response produces a molecule capable of affecting levels of gene expression. The computer operates at a concentration of close to a trillion computers per microlitre and consists of three programmable modules: a computation module, that is, a stochastic molecular automaton12–17; an input module, by which specific mRNA levels or point mutations regulate software molecule concentrations, and hence automaton transition probabilities; and an output module, capable of controlled release of a short single-stranded DNA molecule. This approach might be applied in vivo to biochemical sensing, genetic engineering and even medical diagnosis and treatment. As a proof of principle we programmed the computer to identify and analyse mRNA of disease-related genes18–22 associated with models of small-cell lung cancer and prostate cancer, and to produce a single-stranded DNA molecule modelled after an anticancer drug. PMID:15116117

Selection and testing of reference genes for accurate RT-qPCR in rice seedlings under iron toxicity.

PubMed

Santos, Fabiane Igansi de Castro Dos; Marini, Naciele; Santos, Railson Schreinert Dos; Hoffman, Bianca Silva Fernandes; Alves-Ferreira, Marcio; de Oliveira, Antonio Costa

2018-01-01

Reverse Transcription quantitative PCR (RT-qPCR) is a technique for gene expression profiling with high sensibility and reproducibility. However, to obtain accurate results, it depends on data normalization by using endogenous reference genes whose expression is constitutive or invariable. Although the technique is widely used in plant stress analyzes, the stability of reference genes for iron toxicity in rice (Oryza sativa L.) has not been thoroughly investigated. Here, we tested a set of candidate reference genes for use in rice under this stressful condition. The test was performed using four distinct methods: NormFinder, BestKeeper, geNorm and the comparative ΔCt. To achieve reproducible and reliable results, Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines were followed. Valid reference genes were found for shoot (P2, OsGAPDH and OsNABP), root (OsEF-1a, P8 and OsGAPDH) and root+shoot (OsNABP, OsGAPDH and P8) enabling us to perform further reliable studies for iron toxicity in both indica and japonica subspecies. The importance of the study of other than the traditional endogenous genes for use as normalizers is also shown here.

Selection and testing of reference genes for accurate RT-qPCR in rice seedlings under iron toxicity

PubMed Central

dos Santos, Fabiane Igansi de Castro; Marini, Naciele; dos Santos, Railson Schreinert; Hoffman, Bianca Silva Fernandes; Alves-Ferreira, Marcio

2018-01-01

Reverse Transcription quantitative PCR (RT-qPCR) is a technique for gene expression profiling with high sensibility and reproducibility. However, to obtain accurate results, it depends on data normalization by using endogenous reference genes whose expression is constitutive or invariable. Although the technique is widely used in plant stress analyzes, the stability of reference genes for iron toxicity in rice (Oryza sativa L.) has not been thoroughly investigated. Here, we tested a set of candidate reference genes for use in rice under this stressful condition. The test was performed using four distinct methods: NormFinder, BestKeeper, geNorm and the comparative ΔCt. To achieve reproducible and reliable results, Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines were followed. Valid reference genes were found for shoot (P2, OsGAPDH and OsNABP), root (OsEF-1a, P8 and OsGAPDH) and root+shoot (OsNABP, OsGAPDH and P8) enabling us to perform further reliable studies for iron toxicity in both indica and japonica subspecies. The importance of the study of other than the traditional endogenous genes for use as normalizers is also shown here. PMID:29494624

Integrative structural annotation of de novo RNA-Seq provides an accurate reference gene set of the enormous genome of the onion (Allium cepa L.).

PubMed

Kim, Seungill; Kim, Myung-Shin; Kim, Yong-Min; Yeom, Seon-In; Cheong, Kyeongchae; Kim, Ki-Tae; Jeon, Jongbum; Kim, Sunggil; Kim, Do-Sun; Sohn, Seong-Han; Lee, Yong-Hwan; Choi, Doil

2015-02-01

The onion (Allium cepa L.) is one of the most widely cultivated and consumed vegetable crops in the world. Although a considerable amount of onion transcriptome data has been deposited into public databases, the sequences of the protein-coding genes are not accurate enough to be used, owing to non-coding sequences intermixed with the coding sequences. We generated a high-quality, annotated onion transcriptome from de novo sequence assembly and intensive structural annotation using the integrated structural gene annotation pipeline (ISGAP), which identified 54,165 protein-coding genes among 165,179 assembled transcripts totalling 203.0 Mb by eliminating the intron sequences. ISGAP performed reliable annotation, recognizing accurate gene structures based on reference proteins, and ab initio gene models of the assembled transcripts. Integrative functional annotation and gene-based SNP analysis revealed a whole biological repertoire of genes and transcriptomic variation in the onion. The method developed in this study provides a powerful tool for the construction of reference gene sets for organisms based solely on de novo transcriptome data. Furthermore, the reference genes and their variation described here for the onion represent essential tools for molecular breeding and gene cloning in Allium spp. © The Author 2014. Published by Oxford University Press on behalf of Kazusa DNA Research Institute.

Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment.

PubMed

Lee, Wei-Po; Hsiao, Yu-Ting; Hwang, Wei-Che

2014-01-16

To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks. This work presents a practical framework to infer large gene networks, by developing and parallelizing a hybrid GA-PSO optimization method. Our parallel method is extended to work with the Hadoop MapReduce programming model and is executed in different cloud computing environments. To evaluate the proposed approach, we use a well-known open-source software GeneNetWeaver to create several yeast S. cerevisiae sub-networks and use them to produce gene profiles. Experiments have been conducted and the results have been analyzed. They show that our parallel approach can be successfully used to infer networks with desired behaviors and the computation time can be largely reduced. Parallel population-based algorithms can effectively determine network parameters and they perform better than the widely-used sequential algorithms in gene network inference. These parallel algorithms can be distributed to the cloud computing environment to speed up the computation. By coupling the parallel model population-based optimization method and the parallel computational framework, high

Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment

PubMed Central

2014-01-01

Background To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks. Results This work presents a practical framework to infer large gene networks, by developing and parallelizing a hybrid GA-PSO optimization method. Our parallel method is extended to work with the Hadoop MapReduce programming model and is executed in different cloud computing environments. To evaluate the proposed approach, we use a well-known open-source software GeneNetWeaver to create several yeast S. cerevisiae sub-networks and use them to produce gene profiles. Experiments have been conducted and the results have been analyzed. They show that our parallel approach can be successfully used to infer networks with desired behaviors and the computation time can be largely reduced. Conclusions Parallel population-based algorithms can effectively determine network parameters and they perform better than the widely-used sequential algorithms in gene network inference. These parallel algorithms can be distributed to the cloud computing environment to speed up the computation. By coupling the parallel model population-based optimization method and the parallel

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

DOE PAGES

Faria, José P.; Davis, James J.; Edirisinghe, Janaka N.; ...

2016-11-24

Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. A multitude of technologies, abstractions, and interpretive frameworks have emerged to answer the challenges presented by genome function and regulatory network inference. Here, we propose a new approach for producing biologically meaningful clusters of coexpressed genes, called Atomic Regulons (ARs), based on expression data, gene context, and functional relationships. We demonstrate this new approach by computing ARs for Escherichia coli, which we compare with the coexpressed gene clusters predicted by two prevalent existing methods: hierarchical clustering and k-meansmore » clustering. We test the consistency of ARs predicted by all methods against expected interactions predicted by the Context Likelihood of Relatedness (CLR) mutual information based method, finding that the ARs produced by our approach show better agreement with CLR interactions. We then apply our method to compute ARs for four other genomes: Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus. We compare the AR clusters from all genomes to study the similarity of coexpression among a phylogenetically diverse set of species, identifying subsystems that show remarkable similarity over wide phylogenetic distances. We also study the sensitivity of our method for computing ARs to the expression data used in the computation, showing that our new approach requires less data than competing approaches to converge to a near final configuration of ARs. We go on to use our sensitivity analysis to identify the specific experiments that lead most rapidly to the final set of ARs for E. coli. As a result, this analysis produces insights into improving the design of gene expression experiments.« less

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Faria, José P.; Davis, James J.; Edirisinghe, Janaka N.

Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. A multitude of technologies, abstractions, and interpretive frameworks have emerged to answer the challenges presented by genome function and regulatory network inference. Here, we propose a new approach for producing biologically meaningful clusters of coexpressed genes, called Atomic Regulons (ARs), based on expression data, gene context, and functional relationships. We demonstrate this new approach by computing ARs for Escherichia coli, which we compare with the coexpressed gene clusters predicted by two prevalent existing methods: hierarchical clustering and k-meansmore » clustering. We test the consistency of ARs predicted by all methods against expected interactions predicted by the Context Likelihood of Relatedness (CLR) mutual information based method, finding that the ARs produced by our approach show better agreement with CLR interactions. We then apply our method to compute ARs for four other genomes: Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus. We compare the AR clusters from all genomes to study the similarity of coexpression among a phylogenetically diverse set of species, identifying subsystems that show remarkable similarity over wide phylogenetic distances. We also study the sensitivity of our method for computing ARs to the expression data used in the computation, showing that our new approach requires less data than competing approaches to converge to a near final configuration of ARs. We go on to use our sensitivity analysis to identify the specific experiments that lead most rapidly to the final set of ARs for E. coli. As a result, this analysis produces insights into improving the design of gene expression experiments.« less

A Machine Learned Classifier That Uses Gene Expression Data to Accurately Predict Estrogen Receptor Status

PubMed Central

Bastani, Meysam; Vos, Larissa; Asgarian, Nasimeh; Deschenes, Jean; Graham, Kathryn; Mackey, John; Greiner, Russell

2013-01-01

Background Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER) status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results. Methods To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from 176 frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor. Results This produced a three-gene classifier that can predict the ER-status of a novel tumor, with a cross-validation accuracy of 93.17±2.44%. When applied to an independent validation set and to four other public databases, some on different platforms, this classifier obtained over 90% accuracy in each. In addition, we found that this prediction rule separated the patients' recurrence-free survival curves with a hazard ratio lower than the one based on the IHC analysis of ER-status. Conclusions Our efficient and parsimonious classifier lends itself to high throughput, highly accurate and low-cost RNA-based assessments of ER-status, suitable for routine high-throughput clinical use. This analytic method provides a proof-of-principle that may be applicable to developing effective RNA-based tests for other biomarkers and conditions. PMID:24312637

Toward Bridging the Mechanistic Gap Between Genes and Traits by Emphasizing the Role of Proteins in a Computational Environment

NASA Astrophysics Data System (ADS)

Haskel-Ittah, Michal; Yarden, Anat

2017-12-01

Previous studies have shown that students often ignore molecular mechanisms when describing genetic phenomena. Specifically, students tend to directly link genes to their encoded traits, ignoring the role of proteins as mediators in this process. We tested the ability of 10th grade students to connect genes to traits through proteins, using concept maps and reasoning questions. The context of this study was a computational learning environment developed specifically to foster this ability. This environment presents proteins as the mechanism-mediating genetic phenomena. We found that students' ability to connect genes, proteins, and traits, or to reason using this connection, was initially poor. However, significant improvement was obtained when using the learning environment. Our results suggest that visual representations of proteins' functions in the context of a specific trait contributed to this improvement. One significant aspect of these results is the indication that 10th graders are capable of accurately describing genetic phenomena and their underlying mechanisms, a task that has been shown to raise difficulties, even in higher grades of high school.

A study of structural properties of gene network graphs for mathematical modeling of integrated mosaic gene networks.

PubMed

Petrovskaya, Olga V; Petrovskiy, Evgeny D; Lavrik, Inna N; Ivanisenko, Vladimir A

2017-04-01

Gene network modeling is one of the widely used approaches in systems biology. It allows for the study of complex genetic systems function, including so-called mosaic gene networks, which consist of functionally interacting subnetworks. We conducted a study of a mosaic gene networks modeling method based on integration of models of gene subnetworks by linear control functionals. An automatic modeling of 10,000 synthetic mosaic gene regulatory networks was carried out using computer experiments on gene knockdowns/knockouts. Structural analysis of graphs of generated mosaic gene regulatory networks has revealed that the most important factor for building accurate integrated mathematical models, among those analyzed in the study, is data on expression of genes corresponding to the vertices with high properties of centrality.

Computation and application of tissue-specific gene set weights.

PubMed

Frost, H Robert

2018-04-06

Gene set testing, or pathway analysis, has become a critical tool for the analysis of highdimensional genomic data. Although the function and activity of many genes and higher-level processes is tissue-specific, gene set testing is typically performed in a tissue agnostic fashion, which impacts statistical power and the interpretation and replication of results. To address this challenge, we have developed a bioinformatics approach to compute tissuespecific weights for individual gene sets using information on tissue-specific gene activity from the Human Protein Atlas (HPA). We used this approach to create a public repository of tissue-specific gene set weights for 37 different human tissue types from the HPA and all collections in the Molecular Signatures Database (MSigDB). To demonstrate the validity and utility of these weights, we explored three different applications: the functional characterization of human tissues, multi-tissue analysis for systemic diseases and tissue-specific gene set testing. All data used in the reported analyses is publicly available. An R implementation of the method and tissue-specific weights for MSigDB gene set collections can be downloaded at http://www.dartmouth.edu/∼hrfrost/TissueSpecificGeneSets. rob.frost@dartmouth.edu.

A new computational strategy for predicting essential genes.

PubMed

Cheng, Jian; Wu, Wenwu; Zhang, Yinwen; Li, Xiangchen; Jiang, Xiaoqian; Wei, Gehong; Tao, Shiheng

2013-12-21

Determination of the minimum gene set for cellular life is one of the central goals in biology. Genome-wide essential gene identification has progressed rapidly in certain bacterial species; however, it remains difficult to achieve in most eukaryotic species. Several computational models have recently been developed to integrate gene features and used as alternatives to transfer gene essentiality annotations between organisms. We first collected features that were widely used by previous predictive models and assessed the relationships between gene features and gene essentiality using a stepwise regression model. We found two issues that could significantly reduce model accuracy: (i) the effect of multicollinearity among gene features and (ii) the diverse and even contrasting correlations between gene features and gene essentiality existing within and among different species. To address these issues, we developed a novel model called feature-based weighted Naïve Bayes model (FWM), which is based on Naïve Bayes classifiers, logistic regression, and genetic algorithm. The proposed model assesses features and filters out the effects of multicollinearity and diversity. The performance of FWM was compared with other popular models, such as support vector machine, Naïve Bayes model, and logistic regression model, by applying FWM to reciprocally predict essential genes among and within 21 species. Our results showed that FWM significantly improves the accuracy and robustness of essential gene prediction. FWM can remarkably improve the accuracy of essential gene prediction and may be used as an alternative method for other classification work. This method can contribute substantially to the knowledge of the minimum gene sets required for living organisms and the discovery of new drug targets.

[Key effect genes responding to nerve injury identified by gene ontology and computer pattern recognition].

PubMed

Pan, Qian; Peng, Jin; Zhou, Xue; Yang, Hao; Zhang, Wei

2012-07-01

In order to screen out important genes from large gene data of gene microarray after nerve injury, we combine gene ontology (GO) method and computer pattern recognition technology to find key genes responding to nerve injury, and then verify one of these screened-out genes. Data mining and gene ontology analysis of gene chip data GSE26350 was carried out through MATLAB software. Cd44 was selected from screened-out key gene molecular spectrum by comparing genes' different GO terms and positions on score map of principal component. Function interferences were employed to influence the normal binding of Cd44 and one of its ligands, chondroitin sulfate C (CSC), to observe neurite extension. Gene ontology analysis showed that the first genes on score map (marked by red *) mainly distributed in molecular transducer activity, receptor activity, protein binding et al molecular function GO terms. Cd44 is one of six effector protein genes, and attracted us with its function diversity. After adding different reagents into the medium to interfere the normal binding of CSC and Cd44, varying-degree remissions of CSC's inhibition on neurite extension were observed. CSC can inhibit neurite extension through binding Cd44 on the neuron membrane. This verifies that important genes in given physiological processes can be identified by gene ontology analysis of gene chip data.

Accurate Arabic Script Language/Dialect Classification

DTIC Science & Technology

2014-01-01

Army Research Laboratory Accurate Arabic Script Language/Dialect Classification by Stephen C. Tratz ARL-TR-6761 January 2014 Approved for public...1197 ARL-TR-6761 January 2014 Accurate Arabic Script Language/Dialect Classification Stephen C. Tratz Computational and Information Sciences...Include area code) Standard Form 298 (Rev. 8/98) Prescribed by ANSI Std. Z39.18 January 2014 Final Accurate Arabic Script Language/Dialect Classification

CodingQuarry: highly accurate hidden Markov model gene prediction in fungal genomes using RNA-seq transcripts.

PubMed

Testa, Alison C; Hane, James K; Ellwood, Simon R; Oliver, Richard P

2015-03-11

The impact of gene annotation quality on functional and comparative genomics makes gene prediction an important process, particularly in non-model species, including many fungi. Sets of homologous protein sequences are rarely complete with respect to the fungal species of interest and are often small or unreliable, especially when closely related species have not been sequenced or annotated in detail. In these cases, protein homology-based evidence fails to correctly annotate many genes, or significantly improve ab initio predictions. Generalised hidden Markov models (GHMM) have proven to be invaluable tools in gene annotation and, recently, RNA-seq has emerged as a cost-effective means to significantly improve the quality of automated gene annotation. As these methods do not require sets of homologous proteins, improving gene prediction from these resources is of benefit to fungal researchers. While many pipelines now incorporate RNA-seq data in training GHMMs, there has been relatively little investigation into additionally combining RNA-seq data at the point of prediction, and room for improvement in this area motivates this study. CodingQuarry is a highly accurate, self-training GHMM fungal gene predictor designed to work with assembled, aligned RNA-seq transcripts. RNA-seq data informs annotations both during gene-model training and in prediction. Our approach capitalises on the high quality of fungal transcript assemblies by incorporating predictions made directly from transcript sequences. Correct predictions are made despite transcript assembly problems, including those caused by overlap between the transcripts of adjacent gene loci. Stringent benchmarking against high-confidence annotation subsets showed CodingQuarry predicted 91.3% of Schizosaccharomyces pombe genes and 90.4% of Saccharomyces cerevisiae genes perfectly. These results are 4-5% better than those of AUGUSTUS, the next best performing RNA-seq driven gene predictor tested. Comparisons against

Accurate and fast multiple-testing correction in eQTL studies.

PubMed

Sul, Jae Hoon; Raj, Towfique; de Jong, Simone; de Bakker, Paul I W; Raychaudhuri, Soumya; Ophoff, Roel A; Stranger, Barbara E; Eskin, Eleazar; Han, Buhm

2015-06-04

In studies of expression quantitative trait loci (eQTLs), it is of increasing interest to identify eGenes, the genes whose expression levels are associated with variation at a particular genetic variant. Detecting eGenes is important for follow-up analyses and prioritization because genes are the main entities in biological processes. To detect eGenes, one typically focuses on the genetic variant with the minimum p value among all variants in cis with a gene and corrects for multiple testing to obtain a gene-level p value. For performing multiple-testing correction, a permutation test is widely used. Because of growing sample sizes of eQTL studies, however, the permutation test has become a computational bottleneck in eQTL studies. In this paper, we propose an efficient approach for correcting for multiple testing and assess eGene p values by utilizing a multivariate normal distribution. Our approach properly takes into account the linkage-disequilibrium structure among variants, and its time complexity is independent of sample size. By applying our small-sample correction techniques, our method achieves high accuracy in both small and large studies. We have shown that our method consistently produces extremely accurate p values (accuracy > 98%) for three human eQTL datasets with different sample sizes and SNP densities: the Genotype-Tissue Expression pilot dataset, the multi-region brain dataset, and the HapMap 3 dataset. Copyright © 2015 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Enabling high grayscale resolution displays and accurate response time measurements on conventional computers.

PubMed

Li, Xiangrui; Lu, Zhong-Lin

2012-02-29

Display systems based on conventional computer graphics cards are capable of generating images with 8-bit gray level resolution. However, most experiments in vision research require displays with more than 12 bits of luminance resolution. Several solutions are available. Bit++ (1) and DataPixx (2) use the Digital Visual Interface (DVI) output from graphics cards and high resolution (14 or 16-bit) digital-to-analog converters to drive analog display devices. The VideoSwitcher (3) described here combines analog video signals from the red and blue channels of graphics cards with different weights using a passive resister network (4) and an active circuit to deliver identical video signals to the three channels of color monitors. The method provides an inexpensive way to enable high-resolution monochromatic displays using conventional graphics cards and analog monitors. It can also provide trigger signals that can be used to mark stimulus onsets, making it easy to synchronize visual displays with physiological recordings or response time measurements. Although computer keyboards and mice are frequently used in measuring response times (RT), the accuracy of these measurements is quite low. The RTbox is a specialized hardware and software solution for accurate RT measurements. Connected to the host computer through a USB connection, the driver of the RTbox is compatible with all conventional operating systems. It uses a microprocessor and high-resolution clock to record the identities and timing of button events, which are buffered until the host computer retrieves them. The recorded button events are not affected by potential timing uncertainties or biases associated with data transmission and processing in the host computer. The asynchronous storage greatly simplifies the design of user programs. Several methods are available to synchronize the clocks of the RTbox and the host computer. The RTbox can also receive external triggers and be used to measure RT with respect

Validation of reference genes for accurate normalization of gene expression for real time-quantitative PCR in strawberry fruits using different cultivars and osmotic stresses.

PubMed

Galli, Vanessa; Borowski, Joyce Moura; Perin, Ellen Cristina; Messias, Rafael da Silva; Labonde, Julia; Pereira, Ivan dos Santos; Silva, Sérgio Delmar Dos Anjos; Rombaldi, Cesar Valmor

2015-01-10

The increasing demand of strawberry (Fragaria×ananassa Duch) fruits is associated mainly with their sensorial characteristics and the content of antioxidant compounds. Nevertheless, the strawberry production has been hampered due to its sensitivity to abiotic stresses. Therefore, to understand the molecular mechanisms highlighting stress response is of great importance to enable genetic engineering approaches aiming to improve strawberry tolerance. However, the study of expression of genes in strawberry requires the use of suitable reference genes. In the present study, seven traditional and novel candidate reference genes were evaluated for transcript normalization in fruits of ten strawberry cultivars and two abiotic stresses, using RefFinder, which integrates the four major currently available software programs: geNorm, NormFinder, BestKeeper and the comparative delta-Ct method. The results indicate that the expression stability is dependent on the experimental conditions. The candidate reference gene DBP (DNA binding protein) was considered the most suitable to normalize expression data in samples of strawberry cultivars and under drought stress condition, and the candidate reference gene HISTH4 (histone H4) was the most stable under osmotic stresses and salt stress. The traditional genes GAPDH (glyceraldehyde-3-phosphate dehydrogenase) and 18S (18S ribosomal RNA) were considered the most unstable genes in all conditions. The expression of phenylalanine ammonia lyase (PAL) and 9-cis epoxycarotenoid dioxygenase (NCED1) genes were used to further confirm the validated candidate reference genes, showing that the use of an inappropriate reference gene may induce erroneous results. This study is the first survey on the stability of reference genes in strawberry cultivars and osmotic stresses and provides guidelines to obtain more accurate RT-qPCR results for future breeding efforts. Copyright © 2014 Elsevier B.V. All rights reserved.

An algorithm for computing the gene tree probability under the multispecies coalescent and its application in the inference of population tree

PubMed Central

2016-01-01

Motivation: Gene tree represents the evolutionary history of gene lineages that originate from multiple related populations. Under the multispecies coalescent model, lineages may coalesce outside the species (population) boundary. Given a species tree (with branch lengths), the gene tree probability is the probability of observing a specific gene tree topology under the multispecies coalescent model. There are two existing algorithms for computing the exact gene tree probability. The first algorithm is due to Degnan and Salter, where they enumerate all the so-called coalescent histories for the given species tree and the gene tree topology. Their algorithm runs in exponential time in the number of gene lineages in general. The second algorithm is the STELLS algorithm (2012), which is usually faster but also runs in exponential time in almost all the cases. Results: In this article, we present a new algorithm, called CompactCH, for computing the exact gene tree probability. This new algorithm is based on the notion of compact coalescent histories: multiple coalescent histories are represented by a single compact coalescent history. The key advantage of our new algorithm is that it runs in polynomial time in the number of gene lineages if the number of populations is fixed to be a constant. The new algorithm is more efficient than the STELLS algorithm both in theory and in practice when the number of populations is small and there are multiple gene lineages from each population. As an application, we show that CompactCH can be applied in the inference of population tree (i.e. the population divergence history) from population haplotypes. Simulation results show that the CompactCH algorithm enables efficient and accurate inference of population trees with much more haplotypes than a previous approach. Availability: The CompactCH algorithm is implemented in the STELLS software package, which is available for download at http

miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades.

PubMed

Friedländer, Marc R; Mackowiak, Sebastian D; Li, Na; Chen, Wei; Rajewsky, Nikolaus

2012-01-01

microRNAs (miRNAs) are a large class of small non-coding RNAs which post-transcriptionally regulate the expression of a large fraction of all animal genes and are important in a wide range of biological processes. Recent advances in high-throughput sequencing allow miRNA detection at unprecedented sensitivity, but the computational task of accurately identifying the miRNAs in the background of sequenced RNAs remains challenging. For this purpose, we have designed miRDeep2, a substantially improved algorithm which identifies canonical and non-canonical miRNAs such as those derived from transposable elements and informs on high-confidence candidates that are detected in multiple independent samples. Analyzing data from seven animal species representing the major animal clades, miRDeep2 identified miRNAs with an accuracy of 98.6-99.9% and reported hundreds of novel miRNAs. To test the accuracy of miRDeep2, we knocked down the miRNA biogenesis pathway in a human cell line and sequenced small RNAs before and after. The vast majority of the >100 novel miRNAs expressed in this cell line were indeed specifically downregulated, validating most miRDeep2 predictions. Last, a new miRNA expression profiling routine, low time and memory usage and user-friendly interactive graphic output can make miRDeep2 useful to a wide range of researchers.

Inference of cancer-specific gene regulatory networks using soft computing rules.

PubMed

Wang, Xiaosheng; Gotoh, Osamu

2010-03-24

Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer) using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One is that upregulated genes are regulated by more genes than downregulated ones, while downregulated genes regulate more genes than upregulated ones. The other one is that tumor suppressors suppress tumor activators and activate other tumor suppressors strongly, while tumor activators activate other tumor activators and suppress tumor suppressors weakly, indicating the robustness of biological systems. These findings provide valuable insights into the pathogenesis of cancer.

Computational Identification of Novel Genes: Current and Future Perspectives.

PubMed

Klasberg, Steffen; Bitard-Feildel, Tristan; Mallet, Ludovic

2016-01-01

While it has long been thought that all genomic novelties are derived from the existing material, many genes lacking homology to known genes were found in recent genome projects. Some of these novel genes were proposed to have evolved de novo, ie, out of noncoding sequences, whereas some have been shown to follow a duplication and divergence process. Their discovery called for an extension of the historical hypotheses about gene origination. Besides the theoretical breakthrough, increasing evidence accumulated that novel genes play important roles in evolutionary processes, including adaptation and speciation events. Different techniques are available to identify genes and classify them as novel. Their classification as novel is usually based on their similarity to known genes, or lack thereof, detected by comparative genomics or against databases. Computational approaches are further prime methods that can be based on existing models or leveraging biological evidences from experiments. Identification of novel genes remains however a challenging task. With the constant software and technologies updates, no gold standard, and no available benchmark, evaluation and characterization of genomic novelty is a vibrant field. In this review, the classical and state-of-the-art tools for gene prediction are introduced. The current methods for novel gene detection are presented; the methodological strategies and their limits are discussed along with perspective approaches for further studies.

Computational screening of disease-associated mutations in OCA2 gene.

PubMed

Kamaraj, Balu; Purohit, Rituraj

2014-01-01

Oculocutaneous albinism type 2 (OCA2), caused by mutations of OCA2 gene, is an autosomal recessive disorder characterized by reduced biosynthesis of melanin pigment in the skin, hair, and eyes. The OCA2 gene encodes instructions for making a protein called the P protein. This protein plays a crucial role in melanosome biogenesis, and controls the eumelanin content in melanocytes in part via the processing and trafficking of tyrosinase which is the rate-limiting enzyme in melanin synthesis. In this study we analyzed the pathogenic effect of 95 non-synonymous single nucleotide polymorphisms reported in OCA2 gene using computational methods. We found R305W mutation as most deleterious and disease associated using SIFT, PolyPhen, PANTHER, PhD-SNP, Pmut, and MutPred tools. To understand the atomic arrangement in 3D space, the native and mutant (R305W) structures were modeled. Molecular dynamics simulation was conducted to observe the structural significance of computationally prioritized disease-associated mutation (R305W). Root-mean-square deviation, root-mean-square fluctuation, radius of gyration, solvent accessibility surface area, hydrogen bond (NH bond), trace of covariance matrix, eigenvector projection analysis, and density analysis results showed prominent loss of stability and rise in mutant flexibility values in 3D space. This study presents a well designed computational methodology to examine the albinism-associated SNPs.

Modeling leaderless transcription and atypical genes results in more accurate gene prediction in prokaryotes.

PubMed

Lomsadze, Alexandre; Gemayel, Karl; Tang, Shiyuyun; Borodovsky, Mark

2018-05-17

In a conventional view of the prokaryotic genome organization, promoters precede operons and ribosome binding sites (RBSs) with Shine-Dalgarno consensus precede genes. However, recent experimental research suggesting a more diverse view motivated us to develop an algorithm with improved gene-finding accuracy. We describe GeneMarkS-2, an ab initio algorithm that uses a model derived by self-training for finding species-specific (native) genes, along with an array of precomputed "heuristic" models designed to identify harder-to-detect genes (likely horizontally transferred). Importantly, we designed GeneMarkS-2 to identify several types of distinct sequence patterns (signals) involved in gene expression control, among them the patterns characteristic for leaderless transcription as well as noncanonical RBS patterns. To assess the accuracy of GeneMarkS-2, we used genes validated by COG (Clusters of Orthologous Groups) annotation, proteomics experiments, and N-terminal protein sequencing. We observed that GeneMarkS-2 performed better on average in all accuracy measures when compared with the current state-of-the-art gene prediction tools. Furthermore, the screening of ∼5000 representative prokaryotic genomes made by GeneMarkS-2 predicted frequent leaderless transcription in both archaea and bacteria. We also observed that the RBS sites in some species with leadered transcription did not necessarily exhibit the Shine-Dalgarno consensus. The modeling of different types of sequence motifs regulating gene expression prompted a division of prokaryotic genomes into five categories with distinct sequence patterns around the gene starts. © 2018 Lomsadze et al.; Published by Cold Spring Harbor Laboratory Press.

Computer Series, 101: Accurate Equations of State in Computational Chemistry Projects.

ERIC Educational Resources Information Center

Albee, David; Jones, Edward

1989-01-01

Discusses the use of computers in chemistry courses at the United States Military Academy. Provides two examples of computer projects: (1) equations of state, and (2) solving for molar volume. Presents BASIC and PASCAL listings for the second project. Lists 10 applications for physical chemistry. (MVL)

How to perform RT-qPCR accurately in plant species? A case study on flower colour gene expression in an azalea (Rhododendron simsii hybrids) mapping population.

PubMed

De Keyser, Ellen; Desmet, Laurence; Van Bockstaele, Erik; De Riek, Jan

2013-06-24

Flower colour variation is one of the most crucial selection criteria in the breeding of a flowering pot plant, as is also the case for azalea (Rhododendron simsii hybrids). Flavonoid biosynthesis was studied intensively in several species. In azalea, flower colour can be described by means of a 3-gene model. However, this model does not clarify pink-coloration. The last decade gene expression studies have been implemented widely for studying flower colour. However, the methods used were often only semi-quantitative or quantification was not done according to the MIQE-guidelines. We aimed to develop an accurate protocol for RT-qPCR and to validate the protocol to study flower colour in an azalea mapping population. An accurate RT-qPCR protocol had to be established. RNA quality was evaluated in a combined approach by means of different techniques e.g. SPUD-assay and Experion-analysis. We demonstrated the importance of testing noRT-samples for all genes under study to detect contaminating DNA. In spite of the limited sequence information available, we prepared a set of 11 reference genes which was validated in flower petals; a combination of three reference genes was most optimal. Finally we also used plasmids for the construction of standard curves. This allowed us to calculate gene-specific PCR efficiencies for every gene to assure an accurate quantification. The validity of the protocol was demonstrated by means of the study of six genes of the flavonoid biosynthesis pathway. No correlations were found between flower colour and the individual expression profiles. However, the combination of early pathway genes (CHS, F3H, F3'H and FLS) is clearly related to co-pigmentation with flavonols. The late pathway genes DFR and ANS are to a minor extent involved in differentiating between coloured and white flowers. Concerning pink coloration, we could demonstrate that the lower intensity in this type of flowers is correlated to the expression of F3'H. Currently in plant

How to perform RT-qPCR accurately in plant species? A case study on flower colour gene expression in an azalea (Rhododendron simsii hybrids) mapping population

PubMed Central

2013-01-01

Background Flower colour variation is one of the most crucial selection criteria in the breeding of a flowering pot plant, as is also the case for azalea (Rhododendron simsii hybrids). Flavonoid biosynthesis was studied intensively in several species. In azalea, flower colour can be described by means of a 3-gene model. However, this model does not clarify pink-coloration. The last decade gene expression studies have been implemented widely for studying flower colour. However, the methods used were often only semi-quantitative or quantification was not done according to the MIQE-guidelines. We aimed to develop an accurate protocol for RT-qPCR and to validate the protocol to study flower colour in an azalea mapping population. Results An accurate RT-qPCR protocol had to be established. RNA quality was evaluated in a combined approach by means of different techniques e.g. SPUD-assay and Experion-analysis. We demonstrated the importance of testing noRT-samples for all genes under study to detect contaminating DNA. In spite of the limited sequence information available, we prepared a set of 11 reference genes which was validated in flower petals; a combination of three reference genes was most optimal. Finally we also used plasmids for the construction of standard curves. This allowed us to calculate gene-specific PCR efficiencies for every gene to assure an accurate quantification. The validity of the protocol was demonstrated by means of the study of six genes of the flavonoid biosynthesis pathway. No correlations were found between flower colour and the individual expression profiles. However, the combination of early pathway genes (CHS, F3H, F3'H and FLS) is clearly related to co-pigmentation with flavonols. The late pathway genes DFR and ANS are to a minor extent involved in differentiating between coloured and white flowers. Concerning pink coloration, we could demonstrate that the lower intensity in this type of flowers is correlated to the expression of F3'H

FILMPAR: A parallel algorithm designed for the efficient and accurate computation of thin film flow on functional surfaces containing micro-structure

NASA Astrophysics Data System (ADS)

Lee, Y. C.; Thompson, H. M.; Gaskell, P. H.

2009-12-01

, industrial and physical applications. However, despite recent modelling advances, the accurate numerical solution of the equations governing such problems is still at a relatively early stage. Indeed, recent studies employing a simplifying long-wave approximation have shown that highly efficient numerical methods are necessary to solve the resulting lubrication equations in order to achieve the level of grid resolution required to accurately capture the effects of micro- and nano-scale topographical features. Solution method: A portable parallel multigrid algorithm has been developed for the above purpose, for the particular case of flow over submerged topographical features. Within the multigrid framework adopted, a W-cycle is used to accelerate convergence in respect of the time dependent nature of the problem, with relaxation sweeps performed using a fixed number of pre- and post-Red-Black Gauss-Seidel Newton iterations. In addition, the algorithm incorporates automatic adaptive time-stepping to avoid the computational expense associated with repeated time-step failure. Running time: 1.31 minutes using 128 processors on BlueGene/P with a problem size of over 16.7 million mesh points.

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

PubMed Central

Faria, José P.; Davis, James J.; Edirisinghe, Janaka N.; Taylor, Ronald C.; Weisenhorn, Pamela; Olson, Robert D.; Stevens, Rick L.; Rocha, Miguel; Rocha, Isabel; Best, Aaron A.; DeJongh, Matthew; Tintle, Nathan L.; Parrello, Bruce; Overbeek, Ross; Henry, Christopher S.

2016-01-01

Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets, Atomic Regulons (ARs), represent fundamental units of function within a cell and could be used to associate genes of unknown function with cellular processes and to enable rational genetic engineering of cellular systems. Here, we describe an approach for inferring ARs that leverages large-scale expression data sets, gene context, and functional relationships among genes. We computed ARs for Escherichia coli based on 907 gene expression experiments and compared our results with gene clusters produced by two prevalent data-driven methods: Hierarchical clustering and k-means clustering. We compared ARs and purely data-driven gene clusters to the curated set of regulatory interactions for E. coli found in RegulonDB, showing that ARs are more consistent with gold standard regulons than are data-driven gene clusters. We further examined the consistency of ARs and data-driven gene clusters in the context of gene interactions predicted by Context Likelihood of Relatedness (CLR) analysis, finding that the ARs show better agreement with CLR predicted interactions. We determined the impact of increasing amounts of expression data on AR construction and find that while more data improve ARs, it is not necessary to use the full set of gene expression experiments available for E. coli to produce high quality ARs. In order to explore the conservation of co-regulated gene sets across different organisms, we computed ARs for Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus, each of which represents increasing degrees of phylogenetic distance from E. coli. Comparison of the organism-specific ARs showed

Accurate Time-Dependent Traveling-Wave Tube Model Developed for Computational Bit-Error-Rate Testing

NASA Technical Reports Server (NTRS)

Kory, Carol L.

2001-01-01

The phenomenal growth of the satellite communications industry has created a large demand for traveling-wave tubes (TWT's) operating with unprecedented specifications requiring the design and production of many novel devices in record time. To achieve this, the TWT industry heavily relies on computational modeling. However, the TWT industry's computational modeling capabilities need to be improved because there are often discrepancies between measured TWT data and that predicted by conventional two-dimensional helical TWT interaction codes. This limits the analysis and design of novel devices or TWT's with parameters differing from what is conventionally manufactured. In addition, the inaccuracy of current computational tools limits achievable TWT performance because optimized designs require highly accurate models. To address these concerns, a fully three-dimensional, time-dependent, helical TWT interaction model was developed using the electromagnetic particle-in-cell code MAFIA (Solution of MAxwell's equations by the Finite-Integration-Algorithm). The model includes a short section of helical slow-wave circuit with excitation fed by radiofrequency input/output couplers, and an electron beam contained by periodic permanent magnet focusing. A cutaway view of several turns of the three-dimensional helical slow-wave circuit with input/output couplers is shown. This has been shown to be more accurate than conventionally used two-dimensional models. The growth of the communications industry has also imposed a demand for increased data rates for the transmission of large volumes of data. To achieve increased data rates, complex modulation and multiple access techniques are employed requiring minimum distortion of the signal as it is passed through the TWT. Thus, intersymbol interference (ISI) becomes a major consideration, as well as suspected causes such as reflections within the TWT. To experimentally investigate effects of the physical TWT on ISI would be

Muver, a computational framework for accurately calling accumulated mutations.

PubMed

Burkholder, Adam B; Lujan, Scott A; Lavender, Christopher A; Grimm, Sara A; Kunkel, Thomas A; Fargo, David C

2018-05-09

Identification of mutations from next-generation sequencing data typically requires a balance between sensitivity and accuracy. This is particularly true of DNA insertions and deletions (indels), that can impart significant phenotypic consequences on cells but are harder to call than substitution mutations from whole genome mutation accumulation experiments. To overcome these difficulties, we present muver, a computational framework that integrates established bioinformatics tools with novel analytical methods to generate mutation calls with the extremely low false positive rates and high sensitivity required for accurate mutation rate determination and comparison. Muver uses statistical comparison of ancestral and descendant allelic frequencies to identify variant loci and assigns genotypes with models that include per-sample assessments of sequencing errors by mutation type and repeat context. Muver identifies maximally parsimonious mutation pathways that connect these genotypes, differentiating potential allelic conversion events and delineating ambiguities in mutation location, type, and size. Benchmarking with a human gold standard father-son pair demonstrates muver's sensitivity and low false positive rates. In DNA mismatch repair (MMR) deficient Saccharomyces cerevisiae, muver detects multi-base deletions in homopolymers longer than the replicative polymerase footprint at rates greater than predicted for sequential single-base deletions, implying a novel multi-repeat-unit slippage mechanism. Benchmarking results demonstrate the high accuracy and sensitivity achieved with muver, particularly for indels, relative to available tools. Applied to an MMR-deficient Saccharomyces cerevisiae system, muver mutation calls facilitate mechanistic insights into DNA replication fidelity.

Spectral gene set enrichment (SGSE).

PubMed

Frost, H Robert; Li, Zhigang; Moore, Jason H

2015-03-03

Gene set testing is typically performed in a supervised context to quantify the association between groups of genes and a clinical phenotype. In many cases, however, a gene set-based interpretation of genomic data is desired in the absence of a phenotype variable. Although methods exist for unsupervised gene set testing, they predominantly compute enrichment relative to clusters of the genomic variables with performance strongly dependent on the clustering algorithm and number of clusters. We propose a novel method, spectral gene set enrichment (SGSE), for unsupervised competitive testing of the association between gene sets and empirical data sources. SGSE first computes the statistical association between gene sets and principal components (PCs) using our principal component gene set enrichment (PCGSE) method. The overall statistical association between each gene set and the spectral structure of the data is then computed by combining the PC-level p-values using the weighted Z-method with weights set to the PC variance scaled by Tracy-Widom test p-values. Using simulated data, we show that the SGSE algorithm can accurately recover spectral features from noisy data. To illustrate the utility of our method on real data, we demonstrate the superior performance of the SGSE method relative to standard cluster-based techniques for testing the association between MSigDB gene sets and the variance structure of microarray gene expression data. Unsupervised gene set testing can provide important information about the biological signal held in high-dimensional genomic data sets. Because it uses the association between gene sets and samples PCs to generate a measure of unsupervised enrichment, the SGSE method is independent of cluster or network creation algorithms and, most importantly, is able to utilize the statistical significance of PC eigenvalues to ignore elements of the data most likely to represent noise.

Development and Validation of a Fast, Accurate and Cost-Effective Aeroservoelastic Method on Advanced Parallel Computing Systems

NASA Technical Reports Server (NTRS)

Goodwin, Sabine A.; Raj, P.

1999-01-01

Progress to date towards the development and validation of a fast, accurate and cost-effective aeroelastic method for advanced parallel computing platforms such as the IBM SP2 and the SGI Origin 2000 is presented in this paper. The ENSAERO code, developed at the NASA-Ames Research Center has been selected for this effort. The code allows for the computation of aeroelastic responses by simultaneously integrating the Euler or Navier-Stokes equations and the modal structural equations of motion. To assess the computational performance and accuracy of the ENSAERO code, this paper reports the results of the Navier-Stokes simulations of the transonic flow over a flexible aeroelastic wing body configuration. In addition, a forced harmonic oscillation analysis in the frequency domain and an analysis in the time domain are done on a wing undergoing a rigid pitch and plunge motion. Finally, to demonstrate the ENSAERO flutter-analysis capability, aeroelastic Euler and Navier-Stokes computations on an L-1011 wind tunnel model including pylon, nacelle and empennage are underway. All computational solutions are compared with experimental data to assess the level of accuracy of ENSAERO. As the computations described above are performed, a meticulous log of computational performance in terms of wall clock time, execution speed, memory and disk storage is kept. Code scalability is also demonstrated by studying the impact of varying the number of processors on computational performance on the IBM SP2 and the Origin 2000 systems.

An Exact Algorithm to Compute the Double-Cut-and-Join Distance for Genomes with Duplicate Genes.

PubMed

Shao, Mingfu; Lin, Yu; Moret, Bernard M E

2015-05-01

Computing the edit distance between two genomes is a basic problem in the study of genome evolution. The double-cut-and-join (DCJ) model has formed the basis for most algorithmic research on rearrangements over the last few years. The edit distance under the DCJ model can be computed in linear time for genomes without duplicate genes, while the problem becomes NP-hard in the presence of duplicate genes. In this article, we propose an integer linear programming (ILP) formulation to compute the DCJ distance between two genomes with duplicate genes. We also provide an efficient preprocessing approach to simplify the ILP formulation while preserving optimality. Comparison on simulated genomes demonstrates that our method outperforms MSOAR in computing the edit distance, especially when the genomes contain long duplicated segments. We also apply our method to assign orthologous gene pairs among human, mouse, and rat genomes, where once again our method outperforms MSOAR.

SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes.

PubMed

Pruesse, Elmar; Peplies, Jörg; Glöckner, Frank Oliver

2012-07-15

In the analysis of homologous sequences, computation of multiple sequence alignments (MSAs) has become a bottleneck. This is especially troublesome for marker genes like the ribosomal RNA (rRNA) where already millions of sequences are publicly available and individual studies can easily produce hundreds of thousands of new sequences. Methods have been developed to cope with such numbers, but further improvements are needed to meet accuracy requirements. In this study, we present the SILVA Incremental Aligner (SINA) used to align the rRNA gene databases provided by the SILVA ribosomal RNA project. SINA uses a combination of k-mer searching and partial order alignment (POA) to maintain very high alignment accuracy while satisfying high throughput performance demands. SINA was evaluated in comparison with the commonly used high throughput MSA programs PyNAST and mothur. The three BRAliBase III benchmark MSAs could be reproduced with 99.3, 97.6 and 96.1 accuracy. A larger benchmark MSA comprising 38 772 sequences could be reproduced with 98.9 and 99.3% accuracy using reference MSAs comprising 1000 and 5000 sequences. SINA was able to achieve higher accuracy than PyNAST and mothur in all performed benchmarks. Alignment of up to 500 sequences using the latest SILVA SSU/LSU Ref datasets as reference MSA is offered at http://www.arb-silva.de/aligner. This page also links to Linux binaries, user manual and tutorial. SINA is made available under a personal use license.

Beyond mean-field approximations for accurate and computationally efficient models of on-lattice chemical kinetics

NASA Astrophysics Data System (ADS)

Pineda, M.; Stamatakis, M.

2017-07-01

Modeling the kinetics of surface catalyzed reactions is essential for the design of reactors and chemical processes. The majority of microkinetic models employ mean-field approximations, which lead to an approximate description of catalytic kinetics by assuming spatially uncorrelated adsorbates. On the other hand, kinetic Monte Carlo (KMC) methods provide a discrete-space continuous-time stochastic formulation that enables an accurate treatment of spatial correlations in the adlayer, but at a significant computation cost. In this work, we use the so-called cluster mean-field approach to develop higher order approximations that systematically increase the accuracy of kinetic models by treating spatial correlations at a progressively higher level of detail. We further demonstrate our approach on a reduced model for NO oxidation incorporating first nearest-neighbor lateral interactions and construct a sequence of approximations of increasingly higher accuracy, which we compare with KMC and mean-field. The latter is found to perform rather poorly, overestimating the turnover frequency by several orders of magnitude for this system. On the other hand, our approximations, while more computationally intense than the traditional mean-field treatment, still achieve tremendous computational savings compared to KMC simulations, thereby opening the way for employing them in multiscale modeling frameworks.

Computational Tools and Algorithms for Designing Customized Synthetic Genes

PubMed Central

Gould, Nathan; Hendy, Oliver; Papamichail, Dimitris

2014-01-01

Advances in DNA synthesis have enabled the construction of artificial genes, gene circuits, and genomes of bacterial scale. Freedom in de novo design of synthetic constructs provides significant power in studying the impact of mutations in sequence features, and verifying hypotheses on the functional information that is encoded in nucleic and amino acids. To aid this goal, a large number of software tools of variable sophistication have been implemented, enabling the design of synthetic genes for sequence optimization based on rationally defined properties. The first generation of tools dealt predominantly with singular objectives such as codon usage optimization and unique restriction site incorporation. Recent years have seen the emergence of sequence design tools that aim to evolve sequences toward combinations of objectives. The design of optimal protein-coding sequences adhering to multiple objectives is computationally hard, and most tools rely on heuristics to sample the vast sequence design space. In this review, we study some of the algorithmic issues behind gene optimization and the approaches that different tools have adopted to redesign genes and optimize desired coding features. We utilize test cases to demonstrate the efficiency of each approach, as well as identify their strengths and limitations. PMID:25340050

A Review of Computational Methods for Finding Non-Coding RNA Genes

PubMed Central

Abbas, Qaisar; Raza, Syed Mansoor; Biyabani, Azizuddin Ahmed; Jaffar, Muhammad Arfan

2016-01-01

Finding non-coding RNA (ncRNA) genes has emerged over the past few years as a cutting-edge trend in bioinformatics. There are numerous computational intelligence (CI) challenges in the annotation and interpretation of ncRNAs because it requires a domain-related expert knowledge in CI techniques. Moreover, there are many classes predicted yet not experimentally verified by researchers. Recently, researchers have applied many CI methods to predict the classes of ncRNAs. However, the diverse CI approaches lack a definitive classification framework to take advantage of past studies. A few review papers have attempted to summarize CI approaches, but focused on the particular methodological viewpoints. Accordingly, in this article, we summarize in greater detail than previously available, the CI techniques for finding ncRNAs genes. We differentiate from the existing bodies of research and discuss concisely the technical merits of various techniques. Lastly, we review the limitations of ncRNA gene-finding CI methods with a point-of-view towards the development of new computational tools. PMID:27918472

Indexed variation graphs for efficient and accurate resistome profiling.

PubMed

Rowe, Will P M; Winn, Martyn D

2018-05-14

Antimicrobial resistance remains a major threat to global health. Profiling the collective antimicrobial resistance genes within a metagenome (the "resistome") facilitates greater understanding of antimicrobial resistance gene diversity and dynamics. In turn, this can allow for gene surveillance, individualised treatment of bacterial infections and more sustainable use of antimicrobials. However, resistome profiling can be complicated by high similarity between reference genes, as well as the sheer volume of sequencing data and the complexity of analysis workflows. We have developed an efficient and accurate method for resistome profiling that addresses these complications and improves upon currently available tools. Our method combines a variation graph representation of gene sets with an LSH Forest indexing scheme to allow for fast classification of metagenomic sequence reads using similarity-search queries. Subsequent hierarchical local alignment of classified reads against graph traversals enables accurate reconstruction of full-length gene sequences using a scoring scheme. We provide our implementation, GROOT, and show it to be both faster and more accurate than a current reference-dependent tool for resistome profiling. GROOT runs on a laptop and can process a typical 2 gigabyte metagenome in 2 minutes using a single CPU. Our method is not restricted to resistome profiling and has the potential to improve current metagenomic workflows. GROOT is written in Go and is available at https://github.com/will-rowe/groot (MIT license). will.rowe@stfc.ac.uk. Supplementary data are available at Bioinformatics online.

GENE-Counter: A Computational Pipeline for the Analysis of RNA-Seq Data for Gene Expression Differences

PubMed Central

Di, Yanming; Schafer, Daniel W.; Wilhelm, Larry J.; Fox, Samuel E.; Sullivan, Christopher M.; Curzon, Aron D.; Carrington, James C.; Mockler, Todd C.; Chang, Jeff H.

2011-01-01

GENE-counter is a complete Perl-based computational pipeline for analyzing RNA-Sequencing (RNA-Seq) data for differential gene expression. In addition to its use in studying transcriptomes of eukaryotic model organisms, GENE-counter is applicable for prokaryotes and non-model organisms without an available genome reference sequence. For alignments, GENE-counter is configured for CASHX, Bowtie, and BWA, but an end user can use any Sequence Alignment/Map (SAM)-compliant program of preference. To analyze data for differential gene expression, GENE-counter can be run with any one of three statistics packages that are based on variations of the negative binomial distribution. The default method is a new and simple statistical test we developed based on an over-parameterized version of the negative binomial distribution. GENE-counter also includes three different methods for assessing differentially expressed features for enriched gene ontology (GO) terms. Results are transparent and data are systematically stored in a MySQL relational database to facilitate additional analyses as well as quality assessment. We used next generation sequencing to generate a small-scale RNA-Seq dataset derived from the heavily studied defense response of Arabidopsis thaliana and used GENE-counter to process the data. Collectively, the support from analysis of microarrays as well as the observed and substantial overlap in results from each of the three statistics packages demonstrates that GENE-counter is well suited for handling the unique characteristics of small sample sizes and high variability in gene counts. PMID:21998647

Compression-based distance (CBD): a simple, rapid, and accurate method for microbiota composition comparison

PubMed Central

2013-01-01

Background Perturbations in intestinal microbiota composition have been associated with a variety of gastrointestinal tract-related diseases. The alleviation of symptoms has been achieved using treatments that alter the gastrointestinal tract microbiota toward that of healthy individuals. Identifying differences in microbiota composition through the use of 16S rRNA gene hypervariable tag sequencing has profound health implications. Current computational methods for comparing microbial communities are usually based on multiple alignments and phylogenetic inference, making them time consuming and requiring exceptional expertise and computational resources. As sequencing data rapidly grows in size, simpler analysis methods are needed to meet the growing computational burdens of microbiota comparisons. Thus, we have developed a simple, rapid, and accurate method, independent of multiple alignments and phylogenetic inference, to support microbiota comparisons. Results We create a metric, called compression-based distance (CBD) for quantifying the degree of similarity between microbial communities. CBD uses the repetitive nature of hypervariable tag datasets and well-established compression algorithms to approximate the total information shared between two datasets. Three published microbiota datasets were used as test cases for CBD as an applicable tool. Our study revealed that CBD recaptured 100% of the statistically significant conclusions reported in the previous studies, while achieving a decrease in computational time required when compared to similar tools without expert user intervention. Conclusion CBD provides a simple, rapid, and accurate method for assessing distances between gastrointestinal tract microbiota 16S hypervariable tag datasets. PMID:23617892

Compression-based distance (CBD): a simple, rapid, and accurate method for microbiota composition comparison.

PubMed

Yang, Fang; Chia, Nicholas; White, Bryan A; Schook, Lawrence B

2013-04-23

Perturbations in intestinal microbiota composition have been associated with a variety of gastrointestinal tract-related diseases. The alleviation of symptoms has been achieved using treatments that alter the gastrointestinal tract microbiota toward that of healthy individuals. Identifying differences in microbiota composition through the use of 16S rRNA gene hypervariable tag sequencing has profound health implications. Current computational methods for comparing microbial communities are usually based on multiple alignments and phylogenetic inference, making them time consuming and requiring exceptional expertise and computational resources. As sequencing data rapidly grows in size, simpler analysis methods are needed to meet the growing computational burdens of microbiota comparisons. Thus, we have developed a simple, rapid, and accurate method, independent of multiple alignments and phylogenetic inference, to support microbiota comparisons. We create a metric, called compression-based distance (CBD) for quantifying the degree of similarity between microbial communities. CBD uses the repetitive nature of hypervariable tag datasets and well-established compression algorithms to approximate the total information shared between two datasets. Three published microbiota datasets were used as test cases for CBD as an applicable tool. Our study revealed that CBD recaptured 100% of the statistically significant conclusions reported in the previous studies, while achieving a decrease in computational time required when compared to similar tools without expert user intervention. CBD provides a simple, rapid, and accurate method for assessing distances between gastrointestinal tract microbiota 16S hypervariable tag datasets.

An accurate and computationally efficient algorithm for ground peak identification in large footprint waveform LiDAR data

NASA Astrophysics Data System (ADS)

Zhuang, Wei; Mountrakis, Giorgos

2014-09-01

Large footprint waveform LiDAR sensors have been widely used for numerous airborne studies. Ground peak identification in a large footprint waveform is a significant bottleneck in exploring full usage of the waveform datasets. In the current study, an accurate and computationally efficient algorithm was developed for ground peak identification, called Filtering and Clustering Algorithm (FICA). The method was evaluated on Land, Vegetation, and Ice Sensor (LVIS) waveform datasets acquired over Central NY. FICA incorporates a set of multi-scale second derivative filters and a k-means clustering algorithm in order to avoid detecting false ground peaks. FICA was tested in five different land cover types (deciduous trees, coniferous trees, shrub, grass and developed area) and showed more accurate results when compared to existing algorithms. More specifically, compared with Gaussian decomposition, the RMSE ground peak identification by FICA was 2.82 m (5.29 m for GD) in deciduous plots, 3.25 m (4.57 m for GD) in coniferous plots, 2.63 m (2.83 m for GD) in shrub plots, 0.82 m (0.93 m for GD) in grass plots, and 0.70 m (0.51 m for GD) in plots of developed areas. FICA performance was also relatively consistent under various slope and canopy coverage (CC) conditions. In addition, FICA showed better computational efficiency compared to existing methods. FICA's major computational and accuracy advantage is a result of the adopted multi-scale signal processing procedures that concentrate on local portions of the signal as opposed to the Gaussian decomposition that uses a curve-fitting strategy applied in the entire signal. The FICA algorithm is a good candidate for large-scale implementation on future space-borne waveform LiDAR sensors.

Accurate analytic solution of chemical master equations for gene regulation networks in a single cell

NASA Astrophysics Data System (ADS)

Huang, Guan-Rong; Saakian, David B.; Hu, Chin-Kun

2018-01-01

Studying gene regulation networks in a single cell is an important, interesting, and hot research topic of molecular biology. Such process can be described by chemical master equations (CMEs). We propose a Hamilton-Jacobi equation method with finite-size corrections to solve such CMEs accurately at the intermediate region of switching, where switching rate is comparable to fast protein production rate. We applied this approach to a model of self-regulating proteins [H. Ge et al., Phys. Rev. Lett. 114, 078101 (2015), 10.1103/PhysRevLett.114.078101] and found that as a parameter related to inducer concentration increases the probability of protein production changes from unimodal to bimodal, then to unimodal, consistent with phenotype switching observed in a single cell.

Development of highly accurate approximate scheme for computing the charge transfer integral

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pershin, Anton; Szalay, Péter G.

The charge transfer integral is a key parameter required by various theoretical models to describe charge transport properties, e.g., in organic semiconductors. The accuracy of this important property depends on several factors, which include the level of electronic structure theory and internal simplifications of the applied formalism. The goal of this paper is to identify the performance of various approximate approaches of the latter category, while using the high level equation-of-motion coupled cluster theory for the electronic structure. The calculations have been performed on the ethylene dimer as one of the simplest model systems. By studying different spatial perturbations, itmore » was shown that while both energy split in dimer and fragment charge difference methods are equivalent with the exact formulation for symmetrical displacements, they are less efficient when describing transfer integral along the asymmetric alteration coordinate. Since the “exact” scheme was found computationally expensive, we examine the possibility to obtain the asymmetric fluctuation of the transfer integral by a Taylor expansion along the coordinate space. By exploring the efficiency of this novel approach, we show that the Taylor expansion scheme represents an attractive alternative to the “exact” calculations due to a substantial reduction of computational costs, when a considerably large region of the potential energy surface is of interest. Moreover, we show that the Taylor expansion scheme, irrespective of the dimer symmetry, is very accurate for the entire range of geometry fluctuations that cover the space the molecule accesses at room temperature.« less

Proteogenomics produces comprehensive and highly accurate protein-coding gene annotation in a complete genome assembly of Malassezia sympodialis

PubMed Central

Tellgren-Roth, Christian; Baudo, Charles D.; Kennell, John C.; Sun, Sheng; Billmyre, R. Blake; Schröder, Markus S.; Andersson, Anna; Holm, Tina; Sigurgeirsson, Benjamin; Wu, Guangxi; Sankaranarayanan, Sundar Ram; Siddharthan, Rahul; Sanyal, Kaustuv; Lundeberg, Joakim; Nystedt, Björn; Boekhout, Teun; Dawson, Thomas L.; Heitman, Joseph

2017-01-01

Abstract Complete and accurate genome assembly and annotation is a crucial foundation for comparative and functional genomics. Despite this, few complete eukaryotic genomes are available, and genome annotation remains a major challenge. Here, we present a complete genome assembly of the skin commensal yeast Malassezia sympodialis and demonstrate how proteogenomics can substantially improve gene annotation. Through long-read DNA sequencing, we obtained a gap-free genome assembly for M. sympodialis (ATCC 42132), comprising eight nuclear and one mitochondrial chromosome. We also sequenced and assembled four M. sympodialis clinical isolates, and showed their value for understanding Malassezia reproduction by confirming four alternative allele combinations at the two mating-type loci. Importantly, we demonstrated how proteomics data could be readily integrated with transcriptomics data in standard annotation tools. This increased the number of annotated protein-coding genes by 14% (from 3612 to 4113), compared to using transcriptomics evidence alone. Manual curation further increased the number of protein-coding genes by 9% (to 4493). All of these genes have RNA-seq evidence and 87% were confirmed by proteomics. The M. sympodialis genome assembly and annotation presented here is at a quality yet achieved only for a few eukaryotic organisms, and constitutes an important reference for future host-microbe interaction studies. PMID:28100699

A new computational method for the detection of horizontal gene transfer events.

PubMed

Tsirigos, Aristotelis; Rigoutsos, Isidore

2005-01-01

In recent years, the increase in the amounts of available genomic data has made it easier to appreciate the extent by which organisms increase their genetic diversity through horizontally transferred genetic material. Such transfers have the potential to give rise to extremely dynamic genomes where a significant proportion of their coding DNA has been contributed by external sources. Because of the impact of these horizontal transfers on the ecological and pathogenic character of the recipient organisms, methods are continuously sought that are able to computationally determine which of the genes of a given genome are products of transfer events. In this paper, we introduce and discuss a novel computational method for identifying horizontal transfers that relies on a gene's nucleotide composition and obviates the need for knowledge of codon boundaries. In addition to being applicable to individual genes, the method can be easily extended to the case of clusters of horizontally transferred genes. With the help of an extensive and carefully designed set of experiments on 123 archaeal and bacterial genomes, we demonstrate that the new method exhibits significant improvement in sensitivity when compared to previously published approaches. In fact, it achieves an average relative improvement across genomes of between 11 and 41% compared to the Codon Adaptation Index method in distinguishing native from foreign genes. Our method's horizontal gene transfer predictions for 123 microbial genomes are available online at http://cbcsrv.watson.ibm.com/HGT/.

A hybrid computational method for the discovery of novel reproduction-related genes.

PubMed

Chen, Lei; Chu, Chen; Kong, Xiangyin; Huang, Guohua; Huang, Tao; Cai, Yu-Dong

2015-01-01

Uncovering the molecular mechanisms underlying reproduction is of great importance to infertility treatment and to the generation of healthy offspring. In this study, we discovered novel reproduction-related genes with a hybrid computational method, integrating three different types of method, which offered new clues for further reproduction research. This method was first executed on a weighted graph, constructed based on known protein-protein interactions, to search the shortest paths connecting any two known reproduction-related genes. Genes occurring in these paths were deemed to have a special relationship with reproduction. These newly discovered genes were filtered with a randomization test. Then, the remaining genes were further selected according to their associations with known reproduction-related genes measured by protein-protein interaction score and alignment score obtained by BLAST. The in-depth analysis of the high confidence novel reproduction genes revealed hidden mechanisms of reproduction and provided guidelines for further experimental validations.

A hybrid solution using computational prediction and measured data to accurately determine process corrections with reduced overlay sampling

NASA Astrophysics Data System (ADS)

Noyes, Ben F.; Mokaberi, Babak; Mandoy, Ram; Pate, Alex; Huijgen, Ralph; McBurney, Mike; Chen, Owen

2017-03-01

Reducing overlay error via an accurate APC feedback system is one of the main challenges in high volume production of the current and future nodes in the semiconductor industry. The overlay feedback system directly affects the number of dies meeting overlay specification and the number of layers requiring dedicated exposure tools through the fabrication flow. Increasing the former number and reducing the latter number is beneficial for the overall efficiency and yield of the fabrication process. An overlay feedback system requires accurate determination of the overlay error, or fingerprint, on exposed wafers in order to determine corrections to be automatically and dynamically applied to the exposure of future wafers. Since current and future nodes require correction per exposure (CPE), the resolution of the overlay fingerprint must be high enough to accommodate CPE in the overlay feedback system, or overlay control module (OCM). Determining a high resolution fingerprint from measured data requires extremely dense overlay sampling that takes a significant amount of measurement time. For static corrections this is acceptable, but in an automated dynamic correction system this method creates extreme bottlenecks for the throughput of said system as new lots have to wait until the previous lot is measured. One solution is using a less dense overlay sampling scheme and employing computationally up-sampled data to a dense fingerprint. That method uses a global fingerprint model over the entire wafer; measured localized overlay errors are therefore not always represented in its up-sampled output. This paper will discuss a hybrid system shown in Fig. 1 that combines a computationally up-sampled fingerprint with the measured data to more accurately capture the actual fingerprint, including local overlay errors. Such a hybrid system is shown to result in reduced modelled residuals while determining the fingerprint, and better on-product overlay performance.

Probability-based collaborative filtering model for predicting gene-disease associations.

PubMed

Zeng, Xiangxiang; Ding, Ningxiang; Rodríguez-Patón, Alfonso; Zou, Quan

2017-12-28

Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene-disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our model. Firstly, on the basis of a typical latent factorization model, we propose model I with an average heterogeneous regularization. Secondly, we develop modified model II with personal heterogeneous regularization to enhance the accuracy of aforementioned models. In this model, vector space similarity or Pearson correlation coefficient metrics and data on related species are also used. We compared the results of PCFM with the results of four state-of-arts approaches. The results show that PCFM performs better than other advanced approaches. PCFM model can be leveraged for predictions of disease genes, especially for new human genes or diseases with no known relationships.

A Hybrid Computational Method for the Discovery of Novel Reproduction-Related Genes

PubMed Central

Chen, Lei; Chu, Chen; Kong, Xiangyin; Huang, Guohua; Huang, Tao; Cai, Yu-Dong

2015-01-01

Uncovering the molecular mechanisms underlying reproduction is of great importance to infertility treatment and to the generation of healthy offspring. In this study, we discovered novel reproduction-related genes with a hybrid computational method, integrating three different types of method, which offered new clues for further reproduction research. This method was first executed on a weighted graph, constructed based on known protein-protein interactions, to search the shortest paths connecting any two known reproduction-related genes. Genes occurring in these paths were deemed to have a special relationship with reproduction. These newly discovered genes were filtered with a randomization test. Then, the remaining genes were further selected according to their associations with known reproduction-related genes measured by protein-protein interaction score and alignment score obtained by BLAST. The in-depth analysis of the high confidence novel reproduction genes revealed hidden mechanisms of reproduction and provided guidelines for further experimental validations. PMID:25768094

gene2drug: a computational tool for pathway-based rational drug repositioning.

PubMed

Napolitano, Francesco; Carrella, Diego; Mandriani, Barbara; Pisonero-Vaquero, Sandra; Sirci, Francesco; Medina, Diego L; Brunetti-Pierri, Nicola; di Bernardo, Diego

2018-05-01

Drug repositioning has been proposed as an effective shortcut to drug discovery. The availability of large collections of transcriptional responses to drugs enables computational approaches to drug repositioning directly based on measured molecular effects. We introduce a novel computational methodology for rational drug repositioning, which exploits the transcriptional responses following treatment with small molecule. Specifically, given a therapeutic target gene, a prioritization of potential effective drugs is obtained by assessing their impact on the transcription of genes in the pathway(s) including the target. We performed in silico validation and comparison with a state-of-art technique based on similar principles. We next performed experimental validation in two different real-case drug repositioning scenarios: (i) upregulation of the glutamate-pyruvate transaminase (GPT), which has been shown to induce reduction of oxalate levels in a mouse model of primary hyperoxaluria, and (ii) activation of the transcription factor TFEB, a master regulator of lysosomal biogenesis and autophagy, whose modulation may be beneficial in neurodegenerative disorders. A web tool for Gene2drug is freely available at http://gene2drug.tigem.it. An R package is under development and can be obtained from https://github.com/franapoli/gep2pep. dibernardo@tigem.it. Supplementary data are available at Bioinformatics online.

A Feature Selection Algorithm to Compute Gene Centric Methylation from Probe Level Methylation Data.

PubMed

Baur, Brittany; Bozdag, Serdar

2016-01-01

DNA methylation is an important epigenetic event that effects gene expression during development and various diseases such as cancer. Understanding the mechanism of action of DNA methylation is important for downstream analysis. In the Illumina Infinium HumanMethylation 450K array, there are tens of probes associated with each gene. Given methylation intensities of all these probes, it is necessary to compute which of these probes are most representative of the gene centric methylation level. In this study, we developed a feature selection algorithm based on sequential forward selection that utilized different classification methods to compute gene centric DNA methylation using probe level DNA methylation data. We compared our algorithm to other feature selection algorithms such as support vector machines with recursive feature elimination, genetic algorithms and ReliefF. We evaluated all methods based on the predictive power of selected probes on their mRNA expression levels and found that a K-Nearest Neighbors classification using the sequential forward selection algorithm performed better than other algorithms based on all metrics. We also observed that transcriptional activities of certain genes were more sensitive to DNA methylation changes than transcriptional activities of other genes. Our algorithm was able to predict the expression of those genes with high accuracy using only DNA methylation data. Our results also showed that those DNA methylation-sensitive genes were enriched in Gene Ontology terms related to the regulation of various biological processes.

Using evolutionary computations to understand the design and evolution of gene and cell regulatory networks.

PubMed

Spirov, Alexander; Holloway, David

2013-07-15

This paper surveys modeling approaches for studying the evolution of gene regulatory networks (GRNs). Modeling of the design or 'wiring' of GRNs has become increasingly common in developmental and medical biology, as a means of quantifying gene-gene interactions, the response to perturbations, and the overall dynamic motifs of networks. Drawing from developments in GRN 'design' modeling, a number of groups are now using simulations to study how GRNs evolve, both for comparative genomics and to uncover general principles of evolutionary processes. Such work can generally be termed evolution in silico. Complementary to these biologically-focused approaches, a now well-established field of computer science is Evolutionary Computations (ECs), in which highly efficient optimization techniques are inspired from evolutionary principles. In surveying biological simulation approaches, we discuss the considerations that must be taken with respect to: (a) the precision and completeness of the data (e.g. are the simulations for very close matches to anatomical data, or are they for more general exploration of evolutionary principles); (b) the level of detail to model (we proceed from 'coarse-grained' evolution of simple gene-gene interactions to 'fine-grained' evolution at the DNA sequence level); (c) to what degree is it important to include the genome's cellular context; and (d) the efficiency of computation. With respect to the latter, we argue that developments in computer science EC offer the means to perform more complete simulation searches, and will lead to more comprehensive biological predictions. Copyright © 2013 Elsevier Inc. All rights reserved.

Proteogenomics produces comprehensive and highly accurate protein-coding gene annotation in a complete genome assembly of Malassezia sympodialis.

PubMed

Zhu, Yafeng; Engström, Pär G; Tellgren-Roth, Christian; Baudo, Charles D; Kennell, John C; Sun, Sheng; Billmyre, R Blake; Schröder, Markus S; Andersson, Anna; Holm, Tina; Sigurgeirsson, Benjamin; Wu, Guangxi; Sankaranarayanan, Sundar Ram; Siddharthan, Rahul; Sanyal, Kaustuv; Lundeberg, Joakim; Nystedt, Björn; Boekhout, Teun; Dawson, Thomas L; Heitman, Joseph; Scheynius, Annika; Lehtiö, Janne

2017-03-17

Complete and accurate genome assembly and annotation is a crucial foundation for comparative and functional genomics. Despite this, few complete eukaryotic genomes are available, and genome annotation remains a major challenge. Here, we present a complete genome assembly of the skin commensal yeast Malassezia sympodialis and demonstrate how proteogenomics can substantially improve gene annotation. Through long-read DNA sequencing, we obtained a gap-free genome assembly for M. sympodialis (ATCC 42132), comprising eight nuclear and one mitochondrial chromosome. We also sequenced and assembled four M. sympodialis clinical isolates, and showed their value for understanding Malassezia reproduction by confirming four alternative allele combinations at the two mating-type loci. Importantly, we demonstrated how proteomics data could be readily integrated with transcriptomics data in standard annotation tools. This increased the number of annotated protein-coding genes by 14% (from 3612 to 4113), compared to using transcriptomics evidence alone. Manual curation further increased the number of protein-coding genes by 9% (to 4493). All of these genes have RNA-seq evidence and 87% were confirmed by proteomics. The M. sympodialis genome assembly and annotation presented here is at a quality yet achieved only for a few eukaryotic organisms, and constitutes an important reference for future host-microbe interaction studies. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Parallel Higher-order Finite Element Method for Accurate Field Computations in Wakefield and PIC Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Candel, A.; Kabel, A.; Lee, L.

Over the past years, SLAC's Advanced Computations Department (ACD), under SciDAC sponsorship, has developed a suite of 3D (2D) parallel higher-order finite element (FE) codes, T3P (T2P) and Pic3P (Pic2P), aimed at accurate, large-scale simulation of wakefields and particle-field interactions in radio-frequency (RF) cavities of complex shape. The codes are built on the FE infrastructure that supports SLAC's frequency domain codes, Omega3P and S3P, to utilize conformal tetrahedral (triangular)meshes, higher-order basis functions and quadratic geometry approximation. For time integration, they adopt an unconditionally stable implicit scheme. Pic3P (Pic2P) extends T3P (T2P) to treat charged-particle dynamics self-consistently using the PIC (particle-in-cell)more » approach, the first such implementation on a conformal, unstructured grid using Whitney basis functions. Examples from applications to the International Linear Collider (ILC), Positron Electron Project-II (PEP-II), Linac Coherent Light Source (LCLS) and other accelerators will be presented to compare the accuracy and computational efficiency of these codes versus their counterparts using structured grids.« less

Large-scale extraction of accurate drug-disease treatment pairs from biomedical literature for drug repurposing

PubMed Central

2013-01-01

Background A large-scale, highly accurate, machine-understandable drug-disease treatment relationship knowledge base is important for computational approaches to drug repurposing. The large body of published biomedical research articles and clinical case reports available on MEDLINE is a rich source of FDA-approved drug-disease indication as well as drug-repurposing knowledge that is crucial for applying FDA-approved drugs for new diseases. However, much of this information is buried in free text and not captured in any existing databases. The goal of this study is to extract a large number of accurate drug-disease treatment pairs from published literature. Results In this study, we developed a simple but highly accurate pattern-learning approach to extract treatment-specific drug-disease pairs from 20 million biomedical abstracts available on MEDLINE. We extracted a total of 34,305 unique drug-disease treatment pairs, the majority of which are not included in existing structured databases. Our algorithm achieved a precision of 0.904 and a recall of 0.131 in extracting all pairs, and a precision of 0.904 and a recall of 0.842 in extracting frequent pairs. In addition, we have shown that the extracted pairs strongly correlate with both drug target genes and therapeutic classes, therefore may have high potential in drug discovery. Conclusions We demonstrated that our simple pattern-learning relationship extraction algorithm is able to accurately extract many drug-disease pairs from the free text of biomedical literature that are not captured in structured databases. The large-scale, accurate, machine-understandable drug-disease treatment knowledge base that is resultant of our study, in combination with pairs from structured databases, will have high potential in computational drug repurposing tasks. PMID:23742147

Efficient computation of the phylogenetic likelihood function on multi-gene alignments and multi-core architectures.

PubMed

Stamatakis, Alexandros; Ott, Michael

2008-12-27

The continuous accumulation of sequence data, for example, due to novel wet-laboratory techniques such as pyrosequencing, coupled with the increasing popularity of multi-gene phylogenies and emerging multi-core processor architectures that face problems of cache congestion, poses new challenges with respect to the efficient computation of the phylogenetic maximum-likelihood (ML) function. Here, we propose two approaches that can significantly speed up likelihood computations that typically represent over 95 per cent of the computational effort conducted by current ML or Bayesian inference programs. Initially, we present a method and an appropriate data structure to efficiently compute the likelihood score on 'gappy' multi-gene alignments. By 'gappy' we denote sampling-induced gaps owing to missing sequences in individual genes (partitions), i.e. not real alignment gaps. A first proof-of-concept implementation in RAXML indicates that this approach can accelerate inferences on large and gappy alignments by approximately one order of magnitude. Moreover, we present insights and initial performance results on multi-core architectures obtained during the transition from an OpenMP-based to a Pthreads-based fine-grained parallelization of the ML function.

A Stationary Wavelet Entropy-Based Clustering Approach Accurately Predicts Gene Expression

PubMed Central

Nguyen, Nha; Vo, An; Choi, Inchan

2015-01-01

Abstract Studying epigenetic landscapes is important to understand the condition for gene regulation. Clustering is a useful approach to study epigenetic landscapes by grouping genes based on their epigenetic conditions. However, classical clustering approaches that often use a representative value of the signals in a fixed-sized window do not fully use the information written in the epigenetic landscapes. Clustering approaches to maximize the information of the epigenetic signals are necessary for better understanding gene regulatory environments. For effective clustering of multidimensional epigenetic signals, we developed a method called Dewer, which uses the entropy of stationary wavelet of epigenetic signals inside enriched regions for gene clustering. Interestingly, the gene expression levels were highly correlated with the entropy levels of epigenetic signals. Dewer separates genes better than a window-based approach in the assessment using gene expression and achieved a correlation coefficient above 0.9 without using any training procedure. Our results show that the changes of the epigenetic signals are useful to study gene regulation. PMID:25383910

Using the gene ontology to scan multilevel gene sets for associations in genome wide association studies.

PubMed

Schaid, Daniel J; Sinnwell, Jason P; Jenkins, Gregory D; McDonnell, Shannon K; Ingle, James N; Kubo, Michiaki; Goss, Paul E; Costantino, Joseph P; Wickerham, D Lawrence; Weinshilboum, Richard M

2012-01-01

Gene-set analyses have been widely used in gene expression studies, and some of the developed methods have been extended to genome wide association studies (GWAS). Yet, complications due to linkage disequilibrium (LD) among single nucleotide polymorphisms (SNPs), and variable numbers of SNPs per gene and genes per gene-set, have plagued current approaches, often leading to ad hoc "fixes." To overcome some of the current limitations, we developed a general approach to scan GWAS SNP data for both gene-level and gene-set analyses, building on score statistics for generalized linear models, and taking advantage of the directed acyclic graph structure of the gene ontology when creating gene-sets. However, other types of gene-set structures can be used, such as the popular Kyoto Encyclopedia of Genes and Genomes (KEGG). Our approach combines SNPs into genes, and genes into gene-sets, but assures that positive and negative effects of genes on a trait do not cancel. To control for multiple testing of many gene-sets, we use an efficient computational strategy that accounts for LD and provides accurate step-down adjusted P-values for each gene-set. Application of our methods to two different GWAS provide guidance on the potential strengths and weaknesses of our proposed gene-set analyses. © 2011 Wiley Periodicals, Inc.

Heterogeneous High Throughput Scientific Computing with APM X-Gene and Intel Xeon Phi

NASA Astrophysics Data System (ADS)

Abdurachmanov, David; Bockelman, Brian; Elmer, Peter; Eulisse, Giulio; Knight, Robert; Muzaffar, Shahzad

2015-05-01

Electrical power requirements will be a constraint on the future growth of Distributed High Throughput Computing (DHTC) as used by High Energy Physics. Performance-per-watt is a critical metric for the evaluation of computer architectures for cost- efficient computing. Additionally, future performance growth will come from heterogeneous, many-core, and high computing density platforms with specialized processors. In this paper, we examine the Intel Xeon Phi Many Integrated Cores (MIC) co-processor and Applied Micro X-Gene ARMv8 64-bit low-power server system-on-a-chip (SoC) solutions for scientific computing applications. We report our experience on software porting, performance and energy efficiency and evaluate the potential for use of such technologies in the context of distributed computing systems such as the Worldwide LHC Computing Grid (WLCG).

Seqping: gene prediction pipeline for plant genomes using self-training gene models and transcriptomic data.

PubMed

Chan, Kuang-Lim; Rosli, Rozana; Tatarinova, Tatiana V; Hogan, Michael; Firdaus-Raih, Mohd; Low, Eng-Ti Leslie

2017-01-27

Gene prediction is one of the most important steps in the genome annotation process. A large number of software tools and pipelines developed by various computing techniques are available for gene prediction. However, these systems have yet to accurately predict all or even most of the protein-coding regions. Furthermore, none of the currently available gene-finders has a universal Hidden Markov Model (HMM) that can perform gene prediction for all organisms equally well in an automatic fashion. We present an automated gene prediction pipeline, Seqping that uses self-training HMM models and transcriptomic data. The pipeline processes the genome and transcriptome sequences of the target species using GlimmerHMM, SNAP, and AUGUSTUS pipelines, followed by MAKER2 program to combine predictions from the three tools in association with the transcriptomic evidence. Seqping generates species-specific HMMs that are able to offer unbiased gene predictions. The pipeline was evaluated using the Oryza sativa and Arabidopsis thaliana genomes. Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis showed that the pipeline was able to identify at least 95% of BUSCO's plantae dataset. Our evaluation shows that Seqping was able to generate better gene predictions compared to three HMM-based programs (MAKER2, GlimmerHMM and AUGUSTUS) using their respective available HMMs. Seqping had the highest accuracy in rice (0.5648 for CDS, 0.4468 for exon, and 0.6695 nucleotide structure) and A. thaliana (0.5808 for CDS, 0.5955 for exon, and 0.8839 nucleotide structure). Seqping provides researchers a seamless pipeline to train species-specific HMMs and predict genes in newly sequenced or less-studied genomes. We conclude that the Seqping pipeline predictions are more accurate than gene predictions using the other three approaches with the default or available HMMs.

Gene regulatory networks: a coarse-grained, equation-free approach to multiscale computation.

PubMed

Erban, Radek; Kevrekidis, Ioannis G; Adalsteinsson, David; Elston, Timothy C

2006-02-28

We present computer-assisted methods for analyzing stochastic models of gene regulatory networks. The main idea that underlies this equation-free analysis is the design and execution of appropriately initialized short bursts of stochastic simulations; the results of these are processed to estimate coarse-grained quantities of interest, such as mesoscopic transport coefficients. In particular, using a simple model of a genetic toggle switch, we illustrate the computation of an effective free energy Phi and of a state-dependent effective diffusion coefficient D that characterize an unavailable effective Fokker-Planck equation. Additionally we illustrate the linking of equation-free techniques with continuation methods for performing a form of stochastic "bifurcation analysis"; estimation of mean switching times in the case of a bistable switch is also implemented in this equation-free context. The accuracy of our methods is tested by direct comparison with long-time stochastic simulations. This type of equation-free analysis appears to be a promising approach to computing features of the long-time, coarse-grained behavior of certain classes of complex stochastic models of gene regulatory networks, circumventing the need for long Monte Carlo simulations.

Integrated computational biology analysis to evaluate target genes for chronic myelogenous leukemia.

PubMed

Zheng, Yu; Wang, Yu-Ping; Cao, Hongbao; Chen, Qiusheng; Zhang, Xi

2018-06-05

Although hundreds of genes have been linked to chronic myelogenous leukemia (CML), many of the results lack reproducibility. In the present study, data across multiple modalities were integrated to evaluate 579 CML candidate genes, including literature‑based CML‑gene relation data, Gene Expression Omnibus RNA expression data and pathway‑based gene‑gene interaction data. The expression data included samples from 76 patients with CML and 73 healthy controls. For each target gene, four metrics were proposed and tested with case/control classification. The effectiveness of the four metrics presented was demonstrated by the high classification accuracy (94.63%; P<2x10‑4). Cross metric analysis suggested nine top candidate genes for CML: Epidermal growth factor receptor, tumor protein p53, catenin β 1, janus kinase 2, tumor necrosis factor, abelson murine leukemia viral oncogene homolog 1, vascular endothelial growth factor A, B‑cell lymphoma 2 and proto‑oncogene tyrosine‑protein kinase. In addition, 145 CML candidate pathways enriched with 485 out of 579 genes were identified (P<8.2x10‑11; q=0.005). In conclusion, weighted genetic networks generated using computational biology may be complementary to biological experiments for the evaluation of known or novel CML target genes.

Heterogeneous high throughput scientific computing with APM X-Gene and Intel Xeon Phi

DOE PAGES

Abdurachmanov, David; Bockelman, Brian; Elmer, Peter; ...

2015-05-22

Electrical power requirements will be a constraint on the future growth of Distributed High Throughput Computing (DHTC) as used by High Energy Physics. Performance-per-watt is a critical metric for the evaluation of computer architectures for cost- efficient computing. Additionally, future performance growth will come from heterogeneous, many-core, and high computing density platforms with specialized processors. In this paper, we examine the Intel Xeon Phi Many Integrated Cores (MIC) co-processor and Applied Micro X-Gene ARMv8 64-bit low-power server system-on-a-chip (SoC) solutions for scientific computing applications. As a result, we report our experience on software porting, performance and energy efficiency and evaluatemore » the potential for use of such technologies in the context of distributed computing systems such as the Worldwide LHC Computing Grid (WLCG).« less

WegoLoc: accurate prediction of protein subcellular localization using weighted Gene Ontology terms.

PubMed

Chi, Sang-Mun; Nam, Dougu

2012-04-01

We present an accurate and fast web server, WegoLoc for predicting subcellular localization of proteins based on sequence similarity and weighted Gene Ontology (GO) information. A term weighting method in the text categorization process is applied to GO terms for a support vector machine classifier. As a result, WegoLoc surpasses the state-of-the-art methods for previously used test datasets. WegoLoc supports three eukaryotic kingdoms (animals, fungi and plants) and provides human-specific analysis, and covers several sets of cellular locations. In addition, WegoLoc provides (i) multiple possible localizations of input protein(s) as well as their corresponding probability scores, (ii) weights of GO terms representing the contribution of each GO term in the prediction, and (iii) a BLAST E-value for the best hit with GO terms. If the similarity score does not meet a given threshold, an amino acid composition-based prediction is applied as a backup method. WegoLoc and User's guide are freely available at the website http://www.btool.org/WegoLoc smchiks@ks.ac.kr; dougnam@unist.ac.kr Supplementary data is available at http://www.btool.org/WegoLoc.

A streamline splitting pore-network approach for computationally inexpensive and accurate simulation of transport in porous media

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mehmani, Yashar; Oostrom, Martinus; Balhoff, Matthew

2014-03-20

Several approaches have been developed in the literature for solving flow and transport at the pore-scale. Some authors use a direct modeling approach where the fundamental flow and transport equations are solved on the actual pore-space geometry. Such direct modeling, while very accurate, comes at a great computational cost. Network models are computationally more efficient because the pore-space morphology is approximated. Typically, a mixed cell method (MCM) is employed for solving the flow and transport system which assumes pore-level perfect mixing. This assumption is invalid at moderate to high Peclet regimes. In this work, a novel Eulerian perspective on modelingmore » flow and transport at the pore-scale is developed. The new streamline splitting method (SSM) allows for circumventing the pore-level perfect mixing assumption, while maintaining the computational efficiency of pore-network models. SSM was verified with direct simulations and excellent matches were obtained against micromodel experiments across a wide range of pore-structure and fluid-flow parameters. The increase in the computational cost from MCM to SSM is shown to be minimal, while the accuracy of SSM is much higher than that of MCM and comparable to direct modeling approaches. Therefore, SSM can be regarded as an appropriate balance between incorporating detailed physics and controlling computational cost. The truly predictive capability of the model allows for the study of pore-level interactions of fluid flow and transport in different porous materials. In this paper, we apply SSM and MCM to study the effects of pore-level mixing on transverse dispersion in 3D disordered granular media.« less

Evaluation of amplification refractory mutation system (ARMS) technique for quick and accurate prenatal gene diagnosis of CHM variant in choroideremia.

PubMed

Yang, Lisha; Ijaz, Iqra; Cheng, Jingliang; Wei, Chunli; Tan, Xiaojun; Khan, Md Asaduzzaman; Fu, Xiaodong; Fu, Junjiang

2018-01-01

Choroideremia is a rare X-linked recessive inherited disorder that causes chorioretinal dystrophy leading to visual impairment in its early stages which finally causes total blindness in the affected person. It is caused due to mutations in the CHM gene. In this study, we have recruited a pedigree with choroideremia and detected a nonsense variant (c.C799T:p.R267X) in CHM of the proband (I:1). Different primer sets for amplification refractory mutation system (ARMS) were designed and PCR conditions were optimized. Then, we evaluated the sequence variant in the patient, carrier, and a fetus by using ARMS technique to identify if they inherited the pathogenic gene from parental generation; we used amniotic fluid DNA for the diagnosis of the gene in the fetus. The primer pairs, WT2+C and MT+C, amplified high specific products in different DNAs which were verified by Sanger sequencing. Based on our results, ARMS technique is fast, accurate, and reliable prenatal gene diagnostic tool to assess CHM variants. Taken together, our study indicates that ARMS technique can be used as a potential molecular tool in the diagnosis of prenatal mutation for choroideremia as well as other genetic diseases in undeveloped and developing countries, where there might be shortage of medical resources and supplies.

A sparse matrix-vector multiplication based algorithm for accurate density matrix computations on systems of millions of atoms

NASA Astrophysics Data System (ADS)

Ghale, Purnima; Johnson, Harley T.

2018-06-01

We present an efficient sparse matrix-vector (SpMV) based method to compute the density matrix P from a given Hamiltonian in electronic structure computations. Our method is a hybrid approach based on Chebyshev-Jackson approximation theory and matrix purification methods like the second order spectral projection purification (SP2). Recent methods to compute the density matrix scale as O(N) in the number of floating point operations but are accompanied by large memory and communication overhead, and they are based on iterative use of the sparse matrix-matrix multiplication kernel (SpGEMM), which is known to be computationally irregular. In addition to irregularity in the sparse Hamiltonian H, the nonzero structure of intermediate estimates of P depends on products of H and evolves over the course of computation. On the other hand, an expansion of the density matrix P in terms of Chebyshev polynomials is straightforward and SpMV based; however, the resulting density matrix may not satisfy the required constraints exactly. In this paper, we analyze the strengths and weaknesses of the Chebyshev-Jackson polynomials and the second order spectral projection purification (SP2) method, and propose to combine them so that the accurate density matrix can be computed using the SpMV computational kernel only, and without having to store the density matrix P. Our method accomplishes these objectives by using the Chebyshev polynomial estimate as the initial guess for SP2, which is followed by using sparse matrix-vector multiplications (SpMVs) to replicate the behavior of the SP2 algorithm for purification. We demonstrate the method on a tight-binding model system of an oxide material containing more than 3 million atoms. In addition, we also present the predicted behavior of our method when applied to near-metallic Hamiltonians with a wide energy spectrum.

Computational modeling identifies key gene regulatory interactions underlying phenobarbital-mediated tumor promotion

PubMed Central

Luisier, Raphaëlle; Unterberger, Elif B.; Goodman, Jay I.; Schwarz, Michael; Moggs, Jonathan; Terranova, Rémi; van Nimwegen, Erik

2014-01-01

Gene regulatory interactions underlying the early stages of non-genotoxic carcinogenesis are poorly understood. Here, we have identified key candidate regulators of phenobarbital (PB)-mediated mouse liver tumorigenesis, a well-characterized model of non-genotoxic carcinogenesis, by applying a new computational modeling approach to a comprehensive collection of in vivo gene expression studies. We have combined our previously developed motif activity response analysis (MARA), which models gene expression patterns in terms of computationally predicted transcription factor binding sites with singular value decomposition (SVD) of the inferred motif activities, to disentangle the roles that different transcriptional regulators play in specific biological pathways of tumor promotion. Furthermore, transgenic mouse models enabled us to identify which of these regulatory activities was downstream of constitutive androstane receptor and β-catenin signaling, both crucial components of PB-mediated liver tumorigenesis. We propose novel roles for E2F and ZFP161 in PB-mediated hepatocyte proliferation and suggest that PB-mediated suppression of ESR1 activity contributes to the development of a tumor-prone environment. Our study shows that combining MARA with SVD allows for automated identification of independent transcription regulatory programs within a complex in vivo tissue environment and provides novel mechanistic insights into PB-mediated hepatocarcinogenesis. PMID:24464994

Computational design of a Zn2+ receptor that controls bacterial gene expression

NASA Astrophysics Data System (ADS)

Dwyer, M. A.; Looger, L. L.; Hellinga, H. W.

2003-09-01

The control of cellular physiology and gene expression in response to extracellular signals is a basic property of living systems. We have constructed a synthetic bacterial signal transduction pathway in which gene expression is controlled by extracellular Zn2+. In this system a computationally designed Zn2+-binding periplasmic receptor senses the extracellular solute and triggers a two-component signal transduction pathway via a chimeric transmembrane protein, resulting in transcriptional up-regulation of a -galactosidase reporter gene. The Zn2+-binding site in the designed receptor is based on a four-coordinate, tetrahedral primary coordination sphere consisting of histidines and glutamates. In addition, mutations were introduced in a secondary coordination sphere to satisfy the residual hydrogen-bonding potential of the histidines coordinated to the metal. The importance of the secondary shell interactions is demonstrated by their effect on metal affinity and selectivity, as well as protein stability. Three designed protein sequences, comprising two distinct metal-binding positions, were all shown to bind Zn2+ and to function in the cell-based assay, indicating the generality of the design methodology. These experiments demonstrate that biological systems can be manipulated with computationally designed proteins that have drastically altered ligand-binding specificities, thereby extending the repertoire of genetic control by extracellular signals.

A More Accurate and Efficient Technique Developed for Using Computational Methods to Obtain Helical Traveling-Wave Tube Interaction Impedance

NASA Technical Reports Server (NTRS)

Kory, Carol L.

1999-01-01

The phenomenal growth of commercial communications has created a great demand for traveling-wave tube (TWT) amplifiers. Although the helix slow-wave circuit remains the mainstay of the TWT industry because of its exceptionally wide bandwidth, until recently it has been impossible to accurately analyze a helical TWT using its exact dimensions because of the complexity of its geometrical structure. For the first time, an accurate three-dimensional helical model was developed that allows accurate prediction of TWT cold-test characteristics including operating frequency, interaction impedance, and attenuation. This computational model, which was developed at the NASA Lewis Research Center, allows TWT designers to obtain a more accurate value of interaction impedance than is possible using experimental methods. Obtaining helical slow-wave circuit interaction impedance is an important part of the design process for a TWT because it is related to the gain and efficiency of the tube. This impedance cannot be measured directly; thus, conventional methods involve perturbing a helical circuit with a cylindrical dielectric rod placed on the central axis of the circuit and obtaining the difference in resonant frequency between the perturbed and unperturbed circuits. A mathematical relationship has been derived between this frequency difference and the interaction impedance (ref. 1). However, because of the complex configuration of the helical circuit, deriving this relationship involves several approximations. In addition, this experimental procedure is time-consuming and expensive, but until recently it was widely accepted as the most accurate means of determining interaction impedance. The advent of an accurate three-dimensional helical circuit model (ref. 2) made it possible for Lewis researchers to fully investigate standard approximations made in deriving the relationship between measured perturbation data and interaction impedance. The most prominent approximations made

A novel approach for discovering condition-specific correlations of gene expressions within biological pathways by using cloud computing technology.

PubMed

Chang, Tzu-Hao; Wu, Shih-Lin; Wang, Wei-Jen; Horng, Jorng-Tzong; Chang, Cheng-Wei

2014-01-01

Microarrays are widely used to assess gene expressions. Most microarray studies focus primarily on identifying differential gene expressions between conditions (e.g., cancer versus normal cells), for discovering the major factors that cause diseases. Because previous studies have not identified the correlations of differential gene expression between conditions, crucial but abnormal regulations that cause diseases might have been disregarded. This paper proposes an approach for discovering the condition-specific correlations of gene expressions within biological pathways. Because analyzing gene expression correlations is time consuming, an Apache Hadoop cloud computing platform was implemented. Three microarray data sets of breast cancer were collected from the Gene Expression Omnibus, and pathway information from the Kyoto Encyclopedia of Genes and Genomes was applied for discovering meaningful biological correlations. The results showed that adopting the Hadoop platform considerably decreased the computation time. Several correlations of differential gene expressions were discovered between the relapse and nonrelapse breast cancer samples, and most of them were involved in cancer regulation and cancer-related pathways. The results showed that breast cancer recurrence might be highly associated with the abnormal regulations of these gene pairs, rather than with their individual expression levels. The proposed method was computationally efficient and reliable, and stable results were obtained when different data sets were used. The proposed method is effective in identifying meaningful biological regulation patterns between conditions.

On numerically accurate finite element

NASA Technical Reports Server (NTRS)

Nagtegaal, J. C.; Parks, D. M.; Rice, J. R.

1974-01-01

A general criterion for testing a mesh with topologically similar repeat units is given, and the analysis shows that only a few conventional element types and arrangements are, or can be made suitable for computations in the fully plastic range. Further, a new variational principle, which can easily and simply be incorporated into an existing finite element program, is presented. This allows accurate computations to be made even for element designs that would not normally be suitable. Numerical results are given for three plane strain problems, namely pure bending of a beam, a thick-walled tube under pressure, and a deep double edge cracked tensile specimen. The effects of various element designs and of the new variational procedure are illustrated. Elastic-plastic computation at finite strain are discussed.

Fractionation and reconstitution of factors required for accurate transcription of mammalian ribosomal RNA genes: identification of a species-dependent initiation factor.

PubMed Central

Mishima, Y; Financsek, I; Kominami, R; Muramatsu, M

1982-01-01

Mouse and human cell extracts (S100) can support an accurate and efficient transcription initiation on homologous ribosomal RNA gene (rDNA) templates. The cell extracts were fractionated with the aid of a phosphocellulose column into four fractions (termed A, B, C and D), including one containing a major part of the RNA polymerase I activity. Various reconstitution experiments indicate that fraction D is an absolute requirement for the correct and efficient transcription initiation by RNA polymerase I on both mouse and human genes. Fraction B effectively suppresses random initiation on these templates. Fraction A appears to further enhance the transcription which takes place with fractions C and D. Although fractions A, B and C are interchangeable between mouse and human extracts, fraction D is not; i.e. initiation of transcription required the presence of a homologous fraction D for both templates. The factor(s) in fraction D, however, is not literally species-specific, since mouse D fraction is capable of supporting accurate transcription initiation on a rat rDNA template in the presence of all the other fractions from human cell extract under the conditions where human D fraction is unable to support it. We conclude from these experiments that a species-dependent factor in fraction D plays an important role in the initiation of rDNA transcription in each animal species. Images PMID:7177852

Fast and accurate mock catalogue generation for low-mass galaxies

NASA Astrophysics Data System (ADS)

Koda, Jun; Blake, Chris; Beutler, Florian; Kazin, Eyal; Marin, Felipe

2016-06-01

We present an accurate and fast framework for generating mock catalogues including low-mass haloes, based on an implementation of the COmoving Lagrangian Acceleration (COLA) technique. Multiple realisations of mock catalogues are crucial for analyses of large-scale structure, but conventional N-body simulations are too computationally expensive for the production of thousands of realizations. We show that COLA simulations can produce accurate mock catalogues with a moderate computation resource for low- to intermediate-mass galaxies in 1012 M⊙ haloes, both in real and redshift space. COLA simulations have accurate peculiar velocities, without systematic errors in the velocity power spectra for k ≤ 0.15 h Mpc-1, and with only 3-per cent error for k ≤ 0.2 h Mpc-1. We use COLA with 10 time steps and a Halo Occupation Distribution to produce 600 mock galaxy catalogues of the WiggleZ Dark Energy Survey. Our parallelized code for efficient generation of accurate halo catalogues is publicly available at github.com/junkoda/cola_halo.

ACCURATE CHEMICAL MASTER EQUATION SOLUTION USING MULTI-FINITE BUFFERS

PubMed Central

Cao, Youfang; Terebus, Anna; Liang, Jie

2016-01-01

The discrete chemical master equation (dCME) provides a fundamental framework for studying stochasticity in mesoscopic networks. Because of the multi-scale nature of many networks where reaction rates have large disparity, directly solving dCMEs is intractable due to the exploding size of the state space. It is important to truncate the state space effectively with quantified errors, so accurate solutions can be computed. It is also important to know if all major probabilistic peaks have been computed. Here we introduce the Accurate CME (ACME) algorithm for obtaining direct solutions to dCMEs. With multi-finite buffers for reducing the state space by O(n!), exact steady-state and time-evolving network probability landscapes can be computed. We further describe a theoretical framework of aggregating microstates into a smaller number of macrostates by decomposing a network into independent aggregated birth and death processes, and give an a priori method for rapidly determining steady-state truncation errors. The maximal sizes of the finite buffers for a given error tolerance can also be pre-computed without costly trial solutions of dCMEs. We show exactly computed probability landscapes of three multi-scale networks, namely, a 6-node toggle switch, 11-node phage-lambda epigenetic circuit, and 16-node MAPK cascade network, the latter two with no known solutions. We also show how probabilities of rare events can be computed from first-passage times, another class of unsolved problems challenging for simulation-based techniques due to large separations in time scales. Overall, the ACME method enables accurate and efficient solutions of the dCME for a large class of networks. PMID:27761104

An accurate model for the computation of the dose of protons in water.

PubMed

Embriaco, A; Bellinzona, V E; Fontana, A; Rotondi, A

2017-06-01

The accurate and fast calculation of the dose in proton radiation therapy is an essential ingredient for successful treatments. We propose a novel approach with a minimal number of parameters. The approach is based on the exact calculation of the electromagnetic part of the interaction, namely the Molière theory of the multiple Coulomb scattering for the transversal 1D projection and the Bethe-Bloch formula for the longitudinal stopping power profile, including a gaussian energy straggling. To this e.m. contribution the nuclear proton-nucleus interaction is added with a simple two-parameter model. Then, the non gaussian lateral profile is used to calculate the radial dose distribution with a method that assumes the cylindrical symmetry of the distribution. The results, obtained with a fast C++ based computational code called MONET (MOdel of ioN dosE for Therapy), are in very good agreement with the FLUKA MC code, within a few percent in the worst case. This study provides a new tool for fast dose calculation or verification, possibly for clinical use. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Accurate, simple, and inexpensive assays to diagnose F8 gene inversion mutations in hemophilia A patients and carriers.

PubMed

Dutta, Debargh; Gunasekera, Devi; Ragni, Margaret V; Pratt, Kathleen P

2016-12-27

The most frequent mutations resulting in hemophilia A are an intron 22 or intron 1 gene inversion, which together cause ∼50% of severe hemophilia A cases. We report a simple and accurate RNA-based assay to detect these mutations in patients and heterozygous carriers. The assays do not require specialized equipment or expensive reagents; therefore, they may provide useful and economic protocols that could be standardized for central laboratory testing. RNA is purified from a blood sample, and reverse transcription nested polymerase chain reaction (RT-NPCR) reactions amplify DNA fragments with the F8 sequence spanning the exon 22 to 23 splice site (intron 22 inversion test) or the exon 1 to 2 splice site (intron 1 inversion test). These sequences will be amplified only from F8 RNA without an intron 22 or intron 1 inversion mutation, respectively. Additional RT-NPCR reactions are then carried out to amplify the inverted sequences extending from F8 exon 19 to the first in-frame stop codon within intron 22 or a chimeric transcript containing F8 exon 1 and the VBP1 gene. These latter 2 products are produced only by individuals with an intron 22 or intron 1 inversion mutation, respectively. The intron 22 inversion mutations may be further classified (eg, as type 1 or type 2, reflecting the specific homologous recombination sites) by the standard DNA-based "inverse-shifting" PCR assay if desired. Efficient Bcl I and T4 DNA ligase enzymes that cleave and ligate DNA in minutes were used, which is a substantial improvement over previous protocols that required overnight incubations. These protocols can accurately detect F8 inversion mutations via same-day testing of patient samples.

Rapid and accurate synthesis of TALE genes from synthetic oligonucleotides.

PubMed

Wang, Fenghua; Zhang, Hefei; Gao, Jingxia; Chen, Fengjiao; Chen, Sijie; Zhang, Cuizhen; Peng, Gang

2016-01-01

Custom synthesis of transcription activator-like effector (TALE) genes has relied upon plasmid libraries of pre-fabricated TALE-repeat monomers or oligomers. Here we describe a novel synthesis method that directly incorporates annealed synthetic oligonucleotides into the TALE-repeat units. Our approach utilizes iterative sets of oligonucleotides and a translational frame check strategy to ensure the high efficiency and accuracy of TALE-gene synthesis. TALE arrays of more than 20 repeats can be constructed, and the majority of the synthesized constructs have perfect sequences. In addition, this novel oligonucleotide-based method can readily accommodate design changes to the TALE repeats. We demonstrated an increased gene targeting efficiency against a genomic site containing a potentially methylated cytosine by incorporating non-conventional repeat variable di-residue (RVD) sequences.

Memory conformity affects inaccurate memories more than accurate memories.

PubMed

Wright, Daniel B; Villalba, Daniella K

2012-01-01

After controlling for initial confidence, inaccurate memories were shown to be more easily distorted than accurate memories. In two experiments groups of participants viewed 50 stimuli and were then presented with these stimuli plus 50 fillers. During this test phase participants reported their confidence that each stimulus was originally shown. This was followed by computer-generated responses from a bogus participant. After being exposed to this response participants again rated the confidence of their memory. The computer-generated responses systematically distorted participants' responses. Memory distortion depended on initial memory confidence, with uncertain memories being more malleable than confident memories. This effect was moderated by whether the participant's memory was initially accurate or inaccurate. Inaccurate memories were more malleable than accurate memories. The data were consistent with a model describing two types of memory (i.e., recollective and non-recollective memories), which differ in how susceptible these memories are to memory distortion.

The preliminary exploration of 64-slice volume computed tomography in the accurate measurement of pleural effusion.

PubMed

Guo, Zhi-Jun; Lin, Qiang; Liu, Hai-Tao; Lu, Jun-Ying; Zeng, Yan-Hong; Meng, Fan-Jie; Cao, Bin; Zi, Xue-Rong; Han, Shu-Ming; Zhang, Yu-Huan

2013-09-01

Using computed tomography (CT) to rapidly and accurately quantify pleural effusion volume benefits medical and scientific research. However, the precise volume of pleural effusions still involves many challenges and currently does not have a recognized accurate measuring. To explore the feasibility of using 64-slice CT volume-rendering technology to accurately measure pleural fluid volume and to then analyze the correlation between the volume of the free pleural effusion and the different diameters of the pleural effusion. The 64-slice CT volume-rendering technique was used to measure and analyze three parts. First, the fluid volume of a self-made thoracic model was measured and compared with the actual injected volume. Second, the pleural effusion volume was measured before and after pleural fluid drainage in 25 patients, and the volume reduction was compared with the actual volume of the liquid extract. Finally, the free pleural effusion volume was measured in 26 patients to analyze the correlation between it and the diameter of the effusion, which was then used to calculate the regression equation. After using the 64-slice CT volume-rendering technique to measure the fluid volume of the self-made thoracic model, the results were compared with the actual injection volume. No significant differences were found, P = 0.836. For the 25 patients with drained pleural effusions, the comparison of the reduction volume with the actual volume of the liquid extract revealed no significant differences, P = 0.989. The following linear regression equation was used to compare the pleural effusion volume (V) (measured by the CT volume-rendering technique) with the pleural effusion greatest depth (d): V = 158.16 × d - 116.01 (r = 0.91, P = 0.000). The following linear regression was used to compare the volume with the product of the pleural effusion diameters (l × h × d): V = 0.56 × (l × h × d) + 39.44 (r = 0.92, P = 0.000). The 64-slice CT volume-rendering technique can

An efficient and accurate 3D displacements tracking strategy for digital volume correlation

NASA Astrophysics Data System (ADS)

Pan, Bing; Wang, Bo; Wu, Dafang; Lubineau, Gilles

2014-07-01

Owing to its inherent computational complexity, practical implementation of digital volume correlation (DVC) for internal displacement and strain mapping faces important challenges in improving its computational efficiency. In this work, an efficient and accurate 3D displacement tracking strategy is proposed for fast DVC calculation. The efficiency advantage is achieved by using three improvements. First, to eliminate the need of updating Hessian matrix in each iteration, an efficient 3D inverse compositional Gauss-Newton (3D IC-GN) algorithm is introduced to replace existing forward additive algorithms for accurate sub-voxel displacement registration. Second, to ensure the 3D IC-GN algorithm that converges accurately and rapidly and avoid time-consuming integer-voxel displacement searching, a generalized reliability-guided displacement tracking strategy is designed to transfer accurate and complete initial guess of deformation for each calculation point from its computed neighbors. Third, to avoid the repeated computation of sub-voxel intensity interpolation coefficients, an interpolation coefficient lookup table is established for tricubic interpolation. The computational complexity of the proposed fast DVC and the existing typical DVC algorithms are first analyzed quantitatively according to necessary arithmetic operations. Then, numerical tests are performed to verify the performance of the fast DVC algorithm in terms of measurement accuracy and computational efficiency. The experimental results indicate that, compared with the existing DVC algorithm, the presented fast DVC algorithm produces similar precision and slightly higher accuracy at a substantially reduced computational cost.

1NON-INVASIVE RADIOIODINE IMAGING FOR ACCURATE QUANTITATION OF NIS REPORTER GENE EXPRESSION IN TRANSPLANTED HEARTS

PubMed Central

Ricci, Davide; Mennander, Ari A; Pham, Linh D; Rao, Vinay P; Miyagi, Naoto; Byrne, Guerard W; Russell, Stephen J; McGregor, Christopher GA

2008-01-01

Objectives We studied the concordance of transgene expression in the transplanted heart using bicistronic adenoviral vector coding for a transgene of interest (human carcinoembryonic antigen: hCEA - beta human chorionic gonadotropin: βhCG) and for a marker imaging transgene (human sodium iodide symporter: hNIS). Methods Inbred Lewis rats were used for syngeneic heterotopic cardiac transplantation. Donor rat hearts were perfused ex vivo for 30 minutes prior to transplantation with University of Wisconsin (UW) solution (n=3), with 109 pfu/ml of adenovirus expressing hNIS (Ad-NIS; n=6), hNIS-hCEA (Ad-NIS-CEA; n=6) and hNIS-βhCG (Ad-NIS-CG; n=6). On post-operative day (POD) 5, 10, 15 all animals underwent micro-SPECT/CT imaging of the donor hearts after tail vein injection of 1000 μCi 123I and blood sample collection for hCEA and βhCG quantification. Results Significantly higher image intensity was noted in the hearts perfused with Ad-NIS (1.1±0.2; 0.9±0.07), Ad-NIS-CEA (1.2±0.3; 0.9±0.1) and Ad-NIS-CG (1.1±0.1; 0.9±0.1) compared to UW group (0.44±0.03; 0.47±0.06) on POD 5 and 10 (p<0.05). Serum levels of hCEA and βhCG increased in animals showing high cardiac 123I uptake, but not in those with lower uptake. Above this threshold, image intensities correlated well with serum levels of hCEA and βhCG (R2=0.99 and R2=0.96 respectively). Conclusions These data demonstrate that hNIS is an excellent reporter gene for the transplanted heart. The expression level of hNIS can be accurately and non-invasively monitored by serial radioisotopic single photon emission computed tomography (SPECT) imaging. High concordance has been demonstrated between imaging and soluble marker peptides at the maximum transgene expression on POD 5. PMID:17980613

Machine-learning approach identifies a pattern of gene expression in peripheral blood that can accurately detect ischaemic stroke

PubMed Central

O’Connell, Grant C; Petrone, Ashley B; Treadway, Madison B; Tennant, Connie S; Lucke-Wold, Noelle; Chantler, Paul D; Barr, Taura L

2016-01-01

Early and accurate diagnosis of stroke improves the probability of positive outcome. The objective of this study was to identify a pattern of gene expression in peripheral blood that could potentially be optimised to expedite the diagnosis of acute ischaemic stroke (AIS). A discovery cohort was recruited consisting of 39 AIS patients and 24 neurologically asymptomatic controls. Peripheral blood was sampled at emergency department admission, and genome-wide expression profiling was performed via microarray. A machine-learning technique known as genetic algorithm k-nearest neighbours (GA/kNN) was then used to identify a pattern of gene expression that could optimally discriminate between groups. This pattern of expression was then assessed via qRT-PCR in an independent validation cohort, where it was evaluated for its ability to discriminate between an additional 39 AIS patients and 30 neurologically asymptomatic controls, as well as 20 acute stroke mimics. GA/kNN identified 10 genes (ANTXR2, STK3, PDK4, CD163, MAL, GRAP, ID3, CTSZ, KIF1B and PLXDC2) whose coordinate pattern of expression was able to identify 98.4% of discovery cohort subjects correctly (97.4% sensitive, 100% specific). In the validation cohort, the expression levels of the same 10 genes were able to identify 95.6% of subjects correctly when comparing AIS patients to asymptomatic controls (92.3% sensitive, 100% specific), and 94.9% of subjects correctly when comparing AIS patients with stroke mimics (97.4% sensitive, 90.0% specific). The transcriptional pattern identified in this study shows strong diagnostic potential, and warrants further evaluation to determine its true clinical efficacy. PMID:29263821

Accurate Finite Difference Algorithms

NASA Technical Reports Server (NTRS)

Goodrich, John W.

1996-01-01

Two families of finite difference algorithms for computational aeroacoustics are presented and compared. All of the algorithms are single step explicit methods, they have the same order of accuracy in both space and time, with examples up to eleventh order, and they have multidimensional extensions. One of the algorithm families has spectral like high resolution. Propagation with high order and high resolution algorithms can produce accurate results after O(10(exp 6)) periods of propagation with eight grid points per wavelength.

Rapid and accurate identification of Mycobacterium tuberculosis complex and common non-tuberculous mycobacteria by multiplex real-time PCR targeting different housekeeping genes.

PubMed

Nasr Esfahani, Bahram; Rezaei Yazdi, Hadi; Moghim, Sharareh; Ghasemian Safaei, Hajieh; Zarkesh Esfahani, Hamid

2012-11-01

Rapid and accurate identification of mycobacteria isolates from primary culture is important due to timely and appropriate antibiotic therapy. Conventional methods for identification of Mycobacterium species based on biochemical tests needs several weeks and may remain inconclusive. In this study, a novel multiplex real-time PCR was developed for rapid identification of Mycobacterium genus, Mycobacterium tuberculosis complex (MTC) and the most common non-tuberculosis mycobacteria species including M. abscessus, M. fortuitum, M. avium complex, M. kansasii, and the M. gordonae in three reaction tubes but under same PCR condition. Genetic targets for primer designing included the 16S rDNA gene, the dnaJ gene, the gyrB gene and internal transcribed spacer (ITS). Multiplex real-time PCR was setup with reference Mycobacterium strains and was subsequently tested with 66 clinical isolates. Results of multiplex real-time PCR were analyzed with melting curves and melting temperature (T (m)) of Mycobacterium genus, MTC, and each of non-tuberculosis Mycobacterium species were determined. Multiplex real-time PCR results were compared with amplification and sequencing of 16S-23S rDNA ITS for identification of Mycobacterium species. Sensitivity and specificity of designed primers were each 100 % for MTC, M. abscessus, M. fortuitum, M. avium complex, M. kansasii, and M. gordonae. Sensitivity and specificity of designed primer for genus Mycobacterium was 96 and 100 %, respectively. According to the obtained results, we conclude that this multiplex real-time PCR with melting curve analysis and these novel primers can be used for rapid and accurate identification of genus Mycobacterium, MTC, and the most common non-tuberculosis Mycobacterium species.

A Computational Network Biology Approach to Uncover Novel Genes Related to Alzheimer's Disease.

PubMed

Zanzoni, Andreas

2016-01-01

Recent advances in the fields of genetics and genomics have enabled the identification of numerous Alzheimer's disease (AD) candidate genes, although for many of them the role in AD pathophysiology has not been uncovered yet. Concomitantly, network biology studies have shown a strong link between protein network connectivity and disease. In this chapter I describe a computational approach that, by combining local and global network analysis strategies, allows the formulation of novel hypotheses on the molecular mechanisms involved in AD and prioritizes candidate genes for further functional studies.

In pursuit of an accurate spatial and temporal model of biomolecules at the atomistic level: a perspective on computer simulation.

PubMed

Gray, Alan; Harlen, Oliver G; Harris, Sarah A; Khalid, Syma; Leung, Yuk Ming; Lonsdale, Richard; Mulholland, Adrian J; Pearson, Arwen R; Read, Daniel J; Richardson, Robin A

2015-01-01

Despite huge advances in the computational techniques available for simulating biomolecules at the quantum-mechanical, atomistic and coarse-grained levels, there is still a widespread perception amongst the experimental community that these calculations are highly specialist and are not generally applicable by researchers outside the theoretical community. In this article, the successes and limitations of biomolecular simulation and the further developments that are likely in the near future are discussed. A brief overview is also provided of the experimental biophysical methods that are commonly used to probe biomolecular structure and dynamics, and the accuracy of the information that can be obtained from each is compared with that from modelling. It is concluded that progress towards an accurate spatial and temporal model of biomacromolecules requires a combination of all of these biophysical techniques, both experimental and computational.

HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology

PubMed Central

Hodzic, Ermin; Sauerwald, Thomas; Dao, Phuong; Wang, Kendric; Yeung, Jake; Anderson, Shawn; Vandin, Fabio; Haffari, Gholamreza; Collins, Colin C.; Sahinalp, S. Cenk

2017-01-01

Prioritizing molecular alterations that act as drivers of cancer remains a crucial bottleneck in therapeutic development. Here we introduce HIT'nDRIVE, a computational method that integrates genomic and transcriptomic data to identify a set of patient-specific, sequence-altered genes, with sufficient collective influence over dysregulated transcripts. HIT'nDRIVE aims to solve the “random walk facility location” (RWFL) problem in a gene (or protein) interaction network, which differs from the standard facility location problem by its use of an alternative distance measure: “multihitting time,” the expected length of the shortest random walk from any one of the set of sequence-altered genes to an expression-altered target gene. When applied to 2200 tumors from four major cancer types, HIT'nDRIVE revealed many potentially clinically actionable driver genes. We also demonstrated that it is possible to perform accurate phenotype prediction for tumor samples by only using HIT'nDRIVE-seeded driver gene modules from gene interaction networks. In addition, we identified a number of breast cancer subtype-specific driver modules that are associated with patients’ survival outcome. Furthermore, HIT'nDRIVE, when applied to a large panel of pan-cancer cell lines, accurately predicted drug efficacy using the driver genes and their seeded gene modules. Overall, HIT'nDRIVE may help clinicians contextualize massive multiomics data in therapeutic decision making, enabling widespread implementation of precision oncology. PMID:28768687

Accurate chemical master equation solution using multi-finite buffers

DOE PAGES

Cao, Youfang; Terebus, Anna; Liang, Jie

2016-06-29

Here, the discrete chemical master equation (dCME) provides a fundamental framework for studying stochasticity in mesoscopic networks. Because of the multiscale nature of many networks where reaction rates have a large disparity, directly solving dCMEs is intractable due to the exploding size of the state space. It is important to truncate the state space effectively with quantified errors, so accurate solutions can be computed. It is also important to know if all major probabilistic peaks have been computed. Here we introduce the accurate CME (ACME) algorithm for obtaining direct solutions to dCMEs. With multifinite buffers for reducing the state spacemore » by $O(n!)$, exact steady-state and time-evolving network probability landscapes can be computed. We further describe a theoretical framework of aggregating microstates into a smaller number of macrostates by decomposing a network into independent aggregated birth and death processes and give an a priori method for rapidly determining steady-state truncation errors. The maximal sizes of the finite buffers for a given error tolerance can also be precomputed without costly trial solutions of dCMEs. We show exactly computed probability landscapes of three multiscale networks, namely, a 6-node toggle switch, 11-node phage-lambda epigenetic circuit, and 16-node MAPK cascade network, the latter two with no known solutions. We also show how probabilities of rare events can be computed from first-passage times, another class of unsolved problems challenging for simulation-based techniques due to large separations in time scales. Overall, the ACME method enables accurate and efficient solutions of the dCME for a large class of networks.« less

Accurate chemical master equation solution using multi-finite buffers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cao, Youfang; Terebus, Anna; Liang, Jie

Here, the discrete chemical master equation (dCME) provides a fundamental framework for studying stochasticity in mesoscopic networks. Because of the multiscale nature of many networks where reaction rates have a large disparity, directly solving dCMEs is intractable due to the exploding size of the state space. It is important to truncate the state space effectively with quantified errors, so accurate solutions can be computed. It is also important to know if all major probabilistic peaks have been computed. Here we introduce the accurate CME (ACME) algorithm for obtaining direct solutions to dCMEs. With multifinite buffers for reducing the state spacemore » by $O(n!)$, exact steady-state and time-evolving network probability landscapes can be computed. We further describe a theoretical framework of aggregating microstates into a smaller number of macrostates by decomposing a network into independent aggregated birth and death processes and give an a priori method for rapidly determining steady-state truncation errors. The maximal sizes of the finite buffers for a given error tolerance can also be precomputed without costly trial solutions of dCMEs. We show exactly computed probability landscapes of three multiscale networks, namely, a 6-node toggle switch, 11-node phage-lambda epigenetic circuit, and 16-node MAPK cascade network, the latter two with no known solutions. We also show how probabilities of rare events can be computed from first-passage times, another class of unsolved problems challenging for simulation-based techniques due to large separations in time scales. Overall, the ACME method enables accurate and efficient solutions of the dCME for a large class of networks.« less

Multiclass classification of microarray data samples with a reduced number of genes

PubMed Central

2011-01-01

Background Multiclass classification of microarray data samples with a reduced number of genes is a rich and challenging problem in Bioinformatics research. The problem gets harder as the number of classes is increased. In addition, the performance of most classifiers is tightly linked to the effectiveness of mandatory gene selection methods. Critical to gene selection is the availability of estimates about the maximum number of genes that can be handled by any classification algorithm. Lack of such estimates may lead to either computationally demanding explorations of a search space with thousands of dimensions or classification models based on gene sets of unrestricted size. In the former case, unbiased but possibly overfitted classification models may arise. In the latter case, biased classification models unable to support statistically significant findings may be obtained. Results A novel bound on the maximum number of genes that can be handled by binary classifiers in binary mediated multiclass classification algorithms of microarray data samples is presented. The bound suggests that high-dimensional binary output domains might favor the existence of accurate and sparse binary mediated multiclass classifiers for microarray data samples. Conclusions A comprehensive experimental work shows that the bound is indeed useful to induce accurate and sparse multiclass classifiers for microarray data samples. PMID:21342522

Toward accurate tooth segmentation from computed tomography images using a hybrid level set model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gan, Yangzhou; Zhao, Qunfei; Xia, Zeyang, E-mail: zy.xia@siat.ac.cn, E-mail: jing.xiong@siat.ac.cn

Purpose: A three-dimensional (3D) model of the teeth provides important information for orthodontic diagnosis and treatment planning. Tooth segmentation is an essential step in generating the 3D digital model from computed tomography (CT) images. The aim of this study is to develop an accurate and efficient tooth segmentation method from CT images. Methods: The 3D dental CT volumetric images are segmented slice by slice in a two-dimensional (2D) transverse plane. The 2D segmentation is composed of a manual initialization step and an automatic slice by slice segmentation step. In the manual initialization step, the user manually picks a starting slicemore » and selects a seed point for each tooth in this slice. In the automatic slice segmentation step, a developed hybrid level set model is applied to segment tooth contours from each slice. Tooth contour propagation strategy is employed to initialize the level set function automatically. Cone beam CT (CBCT) images of two subjects were used to tune the parameters. Images of 16 additional subjects were used to validate the performance of the method. Volume overlap metrics and surface distance metrics were adopted to assess the segmentation accuracy quantitatively. The volume overlap metrics were volume difference (VD, mm{sup 3}) and Dice similarity coefficient (DSC, %). The surface distance metrics were average symmetric surface distance (ASSD, mm), RMS (root mean square) symmetric surface distance (RMSSSD, mm), and maximum symmetric surface distance (MSSD, mm). Computation time was recorded to assess the efficiency. The performance of the proposed method has been compared with two state-of-the-art methods. Results: For the tested CBCT images, the VD, DSC, ASSD, RMSSSD, and MSSD for the incisor were 38.16 ± 12.94 mm{sup 3}, 88.82 ± 2.14%, 0.29 ± 0.03 mm, 0.32 ± 0.08 mm, and 1.25 ± 0.58 mm, respectively; the VD, DSC, ASSD, RMSSSD, and MSSD for the canine were 49.12 ± 9.33 mm{sup 3}, 91.57 ± 0.82%, 0.27 ± 0

Ensemble positive unlabeled learning for disease gene identification.

PubMed

Yang, Peng; Li, Xiaoli; Chua, Hon-Nian; Kwoh, Chee-Keong; Ng, See-Kiong

2014-01-01

An increasing number of genes have been experimentally confirmed in recent years as causative genes to various human diseases. The newly available knowledge can be exploited by machine learning methods to discover additional unknown genes that are likely to be associated with diseases. In particular, positive unlabeled learning (PU learning) methods, which require only a positive training set P (confirmed disease genes) and an unlabeled set U (the unknown candidate genes) instead of a negative training set N, have been shown to be effective in uncovering new disease genes in the current scenario. Using only a single source of data for prediction can be susceptible to bias due to incompleteness and noise in the genomic data and a single machine learning predictor prone to bias caused by inherent limitations of individual methods. In this paper, we propose an effective PU learning framework that integrates multiple biological data sources and an ensemble of powerful machine learning classifiers for disease gene identification. Our proposed method integrates data from multiple biological sources for training PU learning classifiers. A novel ensemble-based PU learning method EPU is then used to integrate multiple PU learning classifiers to achieve accurate and robust disease gene predictions. Our evaluation experiments across six disease groups showed that EPU achieved significantly better results compared with various state-of-the-art prediction methods as well as ensemble learning classifiers. Through integrating multiple biological data sources for training and the outputs of an ensemble of PU learning classifiers for prediction, we are able to minimize the potential bias and errors in individual data sources and machine learning algorithms to achieve more accurate and robust disease gene predictions. In the future, our EPU method provides an effective framework to integrate the additional biological and computational resources for better disease gene predictions.

Combining Genome-Scale Experimental and Computational Methods To Identify Essential Genes in Rhodobacter sphaeroides

DOE PAGES

Burger, Brian T.; Imam, Saheed; Scarborough, Matthew J.; ...

2017-06-06

Rhodobacter sphaeroides is one of the best-studied alphaproteobacteria from biochemical, genetic, and genomic perspectives. To gain a better systems-level understanding of this organism, we generated a large transposon mutant library and used transposon sequencing (Tn-seq) to identify genes that are essential under several growth conditions. Using newly developed Tn-seq analysis software (TSAS), we identified 493 genes as essential for aerobic growth on a rich medium. We then used the mutant library to identify conditionally essential genes under two laboratory growth conditions, identifying 85 additional genes required for aerobic growth in a minimal medium and 31 additional genes required for photosyntheticmore » growth. In all instances, our analyses confirmed essentiality for many known genes and identified genes not previously considered to be essential. We used the resulting Tn-seq data to refine and improve a genome-scale metabolic network model (GEM) for R. sphaeroides. Together, we demonstrate how genetic, genomic, and computational approaches can be combined to obtain a systems-level understanding of the genetic framework underlying metabolic diversity in bacterial species.« less

A biomarker-based screen of a gene expression compendium ...

EPA Pesticide Factsheets

Computational approaches were developed to identify factors that regulate Nrf2 in a large gene expression compendium of microarray profiles including >2000 comparisons which queried the effects of chemicals, genes, diets, and infectious agents on gene expression in the mouse liver. A gene expression biomarker of 48 genes which accurately predicted Nrf2 activation was used to identify factors which resulted in a gene expression profile with significant correlation to the biomarker. A number of novel insights were made. Chemicals that activated the xenosensor constitutive activated receptor (CAR) consistently activated Nrf2 across hundreds of profiles, possibly downstream of Cyp-induced increases in oxidative stress. Nrf2 activation was also found to be negatively regulated by the growth hormone (GH)- and androgen-regulated transcription factor STAT5b, a transcription factor suppressed by CAR. Nrf2 was activated when STAT5b was suppressed in female mice vs. male mice, after exposure to estrogens, or in genetic mutants in which GH signaling was disrupted. A subset of the mutants that show STAT5b suppression and Nrf2 activation result in increased resistance to environmental stressors and increased longevity. This study describes a novel approach for understanding the network of factors that regulate the Nrf2 pathway and highlights novel interactions between Nrf2, CAR and STAT5b transcription factors. (This abstract does not represent EPA policy.) Computational appr

Suitable Reference Genes for Accurate Gene Expression Analysis in Parsley (Petroselinum crispum) for Abiotic Stresses and Hormone Stimuli

PubMed Central

Li, Meng-Yao; Song, Xiong; Wang, Feng; Xiong, Ai-Sheng

2016-01-01

Parsley, one of the most important vegetables in the Apiaceae family, is widely used in the food, medicinal, and cosmetic industries. Recent studies on parsley mainly focus on its chemical composition, and further research involving the analysis of the plant's gene functions and expressions is required. qPCR is a powerful method for detecting very low quantities of target transcript levels and is widely used to study gene expression. To ensure the accuracy of results, a suitable reference gene is necessary for expression normalization. In this study, four software, namely geNorm, NormFinder, BestKeeper, and RefFinder were used to evaluate the expression stabilities of eight candidate reference genes of parsley (GAPDH, ACTIN, eIF-4α, SAND, UBC, TIP41, EF-1α, and TUB) under various conditions, including abiotic stresses (heat, cold, salt, and drought) and hormone stimuli treatments (GA, SA, MeJA, and ABA). Results showed that EF-1α and TUB were the most stable genes for abiotic stresses, whereas EF-1α, GAPDH, and TUB were the top three choices for hormone stimuli treatments. Moreover, EF-1α and TUB were the most stable reference genes among all tested samples, and UBC was the least stable one. Expression analysis of PcDREB1 and PcDREB2 further verified that the selected stable reference genes were suitable for gene expression normalization. This study can guide the selection of suitable reference genes in gene expression in parsley. PMID:27746803

Suitable Reference Genes for Accurate Gene Expression Analysis in Parsley (Petroselinum crispum) for Abiotic Stresses and Hormone Stimuli.

PubMed

Li, Meng-Yao; Song, Xiong; Wang, Feng; Xiong, Ai-Sheng

2016-01-01

Parsley, one of the most important vegetables in the Apiaceae family, is widely used in the food, medicinal, and cosmetic industries. Recent studies on parsley mainly focus on its chemical composition, and further research involving the analysis of the plant's gene functions and expressions is required. qPCR is a powerful method for detecting very low quantities of target transcript levels and is widely used to study gene expression. To ensure the accuracy of results, a suitable reference gene is necessary for expression normalization. In this study, four software, namely geNorm, NormFinder, BestKeeper, and RefFinder were used to evaluate the expression stabilities of eight candidate reference genes of parsley ( GAPDH, ACTIN, eIF-4 α, SAND, UBC, TIP41, EF-1 α, and TUB ) under various conditions, including abiotic stresses (heat, cold, salt, and drought) and hormone stimuli treatments (GA, SA, MeJA, and ABA). Results showed that EF-1 α and TUB were the most stable genes for abiotic stresses, whereas EF-1 α, GAPDH , and TUB were the top three choices for hormone stimuli treatments. Moreover, EF-1 α and TUB were the most stable reference genes among all tested samples, and UBC was the least stable one. Expression analysis of PcDREB1 and PcDREB2 further verified that the selected stable reference genes were suitable for gene expression normalization. This study can guide the selection of suitable reference genes in gene expression in parsley.

Gene Ranking of RNA-Seq Data via Discriminant Non-Negative Matrix Factorization.

PubMed

Jia, Zhilong; Zhang, Xiang; Guan, Naiyang; Bo, Xiaochen; Barnes, Michael R; Luo, Zhigang

2015-01-01

RNA-sequencing is rapidly becoming the method of choice for studying the full complexity of transcriptomes, however with increasing dimensionality, accurate gene ranking is becoming increasingly challenging. This paper proposes an accurate and sensitive gene ranking method that implements discriminant non-negative matrix factorization (DNMF) for RNA-seq data. To the best of our knowledge, this is the first work to explore the utility of DNMF for gene ranking. When incorporating Fisher's discriminant criteria and setting the reduced dimension as two, DNMF learns two factors to approximate the original gene expression data, abstracting the up-regulated or down-regulated metagene by using the sample label information. The first factor denotes all the genes' weights of two metagenes as the additive combination of all genes, while the second learned factor represents the expression values of two metagenes. In the gene ranking stage, all the genes are ranked as a descending sequence according to the differential values of the metagene weights. Leveraging the nature of NMF and Fisher's criterion, DNMF can robustly boost the gene ranking performance. The Area Under the Curve analysis of differential expression analysis on two benchmarking tests of four RNA-seq data sets with similar phenotypes showed that our proposed DNMF-based gene ranking method outperforms other widely used methods. Moreover, the Gene Set Enrichment Analysis also showed DNMF outweighs others. DNMF is also computationally efficient, substantially outperforming all other benchmarked methods. Consequently, we suggest DNMF is an effective method for the analysis of differential gene expression and gene ranking for RNA-seq data.

A Computational Framework for Analyzing Stochasticity in Gene Expression

PubMed Central

Sherman, Marc S.; Cohen, Barak A.

2014-01-01

Stochastic fluctuations in gene expression give rise to distributions of protein levels across cell populations. Despite a mounting number of theoretical models explaining stochasticity in protein expression, we lack a robust, efficient, assumption-free approach for inferring the molecular mechanisms that underlie the shape of protein distributions. Here we propose a method for inferring sets of biochemical rate constants that govern chromatin modification, transcription, translation, and RNA and protein degradation from stochasticity in protein expression. We asked whether the rates of these underlying processes can be estimated accurately from protein expression distributions, in the absence of any limiting assumptions. To do this, we (1) derived analytical solutions for the first four moments of the protein distribution, (2) found that these four moments completely capture the shape of protein distributions, and (3) developed an efficient algorithm for inferring gene expression rate constants from the moments of protein distributions. Using this algorithm we find that most protein distributions are consistent with a large number of different biochemical rate constant sets. Despite this degeneracy, the solution space of rate constants almost always informs on underlying mechanism. For example, we distinguish between regimes where transcriptional bursting occurs from regimes reflecting constitutive transcript production. Our method agrees with the current standard approach, and in the restrictive regime where the standard method operates, also identifies rate constants not previously obtainable. Even without making any assumptions we obtain estimates of individual biochemical rate constants, or meaningful ratios of rate constants, in 91% of tested cases. In some cases our method identified all of the underlying rate constants. The framework developed here will be a powerful tool for deducing the contributions of particular molecular mechanisms to specific patterns

Constructing an integrated gene similarity network for the identification of disease genes.

PubMed

Tian, Zhen; Guo, Maozu; Wang, Chunyu; Xing, LinLin; Wang, Lei; Zhang, Yin

2017-09-20

Discovering novel genes that are involved human diseases is a challenging task in biomedical research. In recent years, several computational approaches have been proposed to prioritize candidate disease genes. Most of these methods are mainly based on protein-protein interaction (PPI) networks. However, since these PPI networks contain false positives and only cover less half of known human genes, their reliability and coverage are very low. Therefore, it is highly necessary to fuse multiple genomic data to construct a credible gene similarity network and then infer disease genes on the whole genomic scale. We proposed a novel method, named RWRB, to infer causal genes of interested diseases. First, we construct five individual gene (protein) similarity networks based on multiple genomic data of human genes. Then, an integrated gene similarity network (IGSN) is reconstructed based on similarity network fusion (SNF) method. Finally, we employee the random walk with restart algorithm on the phenotype-gene bilayer network, which combines phenotype similarity network, IGSN as well as phenotype-gene association network, to prioritize candidate disease genes. We investigate the effectiveness of RWRB through leave-one-out cross-validation methods in inferring phenotype-gene relationships. Results show that RWRB is more accurate than state-of-the-art methods on most evaluation metrics. Further analysis shows that the success of RWRB is benefited from IGSN which has a wider coverage and higher reliability comparing with current PPI networks. Moreover, we conduct a comprehensive case study for Alzheimer's disease and predict some novel disease genes that supported by literature. RWRB is an effective and reliable algorithm in prioritizing candidate disease genes on the genomic scale. Software and supplementary information are available at http://nclab.hit.edu.cn/~tianzhen/RWRB/ .

An integrative approach for measuring semantic similarities using gene ontology.

PubMed

Peng, Jiajie; Li, Hongxiang; Jiang, Qinghua; Wang, Yadong; Chen, Jin

2014-01-01

Gene Ontology (GO) provides rich information and a convenient way to study gene functional similarity, which has been successfully used in various applications. However, the existing GO based similarity measurements have limited functions for only a subset of GO information is considered in each measure. An appropriate integration of the existing measures to take into account more information in GO is demanding. We propose a novel integrative measure called InteGO2 to automatically select appropriate seed measures and then to integrate them using a metaheuristic search method. The experiment results show that InteGO2 significantly improves the performance of gene similarity in human, Arabidopsis and yeast on both molecular function and biological process GO categories. InteGO2 computes gene-to-gene similarities more accurately than tested existing measures and has high robustness. The supplementary document and software are available at http://mlg.hit.edu.cn:8082/.

Validation of reference genes for quantitative gene expression analysis in experimental epilepsy.

PubMed

Sadangi, Chinmaya; Rosenow, Felix; Norwood, Braxton A

2017-12-01

To grasp the molecular mechanisms and pathophysiology underlying epilepsy development (epileptogenesis) and epilepsy itself, it is important to understand the gene expression changes that occur during these phases. Quantitative real-time polymerase chain reaction (qPCR) is a technique that rapidly and accurately determines gene expression changes. It is crucial, however, that stable reference genes are selected for each experimental condition to ensure that accurate values are obtained for genes of interest. If reference genes are unstably expressed, this can lead to inaccurate data and erroneous conclusions. To date, epilepsy studies have used mostly single, nonvalidated reference genes. This is the first study to systematically evaluate reference genes in male Sprague-Dawley rat models of epilepsy. We assessed 15 potential reference genes in hippocampal tissue obtained from 2 different models during epileptogenesis, 1 model during chronic epilepsy, and a model of noninjurious seizures. Reference gene ranking varied between models and also differed between epileptogenesis and chronic epilepsy time points. There was also some variance between the four mathematical models used to rank reference genes. Notably, we found novel reference genes to be more stably expressed than those most often used in experimental epilepsy studies. The consequence of these findings is that reference genes suitable for one epilepsy model may not be appropriate for others and that reference genes can change over time. It is, therefore, critically important to validate potential reference genes before using them as normalizing factors in expression analysis in order to ensure accurate, valid results. © 2017 Wiley Periodicals, Inc.

Selection of reference genes for quantitative gene expression normalization in flax (Linum usitatissimum L.).

PubMed

Huis, Rudy; Hawkins, Simon; Neutelings, Godfrey

2010-04-19

Quantitative real-time PCR (qRT-PCR) is currently the most accurate method for detecting differential gene expression. Such an approach depends on the identification of uniformly expressed 'housekeeping genes' (HKGs). Extensive transcriptomic data mining and experimental validation in different model plants have shown that the reliability of these endogenous controls can be influenced by the plant species, growth conditions and organs/tissues examined. It is therefore important to identify the best reference genes to use in each biological system before using qRT-PCR to investigate differential gene expression. In this paper we evaluate different candidate HKGs for developmental transcriptomic studies in the economically-important flax fiber- and oil-crop (Linum usitatissimum L). Specific primers were designed in order to quantify the expression levels of 20 different potential housekeeping genes in flax roots, internal- and external-stem tissues, leaves and flowers at different developmental stages. After calculations of PCR efficiencies, 13 HKGs were retained and their expression stabilities evaluated by the computer algorithms geNorm and NormFinder. According to geNorm, 2 Transcriptional Elongation Factors (TEFs) and 1 Ubiquitin gene are necessary for normalizing gene expression when all studied samples are considered. However, only 2 TEFs are required for normalizing expression in stem tissues. In contrast, NormFinder identified glyceraldehyde-3-phosphate dehydrogenase (GADPH) as the most stably expressed gene when all samples were grouped together, as well as when samples were classed into different sub-groups.qRT-PCR was then used to investigate the relative expression levels of two splice variants of the flax LuMYB1 gene (homologue of AtMYB59). LuMYB1-1 and LuMYB1-2 were highly expressed in the internal stem tissues as compared to outer stem tissues and other samples. This result was confirmed with both geNorm-designated- and Norm

Bacteria as computers making computers

PubMed Central

Danchin, Antoine

2009-01-01

Various efforts to integrate biological knowledge into networks of interactions have produced a lively microbial systems biology. Putting molecular biology and computer sciences in perspective, we review another trend in systems biology, in which recursivity and information replace the usual concepts of differential equations, feedback and feedforward loops and the like. Noting that the processes of gene expression separate the genome from the cell machinery, we analyse the role of the separation between machine and program in computers. However, computers do not make computers. For cells to make cells requires a specific organization of the genetic program, which we investigate using available knowledge. Microbial genomes are organized into a paleome (the name emphasizes the role of the corresponding functions from the time of the origin of life), comprising a constructor and a replicator, and a cenome (emphasizing community-relevant genes), made up of genes that permit life in a particular context. The cell duplication process supposes rejuvenation of the machine and replication of the program. The paleome also possesses genes that enable information to accumulate in a ratchet-like process down the generations. The systems biology must include the dynamics of information creation in its future developments. PMID:19016882

Bacteria as computers making computers.

PubMed

Danchin, Antoine

2009-01-01

Various efforts to integrate biological knowledge into networks of interactions have produced a lively microbial systems biology. Putting molecular biology and computer sciences in perspective, we review another trend in systems biology, in which recursivity and information replace the usual concepts of differential equations, feedback and feedforward loops and the like. Noting that the processes of gene expression separate the genome from the cell machinery, we analyse the role of the separation between machine and program in computers. However, computers do not make computers. For cells to make cells requires a specific organization of the genetic program, which we investigate using available knowledge. Microbial genomes are organized into a paleome (the name emphasizes the role of the corresponding functions from the time of the origin of life), comprising a constructor and a replicator, and a cenome (emphasizing community-relevant genes), made up of genes that permit life in a particular context. The cell duplication process supposes rejuvenation of the machine and replication of the program. The paleome also possesses genes that enable information to accumulate in a ratchet-like process down the generations. The systems biology must include the dynamics of information creation in its future developments.

Exact solutions for species tree inference from discordant gene trees.

PubMed

Chang, Wen-Chieh; Górecki, Paweł; Eulenstein, Oliver

2013-10-01

Phylogenetic analysis has to overcome the grant challenge of inferring accurate species trees from evolutionary histories of gene families (gene trees) that are discordant with the species tree along whose branches they have evolved. Two well studied approaches to cope with this challenge are to solve either biologically informed gene tree parsimony (GTP) problems under gene duplication, gene loss, and deep coalescence, or the classic RF supertree problem that does not rely on any biological model. Despite the potential of these problems to infer credible species trees, they are NP-hard. Therefore, these problems are addressed by heuristics that typically lack any provable accuracy and precision. We describe fast dynamic programming algorithms that solve the GTP problems and the RF supertree problem exactly, and demonstrate that our algorithms can solve instances with data sets consisting of as many as 22 taxa. Extensions of our algorithms can also report the number of all optimal species trees, as well as the trees themselves. To better asses the quality of the resulting species trees that best fit the given gene trees, we also compute the worst case species trees, their numbers, and optimization score for each of the computational problems. Finally, we demonstrate the performance of our exact algorithms using empirical and simulated data sets, and analyze the quality of heuristic solutions for the studied problems by contrasting them with our exact solutions.

MAVTgsa: An R Package for Gene Set (Enrichment) Analysis

DOE PAGES

Chien, Chih-Yi; Chang, Ching-Wei; Tsai, Chen-An; ...

2014-01-01

Gene semore » t analysis methods aim to determine whether an a priori defined set of genes shows statistically significant difference in expression on either categorical or continuous outcomes. Although many methods for gene set analysis have been proposed, a systematic analysis tool for identification of different types of gene set significance modules has not been developed previously. This work presents an R package, called MAVTgsa, which includes three different methods for integrated gene set enrichment analysis. (1) The one-sided OLS (ordinary least squares) test detects coordinated changes of genes in gene set in one direction, either up- or downregulation. (2) The two-sided MANOVA (multivariate analysis variance) detects changes both up- and downregulation for studying two or more experimental conditions. (3) A random forests-based procedure is to identify gene sets that can accurately predict samples from different experimental conditions or are associated with the continuous phenotypes. MAVTgsa computes the P values and FDR (false discovery rate) q -value for all gene sets in the study. Furthermore, MAVTgsa provides several visualization outputs to support and interpret the enrichment results. This package is available online.« less

Accurate quantum chemical calculations

NASA Technical Reports Server (NTRS)

Bauschlicher, Charles W., Jr.; Langhoff, Stephen R.; Taylor, Peter R.

1989-01-01

An important goal of quantum chemical calculations is to provide an understanding of chemical bonding and molecular electronic structure. A second goal, the prediction of energy differences to chemical accuracy, has been much harder to attain. First, the computational resources required to achieve such accuracy are very large, and second, it is not straightforward to demonstrate that an apparently accurate result, in terms of agreement with experiment, does not result from a cancellation of errors. Recent advances in electronic structure methodology, coupled with the power of vector supercomputers, have made it possible to solve a number of electronic structure problems exactly using the full configuration interaction (FCI) method within a subspace of the complete Hilbert space. These exact results can be used to benchmark approximate techniques that are applicable to a wider range of chemical and physical problems. The methodology of many-electron quantum chemistry is reviewed. Methods are considered in detail for performing FCI calculations. The application of FCI methods to several three-electron problems in molecular physics are discussed. A number of benchmark applications of FCI wave functions are described. Atomic basis sets and the development of improved methods for handling very large basis sets are discussed: these are then applied to a number of chemical and spectroscopic problems; to transition metals; and to problems involving potential energy surfaces. Although the experiences described give considerable grounds for optimism about the general ability to perform accurate calculations, there are several problems that have proved less tractable, at least with current computer resources, and these and possible solutions are discussed.

Novel gene sets improve set-level classification of prokaryotic gene expression data.

PubMed

Holec, Matěj; Kuželka, Ondřej; Železný, Filip

2015-10-28

Set-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation. Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate classifiers. We define two families of gene sets using information on regulatory interactions, and evaluate them on phenotype-classification tasks using public prokaryotic gene expression data sets. From each of the two gene-set families, we first select the best-performing subtype. The two selected subtypes are then evaluated on independent (testing) data sets against state-of-the-art gene sets and against the conventional gene-level approach. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. Novel gene sets defined on the basis of regulatory interactions improve set-level classification of gene expression data. The experimental scripts and other material needed to reproduce the experiments are available at http://ida.felk.cvut.cz/novelgenesets.tar.gz.

MultiPhyl: a high-throughput phylogenomics webserver using distributed computing

PubMed Central

Keane, Thomas M.; Naughton, Thomas J.; McInerney, James O.

2007-01-01

With the number of fully sequenced genomes increasing steadily, there is greater interest in performing large-scale phylogenomic analyses from large numbers of individual gene families. Maximum likelihood (ML) has been shown repeatedly to be one of the most accurate methods for phylogenetic construction. Recently, there have been a number of algorithmic improvements in maximum-likelihood-based tree search methods. However, it can still take a long time to analyse the evolutionary history of many gene families using a single computer. Distributed computing refers to a method of combining the computing power of multiple computers in order to perform some larger overall calculation. In this article, we present the first high-throughput implementation of a distributed phylogenetics platform, MultiPhyl, capable of using the idle computational resources of many heterogeneous non-dedicated machines to form a phylogenetics supercomputer. MultiPhyl allows a user to upload hundreds or thousands of amino acid or nucleotide alignments simultaneously and perform computationally intensive tasks such as model selection, tree searching and bootstrapping of each of the alignments using many desktop machines. The program implements a set of 88 amino acid models and 56 nucleotide maximum likelihood models and a variety of statistical methods for choosing between alternative models. A MultiPhyl webserver is available for public use at: http://www.cs.nuim.ie/distributed/multiphyl.php. PMID:17553837

Reconstructing directed gene regulatory network by only gene expression data.

PubMed

Zhang, Lu; Feng, Xi Kang; Ng, Yen Kaow; Li, Shuai Cheng

2016-08-18

Accurately identifying gene regulatory network is an important task in understanding in vivo biological activities. The inference of such networks is often accomplished through the use of gene expression data. Many methods have been developed to evaluate gene expression dependencies between transcription factor and its target genes, and some methods also eliminate transitive interactions. The regulatory (or edge) direction is undetermined if the target gene is also a transcription factor. Some methods predict the regulatory directions in the gene regulatory networks by locating the eQTL single nucleotide polymorphism, or by observing the gene expression changes when knocking out/down the candidate transcript factors; regrettably, these additional data are usually unavailable, especially for the samples deriving from human tissues. In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are

Fast and Accurate Circuit Design Automation through Hierarchical Model Switching.

PubMed

Huynh, Linh; Tagkopoulos, Ilias

2015-08-21

In computer-aided biological design, the trifecta of characterized part libraries, accurate models and optimal design parameters is crucial for producing reliable designs. As the number of parts and model complexity increase, however, it becomes exponentially more difficult for any optimization method to search the solution space, hence creating a trade-off that hampers efficient design. To address this issue, we present a hierarchical computer-aided design architecture that uses a two-step approach for biological design. First, a simple model of low computational complexity is used to predict circuit behavior and assess candidate circuit branches through branch-and-bound methods. Then, a complex, nonlinear circuit model is used for a fine-grained search of the reduced solution space, thus achieving more accurate results. Evaluation with a benchmark of 11 circuits and a library of 102 experimental designs with known characterization parameters demonstrates a speed-up of 3 orders of magnitude when compared to other design methods that provide optimality guarantees.

Extracting Time-Accurate Acceleration Vectors From Nontrivial Accelerometer Arrangements.

PubMed

Franck, Jennifer A; Blume, Janet; Crisco, Joseph J; Franck, Christian

2015-09-01

Sports-related concussions are of significant concern in many impact sports, and their detection relies on accurate measurements of the head kinematics during impact. Among the most prevalent recording technologies are videography, and more recently, the use of single-axis accelerometers mounted in a helmet, such as the HIT system. Successful extraction of the linear and angular impact accelerations depends on an accurate analysis methodology governed by the equations of motion. Current algorithms are able to estimate the magnitude of acceleration and hit location, but make assumptions about the hit orientation and are often limited in the position and/or orientation of the accelerometers. The newly formulated algorithm presented in this manuscript accurately extracts the full linear and rotational acceleration vectors from a broad arrangement of six single-axis accelerometers directly from the governing set of kinematic equations. The new formulation linearizes the nonlinear centripetal acceleration term with a finite-difference approximation and provides a fast and accurate solution for all six components of acceleration over long time periods (>250 ms). The approximation of the nonlinear centripetal acceleration term provides an accurate computation of the rotational velocity as a function of time and allows for reconstruction of a multiple-impact signal. Furthermore, the algorithm determines the impact location and orientation and can distinguish between glancing, high rotational velocity impacts, or direct impacts through the center of mass. Results are shown for ten simulated impact locations on a headform geometry computed with three different accelerometer configurations in varying degrees of signal noise. Since the algorithm does not require simplifications of the actual impacted geometry, the impact vector, or a specific arrangement of accelerometer orientations, it can be easily applied to many impact investigations in which accurate kinematics need

Efficient experimental design for uncertainty reduction in gene regulatory networks.

PubMed

Dehghannasiri, Roozbeh; Yoon, Byung-Jun; Dougherty, Edward R

2015-01-01

An accurate understanding of interactions among genes plays a major role in developing therapeutic intervention methods. Gene regulatory networks often contain a significant amount of uncertainty. The process of prioritizing biological experiments to reduce the uncertainty of gene regulatory networks is called experimental design. Under such a strategy, the experiments with high priority are suggested to be conducted first. The authors have already proposed an optimal experimental design method based upon the objective for modeling gene regulatory networks, such as deriving therapeutic interventions. The experimental design method utilizes the concept of mean objective cost of uncertainty (MOCU). MOCU quantifies the expected increase of cost resulting from uncertainty. The optimal experiment to be conducted first is the one which leads to the minimum expected remaining MOCU subsequent to the experiment. In the process, one must find the optimal intervention for every gene regulatory network compatible with the prior knowledge, which can be prohibitively expensive when the size of the network is large. In this paper, we propose a computationally efficient experimental design method. This method incorporates a network reduction scheme by introducing a novel cost function that takes into account the disruption in the ranking of potential experiments. We then estimate the approximate expected remaining MOCU at a lower computational cost using the reduced networks. Simulation results based on synthetic and real gene regulatory networks show that the proposed approximate method has close performance to that of the optimal method but at lower computational cost. The proposed approximate method also outperforms the random selection policy significantly. A MATLAB software implementing the proposed experimental design method is available at http://gsp.tamu.edu/Publications/supplementary/roozbeh15a/.

A flexible and accurate digital volume correlation method applicable to high-resolution volumetric images

NASA Astrophysics Data System (ADS)

Pan, Bing; Wang, Bo

2017-10-01

Digital volume correlation (DVC) is a powerful technique for quantifying interior deformation within solid opaque materials and biological tissues. In the last two decades, great efforts have been made to improve the accuracy and efficiency of the DVC algorithm. However, there is still a lack of a flexible, robust and accurate version that can be efficiently implemented in personal computers with limited RAM. This paper proposes an advanced DVC method that can realize accurate full-field internal deformation measurement applicable to high-resolution volume images with up to billions of voxels. Specifically, a novel layer-wise reliability-guided displacement tracking strategy combined with dynamic data management is presented to guide the DVC computation from slice to slice. The displacements at specified calculation points in each layer are computed using the advanced 3D inverse-compositional Gauss-Newton algorithm with the complete initial guess of the deformation vector accurately predicted from the computed calculation points. Since only limited slices of interest in the reference and deformed volume images rather than the whole volume images are required, the DVC calculation can thus be efficiently implemented on personal computers. The flexibility, accuracy and efficiency of the presented DVC approach are demonstrated by analyzing computer-simulated and experimentally obtained high-resolution volume images.

Accurate prediction of protein–protein interactions from sequence alignments using a Bayesian method

PubMed Central

Burger, Lukas; van Nimwegen, Erik

2008-01-01

Accurate and large-scale prediction of protein–protein interactions directly from amino-acid sequences is one of the great challenges in computational biology. Here we present a new Bayesian network method that predicts interaction partners using only multiple alignments of amino-acid sequences of interacting protein domains, without tunable parameters, and without the need for any training examples. We first apply the method to bacterial two-component systems and comprehensively reconstruct two-component signaling networks across all sequenced bacteria. Comparisons of our predictions with known interactions show that our method infers interaction partners genome-wide with high accuracy. To demonstrate the general applicability of our method we show that it also accurately predicts interaction partners in a recent dataset of polyketide synthases. Analysis of the predicted genome-wide two-component signaling networks shows that cognates (interacting kinase/regulator pairs, which lie adjacent on the genome) and orphans (which lie isolated) form two relatively independent components of the signaling network in each genome. In addition, while most genes are predicted to have only a small number of interaction partners, we find that 10% of orphans form a separate class of ‘hub' nodes that distribute and integrate signals to and from up to tens of different interaction partners. PMID:18277381

Computational gene network study on antibiotic resistance genes of Acinetobacter baumannii.

PubMed

Anitha, P; Anbarasu, Anand; Ramaiah, Sudha

2014-05-01

Multi Drug Resistance (MDR) in Acinetobacter baumannii is one of the major threats for emerging nosocomial infections in hospital environment. Multidrug-resistance in A. baumannii may be due to the implementation of multi-combination resistance mechanisms such as β-lactamase synthesis, Penicillin-Binding Proteins (PBPs) changes, alteration in porin proteins and in efflux pumps against various existing classes of antibiotics. Multiple antibiotic resistance genes are involved in MDR. These resistance genes are transferred through plasmids, which are responsible for the dissemination of antibiotic resistance among Acinetobacter spp. In addition, these resistance genes may also have a tendency to interact with each other or with their gene products. Therefore, it becomes necessary to understand the impact of these interactions in antibiotic resistance mechanism. Hence, our study focuses on protein and gene network analysis on various resistance genes, to elucidate the role of the interacting proteins and to study their functional contribution towards antibiotic resistance. From the search tool for the retrieval of interacting gene/protein (STRING), a total of 168 functional partners for 15 resistance genes were extracted based on the confidence scoring system. The network study was then followed up with functional clustering of associated partners using molecular complex detection (MCODE). Later, we selected eight efficient clusters based on score. Interestingly, the associated protein we identified from the network possessed greater functional similarity with known resistance genes. This network-based approach on resistance genes of A. baumannii could help in identifying new genes/proteins and provide clues on their association in antibiotic resistance. Copyright © 2014 Elsevier Ltd. All rights reserved.

Computational annotation of genes differentially expressed along olive fruit development

PubMed Central

Galla, Giulio; Barcaccia, Gianni; Ramina, Angelo; Collani, Silvio; Alagna, Fiammetta; Baldoni, Luciana; Cultrera, Nicolò GM; Martinelli, Federico; Sebastiani, Luca; Tonutti, Pietro

2009-01-01

Background Olea europaea L. is a traditional tree crop of the Mediterranean basin with a worldwide economical high impact. Differently from other fruit tree species, little is known about the physiological and molecular basis of the olive fruit development and a few sequences of genes and gene products are available for olive in public databases. This study deals with the identification of large sets of differentially expressed genes in developing olive fruits and the subsequent computational annotation by means of different software. Results mRNA from fruits of the cv. Leccino sampled at three different stages [i.e., initial fruit set (stage 1), completed pit hardening (stage 2) and veraison (stage 3)] was used for the identification of differentially expressed genes putatively involved in main processes along fruit development. Four subtractive hybridization libraries were constructed: forward and reverse between stage 1 and 2 (libraries A and B), and 2 and 3 (libraries C and D). All sequenced clones (1,132 in total) were analyzed through BlastX against non-redundant NCBI databases and about 60% of them showed similarity to known proteins. A total of 89 out of 642 differentially expressed unique sequences was further investigated by Real-Time PCR, showing a validation of the SSH results as high as 69%. Library-specific cDNA repertories were annotated according to the three main vocabularies of the gene ontology (GO): cellular component, biological process and molecular function. BlastX analysis, GO terms mapping and annotation analysis were performed using the Blast2GO software, a research tool designed with the main purpose of enabling GO based data mining on sequence sets for which no GO annotation is yet available. Bioinformatic analysis pointed out a significantly different distribution of the annotated sequences for each GO category, when comparing the three fruit developmental stages. The olive fruit-specific transcriptome dataset was used to query all

Sentinel nodes identified by computed tomography-lymphography accurately stage the axilla in patients with breast cancer

PubMed Central

2013-01-01

Background Sentinel node biopsy often results in the identification and removal of multiple nodes as sentinel nodes, although most of these nodes could be non-sentinel nodes. This study investigated whether computed tomography-lymphography (CT-LG) can distinguish sentinel nodes from non-sentinel nodes and whether sentinel nodes identified by CT-LG can accurately stage the axilla in patients with breast cancer. Methods This study included 184 patients with breast cancer and clinically negative nodes. Contrast agent was injected interstitially. The location of sentinel nodes was marked on the skin surface using a CT laser light navigator system. Lymph nodes located just under the marks were first removed as sentinel nodes. Then, all dyed nodes or all hot nodes were removed. Results The mean number of sentinel nodes identified by CT-LG was significantly lower than that of dyed and/or hot nodes removed (1.1 vs 1.8, p <0.0001). Twenty-three (12.5%) patients had ≥2 sentinel nodes identified by CT-LG removed, whereas 94 (51.1%) of patients had ≥2 dyed and/or hot nodes removed (p <0.0001). Pathological evaluation demonstrated that 47 (25.5%) of 184 patients had metastasis to at least one node. All 47 patients demonstrated metastases to at least one of the sentinel nodes identified by CT-LG. Conclusions CT-LG can distinguish sentinel nodes from non-sentinel nodes, and sentinel nodes identified by CT-LG can accurately stage the axilla in patients with breast cancer. Successful identification of sentinel nodes using CT-LG may facilitate image-based diagnosis of metastasis, possibly leading to the omission of sentinel node biopsy. PMID:24321242

Time-Accurate Computations of Isolated Circular Synthetic Jets in Crossflow

NASA Technical Reports Server (NTRS)

Rumsey, C. L.; Schaeffler, N. W.; Milanovic, I. M.; Zaman, K. B. M. Q.

2007-01-01

Results from unsteady Reynolds-averaged Navier-Stokes computations are described for two different synthetic jet flows issuing into a turbulent boundary layer crossflow through a circular orifice. In one case the jet effect is mostly contained within the boundary layer, while in the other case the jet effect extends beyond the boundary layer edge. Both cases have momentum flux ratios less than 2. Several numerical parameters are investigated, and some lessons learned regarding the CFD methods for computing these types of flow fields are summarized. Results in both cases are compared to experiment.

Computational Gene Expression Modeling Identifies Salivary Biomarker Analysis that Predict Oral Feeding Readiness in the Newborn

PubMed Central

Maron, Jill L.; Hwang, Jooyeon S.; Pathak, Subash; Ruthazer, Robin; Russell, Ruby L.; Alterovitz, Gil

2014-01-01

Objective To combine mathematical modeling of salivary gene expression microarray data and systems biology annotation with RT-qPCR amplification to identify (phase I) and validate (phase II) salivary biomarker analysis for the prediction of oral feeding readiness in preterm infants. Study design Comparative whole transcriptome microarray analysis from 12 preterm newborns pre- and post-oral feeding success was used for computational modeling and systems biology analysis to identify potential salivary transcripts associated with oral feeding success (phase I). Selected gene expression biomarkers (15 from computational modeling; 6 evidence-based; and 3 reference) were evaluated by RT-qPCR amplification on 400 salivary samples from successful (n=200) and unsuccessful (n=200) oral feeders (phase II). Genes, alone and in combination, were evaluated by a multivariate analysis controlling for sex and post-conceptional age (PCA) to determine the probability that newborns achieved successful oral feeding. Results Advancing post-conceptional age (p < 0.001) and female sex (p = 0.05) positively predicted an infant’s ability to feed orally. A combination of five genes, NPY2R (hunger signaling), AMPK (energy homeostasis), PLXNA1 (olfactory neurogenesis), NPHP4 (visual behavior) and WNT3 (facial development), in addition to PCA and sex, demonstrated good accuracy for determining feeding success (AUROC = 0.78). Conclusions We have identified objective and biologically relevant salivary biomarkers that noninvasively assess a newborn’s developing brain, sensory and facial development as they relate to oral feeding success. Understanding the mechanisms that underlie the development of oral feeding readiness through translational and computational methods may improve clinical decision making while decreasing morbidities and health care costs. PMID:25620512

Computational discovery and in vivo validation of hnf4 as a regulatory gene in planarian regeneration.

PubMed

Lobo, Daniel; Morokuma, Junji; Levin, Michael

2016-09-01

Automated computational methods can infer dynamic regulatory network models directly from temporal and spatial experimental data, such as genetic perturbations and their resultant morphologies. Recently, a computational method was able to reverse-engineer the first mechanistic model of planarian regeneration that can recapitulate the main anterior-posterior patterning experiments published in the literature. Validating this comprehensive regulatory model via novel experiments that had not yet been performed would add in our understanding of the remarkable regeneration capacity of planarian worms and demonstrate the power of this automated methodology. Using the Michigan Molecular Interactions and STRING databases and the MoCha software tool, we characterized as hnf4 an unknown regulatory gene predicted to exist by the reverse-engineered dynamic model of planarian regeneration. Then, we used the dynamic model to predict the morphological outcomes under different single and multiple knock-downs (RNA interference) of hnf4 and its predicted gene pathway interactors β-catenin and hh Interestingly, the model predicted that RNAi of hnf4 would rescue the abnormal regenerated phenotype (tailless) of RNAi of hh in amputated trunk fragments. Finally, we validated these predictions in vivo by performing the same surgical and genetic experiments with planarian worms, obtaining the same phenotypic outcomes predicted by the reverse-engineered model. These results suggest that hnf4 is a regulatory gene in planarian regeneration, validate the computational predictions of the reverse-engineered dynamic model, and demonstrate the automated methodology for the discovery of novel genes, pathways and experimental phenotypes. michael.levin@tufts.edu. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Accurate Evaluation Method of Molecular Binding Affinity from Fluctuation Frequency

NASA Astrophysics Data System (ADS)

Hoshino, Tyuji; Iwamoto, Koji; Ode, Hirotaka; Ohdomari, Iwao

2008-05-01

Exact estimation of the molecular binding affinity is significantly important for drug discovery. The energy calculation is a direct method to compute the strength of the interaction between two molecules. This energetic approach is, however, not accurate enough to evaluate a slight difference in binding affinity when distinguishing a prospective substance from dozens of candidates for medicine. Hence more accurate estimation of drug efficacy in a computer is currently demanded. Previously we proposed a concept of estimating molecular binding affinity, focusing on the fluctuation at an interface between two molecules. The aim of this paper is to demonstrate the compatibility between the proposed computational technique and experimental measurements, through several examples for computer simulations of an association of human immunodeficiency virus type-1 (HIV-1) protease and its inhibitor (an example for a drug-enzyme binding), a complexation of an antigen and its antibody (an example for a protein-protein binding), and a combination of estrogen receptor and its ligand chemicals (an example for a ligand-receptor binding). The proposed affinity estimation has proven to be a promising technique in the advanced stage of the discovery and the design of drugs.

High accurate time system of the Low Latitude Meridian Circle.

NASA Astrophysics Data System (ADS)

Yang, Jing; Wang, Feng; Li, Zhiming

In order to obtain the high accurate time signal for the Low Latitude Meridian Circle (LLMC), a new GPS accurate time system is developed which include GPS, 1 MC frequency source and self-made clock system. The second signal of GPS is synchronously used in the clock system and information can be collected by a computer automatically. The difficulty of the cancellation of the time keeper can be overcomed by using this system.

SIFTER search: a web server for accurate phylogeny-based protein function prediction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access tomore » precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.« less

SIFTER search: a web server for accurate phylogeny-based protein function prediction

DOE PAGES

Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

2015-05-15

We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access tomore » precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.« less

Selection of reliable reference genes for quantitative real-time PCR gene expression analysis in Jute (Corchorus capsularis) under stress treatments

PubMed Central

Niu, Xiaoping; Qi, Jianmin; Zhang, Gaoyang; Xu, Jiantang; Tao, Aifen; Fang, Pingping; Su, Jianguang

2015-01-01

To accurately measure gene expression using quantitative reverse transcription PCR (qRT-PCR), reliable reference gene(s) are required for data normalization. Corchorus capsularis, an annual herbaceous fiber crop with predominant biodegradability and renewability, has not been investigated for the stability of reference genes with qRT-PCR. In this study, 11 candidate reference genes were selected and their expression levels were assessed using qRT-PCR. To account for the influence of experimental approach and tissue type, 22 different jute samples were selected from abiotic and biotic stress conditions as well as three different tissue types. The stability of the candidate reference genes was evaluated using geNorm, NormFinder, and BestKeeper programs, and the comprehensive rankings of gene stability were generated by aggregate analysis. For the biotic stress and NaCl stress subsets, ACT7 and RAN were suitable as stable reference genes for gene expression normalization. For the PEG stress subset, UBC, and DnaJ were sufficient for accurate normalization. For the tissues subset, four reference genes TUBβ, UBI, EF1α, and RAN were sufficient for accurate normalization. The selected genes were further validated by comparing expression profiles of WRKY15 in various samples, and two stable reference genes were recommended for accurate normalization of qRT-PCR data. Our results provide researchers with appropriate reference genes for qRT-PCR in C. capsularis, and will facilitate gene expression study under these conditions. PMID:26528312

Computational analysis of gene-gene interactions using multifactor dimensionality reduction.

PubMed

Moore, Jason H

2004-11-01

Understanding the relationship between DNA sequence variations and biologic traits is expected to improve the diagnosis, prevention and treatment of common human diseases. Success in characterizing genetic architecture will depend on our ability to address nonlinearities in the genotype-to-phenotype mapping relationship as a result of gene-gene interactions, or epistasis. This review addresses the challenges associated with the detection and characterization of epistasis. A novel strategy known as multifactor dimensionality reduction that was specifically designed for the identification of multilocus genetic effects is presented. Several case studies that demonstrate the detection of gene-gene interactions in common diseases such as atrial fibrillation, Type II diabetes and essential hypertension are also discussed.

Translating natural genetic variation to gene expression in a computational model of the Drosophila gap gene regulatory network

PubMed Central

Kozlov, Konstantin N.; Kulakovskiy, Ivan V.; Zubair, Asif; Marjoram, Paul; Lawrie, David S.; Nuzhdin, Sergey V.; Samsonova, Maria G.

2017-01-01

Annotating the genotype-phenotype relationship, and developing a proper quantitative description of the relationship, requires understanding the impact of natural genomic variation on gene expression. We apply a sequence-level model of gap gene expression in the early development of Drosophila to analyze single nucleotide polymorphisms (SNPs) in a panel of natural sequenced D. melanogaster lines. Using a thermodynamic modeling framework, we provide both analytical and computational descriptions of how single-nucleotide variants affect gene expression. The analysis reveals that the sequence variants increase (decrease) gene expression if located within binding sites of repressors (activators). We show that the sign of SNP influence (activation or repression) may change in time and space and elucidate the origin of this change in specific examples. The thermodynamic modeling approach predicts non-local and non-linear effects arising from SNPs, and combinations of SNPs, in individual fly genotypes. Simulation of individual fly genotypes using our model reveals that this non-linearity reduces to almost additive inputs from multiple SNPs. Further, we see signatures of the action of purifying selection in the gap gene regulatory regions. To infer the specific targets of purifying selection, we analyze the patterns of polymorphism in the data at two phenotypic levels: the strengths of binding and expression. We find that combinations of SNPs show evidence of being under selective pressure, while individual SNPs do not. The model predicts that SNPs appear to accumulate in the genotypes of the natural population in a way biased towards small increases in activating action on the expression pattern. Taken together, these results provide a systems-level view of how genetic variation translates to the level of gene regulatory networks via combinatorial SNP effects. PMID:28898266

Plasticity in the Rat Prefrontal Cortex: Linking Gene Expression and an Operant Learning with a Computational Theory

PubMed Central

Rapanelli, Maximiliano; Lew, Sergio Eduardo; Frick, Luciana Romina; Zanutto, Bonifacio Silvano

2010-01-01

The plasticity in the medial Prefrontal Cortex (mPFC) of rodents or lateral prefrontal cortex in non human primates (lPFC), plays a key role neural circuits involved in learning and memory. Several genes, like brain-derived neurotrophic factor (BDNF), cAMP response element binding (CREB), Synapsin I, Calcium/calmodulin-dependent protein kinase II (CamKII), activity-regulated cytoskeleton-associated protein (Arc), c-jun and c-fos have been related to plasticity processes. We analysed differential expression of related plasticity genes and immediate early genes in the mPFC of rats during learning an operant conditioning task. Incompletely and completely trained animals were studied because of the distinct events predicted by our computational model at different learning stages. During learning an operant conditioning task, we measured changes in the mRNA levels by Real-Time RT-PCR during learning; expression of these markers associated to plasticity was incremented while learning and such increments began to decline when the task was learned. The plasticity changes in the lPFC during learning predicted by the model matched up with those of the representative gene BDNF. Herein, we showed for the first time that plasticity in the mPFC in rats during learning of an operant conditioning is higher while learning than when the task is learned, using an integrative approach of a computational model and gene expression. PMID:20111591

A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks.

PubMed

Jambusaria, Ankit; Klomp, Jeff; Hong, Zhigang; Rafii, Shahin; Dai, Yang; Malik, Asrar B; Rehman, Jalees

2018-06-07

The heterogeneity of cells across tissue types represents a major challenge for studying biological mechanisms as well as for therapeutic targeting of distinct tissues. Computational prediction of tissue-specific gene regulatory networks may provide important insights into the mechanisms underlying the cellular heterogeneity of cells in distinct organs and tissues. Using three pathway analysis techniques, gene set enrichment analysis (GSEA), parametric analysis of gene set enrichment (PGSEA), alongside our novel model (HeteroPath), which assesses heterogeneously upregulated and downregulated genes within the context of pathways, we generated distinct tissue-specific gene regulatory networks. We analyzed gene expression data derived from freshly isolated heart, brain, and lung endothelial cells and populations of neurons in the hippocampus, cingulate cortex, and amygdala. In both datasets, we found that HeteroPath segregated the distinct cellular populations by identifying regulatory pathways that were not identified by GSEA or PGSEA. Using simulated datasets, HeteroPath demonstrated robustness that was comparable to what was seen using existing gene set enrichment methods. Furthermore, we generated tissue-specific gene regulatory networks involved in vascular heterogeneity and neuronal heterogeneity by performing motif enrichment of the heterogeneous genes identified by HeteroPath and linking the enriched motifs to regulatory transcription factors in the ENCODE database. HeteroPath assesses contextual bidirectional gene expression within pathways and thus allows for transcriptomic assessment of cellular heterogeneity. Unraveling tissue-specific heterogeneity of gene expression can lead to a better understanding of the molecular underpinnings of tissue-specific phenotypes.

Computational Analysis of Candidate Disease Genes and Variants for Salt-Sensitive Hypertension in Indigenous Southern Africans

PubMed Central

Tiffin, Nicki; Meintjes, Ayton; Ramesar, Rajkumar; Bajic, Vladimir B.; Rayner, Brian

2010-01-01

Multiple factors underlie susceptibility to essential hypertension, including a significant genetic and ethnic component, and environmental effects. Blood pressure response of hypertensive individuals to salt is heterogeneous, but salt sensitivity appears more prevalent in people of indigenous African origin. The underlying genetics of salt-sensitive hypertension, however, are poorly understood. In this study, computational methods including text- and data-mining have been used to select and prioritize candidate aetiological genes for salt-sensitive hypertension. Additionally, we have compared allele frequencies and copy number variation for single nucleotide polymorphisms in candidate genes between indigenous Southern African and Caucasian populations, with the aim of identifying candidate genes with significant variability between the population groups: identifying genetic variability between population groups can exploit ethnic differences in disease prevalence to aid with prioritisation of good candidate genes. Our top-ranking candidate genes include parathyroid hormone precursor (PTH) and type-1angiotensin II receptor (AGTR1). We propose that the candidate genes identified in this study warrant further investigation as potential aetiological genes for salt-sensitive hypertension. PMID:20886000

Second-order accurate nonoscillatory schemes for scalar conservation laws

NASA Technical Reports Server (NTRS)

Huynh, Hung T.

1989-01-01

Explicit finite difference schemes for the computation of weak solutions of nonlinear scalar conservation laws is presented and analyzed. These schemes are uniformly second-order accurate and nonoscillatory in the sense that the number of extrema of the discrete solution is not increasing in time.

Accurate computational design of multipass transmembrane proteins.

PubMed

Lu, Peilong; Min, Duyoung; DiMaio, Frank; Wei, Kathy Y; Vahey, Michael D; Boyken, Scott E; Chen, Zibo; Fallas, Jorge A; Ueda, George; Sheffler, William; Mulligan, Vikram Khipple; Xu, Wenqing; Bowie, James U; Baker, David

2018-03-02

The computational design of transmembrane proteins with more than one membrane-spanning region remains a major challenge. We report the design of transmembrane monomers, homodimers, trimers, and tetramers with 76 to 215 residue subunits containing two to four membrane-spanning regions and up to 860 total residues that adopt the target oligomerization state in detergent solution. The designed proteins localize to the plasma membrane in bacteria and in mammalian cells, and magnetic tweezer unfolding experiments in the membrane indicate that they are very stable. Crystal structures of the designed dimer and tetramer-a rocket-shaped structure with a wide cytoplasmic base that funnels into eight transmembrane helices-are very close to the design models. Our results pave the way for the design of multispan membrane proteins with new functions. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Efficient experimental design for uncertainty reduction in gene regulatory networks

PubMed Central

2015-01-01

Background An accurate understanding of interactions among genes plays a major role in developing therapeutic intervention methods. Gene regulatory networks often contain a significant amount of uncertainty. The process of prioritizing biological experiments to reduce the uncertainty of gene regulatory networks is called experimental design. Under such a strategy, the experiments with high priority are suggested to be conducted first. Results The authors have already proposed an optimal experimental design method based upon the objective for modeling gene regulatory networks, such as deriving therapeutic interventions. The experimental design method utilizes the concept of mean objective cost of uncertainty (MOCU). MOCU quantifies the expected increase of cost resulting from uncertainty. The optimal experiment to be conducted first is the one which leads to the minimum expected remaining MOCU subsequent to the experiment. In the process, one must find the optimal intervention for every gene regulatory network compatible with the prior knowledge, which can be prohibitively expensive when the size of the network is large. In this paper, we propose a computationally efficient experimental design method. This method incorporates a network reduction scheme by introducing a novel cost function that takes into account the disruption in the ranking of potential experiments. We then estimate the approximate expected remaining MOCU at a lower computational cost using the reduced networks. Conclusions Simulation results based on synthetic and real gene regulatory networks show that the proposed approximate method has close performance to that of the optimal method but at lower computational cost. The proposed approximate method also outperforms the random selection policy significantly. A MATLAB software implementing the proposed experimental design method is available at http://gsp.tamu.edu/Publications/supplementary/roozbeh15a/. PMID:26423515

DGCA: A comprehensive R package for Differential Gene Correlation Analysis.

PubMed

McKenzie, Andrew T; Katsyv, Igor; Song, Won-Min; Wang, Minghui; Zhang, Bin

2016-11-15

Dissecting the regulatory relationships between genes is a critical step towards building accurate predictive models of biological systems. A powerful approach towards this end is to systematically study the differences in correlation between gene pairs in more than one distinct condition. In this study we develop an R package, DGCA (for Differential Gene Correlation Analysis), which offers a suite of tools for computing and analyzing differential correlations between gene pairs across multiple conditions. To minimize parametric assumptions, DGCA computes empirical p-values via permutation testing. To understand differential correlations at a systems level, DGCA performs higher-order analyses such as measuring the average difference in correlation and multiscale clustering analysis of differential correlation networks. Through a simulation study, we show that the straightforward z-score based method that DGCA employs significantly outperforms the existing alternative methods for calculating differential correlation. Application of DGCA to the TCGA RNA-seq data in breast cancer not only identifies key changes in the regulatory relationships between TP53 and PTEN and their target genes in the presence of inactivating mutations, but also reveals an immune-related differential correlation module that is specific to triple negative breast cancer (TNBC). DGCA is an R package for systematically assessing the difference in gene-gene regulatory relationships under different conditions. This user-friendly, effective, and comprehensive software tool will greatly facilitate the application of differential correlation analysis in many biological studies and thus will help identification of novel signaling pathways, biomarkers, and targets in complex biological systems and diseases.

Funnel metadynamics as accurate binding free-energy method

PubMed Central

Limongelli, Vittorio; Bonomi, Massimiliano; Parrinello, Michele

2013-01-01

A detailed description of the events ruling ligand/protein interaction and an accurate estimation of the drug affinity to its target is of great help in speeding drug discovery strategies. We have developed a metadynamics-based approach, named funnel metadynamics, that allows the ligand to enhance the sampling of the target binding sites and its solvated states. This method leads to an efficient characterization of the binding free-energy surface and an accurate calculation of the absolute protein–ligand binding free energy. We illustrate our protocol in two systems, benzamidine/trypsin and SC-558/cyclooxygenase 2. In both cases, the X-ray conformation has been found as the lowest free-energy pose, and the computed protein–ligand binding free energy in good agreement with experiments. Furthermore, funnel metadynamics unveils important information about the binding process, such as the presence of alternative binding modes and the role of waters. The results achieved at an affordable computational cost make funnel metadynamics a valuable method for drug discovery and for dealing with a variety of problems in chemistry, physics, and material science. PMID:23553839

bc-GenExMiner 3.0: new mining module computes breast cancer gene expression correlation analyses.

PubMed

Jézéquel, Pascal; Frénel, Jean-Sébastien; Campion, Loïc; Guérin-Charbonnel, Catherine; Gouraud, Wilfried; Ricolleau, Gabriel; Campone, Mario

2013-01-01

We recently developed a user-friendly web-based application called bc-GenExMiner (http://bcgenex.centregauducheau.fr), which offered the possibility to evaluate prognostic informativity of genes in breast cancer by means of a 'prognostic module'. In this study, we develop a new module called 'correlation module', which includes three kinds of gene expression correlation analyses. The first one computes correlation coefficient between 2 or more (up to 10) chosen genes. The second one produces two lists of genes that are most correlated (positively and negatively) to a 'tested' gene. A gene ontology (GO) mining function is also proposed to explore GO 'biological process', 'molecular function' and 'cellular component' terms enrichment for the output lists of most correlated genes. The third one explores gene expression correlation between the 15 telomeric and 15 centromeric genes surrounding a 'tested' gene. These correlation analyses can be performed in different groups of patients: all patients (without any subtyping), in molecular subtypes (basal-like, HER2+, luminal A and luminal B) and according to oestrogen receptor status. Validation tests based on published data showed that these automatized analyses lead to results consistent with studies' conclusions. In brief, this new module has been developed to help basic researchers explore molecular mechanisms of breast cancer. DATABASE URL: http://bcgenex.centregauducheau.fr

CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation.

PubMed

Whiffin, Nicola; Walsh, Roddy; Govind, Risha; Edwards, Matthew; Ahmad, Mian; Zhang, Xiaolei; Tayal, Upasana; Buchan, Rachel; Midwinter, William; Wilk, Alicja E; Najgebauer, Hanna; Francis, Catherine; Wilkinson, Sam; Monk, Thomas; Brett, Laura; O'Regan, Declan P; Prasad, Sanjay K; Morris-Rosendahl, Deborah J; Barton, Paul J R; Edwards, Elizabeth; Ware, James S; Cook, Stuart A

2018-01-25

PurposeInternationally adopted variant interpretation guidelines from the American College of Medical Genetics and Genomics (ACMG) are generic and require disease-specific refinement. Here we developed CardioClassifier (http://www.cardioclassifier.org), a semiautomated decision-support tool for inherited cardiac conditions (ICCs).MethodsCardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support variant interpretation. Combining disease- and gene-specific knowledge with variant observations in large cohorts of cases and controls, we refined 14 computational ACMG criteria and created three ICC-specific rules.ResultsWe benchmarked CardioClassifier on 57 expertly curated variants and show full retrieval of all computational data, concordantly activating 87.3% of rules. A generic annotation tool identified fewer than half as many clinically actionable variants (64/219 vs. 156/219, Fisher's P = 1.1  ×  10 -18 ), with important false positives, illustrating the critical importance of disease and gene-specific annotations. CardioClassifier identified putatively disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with leading ICC laboratories. Through addition of manually curated data, variants found in over 40% of cardiomyopathy cases are fully annotated, without requiring additional user-input data.ConclusionCardioClassifier is an ICC-specific decision-support tool that integrates expertly curated computational annotations with case-specific data to generate fast, reproducible, and interactive variant pathogenicity reports, according to best practice guidelines.GENETICS in MEDICINE advance online publication, 25 January 2018; doi:10.1038/gim.2017.258.

A computational search for box C/D snoRNA genes in the Drosophila melanogaster genome.

PubMed

Accardo, M C; Giordano, E; Riccardo, S; Digilio, F A; Iazzetti, G; Calogero, R A; Furia, M

2004-12-12

In eukaryotes, the family of non-coding RNA genes includes a number of genes encoding small nucleolar RNAs (mainly C/D and H/ACA snoRNAs), which act as guides in the maturation or post-transcriptional modifications of target RNA molecules. Since in Drosophila melanogaster (Dm) only few examples of snoRNAs have been identified so far by cDNA libraries screening, integration of the molecular data with in silico identification of these types of genes could throw light on their organization in the Dm genome. We have performed a computational screening of the Dm genome for C/D snoRNA genes, followed by experimental validation of the putative candidates. Few of the 26 confirmed snoRNAs had been recognized by cDNA library analysis. Organization of the Dm genome was also found to be more variegated than previously suspected, with snoRNA genes nested in both the introns and exons of protein-coding genes. This finding suggests that the presence of additional mechanisms of snoRNA biogenesis based on the alternative production of overlapping mRNA/snoRNA molecules. Additional information is available at http://www.bioinformatica.unito.it/bioinformatics/snoRNAs.

DNA sequence requirements for the accurate transcription of a protein-coding plastid gene in a plastid in vitro system from mustard (Sinapis alba L.)

PubMed Central

Link, Gerhard

1984-01-01

A nuclease-treated plastid extract from mustard (Sinapis alba L.) allows efficient transcription of cloned plastid DNA templates. In this in vitro system, the major runoff transcript of the truncated gene for the 32 000 mol. wt. photosystem II protein was accurately initiated from a site close to or identical with the in vivo start site. By using plasmids with deletions in the 5'-flanking region of this gene as templates, a DNA region required for efficient and selective initiation was detected ˜28-35 nucleotides upstream of the transcription start site. This region contains the sequence element TTGACA, which matches the consensus sequence for prokaryotic `−35' promoter elements. In the absence of this region, a region ˜13-27 nucleotides upstream of the start site still enables a basic level of specific transcription. This second region contains the sequence element TATATAA, which matches the consensus sequence for the `TATA' box of genes transcribed by RNA polymerase II (or B). The region between the `TATA'-like element and the transcription start site is not sufficient but may be required for specific transcription of the plastid gene. This latter region contains the sequence element TATACT, which resembles the prokaryotic `−10' (Pribnow) box. Based on the structural and transcriptional features of the 5' upstream region, a `promoter switch' mechanism is proposed, which may account for the developmentally regulated expression of this plastid gene. ImagesFig. 1.Fig. 2.Fig. 3.Fig. 4.Figure 5. PMID:16453540

An implicit higher-order spatially accurate scheme for solving time dependent flows on unstructured meshes

NASA Astrophysics Data System (ADS)

Tomaro, Robert F.

1998-07-01

The present research is aimed at developing a higher-order, spatially accurate scheme for both steady and unsteady flow simulations using unstructured meshes. The resulting scheme must work on a variety of general problems to ensure the creation of a flexible, reliable and accurate aerodynamic analysis tool. To calculate the flow around complex configurations, unstructured grids and the associated flow solvers have been developed. Efficient simulations require the minimum use of computer memory and computational times. Unstructured flow solvers typically require more computer memory than a structured flow solver due to the indirect addressing of the cells. The approach taken in the present research was to modify an existing three-dimensional unstructured flow solver to first decrease the computational time required for a solution and then to increase the spatial accuracy. The terms required to simulate flow involving non-stationary grids were also implemented. First, an implicit solution algorithm was implemented to replace the existing explicit procedure. Several test cases, including internal and external, inviscid and viscous, two-dimensional, three-dimensional and axi-symmetric problems, were simulated for comparison between the explicit and implicit solution procedures. The increased efficiency and robustness of modified code due to the implicit algorithm was demonstrated. Two unsteady test cases, a plunging airfoil and a wing undergoing bending and torsion, were simulated using the implicit algorithm modified to include the terms required for a moving and/or deforming grid. Secondly, a higher than second-order spatially accurate scheme was developed and implemented into the baseline code. Third- and fourth-order spatially accurate schemes were implemented and tested. The original dissipation was modified to include higher-order terms and modified near shock waves to limit pre- and post-shock oscillations. The unsteady cases were repeated using the higher

Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation.

PubMed

Technow, Frank; Messina, Carlos D; Totir, L Radu; Cooper, Mark

2015-01-01

Genomic selection, enabled by whole genome prediction (WGP) methods, is revolutionizing plant breeding. Existing WGP methods have been shown to deliver accurate predictions in the most common settings, such as prediction of across environment performance for traits with additive gene effects. However, prediction of traits with non-additive gene effects and prediction of genotype by environment interaction (G×E), continues to be challenging. Previous attempts to increase prediction accuracy for these particularly difficult tasks employed prediction methods that are purely statistical in nature. Augmenting the statistical methods with biological knowledge has been largely overlooked thus far. Crop growth models (CGMs) attempt to represent the impact of functional relationships between plant physiology and the environment in the formation of yield and similar output traits of interest. Thus, they can explain the impact of G×E and certain types of non-additive gene effects on the expressed phenotype. Approximate Bayesian computation (ABC), a novel and powerful computational procedure, allows the incorporation of CGMs directly into the estimation of whole genome marker effects in WGP. Here we provide a proof of concept study for this novel approach and demonstrate its use with synthetic data sets. We show that this novel approach can be considerably more accurate than the benchmark WGP method GBLUP in predicting performance in environments represented in the estimation set as well as in previously unobserved environments for traits determined by non-additive gene effects. We conclude that this proof of concept demonstrates that using ABC for incorporating biological knowledge in the form of CGMs into WGP is a very promising and novel approach to improving prediction accuracy for some of the most challenging scenarios in plant breeding and applied genetics.

Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation

PubMed Central

Technow, Frank; Messina, Carlos D.; Totir, L. Radu; Cooper, Mark

2015-01-01

Genomic selection, enabled by whole genome prediction (WGP) methods, is revolutionizing plant breeding. Existing WGP methods have been shown to deliver accurate predictions in the most common settings, such as prediction of across environment performance for traits with additive gene effects. However, prediction of traits with non-additive gene effects and prediction of genotype by environment interaction (G×E), continues to be challenging. Previous attempts to increase prediction accuracy for these particularly difficult tasks employed prediction methods that are purely statistical in nature. Augmenting the statistical methods with biological knowledge has been largely overlooked thus far. Crop growth models (CGMs) attempt to represent the impact of functional relationships between plant physiology and the environment in the formation of yield and similar output traits of interest. Thus, they can explain the impact of G×E and certain types of non-additive gene effects on the expressed phenotype. Approximate Bayesian computation (ABC), a novel and powerful computational procedure, allows the incorporation of CGMs directly into the estimation of whole genome marker effects in WGP. Here we provide a proof of concept study for this novel approach and demonstrate its use with synthetic data sets. We show that this novel approach can be considerably more accurate than the benchmark WGP method GBLUP in predicting performance in environments represented in the estimation set as well as in previously unobserved environments for traits determined by non-additive gene effects. We conclude that this proof of concept demonstrates that using ABC for incorporating biological knowledge in the form of CGMs into WGP is a very promising and novel approach to improving prediction accuracy for some of the most challenging scenarios in plant breeding and applied genetics. PMID:26121133

Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing.

PubMed

Lim, Hansaim; Poleksic, Aleksandar; Yao, Yuan; Tong, Hanghang; He, Di; Zhuang, Luke; Meng, Patrick; Xie, Lei

2016-10-01

Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification. In addition, the performances of most machine learning based algorithms have been mainly evaluated to predict off-target interactions in the same gene family for hundreds of chemicals. It is not clear how these algorithms perform in terms of detecting off-targets across gene families on a proteome scale. Here, we are presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regularized one-class collaborative filtering algorithm, to explore continuous chemical space, protein space, and their interactome on a large scale. When tested in a reliable, extensive, and cross-gene family benchmark, REMAP outperforms the state-of-the-art methods. Furthermore, REMAP is highly scalable. It can screen a dataset of 200 thousands chemicals against 20 thousands proteins within 2 hours. Using the reconstructed genome-wide target profile as the fingerprint of a chemical compound, we predicted that seven FDA-approved drugs can be repurposed as novel anti-cancer therapies. The anti-cancer activity of six of them is supported by experimental evidences. Thus, REMAP is a valuable addition to the existing in silico toolbox for drug target identification, drug repurposing, phenotypic screening, and

Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing

PubMed Central

Poleksic, Aleksandar; Yao, Yuan; Tong, Hanghang; Meng, Patrick; Xie, Lei

2016-01-01

Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification. In addition, the performances of most machine learning based algorithms have been mainly evaluated to predict off-target interactions in the same gene family for hundreds of chemicals. It is not clear how these algorithms perform in terms of detecting off-targets across gene families on a proteome scale. Here, we are presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regularized one-class collaborative filtering algorithm, to explore continuous chemical space, protein space, and their interactome on a large scale. When tested in a reliable, extensive, and cross-gene family benchmark, REMAP outperforms the state-of-the-art methods. Furthermore, REMAP is highly scalable. It can screen a dataset of 200 thousands chemicals against 20 thousands proteins within 2 hours. Using the reconstructed genome-wide target profile as the fingerprint of a chemical compound, we predicted that seven FDA-approved drugs can be repurposed as novel anti-cancer therapies. The anti-cancer activity of six of them is supported by experimental evidences. Thus, REMAP is a valuable addition to the existing in silico toolbox for drug target identification, drug repurposing, phenotypic screening, and

Reverse engineering highlights potential principles of large gene regulatory network design and learning.

PubMed

Carré, Clément; Mas, André; Krouk, Gabriel

2017-01-01

Inferring transcriptional gene regulatory networks from transcriptomic datasets is a key challenge of systems biology, with potential impacts ranging from medicine to agronomy. There are several techniques used presently to experimentally assay transcription factors to target relationships, defining important information about real gene regulatory networks connections. These techniques include classical ChIP-seq, yeast one-hybrid, or more recently, DAP-seq or target technologies. These techniques are usually used to validate algorithm predictions. Here, we developed a reverse engineering approach based on mathematical and computer simulation to evaluate the impact that this prior knowledge on gene regulatory networks may have on training machine learning algorithms. First, we developed a gene regulatory networks-simulating engine called FRANK (Fast Randomizing Algorithm for Network Knowledge) that is able to simulate large gene regulatory networks (containing 10 4 genes) with characteristics of gene regulatory networks observed in vivo. FRANK also generates stable or oscillatory gene expression directly produced by the simulated gene regulatory networks. The development of FRANK leads to important general conclusions concerning the design of large and stable gene regulatory networks harboring scale free properties (built ex nihilo). In combination with supervised (accepting prior knowledge) support vector machine algorithm we (i) address biologically oriented questions concerning our capacity to accurately reconstruct gene regulatory networks and in particular we demonstrate that prior-knowledge structure is crucial for accurate learning, and (ii) draw conclusions to inform experimental design to performed learning able to solve gene regulatory networks in the future. By demonstrating that our predictions concerning the influence of the prior-knowledge structure on support vector machine learning capacity holds true on real data ( Escherichia coli K14 network

Computer face-matching technology using two-dimensional photographs accurately matches the facial gestalt of unrelated individuals with the same syndromic form of intellectual disability.

PubMed

Dudding-Byth, Tracy; Baxter, Anne; Holliday, Elizabeth G; Hackett, Anna; O'Donnell, Sheridan; White, Susan M; Attia, John; Brunner, Han; de Vries, Bert; Koolen, David; Kleefstra, Tjitske; Ratwatte, Seshika; Riveros, Carlos; Brain, Steve; Lovell, Brian C

2017-12-19

Massively parallel genetic sequencing allows rapid testing of known intellectual disability (ID) genes. However, the discovery of novel syndromic ID genes requires molecular confirmation in at least a second or a cluster of individuals with an overlapping phenotype or similar facial gestalt. Using computer face-matching technology we report an automated approach to matching the faces of non-identical individuals with the same genetic syndrome within a database of 3681 images [1600 images of one of 10 genetic syndrome subgroups together with 2081 control images]. Using the leave-one-out method, two research questions were specified: 1) Using two-dimensional (2D) photographs of individuals with one of 10 genetic syndromes within a database of images, did the technology correctly identify more than expected by chance: i) a top match? ii) at least one match within the top five matches? or iii) at least one in the top 10 with an individual from the same syndrome subgroup? 2) Was there concordance between correct technology-based matches and whether two out of three clinical geneticists would have considered the diagnosis based on the image alone? The computer face-matching technology correctly identifies a top match, at least one correct match in the top five and at least one in the top 10 more than expected by chance (P < 0.00001). There was low agreement between the technology and clinicians, with higher accuracy of the technology when results were discordant (P < 0.01) for all syndromes except Kabuki syndrome. Although the accuracy of the computer face-matching technology was tested on images of individuals with known syndromic forms of intellectual disability, the results of this pilot study illustrate the potential utility of face-matching technology within deep phenotyping platforms to facilitate the interpretation of DNA sequencing data for individuals who remain undiagnosed despite testing the known developmental disorder genes.

Where we stand, where we are moving: Surveying computational techniques for identifying miRNA genes and uncovering their regulatory role.

PubMed

Kleftogiannis, Dimitrios; Korfiati, Aigli; Theofilatos, Konstantinos; Likothanassis, Spiros; Tsakalidis, Athanasios; Mavroudi, Seferina

2013-06-01

Traditional biology was forced to restate some of its principles when the microRNA (miRNA) genes and their regulatory role were firstly discovered. Typically, miRNAs are small non-coding RNA molecules which have the ability to bind to the 3'untraslated region (UTR) of their mRNA target genes for cleavage or translational repression. Existing experimental techniques for their identification and the prediction of the target genes share some important limitations such as low coverage, time consuming experiments and high cost reagents. Hence, many computational methods have been proposed for these tasks to overcome these limitations. Recently, many researchers emphasized on the development of computational approaches to predict the participation of miRNA genes in regulatory networks and to analyze their transcription mechanisms. All these approaches have certain advantages and disadvantages which are going to be described in the present survey. Our work is differentiated from existing review papers by updating the methodologies list and emphasizing on the computational issues that arise from the miRNA data analysis. Furthermore, in the present survey, the various miRNA data analysis steps are treated as an integrated procedure whose aims and scope is to uncover the regulatory role and mechanisms of the miRNA genes. This integrated view of the miRNA data analysis steps may be extremely useful for all researchers even if they work on just a single step. Copyright © 2013 Elsevier Inc. All rights reserved.

A fourth order accurate finite difference scheme for the computation of elastic waves

NASA Technical Reports Server (NTRS)

Bayliss, A.; Jordan, K. E.; Lemesurier, B. J.; Turkel, E.

1986-01-01

A finite difference for elastic waves is introduced. The model is based on the first order system of equations for the velocities and stresses. The differencing is fourth order accurate on the spatial derivatives and second order accurate in time. The model is tested on a series of examples including the Lamb problem, scattering from plane interf aces and scattering from a fluid-elastic interface. The scheme is shown to be effective for these problems. The accuracy and stability is insensitive to the Poisson ratio. For the class of problems considered here it is found that the fourth order scheme requires for two-thirds to one-half the resolution of a typical second order scheme to give comparable accuracy.

Time-Accurate Solutions of Incompressible Navier-Stokes Equations for Potential Turbopump Applications

NASA Technical Reports Server (NTRS)

Kiris, Cetin; Kwak, Dochan

2001-01-01

Two numerical procedures, one based on artificial compressibility method and the other pressure projection method, are outlined for obtaining time-accurate solutions of the incompressible Navier-Stokes equations. The performance of the two method are compared by obtaining unsteady solutions for the evolution of twin vortices behind a at plate. Calculated results are compared with experimental and other numerical results. For an un- steady ow which requires small physical time step, pressure projection method was found to be computationally efficient since it does not require any subiterations procedure. It was observed that the artificial compressibility method requires a fast convergence scheme at each physical time step in order to satisfy incompressibility condition. This was obtained by using a GMRES-ILU(0) solver in our computations. When a line-relaxation scheme was used, the time accuracy was degraded and time-accurate computations became very expensive.

Cooperative gene regulation by microRNA pairs and their identification using a computational workflow

PubMed Central

Schmitz, Ulf; Lai, Xin; Winter, Felix; Wolkenhauer, Olaf; Vera, Julio; Gupta, Shailendra K.

2014-01-01

MicroRNAs (miRNAs) are an integral part of gene regulation at the post-transcriptional level. Recently, it has been shown that pairs of miRNAs can repress the translation of a target mRNA in a cooperative manner, which leads to an enhanced effectiveness and specificity in target repression. However, it remains unclear which miRNA pairs can synergize and which genes are target of cooperative miRNA regulation. In this paper, we present a computational workflow for the prediction and analysis of cooperating miRNAs and their mutual target genes, which we refer to as RNA triplexes. The workflow integrates methods of miRNA target prediction; triplex structure analysis; molecular dynamics simulations and mathematical modeling for a reliable prediction of functional RNA triplexes and target repression efficiency. In a case study we analyzed the human genome and identified several thousand targets of cooperative gene regulation. Our results suggest that miRNA cooperativity is a frequent mechanism for an enhanced target repression by pairs of miRNAs facilitating distinctive and fine-tuned target gene expression patterns. Human RNA triplexes predicted and characterized in this study are organized in a web resource at www.sbi.uni-rostock.de/triplexrna/. PMID:24875477

Rapid Identification of Sequences for Orphan Enzymes to Power Accurate Protein Annotation

PubMed Central

Ojha, Sunil; Watson, Douglas S.; Bomar, Martha G.; Galande, Amit K.; Shearer, Alexander G.

2013-01-01

The power of genome sequencing depends on the ability to understand what those genes and their proteins products actually do. The automated methods used to assign functions to putative proteins in newly sequenced organisms are limited by the size of our library of proteins with both known function and sequence. Unfortunately this library grows slowly, lagging well behind the rapid increase in novel protein sequences produced by modern genome sequencing methods. One potential source for rapidly expanding this functional library is the “back catalog” of enzymology – “orphan enzymes,” those enzymes that have been characterized and yet lack any associated sequence. There are hundreds of orphan enzymes in the Enzyme Commission (EC) database alone. In this study, we demonstrate how this orphan enzyme “back catalog” is a fertile source for rapidly advancing the state of protein annotation. Starting from three orphan enzyme samples, we applied mass-spectrometry based analysis and computational methods (including sequence similarity networks, sequence and structural alignments, and operon context analysis) to rapidly identify the specific sequence for each orphan while avoiding the most time- and labor-intensive aspects of typical sequence identifications. We then used these three new sequences to more accurately predict the catalytic function of 385 previously uncharacterized or misannotated proteins. We expect that this kind of rapid sequence identification could be efficiently applied on a larger scale to make enzymology’s “back catalog” another powerful tool to drive accurate genome annotation. PMID:24386392

Rapid identification of sequences for orphan enzymes to power accurate protein annotation.

PubMed

Ramkissoon, Kevin R; Miller, Jennifer K; Ojha, Sunil; Watson, Douglas S; Bomar, Martha G; Galande, Amit K; Shearer, Alexander G

2013-01-01

The power of genome sequencing depends on the ability to understand what those genes and their proteins products actually do. The automated methods used to assign functions to putative proteins in newly sequenced organisms are limited by the size of our library of proteins with both known function and sequence. Unfortunately this library grows slowly, lagging well behind the rapid increase in novel protein sequences produced by modern genome sequencing methods. One potential source for rapidly expanding this functional library is the "back catalog" of enzymology--"orphan enzymes," those enzymes that have been characterized and yet lack any associated sequence. There are hundreds of orphan enzymes in the Enzyme Commission (EC) database alone. In this study, we demonstrate how this orphan enzyme "back catalog" is a fertile source for rapidly advancing the state of protein annotation. Starting from three orphan enzyme samples, we applied mass-spectrometry based analysis and computational methods (including sequence similarity networks, sequence and structural alignments, and operon context analysis) to rapidly identify the specific sequence for each orphan while avoiding the most time- and labor-intensive aspects of typical sequence identifications. We then used these three new sequences to more accurately predict the catalytic function of 385 previously uncharacterized or misannotated proteins. We expect that this kind of rapid sequence identification could be efficiently applied on a larger scale to make enzymology's "back catalog" another powerful tool to drive accurate genome annotation.

GEOMetaCuration: a web-based application for accurate manual curation of Gene Expression Omnibus metadata

PubMed Central

Li, Zhao; Li, Jin; Yu, Peng

2018-01-01

Abstract Metadata curation has become increasingly important for biological discovery and biomedical research because a large amount of heterogeneous biological data is currently freely available. To facilitate efficient metadata curation, we developed an easy-to-use web-based curation application, GEOMetaCuration, for curating the metadata of Gene Expression Omnibus datasets. It can eliminate mechanical operations that consume precious curation time and can help coordinate curation efforts among multiple curators. It improves the curation process by introducing various features that are critical to metadata curation, such as a back-end curation management system and a curator-friendly front-end. The application is based on a commonly used web development framework of Python/Django and is open-sourced under the GNU General Public License V3. GEOMetaCuration is expected to benefit the biocuration community and to contribute to computational generation of biological insights using large-scale biological data. An example use case can be found at the demo website: http://geometacuration.yubiolab.org. Database URL: https://bitbucket.com/yubiolab/GEOMetaCuration PMID:29688376

Accurate Bit Error Rate Calculation for Asynchronous Chaos-Based DS-CDMA over Multipath Channel

NASA Astrophysics Data System (ADS)

Kaddoum, Georges; Roviras, Daniel; Chargé, Pascal; Fournier-Prunaret, Daniele

2009-12-01

An accurate approach to compute the bit error rate expression for multiuser chaosbased DS-CDMA system is presented in this paper. For more realistic communication system a slow fading multipath channel is considered. A simple RAKE receiver structure is considered. Based on the bit energy distribution, this approach compared to others computation methods existing in literature gives accurate results with low computation charge. Perfect estimation of the channel coefficients with the associated delays and chaos synchronization is assumed. The bit error rate is derived in terms of the bit energy distribution, the number of paths, the noise variance, and the number of users. Results are illustrated by theoretical calculations and numerical simulations which point out the accuracy of our approach.

Computational Models of HIV-1 Resistance to Gene Therapy Elucidate Therapy Design Principles

PubMed Central

Aviran, Sharon; Shah, Priya S.; Schaffer, David V.; Arkin, Adam P.

2010-01-01

Gene therapy is an emerging alternative to conventional anti-HIV-1 drugs, and can potentially control the virus while alleviating major limitations of current approaches. Yet, HIV-1's ability to rapidly acquire mutations and escape therapy presents a critical challenge to any novel treatment paradigm. Viral escape is thus a key consideration in the design of any gene-based technique. We develop a computational model of HIV's evolutionary dynamics in vivo in the presence of a genetic therapy to explore the impact of therapy parameters and strategies on the development of resistance. Our model is generic and captures the properties of a broad class of gene-based agents that inhibit early stages of the viral life cycle. We highlight the differences in viral resistance dynamics between gene and standard antiretroviral therapies, and identify key factors that impact long-term viral suppression. In particular, we underscore the importance of mutationally-induced viral fitness losses in cells that are not genetically modified, as these can severely constrain the replication of resistant virus. We also propose and investigate a novel treatment strategy that leverages upon gene therapy's unique capacity to deliver different genes to distinct cell populations, and we find that such a strategy can dramatically improve efficacy when used judiciously within a certain parametric regime. Finally, we revisit a previously-suggested idea of improving clinical outcomes by boosting the proliferation of the genetically-modified cells, but we find that such an approach has mixed effects on resistance dynamics. Our results provide insights into the short- and long-term effects of gene therapy and the role of its key properties in the evolution of resistance, which can serve as guidelines for the choice and optimization of effective therapeutic agents. PMID:20711350

The Probability of a Gene Tree Topology within a Phylogenetic Network with Applications to Hybridization Detection

PubMed Central

Yu, Yun; Degnan, James H.; Nakhleh, Luay

2012-01-01

Gene tree topologies have proven a powerful data source for various tasks, including species tree inference and species delimitation. Consequently, methods for computing probabilities of gene trees within species trees have been developed and widely used in probabilistic inference frameworks. All these methods assume an underlying multispecies coalescent model. However, when reticulate evolutionary events such as hybridization occur, these methods are inadequate, as they do not account for such events. Methods that account for both hybridization and deep coalescence in computing the probability of a gene tree topology currently exist for very limited cases. However, no such methods exist for general cases, owing primarily to the fact that it is currently unknown how to compute the probability of a gene tree topology within the branches of a phylogenetic network. Here we present a novel method for computing the probability of gene tree topologies on phylogenetic networks and demonstrate its application to the inference of hybridization in the presence of incomplete lineage sorting. We reanalyze a Saccharomyces species data set for which multiple analyses had converged on a species tree candidate. Using our method, though, we show that an evolutionary hypothesis involving hybridization in this group has better support than one of strict divergence. A similar reanalysis on a group of three Drosophila species shows that the data is consistent with hybridization. Further, using extensive simulation studies, we demonstrate the power of gene tree topologies at obtaining accurate estimates of branch lengths and hybridization probabilities of a given phylogenetic network. Finally, we discuss identifiability issues with detecting hybridization, particularly in cases that involve extinction or incomplete sampling of taxa. PMID:22536161

A computational approach to candidate gene prioritization for X-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis

PubMed Central

2011-01-01

Background Several computational candidate gene selection and prioritization methods have recently been developed. These in silico selection and prioritization techniques are usually based on two central approaches - the examination of similarities to known disease genes and/or the evaluation of functional annotation of genes. Each of these approaches has its own caveats. Here we employ a previously described method of candidate gene prioritization based mainly on gene annotation, in accompaniment with a technique based on the evaluation of pertinent sequence motifs or signatures, in an attempt to refine the gene prioritization approach. We apply this approach to X-linked mental retardation (XLMR), a group of heterogeneous disorders for which some of the underlying genetics is known. Results The gene annotation-based binary filtering method yielded a ranked list of putative XLMR candidate genes with good plausibility of being associated with the development of mental retardation. In parallel, a motif finding approach based on linear discriminatory analysis (LDA) was employed to identify short sequence patterns that may discriminate XLMR from non-XLMR genes. High rates (>80%) of correct classification was achieved, suggesting that the identification of these motifs effectively captures genomic signals associated with XLMR vs. non-XLMR genes. The computational tools developed for the motif-based LDA is integrated into the freely available genomic analysis portal Galaxy (http://main.g2.bx.psu.edu/). Nine genes (APLN, ZC4H2, MAGED4, MAGED4B, RAP2C, FAM156A, FAM156B, TBL1X, and UXT) were highlighted as highly-ranked XLMR methods. Conclusions The combination of gene annotation information and sequence motif-orientated computational candidate gene prediction methods highlight an added benefit in generating a list of plausible candidate genes, as has been demonstrated for XLMR. Reviewers: This article was reviewed by Dr Barbara Bardoni (nominated by Prof Juergen Brosius

MED: a new non-supervised gene prediction algorithm for bacterial and archaeal genomes.

PubMed

Zhu, Huaiqiu; Hu, Gang-Qing; Yang, Yi-Fan; Wang, Jin; She, Zhen-Su

2007-03-16

Despite a remarkable success in the computational prediction of genes in Bacteria and Archaea, a lack of comprehensive understanding of prokaryotic gene structures prevents from further elucidation of differences among genomes. It continues to be interesting to develop new ab initio algorithms which not only accurately predict genes, but also facilitate comparative studies of prokaryotic genomes. This paper describes a new prokaryotic genefinding algorithm based on a comprehensive statistical model of protein coding Open Reading Frames (ORFs) and Translation Initiation Sites (TISs). The former is based on a linguistic "Entropy Density Profile" (EDP) model of coding DNA sequence and the latter comprises several relevant features related to the translation initiation. They are combined to form a so-called Multivariate Entropy Distance (MED) algorithm, MED 2.0, that incorporates several strategies in the iterative program. The iterations enable us to develop a non-supervised learning process and to obtain a set of genome-specific parameters for the gene structure, before making the prediction of genes. Results of extensive tests show that MED 2.0 achieves a competitive high performance in the gene prediction for both 5' and 3' end matches, compared to the current best prokaryotic gene finders. The advantage of the MED 2.0 is particularly evident for GC-rich genomes and archaeal genomes. Furthermore, the genome-specific parameters given by MED 2.0 match with the current understanding of prokaryotic genomes and may serve as tools for comparative genomic studies. In particular, MED 2.0 is shown to reveal divergent translation initiation mechanisms in archaeal genomes while making a more accurate prediction of TISs compared to the existing gene finders and the current GenBank annotation.

A Novel Method for Accurate Operon Predictions in All SequencedProkaryotes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Price, Morgan N.; Huang, Katherine H.; Alm, Eric J.

2004-12-01

We combine comparative genomic measures and the distance separating adjacent genes to predict operons in 124 completely sequenced prokaryotic genomes. Our method automatically tailors itself to each genome using sequence information alone, and thus can be applied to any prokaryote. For Escherichia coli K12 and Bacillus subtilis, our method is 85 and 83% accurate, respectively, which is similar to the accuracy of methods that use the same features but are trained on experimentally characterized transcripts. In Halobacterium NRC-1 and in Helicobacterpylori, our method correctly infers that genes in operons are separated by shorter distances than they are in E.coli, andmore » its predictions using distance alone are more accurate than distance-only predictions trained on a database of E.coli transcripts. We use microarray data from sixphylogenetically diverse prokaryotes to show that combining intergenic distance with comparative genomic measures further improves accuracy and that our method is broadly effective. Finally, we survey operon structure across 124 genomes, and find several surprises: H.pylori has many operons, contrary to previous reports; Bacillus anthracis has an unusual number of pseudogenes within conserved operons; and Synechocystis PCC6803 has many operons even though it has unusually wide spacings between conserved adjacent genes.« less

Creation of an idealized nasopharynx geometry for accurate computational fluid dynamics simulations of nasal airflow in patient-specific models lacking the nasopharynx anatomy

PubMed Central

Borojeni, Azadeh A.T.; Frank-Ito, Dennis O.; Kimbell, Julia S.; Rhee, John S.; Garcia, Guilherme J. M.

2016-01-01

Virtual surgery planning based on computational fluid dynamics (CFD) simulations has the potential to improve surgical outcomes for nasal airway obstruction (NAO) patients, but the benefits of virtual surgery planning must outweigh the risks of radiation exposure. Cone beam computed tomography (CBCT) scans represent an attractive imaging modality for virtual surgery planning due to lower costs and lower radiation exposures compared with conventional CT scans. However, to minimize the radiation exposure, the CBCT sinusitis protocol sometimes images only the nasal cavity, excluding the nasopharynx. The goal of this study was to develop an idealized nasopharynx geometry for accurate representation of outlet boundary conditions when the nasopharynx geometry is unavailable. Anatomically-accurate models of the nasopharynx created from thirty CT scans were intersected with planes rotated at different angles to obtain an average geometry. Cross sections of the idealized nasopharynx were approximated as ellipses with cross-sectional areas and aspect ratios equal to the average in the actual patient-specific models. CFD simulations were performed to investigate whether nasal airflow patterns were affected when the CT-based nasopharynx was replaced by the idealized nasopharynx in 10 NAO patients. Despite the simple form of the idealized geometry, all biophysical variables (nasal resistance, airflow rate, and heat fluxes) were very similar in the idealized vs. patient-specific models. The results confirmed the expectation that the nasopharynx geometry has a minimal effect in the nasal airflow patterns during inspiration. The idealized nasopharynx geometry will be useful in future CFD studies of nasal airflow based on medical images that exclude the nasopharynx. PMID:27525807

FASTSIM2: a second-order accurate frictional rolling contact algorithm

NASA Astrophysics Data System (ADS)

Vollebregt, E. A. H.; Wilders, P.

2011-01-01

In this paper we consider the frictional (tangential) steady rolling contact problem. We confine ourselves to the simplified theory, instead of using full elastostatic theory, in order to be able to compute results fast, as needed for on-line application in vehicle system dynamics simulation packages. The FASTSIM algorithm is the leading technology in this field and is employed in all dominant railway vehicle system dynamics packages (VSD) in the world. The main contribution of this paper is a new version "FASTSIM2" of the FASTSIM algorithm, which is second-order accurate. This is relevant for VSD, because with the new algorithm 16 times less grid points are required for sufficiently accurate computations of the contact forces. The approach is based on new insights in the characteristics of the rolling contact problem when using the simplified theory, and on taking precise care of the contact conditions in the numerical integration scheme employed.

Constrained clusters of gene expression profiles with pathological features.

PubMed

Sese, Jun; Kurokawa, Yukinori; Monden, Morito; Kato, Kikuya; Morishita, Shinichi

2004-11-22

Gene expression profiles should be useful in distinguishing variations in disease, since they reflect accurately the status of cells. The primary clustering of gene expression reveals the genotypes that are responsible for the proximity of members within each cluster, while further clustering elucidates the pathological features of the individual members of each cluster. However, since the first clustering process and the second classification step, in which the features are associated with clusters, are performed independently, the initial set of clusters may omit genes that are associated with pathologically meaningful features. Therefore, it is important to devise a way of identifying gene expression clusters that are associated with pathological features. We present the novel technique of 'itemset constrained clustering' (IC-Clustering), which computes the optimal cluster that maximizes the interclass variance of gene expression between groups, which are divided according to the restriction that only divisions that can be expressed using common features are allowed. This constraint automatically labels each cluster with a set of pathological features which characterize that cluster. When applied to liver cancer datasets, IC-Clustering revealed informative gene expression clusters, which could be annotated with various pathological features, such as 'tumor' and 'man', or 'except tumor' and 'normal liver function'. In contrast, the k-means method overlooked these clusters.

Creating and validating cis-regulatory maps of tissue-specific gene expression regulation

PubMed Central

O'Connor, Timothy R.; Bailey, Timothy L.

2014-01-01

Predicting which genomic regions control the transcription of a given gene is a challenge. We present a novel computational approach for creating and validating maps that associate genomic regions (cis-regulatory modules–CRMs) with genes. The method infers regulatory relationships that explain gene expression observed in a test tissue using widely available genomic data for ‘other’ tissues. To predict the regulatory targets of a CRM, we use cross-tissue correlation between histone modifications present at the CRM and expression at genes within 1 Mbp of it. To validate cis-regulatory maps, we show that they yield more accurate models of gene expression than carefully constructed control maps. These gene expression models predict observed gene expression from transcription factor binding in the CRMs linked to that gene. We show that our maps are able to identify long-range regulatory interactions and improve substantially over maps linking genes and CRMs based on either the control maps or a ‘nearest neighbor’ heuristic. Our results also show that it is essential to include CRMs predicted in multiple tissues during map-building, that H3K27ac is the most informative histone modification, and that CAGE is the most informative measure of gene expression for creating cis-regulatory maps. PMID:25200088

Accurate evaluation of exchange fields in finite element micromagnetic solvers

NASA Astrophysics Data System (ADS)

Chang, R.; Escobar, M. A.; Li, S.; Lubarda, M. V.; Lomakin, V.

2012-04-01

Quadratic basis functions (QBFs) are implemented for solving the Landau-Lifshitz-Gilbert equation via the finite element method. This involves the introduction of a set of special testing functions compatible with the QBFs for evaluating the Laplacian operator. The results by using QBFs are significantly more accurate than those via linear basis functions. QBF approach leads to significantly more accurate results than conventionally used approaches based on linear basis functions. Importantly QBFs allow reducing the error of computing the exchange field by increasing the mesh density for structured and unstructured meshes. Numerical examples demonstrate the feasibility of the method.

Spatial reconstruction of single-cell gene expression data.

PubMed

Satija, Rahul; Farrell, Jeffrey A; Gennert, David; Schier, Alexander F; Regev, Aviv

2015-05-01

Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking. RNA staining methods assay only a small number of transcripts, whereas single-cell RNA-seq, which measures global gene expression, separates cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos and generated a transcriptome-wide map of spatial patterning. We confirmed Seurat's accuracy using several experimental approaches, then used the strategy to identify a set of archetypal expression patterns and spatial markers. Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.

Automated particle correspondence and accurate tilt-axis detection in tilted-image pairs

DOE PAGES

Shatsky, Maxim; Arbelaez, Pablo; Han, Bong-Gyoon; ...

2014-07-01

Tilted electron microscope images are routinely collected for an ab initio structure reconstruction as a part of the Random Conical Tilt (RCT) or Orthogonal Tilt Reconstruction (OTR) methods, as well as for various applications using the "free-hand" procedure. These procedures all require identification of particle pairs in two corresponding images as well as accurate estimation of the tilt-axis used to rotate the electron microscope (EM) grid. Here we present a computational approach, PCT (particle correspondence from tilted pairs), based on tilt-invariant context and projection matching that addresses both problems. The method benefits from treating the two problems as a singlemore » optimization task. It automatically finds corresponding particle pairs and accurately computes tilt-axis direction even in the cases when EM grid is not perfectly planar.« less

A convenient and accurate parallel Input/Output USB device for E-Prime.

PubMed

Canto, Rosario; Bufalari, Ilaria; D'Ausilio, Alessandro

2011-03-01

Psychological and neurophysiological experiments require the accurate control of timing and synchrony for Input/Output signals. For instance, a typical Event-Related Potential (ERP) study requires an extremely accurate synchronization of stimulus delivery with recordings. This is typically done via computer software such as E-Prime, and fast communications are typically assured by the Parallel Port (PP). However, the PP is an old and disappearing technology that, for example, is no longer available on portable computers. Here we propose a convenient USB device enabling parallel I/O capabilities. We tested this device against the PP on both a desktop and a laptop machine in different stress tests. Our data demonstrate the accuracy of our system, which suggests that it may be a good substitute for the PP with E-Prime.

An approach for reduction of false predictions in reverse engineering of gene regulatory networks.

PubMed

Khan, Abhinandan; Saha, Goutam; Pal, Rajat Kumar

2018-05-14

A gene regulatory network discloses the regulatory interactions amongst genes, at a particular condition of the human body. The accurate reconstruction of such networks from time-series genetic expression data using computational tools offers a stiff challenge for contemporary computer scientists. This is crucial to facilitate the understanding of the proper functioning of a living organism. Unfortunately, the computational methods produce many false predictions along with the correct predictions, which is unwanted. Investigations in the domain focus on the identification of as many correct regulations as possible in the reverse engineering of gene regulatory networks to make it more reliable and biologically relevant. One way to achieve this is to reduce the number of incorrect predictions in the reconstructed networks. In the present investigation, we have proposed a novel scheme to decrease the number of false predictions by suitably combining several metaheuristic techniques. We have implemented the same using a dataset ensemble approach (i.e. combining multiple datasets) also. We have employed the proposed methodology on real-world experimental datasets of the SOS DNA Repair network of Escherichia coli and the IMRA network of Saccharomyces cerevisiae. Subsequently, we have experimented upon somewhat larger, in silico networks, namely, DREAM3 and DREAM4 Challenge networks, and 15-gene and 20-gene networks extracted from the GeneNetWeaver database. To study the effect of multiple datasets on the quality of the inferred networks, we have used four datasets in each experiment. The obtained results are encouraging enough as the proposed methodology can reduce the number of false predictions significantly, without using any supplementary prior biological information for larger gene regulatory networks. It is also observed that if a small amount of prior biological information is incorporated here, the results improve further w.r.t. the prediction of true positives

An Integrated Tool to Study MHC Region: Accurate SNV Detection and HLA Genes Typing in Human MHC Region Using Targeted High-Throughput Sequencing

PubMed Central

Liu, Xiao; Xu, Yinyin; Liang, Dequan; Gao, Peng; Sun, Yepeng; Gifford, Benjamin; D’Ascenzo, Mark; Liu, Xiaomin; Tellier, Laurent C. A. M.; Yang, Fang; Tong, Xin; Chen, Dan; Zheng, Jing; Li, Weiyang; Richmond, Todd; Xu, Xun; Wang, Jun; Li, Yingrui

2013-01-01

The major histocompatibility complex (MHC) is one of the most variable and gene-dense regions of the human genome. Most studies of the MHC, and associated regions, focus on minor variants and HLA typing, many of which have been demonstrated to be associated with human disease susceptibility and metabolic pathways. However, the detection of variants in the MHC region, and diagnostic HLA typing, still lacks a coherent, standardized, cost effective and high coverage protocol of clinical quality and reliability. In this paper, we presented such a method for the accurate detection of minor variants and HLA types in the human MHC region, using high-throughput, high-coverage sequencing of target regions. A probe set was designed to template upon the 8 annotated human MHC haplotypes, and to encompass the 5 megabases (Mb) of the extended MHC region. We deployed our probes upon three, genetically diverse human samples for probe set evaluation, and sequencing data show that ∼97% of the MHC region, and over 99% of the genes in MHC region, are covered with sufficient depth and good evenness. 98% of genotypes called by this capture sequencing prove consistent with established HapMap genotypes. We have concurrently developed a one-step pipeline for calling any HLA type referenced in the IMGT/HLA database from this target capture sequencing data, which shows over 96% typing accuracy when deployed at 4 digital resolution. This cost-effective and highly accurate approach for variant detection and HLA typing in the MHC region may lend further insight into immune-mediated diseases studies, and may find clinical utility in transplantation medicine research. This one-step pipeline is released for general evaluation and use by the scientific community. PMID:23894464

Third-order accurate conservative method on unstructured meshes for gasdynamic simulations

NASA Astrophysics Data System (ADS)

Shirobokov, D. A.

2017-04-01

A third-order accurate finite-volume method on unstructured meshes is proposed for solving viscous gasdynamic problems. The method is described as applied to the advection equation. The accuracy of the method is verified by computing the evolution of a vortex on meshes of various degrees of detail with variously shaped cells. Additionally, unsteady flows around a cylinder and a symmetric airfoil are computed. The numerical results are presented in the form of plots and tables.

Systematic computation with functional gene-sets among leukemic and hematopoietic stem cells reveals a favorable prognostic signature for acute myeloid leukemia.

PubMed

Yang, Xinan Holly; Li, Meiyi; Wang, Bin; Zhu, Wanqi; Desgardin, Aurelie; Onel, Kenan; de Jong, Jill; Chen, Jianjun; Chen, Luonan; Cunningham, John M

2015-03-24

Genes that regulate stem cell function are suspected to exert adverse effects on prognosis in malignancy. However, diverse cancer stem cell signatures are difficult for physicians to interpret and apply clinically. To connect the transcriptome and stem cell biology, with potential clinical applications, we propose a novel computational "gene-to-function, snapshot-to-dynamics, and biology-to-clinic" framework to uncover core functional gene-sets signatures. This framework incorporates three function-centric gene-set analysis strategies: a meta-analysis of both microarray and RNA-seq data, novel dynamic network mechanism (DNM) identification, and a personalized prognostic indicator analysis. This work uses complex disease acute myeloid leukemia (AML) as a research platform. We introduced an adjustable "soft threshold" to a functional gene-set algorithm and found that two different analysis methods identified distinct gene-set signatures from the same samples. We identified a 30-gene cluster that characterizes leukemic stem cell (LSC)-depleted cells and a 25-gene cluster that characterizes LSC-enriched cells in parallel; both mark favorable-prognosis in AML. Genes within each signature significantly share common biological processes and/or molecular functions (empirical p = 6e-5 and 0.03 respectively). The 25-gene signature reflects the abnormal development of stem cells in AML, such as AURKA over-expression. We subsequently determined that the clinical relevance of both signatures is independent of known clinical risk classifications in 214 patients with cytogenetically normal AML. We successfully validated the prognosis of both signatures in two independent cohorts of 91 and 242 patients respectively (log-rank p < 0.0015 and 0.05; empirical p < 0.015 and 0.08). The proposed algorithms and computational framework will harness systems biology research because they efficiently translate gene-sets (rather than single genes) into biological discoveries about

ZCURVE 3.0: identify prokaryotic genes with higher accuracy as well as automatically and accurately select essential genes

PubMed Central

Hua, Zhi-Gang; Lin, Yan; Yuan, Ya-Zhou; Yang, De-Chang; Wei, Wen; Guo, Feng-Biao

2015-01-01

In 2003, we developed an ab initio program, ZCURVE 1.0, to find genes in bacterial and archaeal genomes. In this work, we present the updated version (i.e. ZCURVE 3.0). Using 422 prokaryotic genomes, the average accuracy was 93.7% with the updated version, compared with 88.7% with the original version. Such results also demonstrate that ZCURVE 3.0 is comparable with Glimmer 3.02 and may provide complementary predictions to it. In fact, the joint application of the two programs generated better results by correctly finding more annotated genes while also containing fewer false-positive predictions. As the exclusive function, ZCURVE 3.0 contains one post-processing program that can identify essential genes with high accuracy (generally >90%). We hope ZCURVE 3.0 will receive wide use with the web-based running mode. The updated ZCURVE can be freely accessed from http://cefg.uestc.edu.cn/zcurve/ or http://tubic.tju.edu.cn/zcurveb/ without any restrictions. PMID:25977299

Compare Gene Calls

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ecale Zhou, Carol L.

2016-07-05

Compare Gene Calls (CGC) is a Python code used for combining and comparing gene calls from any number of gene callers. A gene caller is a computer program that predicts the extends of open reading frames within genomes of biological organisms.

A molecular computational model improves the preoperative diagnosis of thyroid nodules

PubMed Central

2012-01-01

Background Thyroid nodules with indeterminate cytological features on fine needle aspiration (FNA) cytology have a 20% risk of thyroid cancer. The aim of the current study was to determine the diagnostic utility of an 8-gene assay to distinguish benign from malignant thyroid neoplasm. Methods The mRNA expression level of 9 genes (KIT, SYNGR2, C21orf4, Hs.296031, DDI2, CDH1, LSM7, TC1, NATH) was analysed by quantitative PCR (q-PCR) in 93 FNA cytological samples. To evaluate the diagnostic utility of all the genes analysed, we assessed the area under the curve (AUC) for each gene individually and in combination. BRAF exon 15 status was determined by pyrosequencing. An 8-gene computational model (Neural Network Bayesian Classifier) was built and a multiple-variable analysis was then performed to assess the correlation between the markers. Results The AUC for each significant marker ranged between 0.625 and 0.900, thus all the significant markers, alone and in combination, can be used to distinguish between malignant and benign FNA samples. The classifier made up of KIT, CDH1, LSM7, C21orf4, DDI2, TC1, Hs.296031 and BRAF had a predictive power of 88.8%. It proved to be useful for risk stratification of the most critical cytological group of the indeterminate lesions for which there is the greatest need of accurate diagnostic markers. Conclusion The genetic classification obtained with this model is highly accurate at differentiating malignant from benign thyroid lesions and might be a useful adjunct in the preoperative management of patients with thyroid nodules. PMID:22958914

Omics of Brucella: Species-Specific sRNA-Mediated Gene Ontology Regulatory Networks Identified by Computational Biology.

PubMed

Vishnu, Udayakumar S; Sankarasubramanian, Jagadesan; Gunasekaran, Paramasamy; Sridhar, Jayavel; Rajendhran, Jeyaprakash

2016-06-01

Brucella is an intracellular bacterium that causes the zoonotic infectious disease, brucellosis. Brucella species are currently intensively studied with a view to developing novel global health diagnostics and therapeutics. In this context, small RNAs (sRNAs) are one of the emerging topical areas; they play significant roles in regulating gene expression and cellular processes in bacteria. In the present study, we forecast sRNAs in three Brucella species that infect humans, namely Brucella melitensis, Brucella abortus, and Brucella suis, using a computational biology analysis. We combined two bioinformatic algorithms, SIPHT and sRNAscanner. In B. melitensis 16M, 21 sRNA candidates were identified, of which 14 were novel. Similarly, 14 sRNAs were identified in B. abortus, of which four were novel. In B. suis, 16 sRNAs were identified, and five of them were novel. TargetRNA2 software predicted the putative target genes that could be regulated by the identified sRNAs. The identified mRNA targets are involved in carbohydrate, amino acid, lipid, nucleotide, and coenzyme metabolism and transport, energy production and conversion, replication, recombination, repair, and transcription. Additionally, the Gene Ontology (GO) network analysis revealed the species-specific, sRNA-based regulatory networks in B. melitensis, B. abortus, and B. suis. Taken together, although sRNAs are veritable modulators of gene expression in prokaryotes, there are few reports on the significance of sRNAs in Brucella. This report begins to address this literature gap by offering a series of initial observations based on computational biology to pave the way for future experimental analysis of sRNAs and their targets to explain the complex pathogenesis of Brucella.

A computational method for drug repositioning using publicly available gene expression data.

PubMed

Shabana, K M; Abdul Nazeer, K A; Pradhan, Meeta; Palakal, Mathew

2015-01-01

The identification of new therapeutic uses of existing drugs, or drug repositioning, offers the possibility of faster drug development, reduced risk, lesser cost and shorter paths to approval. The advent of high throughput microarray technology has enabled comprehensive monitoring of transcriptional response associated with various disease states and drug treatments. This data can be used to characterize disease and drug effects and thereby give a measure of the association between a given drug and a disease. Several computational methods have been proposed in the literature that make use of publicly available transcriptional data to reposition drugs against diseases. In this work, we carry out a data mining process using publicly available gene expression data sets associated with a few diseases and drugs, to identify the existing drugs that can be used to treat genes causing lung cancer and breast cancer. Three strong candidates for repurposing have been identified- Letrozole and GDC-0941 against lung cancer, and Ribavirin against breast cancer. Letrozole and GDC-0941 are drugs currently used in breast cancer treatment and Ribavirin is used in the treatment of Hepatitis C.

A Penalized Robust Method for Identifying Gene-Environment Interactions

PubMed Central

Shi, Xingjie; Liu, Jin; Huang, Jian; Zhou, Yong; Xie, Yang; Ma, Shuangge

2015-01-01

In high-throughput studies, an important objective is to identify gene-environment interactions associated with disease outcomes and phenotypes. Many commonly adopted methods assume specific parametric or semiparametric models, which may be subject to model mis-specification. In addition, they usually use significance level as the criterion for selecting important interactions. In this study, we adopt the rank-based estimation, which is much less sensitive to model specification than some of the existing methods and includes several commonly encountered data and models as special cases. Penalization is adopted for the identification of gene-environment interactions. It achieves simultaneous estimation and identification and does not rely on significance level. For computation feasibility, a smoothed rank estimation is further proposed. Simulation shows that under certain scenarios, for example with contaminated or heavy-tailed data, the proposed method can significantly outperform the existing alternatives with more accurate identification. We analyze a lung cancer prognosis study with gene expression measurements under the AFT (accelerated failure time) model. The proposed method identifies interactions different from those using the alternatives. Some of the identified genes have important implications. PMID:24616063

Time-Accurate, Unstructured-Mesh Navier-Stokes Computations with the Space-Time CESE Method

NASA Technical Reports Server (NTRS)

Chang, Chau-Lyan

2006-01-01

Application of the newly emerged space-time conservation element solution element (CESE) method to compressible Navier-Stokes equations is studied. In contrast to Euler equations solvers, several issues such as boundary conditions, numerical dissipation, and grid stiffness warrant systematic investigations and validations. Non-reflecting boundary conditions applied at the truncated boundary are also investigated from the stand point of acoustic wave propagation. Validations of the numerical solutions are performed by comparing with exact solutions for steady-state as well as time-accurate viscous flow problems. The test cases cover a broad speed regime for problems ranging from acoustic wave propagation to 3D hypersonic configurations. Model problems pertinent to hypersonic configurations demonstrate the effectiveness of the CESE method in treating flows with shocks, unsteady waves, and separations. Good agreement with exact solutions suggests that the space-time CESE method provides a viable alternative for time-accurate Navier-Stokes calculations of a broad range of problems.

Fractional labelmaps for computing accurate dose volume histograms

NASA Astrophysics Data System (ADS)

Sunderland, Kyle; Pinter, Csaba; Lasso, Andras; Fichtinger, Gabor

2017-03-01

PURPOSE: In radiation therapy treatment planning systems, structures are represented as parallel 2D contours. For treatment planning algorithms, structures must be converted into labelmap (i.e. 3D image denoting structure inside/outside) representations. This is often done by triangulated a surface from contours, which is converted into a binary labelmap. This surface to binary labelmap conversion can cause large errors in small structures. Binary labelmaps are often represented using one byte per voxel, meaning a large amount of memory is unused. Our goal is to develop a fractional labelmap representation containing non-binary values, allowing more information to be stored in the same amount of memory. METHODS: We implemented an algorithm in 3D Slicer, which converts surfaces to fractional labelmaps by creating 216 binary labelmaps, changing the labelmap origin on each iteration. The binary labelmap values are summed to create the fractional labelmap. In addition, an algorithm is implemented in the SlicerRT toolkit that calculates dose volume histograms (DVH) using fractional labelmaps. RESULTS: We found that with manually segmented RANDO head and neck structures, fractional labelmaps represented structure volume up to 19.07% (average 6.81%) more accurately than binary labelmaps, while occupying the same amount of memory. When compared to baseline DVH from treatment planning software, DVH from fractional labelmaps had agreement acceptance percent (1% ΔD, 1% ΔV) up to 57.46% higher (average 4.33%) than DVH from binary labelmaps. CONCLUSION: Fractional labelmaps promise to be an effective method for structure representation, allowing considerably more information to be stored in the same amount of memory.

Accurate Encoding and Decoding by Single Cells: Amplitude Versus Frequency Modulation

PubMed Central

Micali, Gabriele; Aquino, Gerardo; Richards, David M.; Endres, Robert G.

2015-01-01

Cells sense external concentrations and, via biochemical signaling, respond by regulating the expression of target proteins. Both in signaling networks and gene regulation there are two main mechanisms by which the concentration can be encoded internally: amplitude modulation (AM), where the absolute concentration of an internal signaling molecule encodes the stimulus, and frequency modulation (FM), where the period between successive bursts represents the stimulus. Although both mechanisms have been observed in biological systems, the question of when it is beneficial for cells to use either AM or FM is largely unanswered. Here, we first consider a simple model for a single receptor (or ion channel), which can either signal continuously whenever a ligand is bound, or produce a burst in signaling molecule upon receptor binding. We find that bursty signaling is more accurate than continuous signaling only for sufficiently fast dynamics. This suggests that modulation based on bursts may be more common in signaling networks than in gene regulation. We then extend our model to multiple receptors, where continuous and bursty signaling are equivalent to AM and FM respectively, finding that AM is always more accurate. This implies that the reason some cells use FM is related to factors other than accuracy, such as the ability to coordinate expression of multiple genes or to implement threshold crossing mechanisms. PMID:26030820

Gene regulatory network inference using fused LASSO on multiple data sets

PubMed Central

Omranian, Nooshin; Eloundou-Mbebi, Jeanne M. O.; Mueller-Roeber, Bernd; Nikoloski, Zoran

2016-01-01

Devising computational methods to accurately reconstruct gene regulatory networks given gene expression data is key to systems biology applications. Here we propose a method for reconstructing gene regulatory networks by simultaneous consideration of data sets from different perturbation experiments and corresponding controls. The method imposes three biologically meaningful constraints: (1) expression levels of each gene should be explained by the expression levels of a small number of transcription factor coding genes, (2) networks inferred from different data sets should be similar with respect to the type and number of regulatory interactions, and (3) relationships between genes which exhibit similar differential behavior over the considered perturbations should be favored. We demonstrate that these constraints can be transformed in a fused LASSO formulation for the proposed method. The comparative analysis on transcriptomics time-series data from prokaryotic species, Escherichia coli and Mycobacterium tuberculosis, as well as a eukaryotic species, mouse, demonstrated that the proposed method has the advantages of the most recent approaches for regulatory network inference, while obtaining better performance and assigning higher scores to the true regulatory links. The study indicates that the combination of sparse regression techniques with other biologically meaningful constraints is a promising framework for gene regulatory network reconstructions. PMID:26864687

geneGIS: Computational Tools for Spatial Analyses of DNA Profiles with Associated Photo-Identification and Telemetry Records of Marine Mammals

DTIC Science & Technology

2012-09-30

computational tools provide the ability to display, browse, select, filter and summarize spatio-temporal relationships of these individual-based...her research assistant at Esri, Shaun Walbridge, and members of the Marine Mammal Institute ( MMI ), including Tomas Follet and Debbie Steel. This...Genomics Laboratory, MMI , OSU. 4 As part of the geneGIS initiative, these SPLASH photo-identification records and the geneSPLASH DNA profiles

Possible Computer Vision Systems and Automated or Computer-Aided Edging and Trimming

Treesearch

Philip A. Araman

1990-01-01

This paper discusses research which is underway to help our industry reduce costs, increase product volume and value recovery, and market more accurately graded and described products. The research is part of a team effort to help the hardwood sawmill industry automate with computer vision systems, and computer-aided or computer controlled processing. This paper...

On the accurate estimation of gap fraction during daytime with digital cover photography

NASA Astrophysics Data System (ADS)

Hwang, Y. R.; Ryu, Y.; Kimm, H.; Macfarlane, C.; Lang, M.; Sonnentag, O.

2015-12-01

Digital cover photography (DCP) has emerged as an indirect method to obtain gap fraction accurately. Thus far, however, the intervention of subjectivity, such as determining the camera relative exposure value (REV) and threshold in the histogram, hindered computing accurate gap fraction. Here we propose a novel method that enables us to measure gap fraction accurately during daytime under various sky conditions by DCP. The novel method computes gap fraction using a single DCP unsaturated raw image which is corrected for scattering effects by canopies and a reconstructed sky image from the raw format image. To test the sensitivity of the novel method derived gap fraction to diverse REVs, solar zenith angles and canopy structures, we took photos in one hour interval between sunrise to midday under dense and sparse canopies with REV 0 to -5. The novel method showed little variation of gap fraction across different REVs in both dense and spares canopies across diverse range of solar zenith angles. The perforated panel experiment, which was used to test the accuracy of the estimated gap fraction, confirmed that the novel method resulted in the accurate and consistent gap fractions across different hole sizes, gap fractions and solar zenith angles. These findings highlight that the novel method opens new opportunities to estimate gap fraction accurately during daytime from sparse to dense canopies, which will be useful in monitoring LAI precisely and validating satellite remote sensing LAI products efficiently.

ZCURVE 3.0: identify prokaryotic genes with higher accuracy as well as automatically and accurately select essential genes.

PubMed

Hua, Zhi-Gang; Lin, Yan; Yuan, Ya-Zhou; Yang, De-Chang; Wei, Wen; Guo, Feng-Biao

2015-07-01

In 2003, we developed an ab initio program, ZCURVE 1.0, to find genes in bacterial and archaeal genomes. In this work, we present the updated version (i.e. ZCURVE 3.0). Using 422 prokaryotic genomes, the average accuracy was 93.7% with the updated version, compared with 88.7% with the original version. Such results also demonstrate that ZCURVE 3.0 is comparable with Glimmer 3.02 and may provide complementary predictions to it. In fact, the joint application of the two programs generated better results by correctly finding more annotated genes while also containing fewer false-positive predictions. As the exclusive function, ZCURVE 3.0 contains one post-processing program that can identify essential genes with high accuracy (generally >90%). We hope ZCURVE 3.0 will receive wide use with the web-based running mode. The updated ZCURVE can be freely accessed from http://cefg.uestc.edu.cn/zcurve/ or http://tubic.tju.edu.cn/zcurveb/ without any restrictions. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Accurate complex scaling of three dimensional numerical potentials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cerioni, Alessandro; Genovese, Luigi; Duchemin, Ivan

2013-05-28

The complex scaling method, which consists in continuing spatial coordinates into the complex plane, is a well-established method that allows to compute resonant eigenfunctions of the time-independent Schroedinger operator. Whenever it is desirable to apply the complex scaling to investigate resonances in physical systems defined on numerical discrete grids, the most direct approach relies on the application of a similarity transformation to the original, unscaled Hamiltonian. We show that such an approach can be conveniently implemented in the Daubechies wavelet basis set, featuring a very promising level of generality, high accuracy, and no need for artificial convergence parameters. Complex scalingmore » of three dimensional numerical potentials can be efficiently and accurately performed. By carrying out an illustrative resonant state computation in the case of a one-dimensional model potential, we then show that our wavelet-based approach may disclose new exciting opportunities in the field of computational non-Hermitian quantum mechanics.« less

Selective pressures for accurate altruism targeting: evidence from digital evolution for difficult-to-test aspects of inclusive fitness theory.

PubMed

Clune, Jeff; Goldsby, Heather J; Ofria, Charles; Pennock, Robert T

2011-03-07

Inclusive fitness theory predicts that natural selection will favour altruist genes that are more accurate in targeting altruism only to copies of themselves. In this paper, we provide evidence from digital evolution in support of this prediction by competing multiple altruist-targeting mechanisms that vary in their accuracy in determining whether a potential target for altruism carries a copy of the altruist gene. We compete altruism-targeting mechanisms based on (i) kinship (kin targeting), (ii) genetic similarity at a level greater than that expected of kin (similarity targeting), and (iii) perfect knowledge of the presence of an altruist gene (green beard targeting). Natural selection always favoured the most accurate targeting mechanism available. Our investigations also revealed that evolution did not increase the altruism level when all green beard altruists used the same phenotypic marker. The green beard altruism levels stably increased only when mutations that changed the altruism level also changed the marker (e.g. beard colour), such that beard colour reliably indicated the altruism level. For kin- and similarity-targeting mechanisms, we found that evolution was able to stably adjust altruism levels. Our results confirm that natural selection favours altruist genes that are increasingly accurate in targeting altruism to only their copies. Our work also emphasizes that the concept of targeting accuracy must include both the presence of an altruist gene and the level of altruism it produces.

FAMBE-pH: A Fast and Accurate Method to Compute the Total Solvation Free Energies of Proteins

PubMed Central

Vorobjev, Yury N.; Vila, Jorge A.

2009-01-01

A fast and accurate method to compute the total solvation free energies of proteins as a function of pH is presented. The method makes use of a combination of approaches, some of which have already appeared in the literature; (i) the Poisson equation is solved with an optimized fast adaptive multigrid boundary element (FAMBE) method; (ii) the electrostatic free energies of the ionizable sites are calculated for their neutral and charged states by using a detailed model of atomic charges; (iii) a set of optimal atomic radii is used to define a precise dielectric surface interface; (iv) a multilevel adaptive tessellation of this dielectric surface interface is achieved by using multisized boundary elements; and (v) 1:1 salt effects are included. The equilibrium proton binding/release is calculated with the Tanford–Schellman integral if the proteins contain more than ∼20–25 ionizable groups; for a smaller number of ionizable groups, the ionization partition function is calculated directly. The FAMBE method is tested as a function of pH (FAMBE-pH) with three proteins, namely, bovine pancreatic trypsin inhibitor (BPTI), hen egg white lysozyme (HEWL), and bovine pancreatic ribonuclease A (RNaseA). The results are (a) the FAMBE-pH method reproduces the observed pKa's of the ionizable groups of these proteins within an average absolute value of 0.4 pK units and a maximum error of 1.2 pK units and (b) comparison of the calculated total pH-dependent solvation free energy for BPTI, between the exact calculation of the ionization partition function and the Tanford–Schellman integral method, shows agreement within 1.2 kcal/mol. These results indicate that calculation of total solvation free energies with the FAMBE-pH method can provide an accurate prediction of protein conformational stability at a given fixed pH and, if coupled with molecular mechanics or molecular dynamics methods, can also be used for more realistic studies of protein folding, unfolding, and dynamics

ACCURATE SOLUTION AND GRADIENT COMPUTATION FOR ELLIPTIC INTERFACE PROBLEMS WITH VARIABLE COEFFICIENTS

PubMed Central

LI, ZHILIN; JI, HAIFENG; CHEN, XIAOHONG

2016-01-01

A new augmented method is proposed for elliptic interface problems with a piecewise variable coefficient that has a finite jump across a smooth interface. The main motivation is not only to get a second order accurate solution but also a second order accurate gradient from each side of the interface. The key of the new method is to introduce the jump in the normal derivative of the solution as an augmented variable and re-write the interface problem as a new PDE that consists of a leading Laplacian operator plus lower order derivative terms near the interface. In this way, the leading second order derivatives jump relations are independent of the jump in the coefficient that appears only in the lower order terms after the scaling. An upwind type discretization is used for the finite difference discretization at the irregular grid points near or on the interface so that the resulting coefficient matrix is an M-matrix. A multi-grid solver is used to solve the linear system of equations and the GMRES iterative method is used to solve the augmented variable. Second order convergence for the solution and the gradient from each side of the interface has also been proved in this paper. Numerical examples for general elliptic interface problems have confirmed the theoretical analysis and efficiency of the new method. PMID:28983130

Genome-Wide Analysis of Gene-Gene and Gene-Environment Interactions Using Closed-Form Wald Tests.

PubMed

Yu, Zhaoxia; Demetriou, Michael; Gillen, Daniel L

2015-09-01

Despite the successful discovery of hundreds of variants for complex human traits using genome-wide association studies, the degree to which genes and environmental risk factors jointly affect disease risk is largely unknown. One obstacle toward this goal is that the computational effort required for testing gene-gene and gene-environment interactions is enormous. As a result, numerous computationally efficient tests were recently proposed. However, the validity of these methods often relies on unrealistic assumptions such as additive main effects, main effects at only one variable, no linkage disequilibrium between the two single-nucleotide polymorphisms (SNPs) in a pair or gene-environment independence. Here, we derive closed-form and consistent estimates for interaction parameters and propose to use Wald tests for testing interactions. The Wald tests are asymptotically equivalent to the likelihood ratio tests (LRTs), largely considered to be the gold standard tests but generally too computationally demanding for genome-wide interaction analysis. Simulation studies show that the proposed Wald tests have very similar performances with the LRTs but are much more computationally efficient. Applying the proposed tests to a genome-wide study of multiple sclerosis, we identify interactions within the major histocompatibility complex region. In this application, we find that (1) focusing on pairs where both SNPs are marginally significant leads to more significant interactions when compared to focusing on pairs where at least one SNP is marginally significant; and (2) parsimonious parameterization of interaction effects might decrease, rather than increase, statistical power. © 2015 WILEY PERIODICALS, INC.

Progress in fast, accurate multi-scale climate simulations

DOE PAGES

Collins, W. D.; Johansen, H.; Evans, K. J.; ...

2015-06-01

We present a survey of physical and computational techniques that have the potential to contribute to the next generation of high-fidelity, multi-scale climate simulations. Examples of the climate science problems that can be investigated with more depth with these computational improvements include the capture of remote forcings of localized hydrological extreme events, an accurate representation of cloud features over a range of spatial and temporal scales, and parallel, large ensembles of simulations to more effectively explore model sensitivities and uncertainties. Numerical techniques, such as adaptive mesh refinement, implicit time integration, and separate treatment of fast physical time scales are enablingmore » improved accuracy and fidelity in simulation of dynamics and allowing more complete representations of climate features at the global scale. At the same time, partnerships with computer science teams have focused on taking advantage of evolving computer architectures such as many-core processors and GPUs. As a result, approaches which were previously considered prohibitively costly have become both more efficient and scalable. In combination, progress in these three critical areas is poised to transform climate modeling in the coming decades.« less

ModuleMiner - improved computational detection of cis-regulatory modules: are there different modes of gene regulation in embryonic development and adult tissues?

PubMed Central

Van Loo, Peter; Aerts, Stein; Thienpont, Bernard; De Moor, Bart; Moreau, Yves; Marynen, Peter

2008-01-01

We present ModuleMiner, a novel algorithm for computationally detecting cis-regulatory modules (CRMs) in a set of co-expressed genes. ModuleMiner outperforms other methods for CRM detection on benchmark data, and successfully detects CRMs in tissue-specific microarray clusters and in embryonic development gene sets. Interestingly, CRM predictions for differentiated tissues exhibit strong enrichment close to the transcription start site, whereas CRM predictions for embryonic development gene sets are depleted in this region. PMID:18394174

Probabilistic techniques for obtaining accurate patient counts in Clinical Data Warehouses

PubMed Central

Myers, Risa B.; Herskovic, Jorge R.

2011-01-01

Proposal and execution of clinical trials, computation of quality measures and discovery of correlation between medical phenomena are all applications where an accurate count of patients is needed. However, existing sources of this type of patient information, including Clinical Data Warehouses (CDW) may be incomplete or inaccurate. This research explores applying probabilistic techniques, supported by the MayBMS probabilistic database, to obtain accurate patient counts from a clinical data warehouse containing synthetic patient data. We present a synthetic clinical data warehouse (CDW), and populate it with simulated data using a custom patient data generation engine. We then implement, evaluate and compare different techniques for obtaining patients counts. We model billing as a test for the presence of a condition. We compute billing’s sensitivity and specificity both by conducting a “Simulated Expert Review” where a representative sample of records are reviewed and labeled by experts, and by obtaining the ground truth for every record. We compute the posterior probability of a patient having a condition through a “Bayesian Chain”, using Bayes’ Theorem to calculate the probability of a patient having a condition after each visit. The second method is a “one-shot” approach that computes the probability of a patient having a condition based on whether the patient is ever billed for the condition Our results demonstrate the utility of probabilistic approaches, which improve on the accuracy of raw counts. In particular, the simulated review paired with a single application of Bayes’ Theorem produces the best results, with an average error rate of 2.1% compared to 43.7% for the straightforward billing counts. Overall, this research demonstrates that Bayesian probabilistic approaches improve patient counts on simulated patient populations. We believe that total patient counts based on billing data are one of the many possible applications of our

Selection of low-variance expressed Malus x domestica (apple) genes for use as quantitative PCR reference genes (housekeepers)

USDA-ARS?s Scientific Manuscript database

To accurately measure gene expression using PCR-based approaches, there is the need for reference genes that have low variance in expression (housekeeping genes) to normalise the data for RNA quantity and quality. For non-model species such as Malus x domestica (apples), previously, the selection of...

Time Accurate Unsteady Pressure Loads Simulated for the Space Launch System at a Wind Tunnel Condition

NASA Technical Reports Server (NTRS)

Alter, Stephen J.; Brauckmann, Gregory J.; Kleb, Bil; Streett, Craig L; Glass, Christopher E.; Schuster, David M.

2015-01-01

Using the Fully Unstructured Three-Dimensional (FUN3D) computational fluid dynamics code, an unsteady, time-accurate flow field about a Space Launch System configuration was simulated at a transonic wind tunnel condition (Mach = 0.9). Delayed detached eddy simulation combined with Reynolds Averaged Naiver-Stokes and a Spallart-Almaras turbulence model were employed for the simulation. Second order accurate time evolution scheme was used to simulate the flow field, with a minimum of 0.2 seconds of simulated time to as much as 1.4 seconds. Data was collected at 480 pressure taps at locations, 139 of which matched a 3% wind tunnel model, tested in the Transonic Dynamic Tunnel (TDT) facility at NASA Langley Research Center. Comparisons between computation and experiment showed agreement within 5% in terms of location for peak RMS levels, and 20% for frequency and magnitude of power spectral densities. Grid resolution and time step sensitivity studies were performed to identify methods for improved accuracy comparisons to wind tunnel data. With limited computational resources, accurate trends for reduced vibratory loads on the vehicle were observed. Exploratory methods such as determining minimized computed errors based on CFL number and sub-iterations, as well as evaluating frequency content of the unsteady pressures and evaluation of oscillatory shock structures were used in this study to enhance computational efficiency and solution accuracy. These techniques enabled development of a set of best practices, for the evaluation of future flight vehicle designs in terms of vibratory loads.

Identification of fidgety movements and prediction of CP by the use of computer-based video analysis is more accurate when based on two video recordings.

PubMed

Adde, Lars; Helbostad, Jorunn; Jensenius, Alexander R; Langaas, Mette; Støen, Ragnhild

2013-08-01

This study evaluates the role of postterm age at assessment and the use of one or two video recordings for the detection of fidgety movements (FMs) and prediction of cerebral palsy (CP) using computer vision software. Recordings between 9 and 17 weeks postterm age from 52 preterm and term infants (24 boys, 28 girls; 26 born preterm) were used. Recordings were analyzed using computer vision software. Movement variables, derived from differences between subsequent video frames, were used for quantitative analysis. Sensitivities, specificities, and area under curve were estimated for the first and second recording, or a mean of both. FMs were classified based on the Prechtl approach of general movement assessment. CP status was reported at 2 years. Nine children developed CP of whom all recordings had absent FMs. The mean variability of the centroid of motion (CSD) from two recordings was more accurate than using only one recording, and identified all children who were diagnosed with CP at 2 years. Age at assessment did not influence the detection of FMs or prediction of CP. The accuracy of computer vision techniques in identifying FMs and predicting CP based on two recordings should be confirmed in future studies.

Accurate transport properties for H–CO and H–CO{sub 2}

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dagdigian, Paul J., E-mail: pjdagdigian@jhu.edu

2015-08-07

Transport properties for collisions of hydrogen atoms with CO and CO{sub 2} have been computed by means of quantum scattering calculations. The carbon oxides are important species in hydrocarbon combustion. The following potential energy surfaces (PES’s) for the interaction of the molecule fixed in its equilibrium geometry were employed: for H–CO, the PES was taken from the work of Song et al. [J. Phys. Chem. A 117, 7571 (2013)], while the PES for H–CO{sub 2} was computed in this study by a restricted coupled cluster method that included single, double, and (perturbatively) triple excitations. The computed transport properties were foundmore » to be significantly different from those computed by the conventional approach that employs isotropic Lennard-Jones (12-6) potentials. The effect of using the presently computed accurate transport properties in 1-dimensional combustion simulations of methane-air flames was investigated.« less

Matrix-vector multiplication using digital partitioning for more accurate optical computing

NASA Technical Reports Server (NTRS)

Gary, C. K.

1992-01-01

Digital partitioning offers a flexible means of increasing the accuracy of an optical matrix-vector processor. This algorithm can be implemented with the same architecture required for a purely analog processor, which gives optical matrix-vector processors the ability to perform high-accuracy calculations at speeds comparable with or greater than electronic computers as well as the ability to perform analog operations at a much greater speed. Digital partitioning is compared with digital multiplication by analog convolution, residue number systems, and redundant number representation in terms of the size and the speed required for an equivalent throughput as well as in terms of the hardware requirements. Digital partitioning and digital multiplication by analog convolution are found to be the most efficient alogrithms if coding time and hardware are considered, and the architecture for digital partitioning permits the use of analog computations to provide the greatest throughput for a single processor.

UniGene Tabulator: a full parser for the UniGene format.

PubMed

Lenzi, Luca; Frabetti, Flavia; Facchin, Federica; Casadei, Raffaella; Vitale, Lorenza; Canaider, Silvia; Carinci, Paolo; Zannotti, Maria; Strippoli, Pierluigi

2006-10-15

UniGene Tabulator 1.0 provides a solution for full parsing of UniGene flat file format; it implements a structured graphical representation of each data field present in UniGene following import into a common database managing system usable in a personal computer. This database includes related tables for sequence, protein similarity, sequence-tagged site (STS) and transcript map interval (TXMAP) data, plus a summary table where each record represents a UniGene cluster. UniGene Tabulator enables full local management of UniGene data, allowing parsing, querying, indexing, retrieving, exporting and analysis of UniGene data in a relational database form, usable on Macintosh (OS X 10.3.9 or later) and Windows (2000, with service pack 4, XP, with service pack 2 or later) operating systems-based computers. The current release, including both the FileMaker runtime applications, is freely available at http://apollo11.isto.unibo.it/software/

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

PubMed Central

Roy, Janine; Aust, Daniela; Knösel, Thomas; Rümmele, Petra; Jahnke, Beatrix; Hentrich, Vera; Rückert, Felix; Niedergethmann, Marco; Weichert, Wilko; Bahra, Marcus; Schlitt, Hans J.; Settmacher, Utz; Friess, Helmut; Büchler, Markus; Saeger, Hans-Detlev; Schroeder, Michael; Pilarsky, Christian; Grützmann, Robert

2012-01-01

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice. PMID:22615549

A greedy, graph-based algorithm for the alignment of multiple homologous gene lists.

PubMed

Fostier, Jan; Proost, Sebastian; Dhoedt, Bart; Saeys, Yvan; Demeester, Piet; Van de Peer, Yves; Vandepoele, Klaas

2011-03-15

Many comparative genomics studies rely on the correct identification of homologous genomic regions using accurate alignment tools. In such case, the alphabet of the input sequences consists of complete genes, rather than nucleotides or amino acids. As optimal multiple sequence alignment is computationally impractical, a progressive alignment strategy is often employed. However, such an approach is susceptible to the propagation of alignment errors in early pairwise alignment steps, especially when dealing with strongly diverged genomic regions. In this article, we present a novel accurate and efficient greedy, graph-based algorithm for the alignment of multiple homologous genomic segments, represented as ordered gene lists. Based on provable properties of the graph structure, several heuristics are developed to resolve local alignment conflicts that occur due to gene duplication and/or rearrangement events on the different genomic segments. The performance of the algorithm is assessed by comparing the alignment results of homologous genomic segments in Arabidopsis thaliana to those obtained by using both a progressive alignment method and an earlier graph-based implementation. Especially for datasets that contain strongly diverged segments, the proposed method achieves a substantially higher alignment accuracy, and proves to be sufficiently fast for large datasets including a few dozens of eukaryotic genomes. http://bioinformatics.psb.ugent.be/software. The algorithm is implemented as a part of the i-ADHoRe 3.0 package.

Accurate and efficient calculation of response times for groundwater flow

NASA Astrophysics Data System (ADS)

Carr, Elliot J.; Simpson, Matthew J.

2018-03-01

We study measures of the amount of time required for transient flow in heterogeneous porous media to effectively reach steady state, also known as the response time. Here, we develop a new approach that extends the concept of mean action time. Previous applications of the theory of mean action time to estimate the response time use the first two central moments of the probability density function associated with the transition from the initial condition, at t = 0, to the steady state condition that arises in the long time limit, as t → ∞ . This previous approach leads to a computationally convenient estimation of the response time, but the accuracy can be poor. Here, we outline a powerful extension using the first k raw moments, showing how to produce an extremely accurate estimate by making use of asymptotic properties of the cumulative distribution function. Results are validated using an existing laboratory-scale data set describing flow in a homogeneous porous medium. In addition, we demonstrate how the results also apply to flow in heterogeneous porous media. Overall, the new method is: (i) extremely accurate; and (ii) computationally inexpensive. In fact, the computational cost of the new method is orders of magnitude less than the computational effort required to study the response time by solving the transient flow equation. Furthermore, the approach provides a rigorous mathematical connection with the heuristic argument that the response time for flow in a homogeneous porous medium is proportional to L2 / D , where L is a relevant length scale, and D is the aquifer diffusivity. Here, we extend such heuristic arguments by providing a clear mathematical definition of the proportionality constant.

Genometa--a fast and accurate classifier for short metagenomic shotgun reads.

PubMed

Davenport, Colin F; Neugebauer, Jens; Beckmann, Nils; Friedrich, Benedikt; Kameri, Burim; Kokott, Svea; Paetow, Malte; Siekmann, Björn; Wieding-Drewes, Matthias; Wienhöfer, Markus; Wolf, Stefan; Tümmler, Burkhard; Ahlers, Volker; Sprengel, Frauke

2012-01-01

Metagenomic studies use high-throughput sequence data to investigate microbial communities in situ. However, considerable challenges remain in the analysis of these data, particularly with regard to speed and reliable analysis of microbial species as opposed to higher level taxa such as phyla. We here present Genometa, a computationally undemanding graphical user interface program that enables identification of bacterial species and gene content from datasets generated by inexpensive high-throughput short read sequencing technologies. Our approach was first verified on two simulated metagenomic short read datasets, detecting 100% and 94% of the bacterial species included with few false positives or false negatives. Subsequent comparative benchmarking analysis against three popular metagenomic algorithms on an Illumina human gut dataset revealed Genometa to attribute the most reads to bacteria at species level (i.e. including all strains of that species) and demonstrate similar or better accuracy than the other programs. Lastly, speed was demonstrated to be many times that of BLAST due to the use of modern short read aligners. Our method is highly accurate if bacteria in the sample are represented by genomes in the reference sequence but cannot find species absent from the reference. This method is one of the most user-friendly and resource efficient approaches and is thus feasible for rapidly analysing millions of short reads on a personal computer. The Genometa program, a step by step tutorial and Java source code are freely available from http://genomics1.mh-hannover.de/genometa/ and on http://code.google.com/p/genometa/. This program has been tested on Ubuntu Linux and Windows XP/7.

A gene expression biomarker accurately predicts estrogen receptor α modulation in a human gene expression compendium

EPA Science Inventory

The EPA’s vision for the Endocrine Disruptor Screening Program (EDSP) in the 21st Century (EDSP21) includes utilization of high-throughput screening (HTS) assays coupled with computational modeling to prioritize chemicals with the goal of eventually replacing current Tier 1...

QuartetS: A Fast and Accurate Algorithm for Large-Scale Orthology Detection

DTIC Science & Technology

2011-01-01

of these two genes with all other genes of the other one species. In addition, to be considered orthologs, the BBH pairs had to satisfy two conditions ...BBH pair computations employed as part of the outgroup and QuartetS methods, we used the same two conditions as the ones described above. In our...versus proteins. Genetica , 118, 209–216. 4. Serres,M.H., Kerr,A.R., McCormack,T.J. and Riley,M. (2009) Evolution by leaps: gene duplication in bacteria

Computer-assisted adjuncts for aneurysmal morphologic assessment: toward more precise and accurate approaches

NASA Astrophysics Data System (ADS)

Rajabzadeh-Oghaz, Hamidreza; Varble, Nicole; Davies, Jason M.; Mowla, Ashkan; Shakir, Hakeem J.; Sonig, Ashish; Shallwani, Hussain; Snyder, Kenneth V.; Levy, Elad I.; Siddiqui, Adnan H.; Meng, Hui

2017-03-01

Neurosurgeons currently base most of their treatment decisions for intracranial aneurysms (IAs) on morphological measurements made manually from 2D angiographic images. These measurements tend to be inaccurate because 2D measurements cannot capture the complex geometry of IAs and because manual measurements are variable depending on the clinician's experience and opinion. Incorrect morphological measurements may lead to inappropriate treatment strategies. In order to improve the accuracy and consistency of morphological analysis of IAs, we have developed an image-based computational tool, AView. In this study, we quantified the accuracy of computer-assisted adjuncts of AView for aneurysmal morphologic assessment by performing measurement on spheres of known size and anatomical IA models. AView has an average morphological error of 0.56% in size and 2.1% in volume measurement. We also investigate the clinical utility of this tool on a retrospective clinical dataset and compare size and neck diameter measurement between 2D manual and 3D computer-assisted measurement. The average error was 22% and 30% in the manual measurement of size and aneurysm neck diameter, respectively. Inaccuracies due to manual measurements could therefore lead to wrong treatment decisions in 44% and inappropriate treatment strategies in 33% of the IAs. Furthermore, computer-assisted analysis of IAs improves the consistency in measurement among clinicians by 62% in size and 82% in neck diameter measurement. We conclude that AView dramatically improves accuracy for morphological analysis. These results illustrate the necessity of a computer-assisted approach for the morphological analysis of IAs.

Accurate Cold-Test Model of Helical TWT Slow-Wave Circuits

NASA Technical Reports Server (NTRS)

Kory, Carol L.; Dayton, James A., Jr.

1997-01-01

Recently, a method has been established to accurately calculate cold-test data for helical slow-wave structures using the three-dimensional electromagnetic computer code, MAFIA. Cold-test parameters have been calculated for several helical traveling-wave tube (TWT) slow-wave circuits possessing various support rod configurations, and results are presented here showing excellent agreement with experiment. The helical models include tape thickness, dielectric support shapes and material properties consistent with the actual circuits. The cold-test data from this helical model can be used as input into large-signal helical TWT interaction codes making it possible, for the first time, to design a complete TWT via computer simulation.

Inferring species trees from incongruent multi-copy gene trees using the Robinson-Foulds distance

PubMed Central

2013-01-01

Background Constructing species trees from multi-copy gene trees remains a challenging problem in phylogenetics. One difficulty is that the underlying genes can be incongruent due to evolutionary processes such as gene duplication and loss, deep coalescence, or lateral gene transfer. Gene tree estimation errors may further exacerbate the difficulties of species tree estimation. Results We present a new approach for inferring species trees from incongruent multi-copy gene trees that is based on a generalization of the Robinson-Foulds (RF) distance measure to multi-labeled trees (mul-trees). We prove that it is NP-hard to compute the RF distance between two mul-trees; however, it is easy to calculate this distance between a mul-tree and a singly-labeled species tree. Motivated by this, we formulate the RF problem for mul-trees (MulRF) as follows: Given a collection of multi-copy gene trees, find a singly-labeled species tree that minimizes the total RF distance from the input mul-trees. We develop and implement a fast SPR-based heuristic algorithm for the NP-hard MulRF problem. We compare the performance of the MulRF method (available at http://genome.cs.iastate.edu/CBL/MulRF/) with several gene tree parsimony approaches using gene tree simulations that incorporate gene tree error, gene duplications and losses, and/or lateral transfer. The MulRF method produces more accurate species trees than gene tree parsimony approaches. We also demonstrate that the MulRF method infers in minutes a credible plant species tree from a collection of nearly 2,000 gene trees. Conclusions Our new phylogenetic inference method, based on a generalized RF distance, makes it possible to quickly estimate species trees from large genomic data sets. Since the MulRF method, unlike gene tree parsimony, is based on a generic tree distance measure, it is appealing for analyses of genomic data sets, in which many processes such as deep coalescence, recombination, gene duplication and losses as

Mining new crystal protein genes from Bacillus thuringiensis on the basis of mixed plasmid-enriched genome sequencing and a computational pipeline.

PubMed

Ye, Weixing; Zhu, Lei; Liu, Yingying; Crickmore, Neil; Peng, Donghai; Ruan, Lifang; Sun, Ming

2012-07-01

We have designed a high-throughput system for the identification of novel crystal protein genes (cry) from Bacillus thuringiensis strains. The system was developed with two goals: (i) to acquire the mixed plasmid-enriched genomic sequence of B. thuringiensis using next-generation sequencing biotechnology, and (ii) to identify cry genes with a computational pipeline (using BtToxin_scanner). In our pipeline method, we employed three different kinds of well-developed prediction methods, BLAST, hidden Markov model (HMM), and support vector machine (SVM), to predict the presence of Cry toxin genes. The pipeline proved to be fast (average speed, 1.02 Mb/min for proteins and open reading frames [ORFs] and 1.80 Mb/min for nucleotide sequences), sensitive (it detected 40% more protein toxin genes than a keyword extraction method using genomic sequences downloaded from GenBank), and highly specific. Twenty-one strains from our laboratory's collection were selected based on their plasmid pattern and/or crystal morphology. The plasmid-enriched genomic DNA was extracted from these strains and mixed for Illumina sequencing. The sequencing data were de novo assembled, and a total of 113 candidate cry sequences were identified using the computational pipeline. Twenty-seven candidate sequences were selected on the basis of their low level of sequence identity to known cry genes, and eight full-length genes were obtained with PCR. Finally, three new cry-type genes (primary ranks) and five cry holotypes, which were designated cry8Ac1, cry7Ha1, cry21Ca1, cry32Fa1, and cry21Da1 by the B. thuringiensis Toxin Nomenclature Committee, were identified. The system described here is both efficient and cost-effective and can greatly accelerate the discovery of novel cry genes.

LS Bound based gene selection for DNA microarray data.

PubMed

Zhou, Xin; Mao, K Z

2005-04-15

One problem with discriminant analysis of DNA microarray data is that each sample is represented by quite a large number of genes, and many of them are irrelevant, insignificant or redundant to the discriminant problem at hand. Methods for selecting important genes are, therefore, of much significance in microarray data analysis. In the present study, a new criterion, called LS Bound measure, is proposed to address the gene selection problem. The LS Bound measure is derived from leave-one-out procedure of LS-SVMs (least squares support vector machines), and as the upper bound for leave-one-out classification results it reflects to some extent the generalization performance of gene subsets. We applied this LS Bound measure for gene selection on two benchmark microarray datasets: colon cancer and leukemia. We also compared the LS Bound measure with other evaluation criteria, including the well-known Fisher's ratio and Mahalanobis class separability measure, and other published gene selection algorithms, including Weighting factor and SVM Recursive Feature Elimination. The strength of the LS Bound measure is that it provides gene subsets leading to more accurate classification results than the filter method while its computational complexity is at the level of the filter method. A companion website can be accessed at http://www.ntu.edu.sg/home5/pg02776030/lsbound/. The website contains: (1) the source code of the gene selection algorithm; (2) the complete set of tables and figures regarding the experimental study; (3) proof of the inequality (9). ekzmao@ntu.edu.sg.

Training set selection for the prediction of essential genes.

PubMed

Cheng, Jian; Xu, Zhao; Wu, Wenwu; Zhao, Li; Li, Xiangchen; Liu, Yanlin; Tao, Shiheng

2014-01-01

Various computational models have been developed to transfer annotations of gene essentiality between organisms. However, despite the increasing number of microorganisms with well-characterized sets of essential genes, selection of appropriate training sets for predicting the essential genes of poorly-studied or newly sequenced organisms remains challenging. In this study, a machine learning approach was applied reciprocally to predict the essential genes in 21 microorganisms. Results showed that training set selection greatly influenced predictive accuracy. We determined four criteria for training set selection: (1) essential genes in the selected training set should be reliable; (2) the growth conditions in which essential genes are defined should be consistent in training and prediction sets; (3) species used as training set should be closely related to the target organism; and (4) organisms used as training and prediction sets should exhibit similar phenotypes or lifestyles. We then analyzed the performance of an incomplete training set and an integrated training set with multiple organisms. We found that the size of the training set should be at least 10% of the total genes to yield accurate predictions. Additionally, the integrated training sets exhibited remarkable increase in stability and accuracy compared with single sets. Finally, we compared the performance of the integrated training sets with the four criteria and with random selection. The results revealed that a rational selection of training sets based on our criteria yields better performance than random selection. Thus, our results provide empirical guidance on training set selection for the identification of essential genes on a genome-wide scale.

LCC-Demons: a robust and accurate symmetric diffeomorphic registration algorithm.

PubMed

Lorenzi, M; Ayache, N; Frisoni, G B; Pennec, X

2013-11-01

Non-linear registration is a key instrument for computational anatomy to study the morphology of organs and tissues. However, in order to be an effective instrument for the clinical practice, registration algorithms must be computationally efficient, accurate and most importantly robust to the multiple biases affecting medical images. In this work we propose a fast and robust registration framework based on the log-Demons diffeomorphic registration algorithm. The transformation is parameterized by stationary velocity fields (SVFs), and the similarity metric implements a symmetric local correlation coefficient (LCC). Moreover, we show how the SVF setting provides a stable and consistent numerical scheme for the computation of the Jacobian determinant and the flux of the deformation across the boundaries of a given region. Thus, it provides a robust evaluation of spatial changes. We tested the LCC-Demons in the inter-subject registration setting, by comparing with state-of-the-art registration algorithms on public available datasets, and in the intra-subject longitudinal registration problem, for the statistically powered measurements of the longitudinal atrophy in Alzheimer's disease. Experimental results show that LCC-Demons is a generic, flexible, efficient and robust algorithm for the accurate non-linear registration of images, which can find several applications in the field of medical imaging. Without any additional optimization, it solves equally well intra & inter-subject registration problems, and compares favorably to state-of-the-art methods. Copyright © 2013 Elsevier Inc. All rights reserved.

Highly accurate potential energy surface for the He-H2 dimer

NASA Astrophysics Data System (ADS)

Bakr, Brandon W.; Smith, Daniel G. A.; Patkowski, Konrad

2013-10-01

A new highly accurate interaction potential is constructed for the He-H2 van der Waals complex. This potential is fitted to 1900 ab initio energies computed at the very large-basis coupled-cluster level and augmented by corrections for higher-order excitations (up to full configuration interaction level) and the diagonal Born-Oppenheimer correction. At the vibrationally averaged H-H bond length of 1.448736 bohrs, the well depth of our potential, 15.870 ± 0.065 K, is nearly 1 K larger than the most accurate previous studies have indicated. In addition to constructing our own three-dimensional potential in the van der Waals region, we present a reparameterization of the Boothroyd-Martin-Peterson potential surface [A. I. Boothroyd, P. G. Martin, and M. R. Peterson, J. Chem. Phys. 119, 3187 (2003)] that is suitable for all configurations of the triatomic system. Finally, we use the newly developed potentials to compute the properties of the lone bound states of 4He-H2 and 3He-H2 and the interaction second virial coefficient of the hydrogen-helium mixture.

Nasal computed tomography.

PubMed

Kuehn, Ned F

2006-05-01

Chronic nasal disease is often a challenge to diagnose. Computed tomography greatly enhances the ability to diagnose chronic nasal disease in dogs and cats. Nasal computed tomography provides detailed information regarding the extent of disease, accurate discrimination of neoplastic versus nonneoplastic diseases, and identification of areas of the nose to examine rhinoscopically and suspicious regions to target for biopsy.

In silico method for modelling metabolism and gene product expression at genome scale

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lerman, Joshua A.; Hyduke, Daniel R.; Latif, Haythem

2012-07-03

Transcription and translation use raw materials and energy generated metabolically to create the macromolecular machinery responsible for all cellular functions, including metabolism. A biochemically accurate model of molecular biology and metabolism will facilitate comprehensive and quantitative computations of an organism's molecular constitution as a function of genetic and environmental parameters. Here we formulate a model of metabolism and macromolecular expression. Prototyping it using the simple microorganism Thermotoga maritima, we show our model accurately simulates variations in cellular composition and gene expression. Moreover, through in silico comparative transcriptomics, the model allows the discovery of new regulons and improving the genome andmore » transcription unit annotations. Our method presents a framework for investigating molecular biology and cellular physiology in silico and may allow quantitative interpretation of multi-omics data sets in the context of an integrated biochemical description of an organism.« less

EXONSAMPLER: a computer program for genome-wide and candidate gene exon sampling for targeted next-generation sequencing.

PubMed

Cosart, Ted; Beja-Pereira, Albano; Luikart, Gordon

2014-11-01

The computer program EXONSAMPLER automates the sampling of thousands of exon sequences from publicly available reference genome sequences and gene annotation databases. It was designed to provide exon sequences for the efficient, next-generation gene sequencing method called exon capture. The exon sequences can be sampled by a list of gene name abbreviations (e.g. IFNG, TLR1), or by sampling exons from genes spaced evenly across chromosomes. It provides a list of genomic coordinates (a bed file), as well as a set of sequences in fasta format. User-adjustable parameters for collecting exon sequences include a minimum and maximum acceptable exon length, maximum number of exonic base pairs (bp) to sample per gene, and maximum total bp for the entire collection. It allows for partial sampling of very large exons. It can preferentially sample upstream (5 prime) exons, downstream (3 prime) exons, both external exons, or all internal exons. It is written in the Python programming language using its free libraries. We describe the use of EXONSAMPLER to collect exon sequences from the domestic cow (Bos taurus) genome for the design of an exon-capture microarray to sequence exons from related species, including the zebu cow and wild bison. We collected ~10% of the exome (~3 million bp), including 155 candidate genes, and ~16,000 exons evenly spaced genomewide. We prioritized the collection of 5 prime exons to facilitate discovery and genotyping of SNPs near upstream gene regulatory DNA sequences, which control gene expression and are often under natural selection. © 2014 John Wiley & Sons Ltd.

Covariance approximation for fast and accurate computation of channelized Hotelling observer statistics

NASA Astrophysics Data System (ADS)

Bonetto, P.; Qi, Jinyi; Leahy, R. M.

2000-08-01

Describes a method for computing linear observer statistics for maximum a posteriori (MAP) reconstructions of PET images. The method is based on a theoretical approximation for the mean and covariance of MAP reconstructions. In particular, the authors derive here a closed form for the channelized Hotelling observer (CHO) statistic applied to 2D MAP images. The theoretical analysis models both the Poission statistics of PET data and the inhomogeneity of tracer uptake. The authors show reasonably good correspondence between these theoretical results and Monte Carlo studies. The accuracy and low computational cost of the approximation allow the authors to analyze the observer performance over a wide range of operating conditions and parameter settings for the MAP reconstruction algorithm.

Tentacle: distributed quantification of genes in metagenomes.

PubMed

Boulund, Fredrik; Sjögren, Anders; Kristiansson, Erik

2015-01-01

In metagenomics, microbial communities are sequenced at increasingly high resolution, generating datasets with billions of DNA fragments. Novel methods that can efficiently process the growing volumes of sequence data are necessary for the accurate analysis and interpretation of existing and upcoming metagenomes. Here we present Tentacle, which is a novel framework that uses distributed computational resources for gene quantification in metagenomes. Tentacle is implemented using a dynamic master-worker approach in which DNA fragments are streamed via a network and processed in parallel on worker nodes. Tentacle is modular, extensible, and comes with support for six commonly used sequence aligners. It is easy to adapt Tentacle to different applications in metagenomics and easy to integrate into existing workflows. Evaluations show that Tentacle scales very well with increasing computing resources. We illustrate the versatility of Tentacle on three different use cases. Tentacle is written for Linux in Python 2.7 and is published as open source under the GNU General Public License (v3). Documentation, tutorials, installation instructions, and the source code are freely available online at: http://bioinformatics.math.chalmers.se/tentacle.

A guide to best practices for Gene Ontology (GO) manual annotation

PubMed Central

Balakrishnan, Rama; Harris, Midori A.; Huntley, Rachael; Van Auken, Kimberly; Cherry, J. Michael

2013-01-01

The Gene Ontology Consortium (GOC) is a community-based bioinformatics project that classifies gene product function through the use of structured controlled vocabularies. A fundamental application of the Gene Ontology (GO) is in the creation of gene product annotations, evidence-based associations between GO definitions and experimental or sequence-based analysis. Currently, the GOC disseminates 126 million annotations covering >374 000 species including all the kingdoms of life. This number includes two classes of GO annotations: those created manually by experienced biocurators reviewing the literature or by examination of biological data (1.1 million annotations covering 2226 species) and those generated computationally via automated methods. As manual annotations are often used to propagate functional predictions between related proteins within and between genomes, it is critical to provide accurate consistent manual annotations. Toward this goal, we present here the conventions defined by the GOC for the creation of manual annotation. This guide represents the best practices for manual annotation as established by the GOC project over the past 12 years. We hope this guide will encourage research communities to annotate gene products of their interest to enhance the corpus of GO annotations available to all. Database URL: http://www.geneontology.org PMID:23842463

Accurate Phylogenetic Tree Reconstruction from Quartets: A Heuristic Approach

PubMed Central

Reaz, Rezwana; Bayzid, Md. Shamsuzzoha; Rahman, M. Sohel

2014-01-01

Supertree methods construct trees on a set of taxa (species) combining many smaller trees on the overlapping subsets of the entire set of taxa. A ‘quartet’ is an unrooted tree over taxa, hence the quartet-based supertree methods combine many -taxon unrooted trees into a single and coherent tree over the complete set of taxa. Quartet-based phylogeny reconstruction methods have been receiving considerable attentions in the recent years. An accurate and efficient quartet-based method might be competitive with the current best phylogenetic tree reconstruction methods (such as maximum likelihood or Bayesian MCMC analyses), without being as computationally intensive. In this paper, we present a novel and highly accurate quartet-based phylogenetic tree reconstruction method. We performed an extensive experimental study to evaluate the accuracy and scalability of our approach on both simulated and biological datasets. PMID:25117474

Light Field Imaging Based Accurate Image Specular Highlight Removal

PubMed Central

Wang, Haoqian; Xu, Chenxue; Wang, Xingzheng; Zhang, Yongbing; Peng, Bo

2016-01-01

Specular reflection removal is indispensable to many computer vision tasks. However, most existing methods fail or degrade in complex real scenarios for their individual drawbacks. Benefiting from the light field imaging technology, this paper proposes a novel and accurate approach to remove specularity and improve image quality. We first capture images with specularity by the light field camera (Lytro ILLUM). After accurately estimating the image depth, a simple and concise threshold strategy is adopted to cluster the specular pixels into “unsaturated” and “saturated” category. Finally, a color variance analysis of multiple views and a local color refinement are individually conducted on the two categories to recover diffuse color information. Experimental evaluation by comparison with existed methods based on our light field dataset together with Stanford light field archive verifies the effectiveness of our proposed algorithm. PMID:27253083

Accurate first-principles structures and energies of diversely bonded systems from an efficient density functional

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun, Jianwei; Remsing, Richard C.; Zhang, Yubo

2016-06-13

One atom or molecule binds to another through various types of bond, the strengths of which range from several meV to several eV. Although some computational methods can provide accurate descriptions of all bond types, those methods are not efficient enough for many studies (for example, large systems, ab initio molecular dynamics and high-throughput searches for functional materials). Here, we show that the recently developed non-empirical strongly constrained and appropriately normed (SCAN) meta-generalized gradient approximation (meta-GGA) within the density functional theory framework predicts accurate geometries and energies of diversely bonded molecules and materials (including covalent, metallic, ionic, hydrogen and vanmore » der Waals bonds). This represents a significant improvement at comparable efficiency over its predecessors, the GGAs that currently dominate materials computation. Often, SCAN matches or improves on the accuracy of a computationally expensive hybrid functional, at almost-GGA cost. SCAN is therefore expected to have a broad impact on chemistry and materials science.« less

Accurate first-principles structures and energies of diversely bonded systems from an efficient density functional.

PubMed

Sun, Jianwei; Remsing, Richard C; Zhang, Yubo; Sun, Zhaoru; Ruzsinszky, Adrienn; Peng, Haowei; Yang, Zenghui; Paul, Arpita; Waghmare, Umesh; Wu, Xifan; Klein, Michael L; Perdew, John P

2016-09-01

One atom or molecule binds to another through various types of bond, the strengths of which range from several meV to several eV. Although some computational methods can provide accurate descriptions of all bond types, those methods are not efficient enough for many studies (for example, large systems, ab initio molecular dynamics and high-throughput searches for functional materials). Here, we show that the recently developed non-empirical strongly constrained and appropriately normed (SCAN) meta-generalized gradient approximation (meta-GGA) within the density functional theory framework predicts accurate geometries and energies of diversely bonded molecules and materials (including covalent, metallic, ionic, hydrogen and van der Waals bonds). This represents a significant improvement at comparable efficiency over its predecessors, the GGAs that currently dominate materials computation. Often, SCAN matches or improves on the accuracy of a computationally expensive hybrid functional, at almost-GGA cost. SCAN is therefore expected to have a broad impact on chemistry and materials science.

BASIC: A Simple and Accurate Modular DNA Assembly Method.

PubMed

Storch, Marko; Casini, Arturo; Mackrow, Ben; Ellis, Tom; Baldwin, Geoff S

2017-01-01

Biopart Assembly Standard for Idempotent Cloning (BASIC) is a simple, accurate, and robust DNA assembly method. The method is based on linker-mediated DNA assembly and provides highly accurate DNA assembly with 99 % correct assemblies for four parts and 90 % correct assemblies for seven parts [1]. The BASIC standard defines a single entry vector for all parts flanked by the same prefix and suffix sequences and its idempotent nature means that the assembled construct is returned in the same format. Once a part has been adapted into the BASIC format it can be placed at any position within a BASIC assembly without the need for reformatting. This allows laboratories to grow comprehensive and universal part libraries and to share them efficiently. The modularity within the BASIC framework is further extended by the possibility of encoding ribosomal binding sites (RBS) and peptide linker sequences directly on the linkers used for assembly. This makes BASIC a highly versatile library construction method for combinatorial part assembly including the construction of promoter, RBS, gene variant, and protein-tag libraries. In comparison with other DNA assembly standards and methods, BASIC offers a simple robust protocol; it relies on a single entry vector, provides for easy hierarchical assembly, and is highly accurate for up to seven parts per assembly round [2].

Accuracy and speed in computing the Chebyshev collocation derivative

NASA Technical Reports Server (NTRS)

Don, Wai-Sun; Solomonoff, Alex

1991-01-01

We studied several algorithms for computing the Chebyshev spectral derivative and compare their roundoff error. For a large number of collocation points, the elements of the Chebyshev differentiation matrix, if constructed in the usual way, are not computed accurately. A subtle cause is is found to account for the poor accuracy when computing the derivative by the matrix-vector multiplication method. Methods for accurately computing the elements of the matrix are presented, and we find that if the entities of the matrix are computed accurately, the roundoff error of the matrix-vector multiplication is as small as that of the transform-recursion algorithm. Results of CPU time usage are shown for several different algorithms for computing the derivative by the Chebyshev collocation method for a wide variety of two-dimensional grid sizes on both an IBM and a Cray 2 computer. We found that which algorithm is fastest on a particular machine depends not only on the grid size, but also on small details of the computer hardware as well. For most practical grid sizes used in computation, the even-odd decomposition algorithm is found to be faster than the transform-recursion method.

Prediction of regulatory gene pairs using dynamic time warping and gene ontology.

PubMed

Yang, Andy C; Hsu, Hui-Huang; Lu, Ming-Da; Tseng, Vincent S; Shih, Timothy K

2014-01-01

Selecting informative genes is the most important task for data analysis on microarray gene expression data. In this work, we aim at identifying regulatory gene pairs from microarray gene expression data. However, microarray data often contain multiple missing expression values. Missing value imputation is thus needed before further processing for regulatory gene pairs becomes possible. We develop a novel approach to first impute missing values in microarray time series data by combining k-Nearest Neighbour (KNN), Dynamic Time Warping (DTW) and Gene Ontology (GO). After missing values are imputed, we then perform gene regulation prediction based on our proposed DTW-GO distance measurement of gene pairs. Experimental results show that our approach is more accurate when compared with existing missing value imputation methods on real microarray data sets. Furthermore, our approach can also discover more regulatory gene pairs that are known in the literature than other methods.

A Comprehensive Strategy for Accurate Mutation Detection of the Highly Homologous PMS2.

PubMed

Li, Jianli; Dai, Hongzheng; Feng, Yanming; Tang, Jia; Chen, Stella; Tian, Xia; Gorman, Elizabeth; Schmitt, Eric S; Hansen, Terah A A; Wang, Jing; Plon, Sharon E; Zhang, Victor Wei; Wong, Lee-Jun C

2015-09-01

Germline mutations in the DNA mismatch repair gene PMS2 underlie the cancer susceptibility syndrome, Lynch syndrome. However, accurate molecular testing of PMS2 is complicated by a large number of highly homologous sequences. To establish a comprehensive approach for mutation detection of PMS2, we have designed a strategy combining targeted capture next-generation sequencing (NGS), multiplex ligation-dependent probe amplification, and long-range PCR followed by NGS to simultaneously detect point mutations and copy number changes of PMS2. Exonic deletions (E2 to E9, E5 to E9, E8, E10, E14, and E1 to E15), duplications (E11 to E12), and a nonsense mutation, p.S22*, were identified. Traditional multiplex ligation-dependent probe amplification and Sanger sequencing approaches cannot differentiate the origin of the exonic deletions in the 3' region when PMS2 and PMS2CL share identical sequences as a result of gene conversion. Our approach allows unambiguous identification of mutations in the active gene with a straightforward long-range-PCR/NGS method. Breakpoint analysis of multiple samples revealed that recurrent exon 14 deletions are mediated by homologous Alu sequences. Our comprehensive approach provides a reliable tool for accurate molecular analysis of genes containing multiple copies of highly homologous sequences and should improve PMS2 molecular analysis for patients with Lynch syndrome. Copyright © 2015 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.

Accurately Assessing the Risk of Schizophrenia Conferred by Rare Copy-Number Variation Affecting Genes with Brain Function

PubMed Central

Raychaudhuri, Soumya; Korn, Joshua M.; McCarroll, Steven A.; Altshuler, David; Sklar, Pamela; Purcell, Shaun; Daly, Mark J.

2010-01-01

Investigators have linked rare copy number variation (CNVs) to neuropsychiatric diseases, such as schizophrenia. One hypothesis is that CNV events cause disease by affecting genes with specific brain functions. Under these circumstances, we expect that CNV events in cases should impact brain-function genes more frequently than those events in controls. Previous publications have applied “pathway” analyses to genes within neuropsychiatric case CNVs to show enrichment for brain-functions. While such analyses have been suggestive, they often have not rigorously compared the rates of CNVs impacting genes with brain function in cases to controls, and therefore do not address important confounders such as the large size of brain genes and overall differences in rates and sizes of CNVs. To demonstrate the potential impact of confounders, we genotyped rare CNV events in 2,415 unaffected controls with Affymetrix 6.0; we then applied standard pathway analyses using four sets of brain-function genes and observed an apparently highly significant enrichment for each set. The enrichment is simply driven by the large size of brain-function genes. Instead, we propose a case-control statistical test, cnv-enrichment-test, to compare the rate of CNVs impacting specific gene sets in cases versus controls. With simulations, we demonstrate that cnv-enrichment-test is robust to case-control differences in CNV size, CNV rate, and systematic differences in gene size. Finally, we apply cnv-enrichment-test to rare CNV events published by the International Schizophrenia Consortium (ISC). This approach reveals nominal evidence of case-association in neuronal-activity and the learning gene sets, but not the other two examined gene sets. The neuronal-activity genes have been associated in a separate set of schizophrenia cases and controls; however, testing in independent samples is necessary to definitively confirm this association. Our method is implemented in the PLINK software package

Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

PubMed Central

2009-01-01

Background The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes. Results We constructed a decision tree-based meta-classifier and trained it on datasets with individual and grouped attributes-network topological features, cellular compartments and biological processes-to generate various predictors of essential genes. We showed that the predictors with better performances are those generated by datasets with integrated attributes. Using the predictor with all attributes, i.e., network topological features, cellular compartments and biological processes, we obtained the best predictor of essential genes that was then used to classify yeast genes with unknown essentiality status. Finally, we generated decision trees by training the J48 algorithm on datasets with all network topological features, cellular localization and biological process information to discover cellular rules for essentiality. We found that the number of protein physical interactions, the nuclear localization of proteins and the number of regulating transcription factors are the most important factors determining gene essentiality. Conclusion We were able to demonstrate that network topological features, cellular localization and biological process information are reliable predictors of essential genes. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing essentiality. PMID:19758426

Fuzzy measures on the Gene Ontology for gene product similarity.

PubMed

Popescu, Mihail; Keller, James M; Mitchell, Joyce A

2006-01-01

One of the most important objects in bioinformatics is a gene product (protein or RNA). For many gene products, functional information is summarized in a set of Gene Ontology (GO) annotations. For these genes, it is reasonable to include similarity measures based on the terms found in the GO or other taxonomy. In this paper, we introduce several novel measures for computing the similarity of two gene products annotated with GO terms. The fuzzy measure similarity (FMS) has the advantage that it takes into consideration the context of both complete sets of annotation terms when computing the similarity between two gene products. When the two gene products are not annotated by common taxonomy terms, we propose a method that avoids a zero similarity result. To account for the variations in the annotation reliability, we propose a similarity measure based on the Choquet integral. These similarity measures provide extra tools for the biologist in search of functional information for gene products. The initial testing on a group of 194 sequences representing three proteins families shows a higher correlation of the FMS and Choquet similarities to the BLAST sequence similarities than the traditional similarity measures such as pairwise average or pairwise maximum.

Multi-Physics Computational Grains (MPCGs): Newly-Developed Accurate and Efficient Numerical Methods for Micromechanical Modeling of Multifunctional Materials and Composites

NASA Astrophysics Data System (ADS)

Bishay, Peter L.

This study presents a new family of highly accurate and efficient computational methods for modeling the multi-physics of multifunctional materials and composites in the micro-scale named "Multi-Physics Computational Grains" (MPCGs). Each "mathematical grain" has a random polygonal/polyhedral geometrical shape that resembles the natural shapes of the material grains in the micro-scale where each grain is surrounded by an arbitrary number of neighboring grains. The physics that are incorporated in this study include: Linear Elasticity, Electrostatics, Magnetostatics, Piezoelectricity, Piezomagnetism and Ferroelectricity. However, the methods proposed here can be extended to include more physics (thermo-elasticity, pyroelectricity, electric conduction, heat conduction, etc.) in their formulation, different analysis types (dynamics, fracture, fatigue, etc.), nonlinearities, different defect shapes, and some of the 2D methods can also be extended to 3D formulation. We present "Multi-Region Trefftz Collocation Grains" (MTCGs) as a simple and efficient method for direct and inverse problems, "Trefftz-Lekhnitskii Computational Gains" (TLCGs) for modeling porous and composite smart materials, "Hybrid Displacement Computational Grains" (HDCGs) as a general method for modeling multifunctional materials and composites, and finally "Radial-Basis-Functions Computational Grains" (RBFCGs) for modeling functionally-graded materials, magneto-electro-elastic (MEE) materials and the switching phenomena in ferroelectric materials. The first three proposed methods are suitable for direct numerical simulation (DNS) of the micromechanics of smart composite/porous materials with non-symmetrical arrangement of voids/inclusions, and provide minimal effort in meshing and minimal time in computations, since each grain can represent the matrix of a composite and can include a pore or an inclusion. The last three methods provide stiffness matrix in their formulation and hence can be readily

Enzymic colorimetry-based DNA chip: a rapid and accurate assay for detecting mutations for clarithromycin resistance in the 23S rRNA gene of Helicobacter pylori.

PubMed

Xuan, Shi-Hai; Zhou, Yu-Gui; Shao, Bo; Cui, Ya-Lin; Li, Jian; Yin, Hong-Bo; Song, Xiao-Ping; Cong, Hui; Jing, Feng-Xiang; Jin, Qing-Hui; Wang, Hui-Min; Zhou, Jie

2009-11-01

Macrolide drugs, such as clarithromycin (CAM), are a key component of many combination therapies used to eradicate Helicobacter pylori. However, resistance to CAM is increasing in H. pylori and is becoming a serious problem in H. pylori eradication therapy. CAM resistance in H. pylori is mostly due to point mutations (A2142G/C, A2143G) in the peptidyltransferase-encoding region of the 23S rRNA gene. In this study an enzymic colorimetry-based DNA chip was developed to analyse single-nucleotide polymorphisms of the 23S rRNA gene to determine the prevalence of mutations in CAM-related resistance in H. pylori-positive patients. The results of the colorimetric DNA chip were confirmed by direct DNA sequencing. In 63 samples, the incidence of the A2143G mutation was 17.46 % (11/63). The results of the colorimetric DNA chip were concordant with DNA sequencing in 96.83 % of results (61/63). The colorimetric DNA chip could detect wild-type and mutant signals at every site, even at a DNA concentration of 1.53 x 10(2) copies microl(-1). Thus, the colorimetric DNA chip is a reliable assay for rapid and accurate detection of mutations in the 23S rRNA gene of H. pylori that lead to CAM-related resistance, directly from gastric tissues.

Development of a computer code for calculating the steady super/hypersonic inviscid flow around real configurations. Volume 1: Computational technique

NASA Technical Reports Server (NTRS)

Marconi, F.; Salas, M.; Yaeger, L.

1976-01-01

A numerical procedure has been developed to compute the inviscid super/hypersonic flow field about complex vehicle geometries accurately and efficiently. A second order accurate finite difference scheme is used to integrate the three dimensional Euler equations in regions of continuous flow, while all shock waves are computed as discontinuities via the Rankine Hugoniot jump conditions. Conformal mappings are used to develop a computational grid. The effects of blunt nose entropy layers are computed in detail. Real gas effects for equilibrium air are included using curve fits of Mollier charts. Typical calculated results for shuttle orbiter, hypersonic transport, and supersonic aircraft configurations are included to demonstrate the usefulness of this tool.

Efficient Exploration of the Space of Reconciled Gene Trees

PubMed Central

Szöllősi, Gergely J.; Rosikiewicz, Wojciech; Boussau, Bastien; Tannier, Eric; Daubin, Vincent

2013-01-01

Gene trees record the combination of gene-level events, such as duplication, transfer and loss (DTL), and species-level events, such as speciation and extinction. Gene tree–species tree reconciliation methods model these processes by drawing gene trees into the species tree using a series of gene and species-level events. The reconstruction of gene trees based on sequence alone almost always involves choosing between statistically equivalent or weakly distinguishable relationships that could be much better resolved based on a putative species tree. To exploit this potential for accurate reconstruction of gene trees, the space of reconciled gene trees must be explored according to a joint model of sequence evolution and gene tree–species tree reconciliation. Here we present amalgamated likelihood estimation (ALE), a probabilistic approach to exhaustively explore all reconciled gene trees that can be amalgamated as a combination of clades observed in a sample of gene trees. We implement the ALE approach in the context of a reconciliation model (Szöllősi et al. 2013), which allows for the DTL of genes. We use ALE to efficiently approximate the sum of the joint likelihood over amalgamations and to find the reconciled gene tree that maximizes the joint likelihood among all such trees. We demonstrate using simulations that gene trees reconstructed using the joint likelihood are substantially more accurate than those reconstructed using sequence alone. Using realistic gene tree topologies, branch lengths, and alignment sizes, we demonstrate that ALE produces more accurate gene trees even if the model of sequence evolution is greatly simplified. Finally, examining 1099 gene families from 36 cyanobacterial genomes we find that joint likelihood-based inference results in a striking reduction in apparent phylogenetic discord, with respectively. 24%, 59%, and 46% reductions in the mean numbers of duplications, transfers, and losses per gene family. The open source

Robust and accurate vectorization of line drawings.

PubMed

Hilaire, Xavier; Tombre, Karl

2006-06-01

This paper presents a method for vectorizing the graphical parts of paper-based line drawings. The method consists of separating the input binary image into layers of homogeneous thickness, skeletonizing each layer, segmenting the skeleton by a method based on random sampling, and simplifying the result. The segmentation method is robust with a best bound of 50 percent noise reached for indefinitely long primitives. Accurate estimation of the recognized vector's parameters is enabled by explicitly computing their feasibility domains. Theoretical performance analysis and expression of the complexity of the segmentation method are derived. Experimental results and comparisons with other vectorization systems are also provided.

Application of CT-PSF-based computer-simulated lung nodules for evaluating the accuracy of computer-aided volumetry.

PubMed

Funaki, Ayumu; Ohkubo, Masaki; Wada, Shinichi; Murao, Kohei; Matsumoto, Toru; Niizuma, Shinji

2012-07-01

With the wide dissemination of computed tomography (CT) screening for lung cancer, measuring the nodule volume accurately with computer-aided volumetry software is increasingly important. Many studies for determining the accuracy of volumetry software have been performed using a phantom with artificial nodules. These phantom studies are limited, however, in their ability to reproduce the nodules both accurately and in the variety of sizes and densities required. Therefore, we propose a new approach of using computer-simulated nodules based on the point spread function measured in a CT system. The validity of the proposed method was confirmed by the excellent agreement obtained between computer-simulated nodules and phantom nodules regarding the volume measurements. A practical clinical evaluation of the accuracy of volumetry software was achieved by adding simulated nodules onto clinical lung images, including noise and artifacts. The tested volumetry software was revealed to be accurate within an error of 20 % for nodules >5 mm and with the difference between nodule density and background (lung) (CT value) being 400-600 HU. Such a detailed analysis can provide clinically useful information on the use of volumetry software in CT screening for lung cancer. We concluded that the proposed method is effective for evaluating the performance of computer-aided volumetry software.

Accurate traveltime computation in complex anisotropic media with discontinuous Galerkin method

NASA Astrophysics Data System (ADS)

Le Bouteiller, P.; Benjemaa, M.; Métivier, L.; Virieux, J.

2017-12-01

Travel time computation is of major interest for a large range of geophysical applications, among which source localization and characterization, phase identification, data windowing and tomography, from decametric scale up to global Earth scale.Ray-tracing tools, being essentially 1D Lagrangian integration along a path, have been used for their efficiency but present some drawbacks, such as a rather difficult control of the medium sampling. Moreover, they do not provide answers in shadow zones. Eikonal solvers, based on an Eulerian approach, have attracted attention in seismology with the pioneering work of Vidale (1988), while such approach has been proposed earlier by Riznichenko (1946). They have been used now for first-arrival travel-time tomography at various scales (Podvin & Lecomte (1991). The framework for solving this non-linear partial differential equation is now well understood and various finite-difference approaches have been proposed, essentially for smooth media. We propose a novel finite element approach which builds a precise solution for strongly heterogeneous anisotropic medium (still in the limit of Eikonal validity). The discontinuous Galerkin method we have developed allows local refinement of the mesh and local high orders of interpolation inside elements. High precision of the travel times and its spatial derivatives is obtained through this formulation. This finite element method also honors boundary conditions, such as complex topographies and absorbing boundaries for mimicking an infinite medium. Applications from travel-time tomography, slope tomography are expected, but also for migration and take-off angles estimation, thanks to the accuracy obtained when computing first-arrival times.References:Podvin, P. and Lecomte, I., 1991. Finite difference computation of traveltimes in very contrasted velocity model: a massively parallel approach and its associated tools, Geophys. J. Int., 105, 271-284.Riznichenko, Y., 1946. Geometrical

An accurate method for computer-generating tungsten anode x-ray spectra from 30 to 140 kV.

PubMed

Boone, J M; Seibert, J A

1997-11-01

A tungsten anode spectral model using interpolating polynomials (TASMIP) was used to compute x-ray spectra at 1 keV intervals over the range from 30 kV to 140 kV. The TASMIP is not semi-empirical and uses no physical assumptions regarding x-ray production, but rather interpolates measured constant potential x-ray spectra published by Fewell et al. [Handbook of Computed Tomography X-ray Spectra (U.S. Government Printing Office, Washington, D.C., 1981)]. X-ray output measurements (mR/mAs measured at 1 m) were made on a calibrated constant potential generator in our laboratory from 50 kV to 124 kV, and with 0-5 mm added aluminum filtration. The Fewell spectra were slightly modified (numerically hardened) and normalized based on the attenuation and output characteristics of a constant potential generator and metal-insert x-ray tube in our laboratory. Then, using the modified Fewell spectra of different kVs, the photon fluence phi at each 1 keV energy bin (E) over energies from 10 keV to 140 keV was characterized using polynomial functions of the form phi (E) = a0[E] + a1[E] kV + a2[E] kV2 + ... + a(n)[E] kVn. A total of 131 polynomial functions were used to calculate accurate x-ray spectra, each function requiring between two and four terms. The resulting TASMIP algorithm produced x-ray spectra that match both the quality and quantity characteristics of the x-ray system in our laboratory. For photon fluences above 10% of the peak fluence in the spectrum, the average percent difference (and standard deviation) between the modified Fewell spectra and the TASMIP photon fluence was -1.43% (3.8%) for the 50 kV spectrum, -0.89% (1.37%) for the 70 kV spectrum, and for the 80, 90, 100, 110, 120, 130 and 140 kV spectra, the mean differences between spectra were all less than 0.20% and the standard deviations were less than approximately 1.1%. The model was also extended to include the effects of generator-induced kV ripple. Finally, the x-ray photon fluence in the units of

Stable and Spectrally Accurate Schemes for the Navier-Stokes Equations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jia, Jun; Liu, Jie

2011-01-01

In this paper, we present an accurate, efficient and stable numerical method for the incompressible Navier-Stokes equations (NSEs). The method is based on (1) an equivalent pressure Poisson equation formulation of the NSE with proper pressure boundary conditions, which facilitates the design of high-order and stable numerical methods, and (2) the Krylov deferred correction (KDC) accelerated method of lines transpose (mbox MoL{sup T}), which is very stable, efficient, and of arbitrary order in time. Numerical tests with known exact solutions in three dimensions show that the new method is spectrally accurate in time, and a numerical order of convergence 9more » was observed. Two-dimensional computational results of flow past a cylinder and flow in a bifurcated tube are also reported.« less

3ARM: A Fast, Accurate Radiative Transfer Model for Use in Climate Models

NASA Technical Reports Server (NTRS)

Bergstrom, R. W.; Kinne, S.; Sokolik, I. N.; Toon, O. B.; Mlawer, E. J.; Clough, S. A.; Ackerman, T. P.; Mather, J.

1996-01-01

A new radiative transfer model combining the efforts of three groups of researchers is discussed. The model accurately computes radiative transfer in a inhomogeneous absorbing, scattering and emitting atmospheres. As an illustration of the model, results are shown for the effects of dust on the thermal radiation.

3ARM: A Fast, Accurate Radiative Transfer Model for use in Climate Models

NASA Technical Reports Server (NTRS)

Bergstrom, R. W.; Kinne, S.; Sokolik, I. N.; Toon, O. B.; Mlawer, E. J.; Clough, S. A.; Ackerman, T. P.; Mather, J.

1996-01-01

A new radiative transfer model combining the efforts of three groups of researchers is discussed. The model accurately computes radiative transfer in a inhomogeneous absorbing, scattering and emitting atmospheres. As an illustration of the model, results are shown for the effects of dust on the thermal radiation.

3ARM: A Fast, Accurate Radiative Transfer Model For Use in Climate Models

NASA Technical Reports Server (NTRS)

Bergstrom, R. W.; Kinne, S.; Sokolik, I. N.; Toon, O. B.; Mlawer, E. J.; Clough, S. A.; Ackerman, T. P.; Mather, J.

1996-01-01

A new radiative transfer model combining the efforts of three groups of researchers is discussed. The model accurately computes radiative transfer in a inhomogeneous absorbing, scattering and emitting atmospheres. As an illustration of the model, results are shown for the effects of dust on the thermal radiation.

Fast and accurate reference-free alignment of subtomograms.

PubMed

Chen, Yuxiang; Pfeffer, Stefan; Hrabe, Thomas; Schuller, Jan Michael; Förster, Friedrich

2013-06-01

In cryoelectron tomography alignment and averaging of subtomograms, each dnepicting the same macromolecule, improves the resolution compared to the individual subtomogram. Major challenges of subtomogram alignment are noise enhancement due to overfitting, the bias of an initial reference in the iterative alignment process, and the computational cost of processing increasingly large amounts of data. Here, we propose an efficient and accurate alignment algorithm via a generalized convolution theorem, which allows computation of a constrained correlation function using spherical harmonics. This formulation increases computational speed of rotational matching dramatically compared to rotation search in Cartesian space without sacrificing accuracy in contrast to other spherical harmonic based approaches. Using this sampling method, a reference-free alignment procedure is proposed to tackle reference bias and overfitting, which also includes contrast transfer function correction by Wiener filtering. Application of the method to simulated data allowed us to obtain resolutions near the ground truth. For two experimental datasets, ribosomes from yeast lysate and purified 20S proteasomes, we achieved reconstructions of approximately 20Å and 16Å, respectively. The software is ready-to-use and made public to the community. Copyright © 2013 Elsevier Inc. All rights reserved.

Time-Accurate Simulations and Acoustic Analysis of Slat Free-Shear Layer

NASA Technical Reports Server (NTRS)

Khorrami, Mehdi R.; Singer, Bart A.; Berkman, Mert E.

2001-01-01

A detailed computational aeroacoustic analysis of a high-lift flow field is performed. Time-accurate Reynolds Averaged Navier-Stokes (RANS) computations simulate the free shear layer that originates from the slat cusp. Both unforced and forced cases are studied. Preliminary results show that the shear layer is a good amplifier of disturbances in the low to mid-frequency range. The Ffowcs-Williams and Hawkings equation is solved to determine the acoustic field using the unsteady flow data from the RANS calculations. The noise radiated from the excited shear layer has a spectral shape qualitatively similar to that obtained from measurements in a corresponding experimental study of the high-lift system.

Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants

DOE PAGES

Schläpfer, Pascal; Zhang, Peifen; Wang, Chuan; ...

2017-04-01

Plant metabolism underpins many traits of ecological and agronomic importance. Plants produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have not yet been elucidated. To engineer and improve metabolic traits, we will need comprehensive and accurate knowledge of the organization and regulation of plant metabolism at the genome scale. Here, we present a computational pipeline to identify metabolic enzymes, pathways, and gene clusters from a sequenced genome. Using this pipeline, we generated metabolic pathway databases for 22 species and identified metabolic gene clusters from 18 species. This unified resource can bemore » used to conduct a wide array of comparative studies of plant metabolism. Using the resource, we discovered a widespread occurrence of metabolic gene clusters in plants: 11,969 clusters from 18 species. The prevalence of metabolic gene clusters offers an intriguing possibility of an untapped source for uncovering new metabolite biosynthesis pathways. For example, more than 1,700 clusters contain enzymes that could generate a specialized metabolite scaffold (signature enzymes) and enzymes that modify the scaffold (tailoring enzymes). In four species with sufficient gene expression data, we identified 43 highly coexpressed clusters that contain signature and tailoring enzymes, of which eight were characterized previously to be functional pathways. Finally, we identified patterns of genome organization that implicate local gene duplication and, to a lesser extent, single gene transposition as having played roles in the evolution of plant metabolic gene clusters.« less

Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schläpfer, Pascal; Zhang, Peifen; Wang, Chuan

Plant metabolism underpins many traits of ecological and agronomic importance. Plants produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have not yet been elucidated. To engineer and improve metabolic traits, we will need comprehensive and accurate knowledge of the organization and regulation of plant metabolism at the genome scale. Here, we present a computational pipeline to identify metabolic enzymes, pathways, and gene clusters from a sequenced genome. Using this pipeline, we generated metabolic pathway databases for 22 species and identified metabolic gene clusters from 18 species. This unified resource can bemore » used to conduct a wide array of comparative studies of plant metabolism. Using the resource, we discovered a widespread occurrence of metabolic gene clusters in plants: 11,969 clusters from 18 species. The prevalence of metabolic gene clusters offers an intriguing possibility of an untapped source for uncovering new metabolite biosynthesis pathways. For example, more than 1,700 clusters contain enzymes that could generate a specialized metabolite scaffold (signature enzymes) and enzymes that modify the scaffold (tailoring enzymes). In four species with sufficient gene expression data, we identified 43 highly coexpressed clusters that contain signature and tailoring enzymes, of which eight were characterized previously to be functional pathways. Finally, we identified patterns of genome organization that implicate local gene duplication and, to a lesser extent, single gene transposition as having played roles in the evolution of plant metabolic gene clusters.« less

Multiplex-PCR-Based Screening and Computational Modeling of Virulence Factors and T-Cell Mediated Immunity in Helicobacter pylori Infections for Accurate Clinical Diagnosis.

PubMed

Oktem-Okullu, Sinem; Tiftikci, Arzu; Saruc, Murat; Cicek, Bahattin; Vardareli, Eser; Tozun, Nurdan; Kocagoz, Tanil; Sezerman, Ugur; Yavuz, Ahmet Sinan; Sayi-Yazgan, Ayca

2015-01-01

The outcome of H. pylori infection is closely related with bacteria's virulence factors and host immune response. The association between T cells and H. pylori infection has been identified, but the effects of the nine major H. pylori specific virulence factors; cagA, vacA, oipA, babA, hpaA, napA, dupA, ureA, ureB on T cell response in H. pylori infected patients have not been fully elucidated. We developed a multiplex- PCR assay to detect nine H. pylori virulence genes with in a three PCR reactions. Also, the expression levels of Th1, Th17 and Treg cell specific cytokines and transcription factors were detected by using qRT-PCR assays. Furthermore, a novel expert derived model is developed to identify set of factors and rules that can distinguish the ulcer patients from gastritis patients. Within all virulence factors that we tested, we identified a correlation between the presence of napA virulence gene and ulcer disease as a first data. Additionally, a positive correlation between the H. pylori dupA virulence factor and IFN-γ, and H. pylori babA virulence factor and IL-17 was detected in gastritis and ulcer patients respectively. By using computer-based models, clinical outcomes of a patients infected with H. pylori can be predicted by screening the patient's H. pylori vacA m1/m2, ureA and cagA status and IFN-γ (Th1), IL-17 (Th17), and FOXP3 (Treg) expression levels. Herein, we report, for the first time, the relationship between H. pylori virulence factors and host immune responses for diagnostic prediction of gastric diseases using computer-based models.

Computing Life

ERIC Educational Resources Information Center

National Institute of General Medical Sciences (NIGMS), 2009

2009-01-01

Computer advances now let researchers quickly search through DNA sequences to find gene variations that could lead to disease, simulate how flu might spread through one's school, and design three-dimensional animations of molecules that rival any video game. By teaming computers and biology, scientists can answer new and old questions that could…

An extended set of yeast-based functional assays accurately identifies human disease mutations

PubMed Central

Sun, Song; Yang, Fan; Tan, Guihong; Costanzo, Michael; Oughtred, Rose; Hirschman, Jodi; Theesfeld, Chandra L.; Bansal, Pritpal; Sahni, Nidhi; Yi, Song; Yu, Analyn; Tyagi, Tanya; Tie, Cathy; Hill, David E.; Vidal, Marc; Andrews, Brenda J.; Boone, Charles; Dolinski, Kara; Roth, Frederick P.

2016-01-01

We can now routinely identify coding variants within individual human genomes. A pressing challenge is to determine which variants disrupt the function of disease-associated genes. Both experimental and computational methods exist to predict pathogenicity of human genetic variation. However, a systematic performance comparison between them has been lacking. Therefore, we developed and exploited a panel of 26 yeast-based functional complementation assays to measure the impact of 179 variants (101 disease- and 78 non-disease-associated variants) from 22 human disease genes. Using the resulting reference standard, we show that experimental functional assays in a 1-billion-year diverged model organism can identify pathogenic alleles with significantly higher precision and specificity than current computational methods. PMID:26975778

Accurate pressure gradient calculations in hydrostatic atmospheric models

NASA Technical Reports Server (NTRS)

Carroll, John J.; Mendez-Nunez, Luis R.; Tanrikulu, Saffet

1987-01-01

A method for the accurate calculation of the horizontal pressure gradient acceleration in hydrostatic atmospheric models is presented which is especially useful in situations where the isothermal surfaces are not parallel to the vertical coordinate surfaces. The present method is shown to be exact if the potential temperature lapse rate is constant between the vertical pressure integration limits. The technique is applied to both the integration of the hydrostatic equation and the computation of the slope correction term in the horizontal pressure gradient. A fixed vertical grid and a dynamic grid defined by the significant levels in the vertical temperature distribution are employed.

Can a numerically stable subgrid-scale model for turbulent flow computation be ideally accurate?: a preliminary theoretical study for the Gaussian filtered Navier-Stokes equations.

PubMed

Ida, Masato; Taniguchi, Nobuyuki

2003-09-01

This paper introduces a candidate for the origin of the numerical instabilities in large eddy simulation repeatedly observed in academic and practical industrial flow computations. Without resorting to any subgrid-scale modeling, but based on a simple assumption regarding the streamwise component of flow velocity, it is shown theoretically that in a channel-flow computation, the application of the Gaussian filtering to the incompressible Navier-Stokes equations yields a numerically unstable term, a cross-derivative term, which is similar to one appearing in the Gaussian filtered Vlasov equation derived by Klimas [J. Comput. Phys. 68, 202 (1987)] and also to one derived recently by Kobayashi and Shimomura [Phys. Fluids 15, L29 (2003)] from the tensor-diffusivity subgrid-scale term in a dynamic mixed model. The present result predicts that not only the numerical methods and the subgrid-scale models employed but also only the applied filtering process can be a seed of this numerical instability. An investigation concerning the relationship between the turbulent energy scattering and the unstable term shows that the instability of the term does not necessarily represent the backscatter of kinetic energy which has been considered a possible origin of numerical instabilities in large eddy simulation. The present findings raise the question whether a numerically stable subgrid-scale model can be ideally accurate.

GBOOST: a GPU-based tool for detecting gene-gene interactions in genome-wide case control studies.

PubMed

Yung, Ling Sing; Yang, Can; Wan, Xiang; Yu, Weichuan

2011-05-01

Collecting millions of genetic variations is feasible with the advanced genotyping technology. With a huge amount of genetic variations data in hand, developing efficient algorithms to carry out the gene-gene interaction analysis in a timely manner has become one of the key problems in genome-wide association studies (GWAS). Boolean operation-based screening and testing (BOOST), a recent work in GWAS, completes gene-gene interaction analysis in 2.5 days on a desktop computer. Compared with central processing units (CPUs), graphic processing units (GPUs) are highly parallel hardware and provide massive computing resources. We are, therefore, motivated to use GPUs to further speed up the analysis of gene-gene interactions. We implement the BOOST method based on a GPU framework and name it GBOOST. GBOOST achieves a 40-fold speedup compared with BOOST. It completes the analysis of Wellcome Trust Case Control Consortium Type 2 Diabetes (WTCCC T2D) genome data within 1.34 h on a desktop computer equipped with Nvidia GeForce GTX 285 display card. GBOOST code is available at http://bioinformatics.ust.hk/BOOST.html#GBOOST.

A dental vision system for accurate 3D tooth modeling.

PubMed

Zhang, Li; Alemzadeh, K

2006-01-01

This paper describes an active vision system based reverse engineering approach to extract the three-dimensional (3D) geometric information from dental teeth and transfer this information into Computer-Aided Design/Computer-Aided Manufacture (CAD/CAM) systems to improve the accuracy of 3D teeth models and at the same time improve the quality of the construction units to help patient care. The vision system involves the development of a dental vision rig, edge detection, boundary tracing and fast & accurate 3D modeling from a sequence of sliced silhouettes of physical models. The rig is designed using engineering design methods such as a concept selection matrix and weighted objectives evaluation chart. Reconstruction results and accuracy evaluation are presented on digitizing different teeth models.

Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data

PubMed Central

2017-01-01

Mapping gene expression as a quantitative trait using whole genome-sequencing and transcriptome analysis allows to discover the functional consequences of genetic variation. We developed a novel method and ultra-fast software Findr for higly accurate causal inference between gene expression traits using cis-regulatory DNA variations as causal anchors, which improves current methods by taking into consideration hidden confounders and weak regulations. Findr outperformed existing methods on the DREAM5 Systems Genetics challenge and on the prediction of microRNA and transcription factor targets in human lymphoblastoid cells, while being nearly a million times faster. Findr is publicly available at https://github.com/lingfeiwang/findr. PMID:28821014

Integrated Translatome and Proteome: Approach for Accurate Portraying of Widespread Multifunctional Aspects of Trichoderma

PubMed Central

Sharma, Vivek; Salwan, Richa; Sharma, P. N.; Gulati, Arvind

2017-01-01

Genome-wide studies of transcripts expression help in systematic monitoring of genes and allow targeting of candidate genes for future research. In contrast to relatively stable genomic data, the expression of genes is dynamic and regulated both at time and space level at different level in. The variation in the rate of translation is specific for each protein. Both the inherent nature of an mRNA molecule to be translated and the external environmental stimuli can affect the efficiency of the translation process. In biocontrol agents (BCAs), the molecular response at translational level may represents noise-like response of absolute transcript level and an adaptive response to physiological and pathological situations representing subset of mRNAs population actively translated in a cell. The molecular responses of biocontrol are complex and involve multistage regulation of number of genes. The use of high-throughput techniques has led to rapid increase in volume of transcriptomics data of Trichoderma. In general, almost half of the variations of transcriptome and protein level are due to translational control. Thus, studies are required to integrate raw information from different “omics” approaches for accurate depiction of translational response of BCAs in interaction with plants and plant pathogens. The studies on translational status of only active mRNAs bridging with proteome data will help in accurate characterization of only a subset of mRNAs actively engaged in translation. This review highlights the associated bottlenecks and use of state-of-the-art procedures in addressing the gap to accelerate future accomplishment of biocontrol mechanisms. PMID:28900417

Accurate computation and continuation of homoclinic and heteroclinic orbits for singular perturbation problems

NASA Technical Reports Server (NTRS)

Vaughan, William W.; Friedman, Mark J.; Monteiro, Anand C.

1993-01-01

In earlier papers, Doedel and the authors have developed a numerical method and derived error estimates for the computation of branches of heteroclinic orbits for a system of autonomous ordinary differential equations in R(exp n). The idea of the method is to reduce a boundary value problem on the real line to a boundary value problem on a finite interval by using a local (linear or higher order) approximation of the stable and unstable manifolds. A practical limitation for the computation of homoclinic and heteroclinic orbits has been the difficulty in obtaining starting orbits. Typically these were obtained from a closed form solution or via a homotopy from a known solution. Here we consider extensions of our algorithm which allow us to obtain starting orbits on the continuation branch in a more systematic way as well as make the continuation algorithm more flexible. In applications, we use the continuation software package AUTO in combination with some initial value software. The examples considered include computation of homoclinic orbits in a singular perturbation problem and in a turbulent fluid boundary layer in the wall region problem.

Accurate van der Waals coefficients from density functional theory

PubMed Central

Tao, Jianmin; Perdew, John P.; Ruzsinszky, Adrienn

2012-01-01

The van der Waals interaction is a weak, long-range correlation, arising from quantum electronic charge fluctuations. This interaction affects many properties of materials. A simple and yet accurate estimate of this effect will facilitate computer simulation of complex molecular materials and drug design. Here we develop a fast approach for accurate evaluation of dynamic multipole polarizabilities and van der Waals (vdW) coefficients of all orders from the electron density and static multipole polarizabilities of each atom or other spherical object, without empirical fitting. Our dynamic polarizabilities (dipole, quadrupole, octupole, etc.) are exact in the zero- and high-frequency limits, and exact at all frequencies for a metallic sphere of uniform density. Our theory predicts dynamic multipole polarizabilities in excellent agreement with more expensive many-body methods, and yields therefrom vdW coefficients C6, C8, C10 for atom pairs with a mean absolute relative error of only 3%. PMID:22205765

Identification and evaluation of reference genes for accurate gene expression normalization of fresh and frozen-thawed spermatozoa of water buffalo (Bubalus bubalis).

PubMed

Ashish, Shende; Bhure, S K; Harikrishna, Pillai; Ramteke, S S; Muhammed Kutty, V H; Shruthi, N; Ravi Kumar, G V P P S; Manish, Mahawar; Ghosh, S K; Mihir, Sarkar

2017-04-01

The quantitative real time PCR (qRT-PCR) has become an important tool for gene-expression analysis for a selected number of genes in life science. Although large dynamic range, sensitivity and reproducibility of qRT-PCR is good, the reliability majorly depend on the selection of proper reference genes (RGs) employed for normalization. Although, RGs expression has been reported to vary considerably within same cell type with different experimental treatments. No systematic study has been conducted to identify and evaluate the appropriate RGs in spermatozoa of domestic animals. Therefore, this study was conducted to analyze suitable stable RGs in fresh and frozen-thawed spermatozoa. We have assessed 13 candidate RGs (BACT, RPS18s, RPS15A, ATP5F1, HMBS, ATP2B4, RPL13, EEF2, TBP, EIF2B2, MDH1, B2M and GLUT5) of different functions and pathways using five algorithms. Regardless of the approach, the ranking of the most and the least candidate RGs remained almost same. The comprehensive ranking by RefFinder showed GLUT5, ATP2B4 and B2M, MDH1 as the top two stable and least stable RGs, respectively. The expression levels of four heat shock proteins (HSP) were employed as a target gene to evaluate RGs efficiency for normalization. The results demonstrated an exponential difference in expression levels of the four HSP genes upon normalization of the data with the most stable and the least stable RGs. Our study, provides a convenient RGs for normalization of gene-expression of key metabolic pathways effected during freezing and thawing of spermatozoa of buffalo and other closely related bovines. Copyright © 2017 Elsevier Inc. All rights reserved.

Accurate, Streamlined Analysis of mRNA Translation by Sucrose Gradient Fractionation

PubMed Central

Aboulhouda, Soufiane; Di Santo, Rachael; Therizols, Gabriel; Weinberg, David

2017-01-01

The efficiency with which proteins are produced from mRNA molecules can vary widely across transcripts, cell types, and cellular states. Methods that accurately assay the translational efficiency of mRNAs are critical to gaining a mechanistic understanding of post-transcriptional gene regulation. One way to measure translational efficiency is to determine the number of ribosomes associated with an mRNA molecule, normalized to the length of the coding sequence. The primary method for this analysis of individual mRNAs is sucrose gradient fractionation, which physically separates mRNAs based on the number of bound ribosomes. Here, we describe a streamlined protocol for accurate analysis of mRNA association with ribosomes. Compared to previous protocols, our method incorporates internal controls and improved buffer conditions that together reduce artifacts caused by non-specific mRNA–ribosome interactions. Moreover, our direct-from-fraction qRT-PCR protocol eliminates the need for RNA purification from gradient fractions, which greatly reduces the amount of hands-on time required and facilitates parallel analysis of multiple conditions or gene targets. Additionally, no phenol waste is generated during the procedure. We initially developed the protocol to investigate the translationally repressed state of the HAC1 mRNA in S. cerevisiae, but we also detail adapted procedures for mammalian cell lines and tissues. PMID:29170751

Efficiency and Accuracy of Time-Accurate Turbulent Navier-Stokes Computations

NASA Technical Reports Server (NTRS)

Rumsey, Christopher L.; Sanetrik, Mark D.; Biedron, Robert T.; Melson, N. Duane; Parlette, Edward B.

1995-01-01

The accuracy and efficiency of two types of subiterations in both explicit and implicit Navier-Stokes codes are explored for unsteady laminar circular-cylinder flow and unsteady turbulent flow over an 18-percent-thick circular-arc (biconvex) airfoil. Grid and time-step studies are used to assess the numerical accuracy of the methods. Nonsubiterative time-stepping schemes and schemes with physical time subiterations are subject to time-step limitations in practice that are removed by pseudo time sub-iterations. Computations for the circular-arc airfoil indicate that a one-equation turbulence model predicts the unsteady separated flow better than an algebraic turbulence model; also, the hysteresis with Mach number of the self-excited unsteadiness due to shock and boundary-layer separation is well predicted.

Exploiting Locality in Quantum Computation for Quantum Chemistry.

PubMed

McClean, Jarrod R; Babbush, Ryan; Love, Peter J; Aspuru-Guzik, Alán

2014-12-18

Accurate prediction of chemical and material properties from first-principles quantum chemistry is a challenging task on traditional computers. Recent developments in quantum computation offer a route toward highly accurate solutions with polynomial cost; however, this solution still carries a large overhead. In this Perspective, we aim to bring together known results about the locality of physical interactions from quantum chemistry with ideas from quantum computation. We show that the utilization of spatial locality combined with the Bravyi-Kitaev transformation offers an improvement in the scaling of known quantum algorithms for quantum chemistry and provides numerical examples to help illustrate this point. We combine these developments to improve the outlook for the future of quantum chemistry on quantum computers.

Gene prioritization and clustering by multi-view text mining

PubMed Central

2010-01-01

Background Text mining has become a useful tool for biologists trying to understand the genetics of diseases. In particular, it can help identify the most interesting candidate genes for a disease for further experimental analysis. Many text mining approaches have been introduced, but the effect of disease-gene identification varies in different text mining models. Thus, the idea of incorporating more text mining models may be beneficial to obtain more refined and accurate knowledge. However, how to effectively combine these models still remains a challenging question in machine learning. In particular, it is a non-trivial issue to guarantee that the integrated model performs better than the best individual model. Results We present a multi-view approach to retrieve biomedical knowledge using different controlled vocabularies. These controlled vocabularies are selected on the basis of nine well-known bio-ontologies and are applied to index the vast amounts of gene-based free-text information available in the MEDLINE repository. The text mining result specified by a vocabulary is considered as a view and the obtained multiple views are integrated by multi-source learning algorithms. We investigate the effect of integration in two fundamental computational disease gene identification tasks: gene prioritization and gene clustering. The performance of the proposed approach is systematically evaluated and compared on real benchmark data sets. In both tasks, the multi-view approach demonstrates significantly better performance than other comparing methods. Conclusions In practical research, the relevance of specific vocabulary pertaining to the task is usually unknown. In such case, multi-view text mining is a superior and promising strategy for text-based disease gene identification. PMID:20074336

A Time-Accurate Upwind Unstructured Finite Volume Method for Compressible Flow with Cure of Pathological Behaviors

NASA Technical Reports Server (NTRS)

Loh, Ching Y.; Jorgenson, Philip C. E.

2007-01-01

A time-accurate, upwind, finite volume method for computing compressible flows on unstructured grids is presented. The method is second order accurate in space and time and yields high resolution in the presence of discontinuities. For efficiency, the Roe approximate Riemann solver with an entropy correction is employed. In the basic Euler/Navier-Stokes scheme, many concepts of high order upwind schemes are adopted: the surface flux integrals are carefully treated, a Cauchy-Kowalewski time-stepping scheme is used in the time-marching stage, and a multidimensional limiter is applied in the reconstruction stage. However even with these up-to-date improvements, the basic upwind scheme is still plagued by the so-called "pathological behaviors," e.g., the carbuncle phenomenon, the expansion shock, etc. A solution to these limitations is presented which uses a very simple dissipation model while still preserving second order accuracy. This scheme is referred to as the enhanced time-accurate upwind (ETAU) scheme in this paper. The unstructured grid capability renders flexibility for use in complex geometry; and the present ETAU Euler/Navier-Stokes scheme is capable of handling a broad spectrum of flow regimes from high supersonic to subsonic at very low Mach number, appropriate for both CFD (computational fluid dynamics) and CAA (computational aeroacoustics). Numerous examples are included to demonstrate the robustness of the methods.

40 CFR 194.23 - Models and computer codes.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 26 2013-07-01 2013-07-01 false Models and computer codes. 194.23... General Requirements § 194.23 Models and computer codes. (a) Any compliance application shall include: (1... obtain stable solutions; (iv) Computer models accurately implement the numerical models; i.e., computer...

40 CFR 194.23 - Models and computer codes.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 26 2012-07-01 2011-07-01 true Models and computer codes. 194.23... General Requirements § 194.23 Models and computer codes. (a) Any compliance application shall include: (1... obtain stable solutions; (iv) Computer models accurately implement the numerical models; i.e., computer...

40 CFR 194.23 - Models and computer codes.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 25 2014-07-01 2014-07-01 false Models and computer codes. 194.23... General Requirements § 194.23 Models and computer codes. (a) Any compliance application shall include: (1... obtain stable solutions; (iv) Computer models accurately implement the numerical models; i.e., computer...

40 CFR 194.23 - Models and computer codes.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 24 2010-07-01 2010-07-01 false Models and computer codes. 194.23... General Requirements § 194.23 Models and computer codes. (a) Any compliance application shall include: (1... obtain stable solutions; (iv) Computer models accurately implement the numerical models; i.e., computer...

40 CFR 194.23 - Models and computer codes.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 25 2011-07-01 2011-07-01 false Models and computer codes. 194.23... General Requirements § 194.23 Models and computer codes. (a) Any compliance application shall include: (1... obtain stable solutions; (iv) Computer models accurately implement the numerical models; i.e., computer...

Selection of suitable reference genes for gene expression studies in Staphylococcus capitis during growth under erythromycin stress.

PubMed

Cui, Bintao; Smooker, Peter M; Rouch, Duncan A; Deighton, Margaret A

2016-08-01

Accurate and reproducible measurement of gene transcription requires appropriate reference genes, which are stably expressed under different experimental conditions to provide normalization. Staphylococcus capitis is a human pathogen that produces biofilm under stress, such as imposed by antimicrobial agents. In this study, a set of five commonly used staphylococcal reference genes (gyrB, sodA, recA, tuf and rpoB) were systematically evaluated in two clinical isolates of Staphylococcus capitis (S. capitis subspecies urealyticus and capitis, respectively) under erythromycin stress in mid-log and stationary phases. Two public software programs (geNorm and NormFinder) and two manual calculation methods, reference residue normalization (RRN) and relative quantitative (RQ), were applied. The potential reference genes selected by the four algorithms were further validated by comparing the expression of a well-studied biofilm gene (icaA) with phenotypic biofilm formation in S. capitis under four different experimental conditions. The four methods differed considerably in their ability to predict the most suitable reference gene or gene combination for comparing icaA expression under different conditions. Under the conditions used here, the RQ method provided better selection of reference genes than the other three algorithms; however, this finding needs to be confirmed with a larger number of isolates. This study reinforces the need to assess the stability of reference genes for analysis of target gene expression under different conditions and the use of more than one algorithm in such studies. Although this work was conducted using a specific human pathogen, it emphasizes the importance of selecting suitable reference genes for accurate normalization of gene expression more generally.

Tau-independent Phase Analysis: A Novel Method for Accurately Determining Phase Shifts.

PubMed

Tackenberg, Michael C; Jones, Jeff R; Page, Terry L; Hughey, Jacob J

2018-06-01

Estimations of period and phase are essential in circadian biology. While many techniques exist for estimating period, comparatively few methods are available for estimating phase. Current approaches to analyzing phase often vary between studies and are sensitive to coincident changes in period and the stage of the circadian cycle at which the stimulus occurs. Here we propose a new technique, tau-independent phase analysis (TIPA), for quantifying phase shifts in multiple types of circadian time-course data. Through comprehensive simulations, we show that TIPA is both more accurate and more precise than the standard actogram approach. TIPA is computationally simple and therefore will enable accurate and reproducible quantification of phase shifts across multiple subfields of chronobiology.

Lung tumor diagnosis and subtype discovery by gene expression profiling.

PubMed

Wang, Lu-yong; Tu, Zhuowen

2006-01-01

The optimal treatment of patients with complex diseases, such as cancers, depends on the accurate diagnosis by using a combination of clinical and histopathological data. In many scenarios, it becomes tremendously difficult because of the limitations in clinical presentation and histopathology. To accurate diagnose complex diseases, the molecular classification based on gene or protein expression profiles are indispensable for modern medicine. Moreover, many heterogeneous diseases consist of various potential subtypes in molecular basis and differ remarkably in their response to therapies. It is critical to accurate predict subgroup on disease gene expression profiles. More fundamental knowledge of the molecular basis and classification of disease could aid in the prediction of patient outcome, the informed selection of therapies, and identification of novel molecular targets for therapy. In this paper, we propose a new disease diagnostic method, probabilistic boosting tree (PB tree) method, on gene expression profiles of lung tumors. It enables accurate disease classification and subtype discovery in disease. It automatically constructs a tree in which each node combines a number of weak classifiers into a strong classifier. Also, subtype discovery is naturally embedded in the learning process. Our algorithm achieves excellent diagnostic performance, and meanwhile it is capable of detecting the disease subtype based on gene expression profile.

Seed storage protein gene promoters contain conserved DNA motifs in Brassicaceae, Fabaceae and Poaceae

PubMed Central

Fauteux, François; Strömvik, Martina V

2009-01-01

Background Accurate computational identification of cis-regulatory motifs is difficult, particularly in eukaryotic promoters, which typically contain multiple short and degenerate DNA sequences bound by several interacting factors. Enrichment in combinations of rare motifs in the promoter sequence of functionally or evolutionarily related genes among several species is an indicator of conserved transcriptional regulatory mechanisms. This provides a basis for the computational identification of cis-regulatory motifs. Results We have used a discriminative seeding DNA motif discovery algorithm for an in-depth analysis of 54 seed storage protein (SSP) gene promoters from three plant families, namely Brassicaceae (mustards), Fabaceae (legumes) and Poaceae (grasses) using backgrounds based on complete sets of promoters from a representative species in each family, namely Arabidopsis (Arabidopsis thaliana (L.) Heynh.), soybean (Glycine max (L.) Merr.) and rice (Oryza sativa L.) respectively. We have identified three conserved motifs (two RY-like and one ACGT-like) in Brassicaceae and Fabaceae SSP gene promoters that are similar to experimentally characterized seed-specific cis-regulatory elements. Fabaceae SSP gene promoter sequences are also enriched in a novel, seed-specific E2Fb-like motif. Conserved motifs identified in Poaceae SSP gene promoters include a GCN4-like motif, two prolamin-box-like motifs and an Skn-1-like motif. Evidence of the presence of a variant of the TATA-box is found in the SSP gene promoters from the three plant families. Motifs discovered in SSP gene promoters were used to score whole-genome sets of promoters from Arabidopsis, soybean and rice. The highest-scoring promoters are associated with genes coding for different subunits or precursors of seed storage proteins. Conclusion Seed storage protein gene promoter motifs are conserved in diverse species, and different plant families are characterized by a distinct combination of conserved motifs

Case Report: Application of whole exome sequencing for accurate diagnosis of rare syndromes of mineralocorticoid excess

PubMed Central

Narayanan, Ranjit; Karuthedath Vellarikkal, Shamsudheen; Jayarajan, Rijith; Verma, Ankit; Dixit, Vishal; Scaria, Vinod; Sivasubbu, Sridhar

2017-01-01

Syndromes of mineralocorticoid excess (SME) are closely related clinical manifestations occurring within a specific set of diseases. Overlapping clinical manifestations of such syndromes often create a dilemma in accurate diagnosis, which is crucial for disease surveillance and management especially in rare genetic disorders. Here we demonstrate the use of whole exome sequencing (WES) for accurate diagnosis of rare SME and report that p.R337C variation in the HSD11B2 gene causes progressive apparent mineralocorticoid excess (AME) syndrome in a South Indian family of Mappila origin. PMID:29067160

Computational Fluid Dynamics of Whole-Body Aircraft

NASA Astrophysics Data System (ADS)

Agarwal, Ramesh

1999-01-01

The current state of the art in computational aerodynamics for whole-body aircraft flowfield simulations is described. Recent advances in geometry modeling, surface and volume grid generation, and flow simulation algorithms have led to accurate flowfield predictions for increasingly complex and realistic configurations. As a result, computational aerodynamics has emerged as a crucial enabling technology for the design and development of flight vehicles. Examples illustrating the current capability for the prediction of transport and fighter aircraft flowfields are presented. Unfortunately, accurate modeling of turbulence remains a major difficulty in the analysis of viscosity-dominated flows. In the future, inverse design methods, multidisciplinary design optimization methods, artificial intelligence technology, and massively parallel computer technology will be incorporated into computational aerodynamics, opening up greater opportunities for improved product design at substantially reduced costs.

Selection and Validation of Reference Genes for Accurate RT-qPCR Data Normalization in Coffea spp. under a Climate Changes Context of Interacting Elevated [CO2] and Temperature

PubMed Central

Martins, Madlles Q.; Fortunato, Ana S.; Rodrigues, Weverton P.; Partelli, Fábio L.; Campostrini, Eliemar; Lidon, Fernando C.; DaMatta, Fábio M.; Ramalho, José C.; Ribeiro-Barros, Ana I.

2017-01-01

/oxygenase (RLS), results from the in silico aggregation and experimental validation of the best number of reference genes showed that two reference genes are adequate to normalize RT-qPCR data. Altogether, this work highlights the importance of an adequate selection of reference genes for each single or combined experimental condition and constitutes the basis to accurately study molecular responses of Coffea spp. in a context of climate changes and global warming. PMID:28326094

Selection and Validation of Reference Genes for Accurate RT-qPCR Data Normalization in Coffea spp. under a Climate Changes Context of Interacting Elevated [CO2] and Temperature.

PubMed

Martins, Madlles Q; Fortunato, Ana S; Rodrigues, Weverton P; Partelli, Fábio L; Campostrini, Eliemar; Lidon, Fernando C; DaMatta, Fábio M; Ramalho, José C; Ribeiro-Barros, Ana I

2017-01-01

/oxygenase ( RLS ), results from the in silico aggregation and experimental validation of the best number of reference genes showed that two reference genes are adequate to normalize RT-qPCR data. Altogether, this work highlights the importance of an adequate selection of reference genes for each single or combined experimental condition and constitutes the basis to accurately study molecular responses of Coffea spp. in a context of climate changes and global warming.

On canonical cylinder sections for accurate determination of contact angle in microgravity

NASA Technical Reports Server (NTRS)

Concus, Paul; Finn, Robert; Zabihi, Farhad

1992-01-01

Large shifts of liquid arising from small changes in certain container shapes in zero gravity can be used as a basis for accurately determining contact angle. Canonical geometries for this purpose, recently developed mathematically, are investigated here computationally. It is found that the desired nearly-discontinuous behavior can be obtained and that the shifts of liquid have sufficient volume to be readily observed.

Automated and Accurate Estimation of Gene Family Abundance from Shotgun Metagenomes

PubMed Central

Nayfach, Stephen; Bradley, Patrick H.; Wyman, Stacia K.; Laurent, Timothy J.; Williams, Alex; Eisen, Jonathan A.; Pollard, Katherine S.; Sharpton, Thomas J.

2015-01-01

Shotgun metagenomic DNA sequencing is a widely applicable tool for characterizing the functions that are encoded by microbial communities. Several bioinformatic tools can be used to functionally annotate metagenomes, allowing researchers to draw inferences about the functional potential of the community and to identify putative functional biomarkers. However, little is known about how decisions made during annotation affect the reliability of the results. Here, we use statistical simulations to rigorously assess how to optimize annotation accuracy and speed, given parameters of the input data like read length and library size. We identify best practices in metagenome annotation and use them to guide the development of the Shotgun Metagenome Annotation Pipeline (ShotMAP). ShotMAP is an analytically flexible, end-to-end annotation pipeline that can be implemented either on a local computer or a cloud compute cluster. We use ShotMAP to assess how different annotation databases impact the interpretation of how marine metagenome and metatranscriptome functional capacity changes across seasons. We also apply ShotMAP to data obtained from a clinical microbiome investigation of inflammatory bowel disease. This analysis finds that gut microbiota collected from Crohn’s disease patients are functionally distinct from gut microbiota collected from either ulcerative colitis patients or healthy controls, with differential abundance of metabolic pathways related to host-microbiome interactions that may serve as putative biomarkers of disease. PMID:26565399

Stability evaluation of reference genes for gene expression analysis by RT-qPCR in soybean under different conditions.

PubMed

Wan, Qiao; Chen, Shuilian; Shan, Zhihui; Yang, Zhonglu; Chen, Limiao; Zhang, Chanjuan; Yuan, Songli; Hao, Qinnan; Zhang, Xiaojuan; Qiu, Dezhen; Chen, Haifeng; Zhou, Xinan

2017-01-01

Real-time quantitative reverse transcription PCR is a sensitive and widely used technique to quantify gene expression. To achieve a reliable result, appropriate reference genes are highly required for normalization of transcripts in different samples. In this study, 9 previously published reference genes (60S, Fbox, ELF1A, ELF1B, ACT11, TUA5, UBC4, G6PD, CYP2) of soybean [Glycine max (L.) Merr.] were selected. The expression stability of the 9 genes was evaluated under conditions of biotic stress caused by infection with soybean mosaic virus, nitrogen stress, across different cultivars and developmental stages. ΔCt and geNorm algorithms were used to evaluate and rank the expression stability of the 9 reference genes. Results obtained from two algorithms showed high consistency. Moreover, results of pairwise variation showed that two reference genes were sufficient to normalize the expression levels of target genes under each experimental setting. For virus infection, ELF1A and ELF1B were the most stable reference genes for accurate normalization. For different developmental stages, Fbox and G6PD had the highest expression stability between two soybean cultivars (Tanlong No. 1 and Tanlong No. 2). ELF1B and ACT11 were identified as the most stably expressed reference genes both under nitrogen stress and among different cultivars. The results showed that none of the candidate reference genes were uniformly expressed at different conditions, and selecting appropriate reference genes was pivotal for gene expression studies with particular condition and tissue. The most stable combination of genes identified in this study will help to achieve more accurate and reliable results in a wide variety of samples in soybean.

A computer-based physics laboratory apparatus: Signal generator software

NASA Astrophysics Data System (ADS)

Thanakittiviroon, Tharest; Liangrocapart, Sompong

2005-09-01

This paper describes a computer-based physics laboratory apparatus to replace expensive instruments such as high-precision signal generators. This apparatus uses a sound card in a common personal computer to give sinusoidal signals with an accurate frequency that can be programmed to give different frequency signals repeatedly. An experiment on standing waves on an oscillating string uses this apparatus. In conjunction with interactive lab manuals, which have been developed using personal computers in our university, we achieve a complete set of low-cost, accurate, and easy-to-use equipment for teaching a physics laboratory.

The identification of complete domains within protein sequences using accurate E-values for semi-global alignment

PubMed Central

Kann, Maricel G.; Sheetlin, Sergey L.; Park, Yonil; Bryant, Stephen H.; Spouge, John L.

2007-01-01

The sequencing of complete genomes has created a pressing need for automated annotation of gene function. Because domains are the basic units of protein function and evolution, a gene can be annotated from a domain database by aligning domains to the corresponding protein sequence. Ideally, complete domains are aligned to protein subsequences, in a ‘semi-global alignment’. Local alignment, which aligns pieces of domains to subsequences, is common in high-throughput annotation applications, however. It is a mature technique, with the heuristics and accurate E-values required for screening large databases and evaluating the screening results. Hidden Markov models (HMMs) provide an alternative theoretical framework for semi-global alignment, but their use is limited because they lack heuristic acceleration and accurate E-values. Our new tool, GLOBAL, overcomes some limitations of previous semi-global HMMs: it has accurate E-values and the possibility of the heuristic acceleration required for high-throughput applications. Moreover, according to a standard of truth based on protein structure, two semi-global HMM alignment tools (GLOBAL and HMMer) had comparable performance in identifying complete domains, but distinctly outperformed two tools based on local alignment. When searching for complete protein domains, therefore, GLOBAL avoids disadvantages commonly associated with HMMs, yet maintains their superior retrieval performance. PMID:17596268

VH Replacement Footprint Analyzer-I, a Java-Based Computer Program for Analyses of Immunoglobulin Heavy Chain Genes and Potential VH Replacement Products in Human and Mouse

PubMed Central

Huang, Lin; Lange, Miles D.; Zhang, Zhixin

2014-01-01

VH replacement occurs through RAG-mediated secondary recombination between a rearranged VH gene and an upstream unrearranged VH gene. Due to the location of the cryptic recombination signal sequence (cRSS, TACTGTG) at the 3′ end of VH gene coding region, a short stretch of nucleotides from the previous rearranged VH gene can be retained in the newly formed VH–DH junction as a “footprint” of VH replacement. Such footprints can be used as markers to identify Ig heavy chain (IgH) genes potentially generated through VH replacement. To explore the contribution of VH replacement products to the antibody repertoire, we developed a Java-based computer program, VH replacement footprint analyzer-I (VHRFA-I), to analyze published or newly obtained IgH genes from human or mouse. The VHRFA-1 program has multiple functional modules: it first uses service provided by the IMGT/V-QUEST program to assign potential VH, DH, and JH germline genes; then, it searches for VH replacement footprint motifs within the VH–DH junction (N1) regions of IgH gene sequences to identify potential VH replacement products; it can also analyze the frequencies of VH replacement products in correlation with publications, keywords, or VH, DH, and JH gene usages, and mutation status; it can further analyze the amino acid usages encoded by the identified VH replacement footprints. In summary, this program provides a useful computation tool for exploring the biological significance of VH replacement products in human and mouse. PMID:24575092

Thermal Conductivities in Solids from First Principles: Accurate Computations and Rapid Estimates

NASA Astrophysics Data System (ADS)

Carbogno, Christian; Scheffler, Matthias

In spite of significant research efforts, a first-principles determination of the thermal conductivity κ at high temperatures has remained elusive. Boltzmann transport techniques that account for anharmonicity perturbatively become inaccurate under such conditions. Ab initio molecular dynamics (MD) techniques using the Green-Kubo (GK) formalism capture the full anharmonicity, but can become prohibitively costly to converge in time and size. We developed a formalism that accelerates such GK simulations by several orders of magnitude and that thus enables its application within the limited time and length scales accessible in ab initio MD. For this purpose, we determine the effective harmonic potential occurring during the MD, the associated temperature-dependent phonon properties and lifetimes. Interpolation in reciprocal and frequency space then allows to extrapolate to the macroscopic scale. For both force-field and ab initio MD, we validate this approach by computing κ for Si and ZrO2, two materials known for their particularly harmonic and anharmonic character. Eventually, we demonstrate how these techniques facilitate reasonable estimates of κ from existing MD calculations at virtually no additional computational cost.

Integrated Computational Analysis of Genes Associated with Human Hereditary Insensitivity to Pain. A Drug Repurposing Perspective

PubMed Central

Lötsch, Jörn; Lippmann, Catharina; Kringel, Dario; Ultsch, Alfred

2017-01-01

Genes causally involved in human insensitivity to pain provide a unique molecular source of studying the pathophysiology of pain and the development of novel analgesic drugs. The increasing availability of “big data” enables novel research approaches to chronic pain while also requiring novel techniques for data mining and knowledge discovery. We used machine learning to combine the knowledge about n = 20 genes causally involved in human hereditary insensitivity to pain with the knowledge about the functions of thousands of genes. An integrated computational analysis proposed that among the functions of this set of genes, the processes related to nervous system development and to ceramide and sphingosine signaling pathways are particularly important. This is in line with earlier suggestions to use these pathways as therapeutic target in pain. Following identification of the biological processes characterizing hereditary insensitivity to pain, the biological processes were used for a similarity analysis with the functions of n = 4,834 database-queried drugs. Using emergent self-organizing maps, a cluster of n = 22 drugs was identified sharing important functional features with hereditary insensitivity to pain. Several members of this cluster had been implicated in pain in preclinical experiments. Thus, the present concept of machine-learned knowledge discovery for pain research provides biologically plausible results and seems to be suitable for drug discovery by identifying a narrow choice of repurposing candidates, demonstrating that contemporary machine-learned methods offer innovative approaches to knowledge discovery from available evidence. PMID:28848388

Biased Gene Fractionation and Dominant Gene Expression among the Subgenomes of Brassica rapa

PubMed Central

Cheng, Feng; Wu, Jian; Fang, Lu; Sun, Silong; Liu, Bo; Lin, Ke; Bonnema, Guusje; Wang, Xiaowu

2012-01-01

Polyploidization, both ancient and recent, is frequent among plants. A “two-step theory" was proposed to explain the meso-triplication of the Brassica “A" genome: Brassica rapa. By accurately partitioning of this genome, we observed that genes in the less fractioned subgenome (LF) were dominantly expressed over the genes in more fractioned subgenomes (MFs: MF1 and MF2), while the genes in MF1 were slightly dominantly expressed over the genes in MF2. The results indicated that the dominantly expressed genes tended to be resistant against gene fractionation. By re-sequencing two B. rapa accessions: a vegetable turnip (VT117) and a Rapid Cycling line (L144), we found that genes in LF had less non-synonymous or frameshift mutations than genes in MFs; however mutation rates were not significantly different between MF1 and MF2. The differences in gene expression patterns and on-going gene death among the three subgenomes suggest that “two-step" genome triplication and differential subgenome methylation played important roles in the genome evolution of B. rapa. PMID:22567157

Biased gene fractionation and dominant gene expression among the subgenomes of Brassica rapa.

PubMed

Cheng, Feng; Wu, Jian; Fang, Lu; Sun, Silong; Liu, Bo; Lin, Ke; Bonnema, Guusje; Wang, Xiaowu

2012-01-01

Polyploidization, both ancient and recent, is frequent among plants. A "two-step theory" was proposed to explain the meso-triplication of the Brassica "A" genome: Brassica rapa. By accurately partitioning of this genome, we observed that genes in the less fractioned subgenome (LF) were dominantly expressed over the genes in more fractioned subgenomes (MFs: MF1 and MF2), while the genes in MF1 were slightly dominantly expressed over the genes in MF2. The results indicated that the dominantly expressed genes tended to be resistant against gene fractionation. By re-sequencing two B. rapa accessions: a vegetable turnip (VT117) and a Rapid Cycling line (L144), we found that genes in LF had less non-synonymous or frameshift mutations than genes in MFs; however mutation rates were not significantly different between MF1 and MF2. The differences in gene expression patterns and on-going gene death among the three subgenomes suggest that "two-step" genome triplication and differential subgenome methylation played important roles in the genome evolution of B. rapa.

A fast and accurate dihedral interpolation loop subdivision scheme

NASA Astrophysics Data System (ADS)

Shi, Zhuo; An, Yalei; Wang, Zhongshuai; Yu, Ke; Zhong, Si; Lan, Rushi; Luo, Xiaonan

2018-04-01

In this paper, we propose a fast and accurate dihedral interpolation Loop subdivision scheme for subdivision surfaces based on triangular meshes. In order to solve the problem of surface shrinkage, we keep the limit condition unchanged, which is important. Extraordinary vertices are handled using modified Butterfly rules. Subdivision schemes are computationally costly as the number of faces grows exponentially at higher levels of subdivision. To address this problem, our approach is to use local surface information to adaptively refine the model. This is achieved simply by changing the threshold value of the dihedral angle parameter, i.e., the angle between the normals of a triangular face and its adjacent faces. We then demonstrate the effectiveness of the proposed method for various 3D graphic triangular meshes, and extensive experimental results show that it can match or exceed the expected results at lower computational cost.

Enabling fast, stable and accurate peridynamic computations using multi-time-step integration

DOE PAGES

Lindsay, P.; Parks, M. L.; Prakash, A.

2016-04-13

Peridynamics is a nonlocal extension of classical continuum mechanics that is well-suited for solving problems with discontinuities such as cracks. This paper extends the peridynamic formulation to decompose a problem domain into a number of smaller overlapping subdomains and to enable the use of different time steps in different subdomains. This approach allows regions of interest to be isolated and solved at a small time step for increased accuracy while the rest of the problem domain can be solved at a larger time step for greater computational efficiency. Lastly, performance of the proposed method in terms of stability, accuracy, andmore » computational cost is examined and several numerical examples are presented to corroborate the findings.« less

Toward Accurate and Quantitative Comparative Metagenomics

PubMed Central

Nayfach, Stephen; Pollard, Katherine S.

2016-01-01

Shotgun metagenomics and computational analysis are used to compare the taxonomic and functional profiles of microbial communities. Leveraging this approach to understand roles of microbes in human biology and other environments requires quantitative data summaries whose values are comparable across samples and studies. Comparability is currently hampered by the use of abundance statistics that do not estimate a meaningful parameter of the microbial community and biases introduced by experimental protocols and data-cleaning approaches. Addressing these challenges, along with improving study design, data access, metadata standardization, and analysis tools, will enable accurate comparative metagenomics. We envision a future in which microbiome studies are replicable and new metagenomes are easily and rapidly integrated with existing data. Only then can the potential of metagenomics for predictive ecological modeling, well-powered association studies, and effective microbiome medicine be fully realized. PMID:27565341

Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants.

PubMed

Schläpfer, Pascal; Zhang, Peifen; Wang, Chuan; Kim, Taehyong; Banf, Michael; Chae, Lee; Dreher, Kate; Chavali, Arvind K; Nilo-Poyanco, Ricardo; Bernard, Thomas; Kahn, Daniel; Rhee, Seung Y

2017-04-01

Plant metabolism underpins many traits of ecological and agronomic importance. Plants produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have not yet been elucidated. To engineer and improve metabolic traits, we need comprehensive and accurate knowledge of the organization and regulation of plant metabolism at the genome scale. Here, we present a computational pipeline to identify metabolic enzymes, pathways, and gene clusters from a sequenced genome. Using this pipeline, we generated metabolic pathway databases for 22 species and identified metabolic gene clusters from 18 species. This unified resource can be used to conduct a wide array of comparative studies of plant metabolism. Using the resource, we discovered a widespread occurrence of metabolic gene clusters in plants: 11,969 clusters from 18 species. The prevalence of metabolic gene clusters offers an intriguing possibility of an untapped source for uncovering new metabolite biosynthesis pathways. For example, more than 1,700 clusters contain enzymes that could generate a specialized metabolite scaffold (signature enzymes) and enzymes that modify the scaffold (tailoring enzymes). In four species with sufficient gene expression data, we identified 43 highly coexpressed clusters that contain signature and tailoring enzymes, of which eight were characterized previously to be functional pathways. Finally, we identified patterns of genome organization that implicate local gene duplication and, to a lesser extent, single gene transposition as having played roles in the evolution of plant metabolic gene clusters. © 2017 American Society of Plant Biologists. All Rights Reserved.

Interrogating the topological robustness of gene regulatory circuits by randomization

PubMed Central

Levine, Herbert; Onuchic, Jose N.

2017-01-01

One of the most important roles of cells is performing their cellular tasks properly for survival. Cells usually achieve robust functionality, for example, cell-fate decision-making and signal transduction, through multiple layers of regulation involving many genes. Despite the combinatorial complexity of gene regulation, its quantitative behavior has been typically studied on the basis of experimentally verified core gene regulatory circuitry, composed of a small set of important elements. It is still unclear how such a core circuit operates in the presence of many other regulatory molecules and in a crowded and noisy cellular environment. Here we report a new computational method, named random circuit perturbation (RACIPE), for interrogating the robust dynamical behavior of a gene regulatory circuit even without accurate measurements of circuit kinetic parameters. RACIPE generates an ensemble of random kinetic models corresponding to a fixed circuit topology, and utilizes statistical tools to identify generic properties of the circuit. By applying RACIPE to simple toggle-switch-like motifs, we observed that the stable states of all models converge to experimentally observed gene state clusters even when the parameters are strongly perturbed. RACIPE was further applied to a proposed 22-gene network of the Epithelial-to-Mesenchymal Transition (EMT), from which we identified four experimentally observed gene states, including the states that are associated with two different types of hybrid Epithelial/Mesenchymal phenotypes. Our results suggest that dynamics of a gene circuit is mainly determined by its topology, not by detailed circuit parameters. Our work provides a theoretical foundation for circuit-based systems biology modeling. We anticipate RACIPE to be a powerful tool to predict and decode circuit design principles in an unbiased manner, and to quantitatively evaluate the robustness and heterogeneity of gene expression. PMID:28362798

The Global Error Assessment (GEA) model for the selection of differentially expressed genes in microarray data.

PubMed

Mansourian, Robert; Mutch, David M; Antille, Nicolas; Aubert, Jerome; Fogel, Paul; Le Goff, Jean-Marc; Moulin, Julie; Petrov, Anton; Rytz, Andreas; Voegel, Johannes J; Roberts, Matthew-Alan

2004-11-01

Microarray technology has become a powerful research tool in many fields of study; however, the cost of microarrays often results in the use of a low number of replicates (k). Under circumstances where k is low, it becomes difficult to perform standard statistical tests to extract the most biologically significant experimental results. Other more advanced statistical tests have been developed; however, their use and interpretation often remain difficult to implement in routine biological research. The present work outlines a method that achieves sufficient statistical power for selecting differentially expressed genes under conditions of low k, while remaining as an intuitive and computationally efficient procedure. The present study describes a Global Error Assessment (GEA) methodology to select differentially expressed genes in microarray datasets, and was developed using an in vitro experiment that compared control and interferon-gamma treated skin cells. In this experiment, up to nine replicates were used to confidently estimate error, thereby enabling methods of different statistical power to be compared. Gene expression results of a similar absolute expression are binned, so as to enable a highly accurate local estimate of the mean squared error within conditions. The model then relates variability of gene expression in each bin to absolute expression levels and uses this in a test derived from the classical ANOVA. The GEA selection method is compared with both the classical and permutational ANOVA tests, and demonstrates an increased stability, robustness and confidence in gene selection. A subset of the selected genes were validated by real-time reverse transcription-polymerase chain reaction (RT-PCR). All these results suggest that GEA methodology is (i) suitable for selection of differentially expressed genes in microarray data, (ii) intuitive and computationally efficient and (iii) especially advantageous under conditions of low k. The GEA code for R

Absolute Hounsfield unit measurement on noncontrast computed tomography cannot accurately predict struvite stone composition.

PubMed

Marchini, Giovanni Scala; Gebreselassie, Surafel; Liu, Xiaobo; Pynadath, Cindy; Snyder, Grace; Monga, Manoj

2013-02-01

The purpose of our study was to determine, in vivo, whether single-energy noncontrast computed tomography (NCCT) can accurately predict the presence/percentage of struvite stone composition. We retrospectively searched for all patients with struvite components on stone composition analysis between January 2008 and March 2012. Inclusion criteria were NCCT prior to stone analysis and stone size ≥4 mm. A single urologist, blinded to stone composition, reviewed all NCCT to acquire stone location, dimensions, and Hounsfield unit (HU). HU density (HUD) was calculated by dividing mean HU by the stone's largest transverse diameter. Stone analysis was performed via Fourier transform infrared spectrometry. Independent sample Student's t-test and analysis of variance (ANOVA) were used to compare HU/HUD among groups. Spearman's correlation test was used to determine the correlation between HU and stone size and also HU/HUD to % of each component within the stone. Significance was considered if p<0.05. Fourty-four patients met the inclusion criteria. Struvite was the most prevalent component with mean percentage of 50.1%±17.7%. Mean HU and HUD were 820.2±357.9 and 67.5±54.9, respectively. Struvite component analysis revealed a nonsignificant positive correlation with HU (R=0.017; p=0.912) and negative with HUD (R=-0.20; p=0.898). Overall, 3 (6.8%) had <20% of struvite component; 11 (25%), 25 (56.8%), and 5 (11.4%) had 21% to 40%, 41% to 60%, and 61% to 80% of struvite, respectively. ANOVA revealed no difference among groups regarding HU (p=0.68) and HUD (p=0.37), with important overlaps. When comparing pure struvite stones (n=5) with other miscellaneous stones (n=39), no difference was found for HU (p=0.09) but HUD was significantly lower for pure stones (27.9±23.6 v 72.5±55.9, respectively; p=0.006). Again, significant overlaps were seen. Pure struvite stones have significantly lower HUD than mixed struvite stones, but overlap exists. A low HUD may increase the

Accurate quantum Z rotations with less magic

NASA Astrophysics Data System (ADS)

Landahl, Andrew; Cesare, Chris

2013-03-01

We present quantum protocols for executing arbitrarily accurate π /2k rotations of a qubit about its Z axis. Unlike reduced instruction set computing (RISC) protocols which use a two-step process of synthesizing high-fidelity ``magic'' states from which T = Z (π / 4) gates can be teleported and then compiling a sequence of adaptive stabilizer operations and T gates to approximate Z (π /2k) , our complex instruction set computing (CISC) protocol distills magic states for the Z (π /2k) gates directly. Replacing this two-step process with a single step results in substantial reductions in the number of gates needed. The key to our construction is a family of shortened quantum Reed-Muller codes of length 2 k + 2 - 1 , whose distillation threshold shrinks with k but is greater than 0.85% for k <= 6 . AJL and CC were supported in part by the Laboratory Directed Research and Development program at Sandia National Laboratories. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

GenePRIMP: A Gene Prediction Improvement Pipeline For Prokaryotic Genomes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kyrpides, Nikos C.; Ivanova, Natalia N.; Pati, Amrita

2010-07-08

GenePRIMP (Gene Prediction Improvement Pipeline, Http://geneprimp.jgi-psf.org), a computational process that performs evidence-based evaluation of gene models in prokaryotic genomes and reports anomalies including inconsistent start sites, missing genes, and split genes. We show that manual curation of gene models using the anomaly reports generated by GenePRIMP improves their quality and demonstrate the applicability of GenePRIMP in improving finishing quality and comparing different genome sequencing and annotation technologies. Keywords in context: Gene model, Quality Control, Translation start sites, Automatic correction. Hardware requirements; PC, MAC; Operating System: UNIX/LINUX; Compiler/Version: Perl 5.8.5 or higher; Special requirements: NCBI Blast and nr installation; File Types:more » Source Code, Executable module(s), Sample problem input data; installation instructions other; programmer documentation. Location/transmission: http://geneprimp.jgi-psf.org/gp.tar.gz« less

Accurate spectroscopic characterization of oxirane: A valuable route to its identification in Titan's atmosphere and the assignment of unidentified infrared bands

DOE Office of Scientific and Technical Information (OSTI.GOV)

Puzzarini, Cristina; Biczysko, Malgorzata; Bloino, Julien

2014-04-20

In an effort to provide an accurate spectroscopic characterization of oxirane, state-of-the-art computational methods and approaches have been employed to determine highly accurate fundamental vibrational frequencies and rotational parameters. Available experimental data were used to assess the reliability of our computations, and an accuracy on average of 10 cm{sup –1} for fundamental transitions as well as overtones and combination bands has been pointed out. Moving to rotational spectroscopy, relative discrepancies of 0.1%, 2%-3%, and 3%-4% were observed for rotational, quartic, and sextic centrifugal-distortion constants, respectively. We are therefore confident that the highly accurate spectroscopic data provided herein can be usefulmore » for identification of oxirane in Titan's atmosphere and the assignment of unidentified infrared bands. Since oxirane was already observed in the interstellar medium and some astronomical objects are characterized by very high D/H ratios, we also considered the accurate determination of the spectroscopic parameters for the mono-deuterated species, oxirane-d1. For the latter, an empirical scaling procedure allowed us to improve our computed data and to provide predictions for rotational transitions with a relative accuracy of about 0.02% (i.e., an uncertainty of about 40 MHz for a transition lying at 200 GHz).« less

Accurate and computationally efficient prediction of thermochemical properties of biomolecules using the generalized connectivity-based hierarchy.

PubMed

Sengupta, Arkajyoti; Ramabhadran, Raghunath O; Raghavachari, Krishnan

2014-08-14

In this study we have used the connectivity-based hierarchy (CBH) method to derive accurate heats of formation of a range of biomolecules, 18 amino acids and 10 barbituric acid/uracil derivatives. The hierarchy is based on the connectivity of the different atoms in a large molecule. It results in error-cancellation reaction schemes that are automated, general, and can be readily used for a broad range of organic molecules and biomolecules. Herein, we first locate stable conformational and tautomeric forms of these biomolecules using an accurate level of theory (viz. CCSD(T)/6-311++G(3df,2p)). Subsequently, the heats of formation of the amino acids are evaluated using the CBH-1 and CBH-2 schemes and routinely employed density functionals or wave function-based methods. The calculated heats of formation obtained herein using modest levels of theory and are in very good agreement with those obtained using more expensive W1-F12 and W2-F12 methods on amino acids and G3 results on barbituric acid derivatives. Overall, the present study (a) highlights the small effect of including multiple conformers in determining the heats of formation of biomolecules and (b) in concurrence with previous CBH studies, proves that use of the more effective error-cancelling isoatomic scheme (CBH-2) results in more accurate heats of formation with modestly sized basis sets along with common density functionals or wave function-based methods.

Gene Therapy for Fracture Repair

DTIC Science & Technology

2005-12-01

therapeutic benefits. We have identified a murine leukemia virus (MLV) vector that provides robust transgene expression in fracture tissues, and applied it to...During the second year of funding, we used the surgical technique to apply the murine leukemia virus (MLV)-based vector to the fracture tissues and...trochanter. ii ) Fracture Injection The therapeutic gene chosen was the BMP-2/4 hybrid gene. To most accurately establish the expression of the

Time-Accurate Numerical Prediction of Free Flight Aerodynamics of a Finned Projectile

DTIC Science & Technology

2005-09-01

develop (with fewer dollars) more lethal and effective munitions. The munitions must stay abreast of the latest technology available to our...consuming. Computer simulations can and have provided an effective means of determining the unsteady aerodynamics and flight mechanics of guided projectile...Recently, the time-accurate technique was used to obtain improved results for Magnus moment and roll damping moment of a spinning projectile at transonic

Magnetic gaps in organic tri-radicals: From a simple model to accurate estimates.

PubMed

Barone, Vincenzo; Cacelli, Ivo; Ferretti, Alessandro; Prampolini, Giacomo

2017-03-14

The calculation of the energy gap between the magnetic states of organic poly-radicals still represents a challenging playground for quantum chemistry, and high-level techniques are required to obtain accurate estimates. On these grounds, the aim of the present study is twofold. From the one side, it shows that, thanks to recent algorithmic and technical improvements, we are able to compute reliable quantum mechanical results for the systems of current fundamental and technological interest. From the other side, proper parameterization of a simple Hubbard Hamiltonian allows for a sound rationalization of magnetic gaps in terms of basic physical effects, unraveling the role played by electron delocalization, Coulomb repulsion, and effective exchange in tuning the magnetic character of the ground state. As case studies, we have chosen three prototypical organic tri-radicals, namely, 1,3,5-trimethylenebenzene, 1,3,5-tridehydrobenzene, and 1,2,3-tridehydrobenzene, which differ either for geometric or electronic structure. After discussing the differences among the three species and their consequences on the magnetic properties in terms of the simple model mentioned above, accurate and reliable values for the energy gap between the lowest quartet and doublet states are computed by means of the so-called difference dedicated configuration interaction (DDCI) technique, and the final results are discussed and compared to both available experimental and computational estimates.

Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants1[OPEN

PubMed Central

Zhang, Peifen; Kim, Taehyong; Banf, Michael; Chavali, Arvind K.; Nilo-Poyanco, Ricardo; Bernard, Thomas

2017-01-01

Plant metabolism underpins many traits of ecological and agronomic importance. Plants produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have not yet been elucidated. To engineer and improve metabolic traits, we need comprehensive and accurate knowledge of the organization and regulation of plant metabolism at the genome scale. Here, we present a computational pipeline to identify metabolic enzymes, pathways, and gene clusters from a sequenced genome. Using this pipeline, we generated metabolic pathway databases for 22 species and identified metabolic gene clusters from 18 species. This unified resource can be used to conduct a wide array of comparative studies of plant metabolism. Using the resource, we discovered a widespread occurrence of metabolic gene clusters in plants: 11,969 clusters from 18 species. The prevalence of metabolic gene clusters offers an intriguing possibility of an untapped source for uncovering new metabolite biosynthesis pathways. For example, more than 1,700 clusters contain enzymes that could generate a specialized metabolite scaffold (signature enzymes) and enzymes that modify the scaffold (tailoring enzymes). In four species with sufficient gene expression data, we identified 43 highly coexpressed clusters that contain signature and tailoring enzymes, of which eight were characterized previously to be functional pathways. Finally, we identified patterns of genome organization that implicate local gene duplication and, to a lesser extent, single gene transposition as having played roles in the evolution of plant metabolic gene clusters. PMID:28228535

Computer Simulation of Microwave Devices

NASA Technical Reports Server (NTRS)

Kory, Carol L.

1997-01-01

The accurate simulation of cold-test results including dispersion, on-axis beam interaction impedance, and attenuation of a helix traveling-wave tube (TWT) slow-wave circuit using the three-dimensional code MAFIA (Maxwell's Equations Solved by the Finite Integration Algorithm) was demonstrated for the first time. Obtaining these results is a critical step in the design of TWT's. A well-established procedure to acquire these parameters is to actually build and test a model or a scale model of the circuit. However, this procedure is time-consuming and expensive, and it limits freedom to examine new variations to the basic circuit. These limitations make the need for computational methods crucial since they can lower costs, reduce tube development time, and lessen limitations on novel designs. Computer simulation has been used to accurately obtain cold-test parameters for several slow-wave circuits. Although the helix slow-wave circuit remains the mainstay of the TWT industry because of its exceptionally wide bandwidth, until recently it has been impossible to accurately analyze a helical TWT using its exact dimensions because of the complexity of its geometrical structure. A new computer modeling technique developed at the NASA Lewis Research Center overcomes these difficulties. The MAFIA three-dimensional mesh for a C-band helix slow-wave circuit is shown.

Demystifying the GMAT: Computer-Based Testing Terms

ERIC Educational Resources Information Center

Rudner, Lawrence M.

2012-01-01

Computer-based testing can be a powerful means to make all aspects of test administration not only faster and more efficient, but also more accurate and more secure. While the Graduate Management Admission Test (GMAT) exam is a computer adaptive test, there are other approaches. This installment presents a primer of computer-based testing terms.

Spatial reconstruction of single-cell gene expression

PubMed Central

Satija, Rahul; Farrell, Jeffrey A.; Gennert, David; Schier, Alexander F.; Regev, Aviv

2015-01-01

Spatial localization is a key determinant of cellular fate and behavior, but spatial RNA assays traditionally rely on staining for a limited number of RNA species. In contrast, single-cell RNA-seq allows for deep profiling of cellular gene expression, but established methods separate cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos, inferring a transcriptome-wide map of spatial patterning. We confirmed Seurat’s accuracy using several experimental approaches, and used it to identify a set of archetypal expression patterns and spatial markers. Additionally, Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems. PMID:25867923

Combinatorial Pooling Enables Selective Sequencing of the Barley Gene Space

PubMed Central

Lonardi, Stefano; Duma, Denisa; Alpert, Matthew; Cordero, Francesca; Beccuti, Marco; Bhat, Prasanna R.; Wu, Yonghui; Ciardo, Gianfranco; Alsaihati, Burair; Ma, Yaqin; Wanamaker, Steve; Resnik, Josh; Bozdag, Serdar; Luo, Ming-Cheng; Close, Timothy J.

2013-01-01

For the vast majority of species – including many economically or ecologically important organisms, progress in biological research is hampered due to the lack of a reference genome sequence. Despite recent advances in sequencing technologies, several factors still limit the availability of such a critical resource. At the same time, many research groups and international consortia have already produced BAC libraries and physical maps and now are in a position to proceed with the development of whole-genome sequences organized around a physical map anchored to a genetic map. We propose a BAC-by-BAC sequencing protocol that combines combinatorial pooling design and second-generation sequencing technology to efficiently approach denovo selective genome sequencing. We show that combinatorial pooling is a cost-effective and practical alternative to exhaustive DNA barcoding when preparing sequencing libraries for hundreds or thousands of DNA samples, such as in this case gene-bearing minimum-tiling-path BAC clones. The novelty of the protocol hinges on the computational ability to efficiently compare hundred millions of short reads and assign them to the correct BAC clones (deconvolution) so that the assembly can be carried out clone-by-clone. Experimental results on simulated data for the rice genome show that the deconvolution is very accurate, and the resulting BAC assemblies have high quality. Results on real data for a gene-rich subset of the barley genome confirm that the deconvolution is accurate and the BAC assemblies have good quality. While our method cannot provide the level of completeness that one would achieve with a comprehensive whole-genome sequencing project, we show that it is quite successful in reconstructing the gene sequences within BACs. In the case of plants such as barley, this level of sequence knowledge is sufficient to support critical end-point objectives such as map-based cloning and marker-assisted breeding. PMID:23592960

Combinatorial pooling enables selective sequencing of the barley gene space.

PubMed

Lonardi, Stefano; Duma, Denisa; Alpert, Matthew; Cordero, Francesca; Beccuti, Marco; Bhat, Prasanna R; Wu, Yonghui; Ciardo, Gianfranco; Alsaihati, Burair; Ma, Yaqin; Wanamaker, Steve; Resnik, Josh; Bozdag, Serdar; Luo, Ming-Cheng; Close, Timothy J

2013-04-01

For the vast majority of species - including many economically or ecologically important organisms, progress in biological research is hampered due to the lack of a reference genome sequence. Despite recent advances in sequencing technologies, several factors still limit the availability of such a critical resource. At the same time, many research groups and international consortia have already produced BAC libraries and physical maps and now are in a position to proceed with the development of whole-genome sequences organized around a physical map anchored to a genetic map. We propose a BAC-by-BAC sequencing protocol that combines combinatorial pooling design and second-generation sequencing technology to efficiently approach denovo selective genome sequencing. We show that combinatorial pooling is a cost-effective and practical alternative to exhaustive DNA barcoding when preparing sequencing libraries for hundreds or thousands of DNA samples, such as in this case gene-bearing minimum-tiling-path BAC clones. The novelty of the protocol hinges on the computational ability to efficiently compare hundred millions of short reads and assign them to the correct BAC clones (deconvolution) so that the assembly can be carried out clone-by-clone. Experimental results on simulated data for the rice genome show that the deconvolution is very accurate, and the resulting BAC assemblies have high quality. Results on real data for a gene-rich subset of the barley genome confirm that the deconvolution is accurate and the BAC assemblies have good quality. While our method cannot provide the level of completeness that one would achieve with a comprehensive whole-genome sequencing project, we show that it is quite successful in reconstructing the gene sequences within BACs. In the case of plants such as barley, this level of sequence knowledge is sufficient to support critical end-point objectives such as map-based cloning and marker-assisted breeding.

A literature search tool for intelligent extraction of disease-associated genes.

PubMed

Jung, Jae-Yoon; DeLuca, Todd F; Nelson, Tristan H; Wall, Dennis P

2014-01-01

To extract disorder-associated genes from the scientific literature in PubMed with greater sensitivity for literature-based support than existing methods. We developed a PubMed query to retrieve disorder-related, original research articles. Then we applied a rule-based text-mining algorithm with keyword matching to extract target disorders, genes with significant results, and the type of study described by the article. We compared our resulting candidate disorder genes and supporting references with existing databases. We demonstrated that our candidate gene set covers nearly all genes in manually curated databases, and that the references supporting the disorder-gene link are more extensive and accurate than other general purpose gene-to-disorder association databases. We implemented a novel publication search tool to find target articles, specifically focused on links between disorders and genotypes. Through comparison against gold-standard manually updated gene-disorder databases and comparison with automated databases of similar functionality we show that our tool can search through the entirety of PubMed to extract the main gene findings for human diseases rapidly and accurately.

Allele-sharing models: LOD scores and accurate linkage tests.

PubMed

Kong, A; Cox, N J

1997-11-01

Starting with a test statistic for linkage analysis based on allele sharing, we propose an associated one-parameter model. Under general missing-data patterns, this model allows exact calculation of likelihood ratios and LOD scores and has been implemented by a simple modification of existing software. Most important, accurate linkage tests can be performed. Using an example, we show that some previously suggested approaches to handling less than perfectly informative data can be unacceptably conservative. Situations in which this model may not perform well are discussed, and an alternative model that requires additional computations is suggested.

Allele-sharing models: LOD scores and accurate linkage tests.

PubMed Central

Kong, A; Cox, N J

1997-01-01

Starting with a test statistic for linkage analysis based on allele sharing, we propose an associated one-parameter model. Under general missing-data patterns, this model allows exact calculation of likelihood ratios and LOD scores and has been implemented by a simple modification of existing software. Most important, accurate linkage tests can be performed. Using an example, we show that some previously suggested approaches to handling less than perfectly informative data can be unacceptably conservative. Situations in which this model may not perform well are discussed, and an alternative model that requires additional computations is suggested. PMID:9345087

Method and apparatus for accurately manipulating an object during microelectrophoresis

DOEpatents

Parvin, Bahram A.; Maestre, Marcos F.; Fish, Richard H.; Johnston, William E.

1997-01-01

An apparatus using electrophoresis provides accurate manipulation of an object on a microscope stage for further manipulations add reactions. The present invention also provides an inexpensive and easily accessible means to move an object without damage to the object. A plurality of electrodes are coupled to the stage in an array whereby the electrode array allows for distinct manipulations of the electric field for accurate manipulations of the object. There is an electrode array control coupled to the plurality of electrodes for manipulating the electric field. In an alternative embodiment, a chamber is provided on the stage to hold the object. The plurality of electrodes are positioned in the chamber, and the chamber is filled with fluid. The system can be automated using visual servoing, which manipulates the control parameters, i.e., x, y stage, applying the field, etc., after extracting the significant features directly from image data. Visual servoing includes an imaging device and computer system to determine the location of the object. A second stage having a plurality of tubes positioned on top of the second stage, can be accurately positioned by visual servoing so that one end of one of the plurality of tubes surrounds at least part of the object on the first stage.

Method and apparatus for accurately manipulating an object during microelectrophoresis

DOEpatents

Parvin, B.A.; Maestre, M.F.; Fish, R.H.; Johnston, W.E.

1997-09-23

An apparatus using electrophoresis provides accurate manipulation of an object on a microscope stage for further manipulations and reactions. The present invention also provides an inexpensive and easily accessible means to move an object without damage to the object. A plurality of electrodes are coupled to the stage in an array whereby the electrode array allows for distinct manipulations of the electric field for accurate manipulations of the object. There is an electrode array control coupled to the plurality of electrodes for manipulating the electric field. In an alternative embodiment, a chamber is provided on the stage to hold the object. The plurality of electrodes are positioned in the chamber, and the chamber is filled with fluid. The system can be automated using visual servoing, which manipulates the control parameters, i.e., x, y stage, applying the field, etc., after extracting the significant features directly from image data. Visual servoing includes an imaging device and computer system to determine the location of the object. A second stage having a plurality of tubes positioned on top of the second stage, can be accurately positioned by visual servoing so that one end of one of the plurality of tubes surrounds at least part of the object on the first stage. 11 figs.

Mutation databases for inherited renal disease: are they complete, accurate, clinically relevant, and freely available?

PubMed

Savige, Judy; Dagher, Hayat; Povey, Sue

2014-07-01

This study examined whether gene-specific DNA variant databases for inherited diseases of the kidney fulfilled the Human Variome Project recommendations of being complete, accurate, clinically relevant and freely available. A recent review identified 60 inherited renal diseases caused by mutations in 132 genes. The disease name, MIM number, gene name, together with "mutation" or "database," were used to identify web-based databases. Fifty-nine diseases (98%) due to mutations in 128 genes had a variant database. Altogether there were 349 databases (a median of 3 per gene, range 0-6), but no gene had two databases with the same number of variants, and 165 (50%) databases included fewer than 10 variants. About half the databases (180, 54%) had been updated in the previous year. Few (77, 23%) were curated by "experts" but these included nine of the 11 with the most variants. Even fewer databases (41, 12%) included clinical features apart from the name of the associated disease. Most (223, 67%) could be accessed without charge, including those for 50 genes (40%) with the maximum number of variants. Future efforts should focus on encouraging experts to collaborate on a single database for each gene affected in inherited renal disease, including both unpublished variants, and clinical phenotypes. © 2014 WILEY PERIODICALS, INC.

Accurate phylogenetic classification of DNA fragments based onsequence composition

DOE Office of Scientific and Technical Information (OSTI.GOV)

McHardy, Alice C.; Garcia Martin, Hector; Tsirigos, Aristotelis

2006-05-01

Metagenome studies have retrieved vast amounts of sequenceout of a variety of environments, leading to novel discoveries and greatinsights into the uncultured microbial world. Except for very simplecommunities, diversity makes sequence assembly and analysis a verychallenging problem. To understand the structure a 5 nd function ofmicrobial communities, a taxonomic characterization of the obtainedsequence fragments is highly desirable, yet currently limited mostly tothose sequences that contain phylogenetic marker genes. We show that forclades at the rank of domain down to genus, sequence composition allowsthe very accurate phylogenetic 10 characterization of genomic sequence.We developed a composition-based classifier, PhyloPythia, for de novophylogenetic sequencemore » characterization and have trained it on adata setof 340 genomes. By extensive evaluation experiments we show that themethodis accurate across all taxonomic ranks considered, even forsequences that originate fromnovel organisms and are as short as 1kb.Application to two metagenome datasets 15 obtained from samples ofphosphorus-removing sludge showed that the method allows the accurateclassification at genus level of most sequence fragments from thedominant populations, while at the same time correctly characterizingeven larger parts of the samples at higher taxonomic levels.« less

Computing prokaryotic gene ubiquity: rescuing the core from extinction.

PubMed

Charlebois, Robert L; Doolittle, W Ford

2004-12-01

The genomic core concept has found several uses in comparative and evolutionary genomics. Defined as the set of all genes common to (ubiquitous among) all genomes in a phylogenetically coherent group, core size decreases as the number and phylogenetic diversity of the relevant group increases. Here, we focus on methods for defining the size and composition of the core of all genes shared by sequenced genomes of prokaryotes (Bacteria and Archaea). There are few (almost certainly less than 50) genes shared by all of the 147 genomes compared, surely insufficient to conduct all essential functions. Sequencing and annotation errors are responsible for the apparent absence of some genes, while very limited but genuine disappearances (from just one or a few genomes) can account for several others. Core size will continue to decrease as more genome sequences appear, unless the requirement for ubiquity is relaxed. Such relaxation seems consistent with any reasonable biological purpose for seeking a core, but it renders the problem of definition more problematic. We propose an alternative approach (the phylogenetically balanced core), which preserves some of the biological utility of the core concept. Cores, however delimited, preferentially contain informational rather than operational genes; we present a new hypothesis for why this might be so.

Genome-wide prediction and analysis of human tissue-selective genes using microarray expression data

PubMed Central

2013-01-01

Background Understanding how genes are expressed specifically in particular tissues is a fundamental question in developmental biology. Many tissue-specific genes are involved in the pathogenesis of complex human diseases. However, experimental identification of tissue-specific genes is time consuming and difficult. The accurate predictions of tissue-specific gene targets could provide useful information for biomarker development and drug target identification. Results In this study, we have developed a machine learning approach for predicting the human tissue-specific genes using microarray expression data. The lists of known tissue-specific genes for different tissues were collected from UniProt database, and the expression data retrieved from the previously compiled dataset according to the lists were used for input vector encoding. Random Forests (RFs) and Support Vector Machines (SVMs) were used to construct accurate classifiers. The RF classifiers were found to outperform SVM models for tissue-specific gene prediction. The results suggest that the candidate genes for brain or liver specific expression can provide valuable information for further experimental studies. Our approach was also applied for identifying tissue-selective gene targets for different types of tissues. Conclusions A machine learning approach has been developed for accurately identifying the candidate genes for tissue specific/selective expression. The approach provides an efficient way to select some interesting genes for developing new biomedical markers and improve our knowledge of tissue-specific expression. PMID:23369200

Robust TLR4-induced gene expression patterns are not an accurate indicator of human immunity

PubMed Central

2010-01-01

Background Activation of Toll-like receptors (TLRs) is widely accepted as an essential event for defence against infection. Many TLRs utilize a common signalling pathway that relies on activation of the kinase IRAK4 and the transcription factor NFκB for the rapid expression of immunity genes. Methods 21 K DNA microarray technology was used to evaluate LPS-induced (TLR4) gene responses in blood monocytes from a child with an IRAK4-deficiency. In vitro responsiveness to LPS was confirmed by real-time PCR and ELISA and compared to the clinical predisposition of the child and IRAK4-deficient mice to Gram negative infection. Results We demonstrated that the vast majority of LPS-responsive genes in IRAK4-deficient monocytes were greatly suppressed, an observation that is consistent with the described role for IRAK4 as an essential component of TLR4 signalling. The severely impaired response to LPS, however, is inconsistent with a remarkably low incidence of Gram negative infections observed in this child and other children with IRAK4-deficiency. This unpredicted clinical phenotype was validated by demonstrating that IRAK4-deficient mice had a similar resistance to infection with Gram negative S. typhimurium as wildtype mice. A number of immunity genes, such as chemokines, were expressed at normal levels in human IRAK4-deficient monocytes, indicating that particular IRAK4-independent elements within the repertoire of TLR4-induced responses are expressed. Conclusions Sufficient defence to Gram negative immunity does not require IRAK4 or a robust, 'classic' inflammatory and immune response. PMID:20105294

The Joint Effects of Background Selection and Genetic Recombination on Local Gene Genealogies

PubMed Central

Zeng, Kai; Charlesworth, Brian

2011-01-01

Background selection, the effects of the continual removal of deleterious mutations by natural selection on variability at linked sites, is potentially a major determinant of DNA sequence variability. However, the joint effects of background selection and genetic recombination on the shape of the neutral gene genealogy have proved hard to study analytically. The only existing formula concerns the mean coalescent time for a pair of alleles, making it difficult to assess the importance of background selection from genome-wide data on sequence polymorphism. Here we develop a structured coalescent model of background selection with recombination and implement it in a computer program that efficiently generates neutral gene genealogies for an arbitrary sample size. We check the validity of the structured coalescent model against forward-in-time simulations and show that it accurately captures the effects of background selection. The model produces more accurate predictions of the mean coalescent time than the existing formula and supports the conclusion that the effect of background selection is greater in the interior of a deleterious region than at its boundaries. The level of linkage disequilibrium between sites is elevated by background selection, to an extent that is well summarized by a change in effective population size. The structured coalescent model is readily extendable to more realistic situations and should prove useful for analyzing genome-wide polymorphism data. PMID:21705759

The joint effects of background selection and genetic recombination on local gene genealogies.

PubMed

Zeng, Kai; Charlesworth, Brian

2011-09-01

Background selection, the effects of the continual removal of deleterious mutations by natural selection on variability at linked sites, is potentially a major determinant of DNA sequence variability. However, the joint effects of background selection and genetic recombination on the shape of the neutral gene genealogy have proved hard to study analytically. The only existing formula concerns the mean coalescent time for a pair of alleles, making it difficult to assess the importance of background selection from genome-wide data on sequence polymorphism. Here we develop a structured coalescent model of background selection with recombination and implement it in a computer program that efficiently generates neutral gene genealogies for an arbitrary sample size. We check the validity of the structured coalescent model against forward-in-time simulations and show that it accurately captures the effects of background selection. The model produces more accurate predictions of the mean coalescent time than the existing formula and supports the conclusion that the effect of background selection is greater in the interior of a deleterious region than at its boundaries. The level of linkage disequilibrium between sites is elevated by background selection, to an extent that is well summarized by a change in effective population size. The structured coalescent model is readily extendable to more realistic situations and should prove useful for analyzing genome-wide polymorphism data.

Identification and Evaluation of Reliable Reference Genes for Quantitative Real-Time PCR Analysis in Tea Plant (Camellia sinensis (L.) O. Kuntze)

PubMed Central

Hao, Xinyuan; Horvath, David P.; Chao, Wun S.; Yang, Yajun; Wang, Xinchao; Xiao, Bin

2014-01-01

Reliable reference selection for the accurate quantification of gene expression under various experimental conditions is a crucial step in qRT-PCR normalization. To date, only a few housekeeping genes have been identified and used as reference genes in tea plant. The validity of those reference genes are not clear since their expression stabilities have not been rigorously examined. To identify more appropriate reference genes for qRT-PCR studies on tea plant, we examined the expression stability of 11 candidate reference genes from three different sources: the orthologs of Arabidopsis traditional reference genes and stably expressed genes identified from whole-genome GeneChip studies, together with three housekeeping gene commonly used in tea plant research. We evaluated the transcript levels of these genes in 94 experimental samples. The expression stabilities of these 11 genes were ranked using four different computation programs including geNorm, Normfinder, BestKeeper, and the comparative ∆CT method. Results showed that the three commonly used housekeeping genes of CsTUBULIN1, CsACINT1 and Cs18S rRNA1 together with CsUBQ1 were the most unstable genes in all sample ranking order. However, CsPTB1, CsEF1, CsSAND1, CsCLATHRIN1 and CsUBC1 were the top five appropriate reference genes for qRT-PCR analysis in complex experimental conditions. PMID:25474086

Gene Expression Patterns Associated With Histopathology in Toxic Liver Fibrosis.

PubMed

Ippolito, Danielle L; AbdulHameed, Mohamed Diwan M; Tawa, Gregory J; Baer, Christine E; Permenter, Matthew G; McDyre, Bonna C; Dennis, William E; Boyle, Molly H; Hobbs, Cheryl A; Streicker, Michael A; Snowden, Bobbi S; Lewis, John A; Wallqvist, Anders; Stallings, Jonathan D

2016-01-01

Toxic industrial chemicals induce liver injury, which is difficult to diagnose without invasive procedures. Identifying indicators of end organ injury can complement exposure-based assays and improve predictive power. A multiplexed approach was used to experimentally evaluate a panel of 67 genes predicted to be associated with the fibrosis pathology by computationally mining DrugMatrix, a publicly available repository of gene microarray data. Five-day oral gavage studies in male Sprague Dawley rats dosed with varying concentrations of 3 fibrogenic compounds (allyl alcohol, carbon tetrachloride, and 4,4'-methylenedianiline) and 2 nonfibrogenic compounds (bromobenzene and dexamethasone) were conducted. Fibrosis was definitively diagnosed by histopathology. The 67-plex gene panel accurately diagnosed fibrosis in both microarray and multiplexed-gene expression assays. Necrosis and inflammatory infiltration were comorbid with fibrosis. ANOVA with contrasts identified that 51 of the 67 predicted genes were significantly associated with the fibrosis phenotype, with 24 of these specific to fibrosis alone. The protein product of the gene most strongly correlated with the fibrosis phenotype PCOLCE (Procollagen C-Endopeptidase Enhancer) was dose-dependently elevated in plasma from animals administered fibrogenic chemicals (P < .05). Semiquantitative global mass spectrometry analysis of the plasma identified an additional 5 protein products of the gene panel which increased after fibrogenic toxicant administration: fibronectin, ceruloplasmin, vitronectin, insulin-like growth factor binding protein, and α2-macroglobulin. These results support the data mining approach for identifying gene and/or protein panels for assessing liver injury and may suggest bridging biomarkers for molecular mediators linked to histopathology. Published by Oxford University Press on behalf of the Society of Toxicology 2015. This work is written by US Government employees and is in the public

iPcc: a novel feature extraction method for accurate disease class discovery and prediction

PubMed Central

Ren, Xianwen; Wang, Yong; Zhang, Xiang-Sun; Jin, Qi

2013-01-01

Gene expression profiling has gradually become a routine procedure for disease diagnosis and classification. In the past decade, many computational methods have been proposed, resulting in great improvements on various levels, including feature selection and algorithms for classification and clustering. In this study, we present iPcc, a novel method from the feature extraction perspective to further propel gene expression profiling technologies from bench to bedside. We define ‘correlation feature space’ for samples based on the gene expression profiles by iterative employment of Pearson’s correlation coefficient. Numerical experiments on both simulated and real gene expression data sets demonstrate that iPcc can greatly highlight the latent patterns underlying noisy gene expression data and thus greatly improve the robustness and accuracy of the algorithms currently available for disease diagnosis and classification based on gene expression profiles. PMID:23761440

Accurate upwind methods for the Euler equations

NASA Technical Reports Server (NTRS)

Huynh, Hung T.

1993-01-01

A new class of piecewise linear methods for the numerical solution of the one-dimensional Euler equations of gas dynamics is presented. These methods are uniformly second-order accurate, and can be considered as extensions of Godunov's scheme. With an appropriate definition of monotonicity preservation for the case of linear convection, it can be shown that they preserve monotonicity. Similar to Van Leer's MUSCL scheme, they consist of two key steps: a reconstruction step followed by an upwind step. For the reconstruction step, a monotonicity constraint that preserves uniform second-order accuracy is introduced. Computational efficiency is enhanced by devising a criterion that detects the 'smooth' part of the data where the constraint is redundant. The concept and coding of the constraint are simplified by the use of the median function. A slope steepening technique, which has no effect at smooth regions and can resolve a contact discontinuity in four cells, is described. As for the upwind step, existing and new methods are applied in a manner slightly different from those in the literature. These methods are derived by approximating the Euler equations via linearization and diagonalization. At a 'smooth' interface, Harten, Lax, and Van Leer's one intermediate state model is employed. A modification for this model that can resolve contact discontinuities is presented. Near a discontinuity, either this modified model or a more accurate one, namely, Roe's flux-difference splitting. is used. The current presentation of Roe's method, via the conceptually simple flux-vector splitting, not only establishes a connection between the two splittings, but also leads to an admissibility correction with no conditional statement, and an efficient approximation to Osher's approximate Riemann solver. These reconstruction and upwind steps result in schemes that are uniformly second-order accurate and economical at smooth regions, and yield high resolution at discontinuities.

High Order Schemes in Bats-R-US for Faster and More Accurate Predictions

NASA Astrophysics Data System (ADS)

Chen, Y.; Toth, G.; Gombosi, T. I.

2014-12-01

BATS-R-US is a widely used global magnetohydrodynamics model that originally employed second order accurate TVD schemes combined with block based Adaptive Mesh Refinement (AMR) to achieve high resolution in the regions of interest. In the last years we have implemented fifth order accurate finite difference schemes CWENO5 and MP5 for uniform Cartesian grids. Now the high order schemes have been extended to generalized coordinates, including spherical grids and also to the non-uniform AMR grids including dynamic regridding. We present numerical tests that verify the preservation of free-stream solution and high-order accuracy as well as robust oscillation-free behavior near discontinuities. We apply the new high order accurate schemes to both heliospheric and magnetospheric simulations and show that it is robust and can achieve the same accuracy as the second order scheme with much less computational resources. This is especially important for space weather prediction that requires faster than real time code execution.

ArraySolver: an algorithm for colour-coded graphical display and Wilcoxon signed-rank statistics for comparing microarray gene expression data.

PubMed

Khan, Haseeb Ahmad

2004-01-01

The massive surge in the production of microarray data poses a great challenge for proper analysis and interpretation. In recent years numerous computational tools have been developed to extract meaningful interpretation of microarray gene expression data. However, a convenient tool for two-groups comparison of microarray data is still lacking and users have to rely on commercial statistical packages that might be costly and require special skills, in addition to extra time and effort for transferring data from one platform to other. Various statistical methods, including the t-test, analysis of variance, Pearson test and Mann-Whitney U test, have been reported for comparing microarray data, whereas the utilization of the Wilcoxon signed-rank test, which is an appropriate test for two-groups comparison of gene expression data, has largely been neglected in microarray studies. The aim of this investigation was to build an integrated tool, ArraySolver, for colour-coded graphical display and comparison of gene expression data using the Wilcoxon signed-rank test. The results of software validation showed similar outputs with ArraySolver and SPSS for large datasets. Whereas the former program appeared to be more accurate for 25 or fewer pairs (n < or = 25), suggesting its potential application in analysing molecular signatures that usually contain small numbers of genes. The main advantages of ArraySolver are easy data selection, convenient report format, accurate statistics and the familiar Excel platform.

ArraySolver: An Algorithm for Colour-Coded Graphical Display and Wilcoxon Signed-Rank Statistics for Comparing Microarray Gene Expression Data

PubMed Central

2004-01-01

The massive surge in the production of microarray data poses a great challenge for proper analysis and interpretation. In recent years numerous computational tools have been developed to extract meaningful interpretation of microarray gene expression data. However, a convenient tool for two-groups comparison of microarray data is still lacking and users have to rely on commercial statistical packages that might be costly and require special skills, in addition to extra time and effort for transferring data from one platform to other. Various statistical methods, including the t-test, analysis of variance, Pearson test and Mann–Whitney U test, have been reported for comparing microarray data, whereas the utilization of the Wilcoxon signed-rank test, which is an appropriate test for two-groups comparison of gene expression data, has largely been neglected in microarray studies. The aim of this investigation was to build an integrated tool, ArraySolver, for colour-coded graphical display and comparison of gene expression data using the Wilcoxon signed-rank test. The results of software validation showed similar outputs with ArraySolver and SPSS for large datasets. Whereas the former program appeared to be more accurate for 25 or fewer pairs (n ≤ 25), suggesting its potential application in analysing molecular signatures that usually contain small numbers of genes. The main advantages of ArraySolver are easy data selection, convenient report format, accurate statistics and the familiar Excel platform. PMID:18629036

Toward Accurate and Quantitative Comparative Metagenomics.

PubMed

Nayfach, Stephen; Pollard, Katherine S

2016-08-25

Shotgun metagenomics and computational analysis are used to compare the taxonomic and functional profiles of microbial communities. Leveraging this approach to understand roles of microbes in human biology and other environments requires quantitative data summaries whose values are comparable across samples and studies. Comparability is currently hampered by the use of abundance statistics that do not estimate a meaningful parameter of the microbial community and biases introduced by experimental protocols and data-cleaning approaches. Addressing these challenges, along with improving study design, data access, metadata standardization, and analysis tools, will enable accurate comparative metagenomics. We envision a future in which microbiome studies are replicable and new metagenomes are easily and rapidly integrated with existing data. Only then can the potential of metagenomics for predictive ecological modeling, well-powered association studies, and effective microbiome medicine be fully realized. Copyright © 2016 Elsevier Inc. All rights reserved.

Automatic temperature computation for realistic IR simulation

NASA Astrophysics Data System (ADS)

Le Goff, Alain; Kersaudy, Philippe; Latger, Jean; Cathala, Thierry; Stolte, Nilo; Barillot, Philippe

2000-07-01

Polygon temperature computation in 3D virtual scenes is fundamental for IR image simulation. This article describes in detail the temperature calculation software and its current extensions, briefly presented in [1]. This software, called MURET, is used by the simulation workshop CHORALE of the French DGA. MURET is a one-dimensional thermal software, which accurately takes into account the material thermal attributes of three-dimensional scene and the variation of the environment characteristics (atmosphere) as a function of the time. Concerning the environment, absorbed incident fluxes are computed wavelength by wavelength, for each half an hour, druing 24 hours before the time of the simulation. For each polygon, incident fluxes are compsed of: direct solar fluxes, sky illumination (including diffuse solar fluxes). Concerning the materials, classical thermal attributes are associated to several layers, such as conductivity, absorption, spectral emissivity, density, specific heat, thickness and convection coefficients are taken into account. In the future, MURET will be able to simulate permeable natural materials (water influence) and vegetation natural materials (woods). This model of thermal attributes induces a very accurate polygon temperature computation for the complex 3D databases often found in CHORALE simulations. The kernel of MUET consists of an efficient ray tracer allowing to compute the history (over 24 hours) of the shadowed parts of the 3D scene and a library, responsible for the thermal computations. The great originality concerns the way the heating fluxes are computed. Using ray tracing, the flux received in each 3D point of the scene accurately takes into account the masking (hidden surfaces) between objects. By the way, this library supplies other thermal modules such as a thermal shows computation tool.

GeneSigDB—a curated database of gene expression signatures

PubMed Central

Culhane, Aedín C.; Schwarzl, Thomas; Sultana, Razvan; Picard, Kermshlise C.; Picard, Shaita C.; Lu, Tim H.; Franklin, Katherine R.; French, Simon J.; Papenhausen, Gerald; Correll, Mick; Quackenbush, John

2010-01-01

The primary objective of most gene expression studies is the identification of one or more gene signatures; lists of genes whose transcriptional levels are uniquely associated with a specific biological phenotype. Whilst thousands of experimentally derived gene signatures are published, their potential value to the community is limited by their computational inaccessibility. Gene signatures are embedded in published article figures, tables or in supplementary materials, and are frequently presented using non-standard gene or probeset nomenclature. We present GeneSigDB (http://compbio.dfci.harvard.edu/genesigdb) a manually curated database of gene expression signatures. GeneSigDB release 1.0 focuses on cancer and stem cells gene signatures and was constructed from more than 850 publications from which we manually transcribed 575 gene signatures. Most gene signatures (n = 560) were successfully mapped to the genome to extract standardized lists of EnsEMBL gene identifiers. GeneSigDB provides the original gene signature, the standardized gene list and a fully traceable gene mapping history for each gene from the original transcribed data table through to the standardized list of genes. The GeneSigDB web portal is easy to search, allows users to compare their own gene list to those in the database, and download gene signatures in most common gene identifier formats. PMID:19934259

A Gene Ontology Tutorial in Python.

PubMed

Vesztrocy, Alex Warwick; Dessimoz, Christophe

2017-01-01

This chapter is a tutorial on using Gene Ontology resources in the Python programming language. This entails querying the Gene Ontology graph, retrieving Gene Ontology annotations, performing gene enrichment analyses, and computing basic semantic similarity between GO terms. An interactive version of the tutorial, including solutions, is available at http://gohandbook.org .

RICO: A NEW APPROACH FOR FAST AND ACCURATE REPRESENTATION OF THE COSMOLOGICAL RECOMBINATION HISTORY

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fendt, W. A.; Wandelt, B. D.; Chluba, J.

2009-04-15

We present RICO, a code designed to compute the ionization fraction of the universe during the epoch of hydrogen and helium recombination with an unprecedented combination of speed and accuracy. This is accomplished by training the machine learning code PICO on the calculations of a multilevel cosmological recombination code which self-consistently includes several physical processes that were neglected previously. After training, RICO is used to fit the free electron fraction as a function of the cosmological parameters. While, for example, at low redshifts (z {approx}< 900), much of the net change in the ionization fraction can be captured by loweringmore » the hydrogen fudge factor in RECFAST by about 3%, RICO provides a means of effectively using the accurate ionization history of the full recombination code in the standard cosmological parameter estimation framework without the need to add new or refined fudge factors or functions to a simple recombination model. Within the new approach presented here, it is easy to update RICO whenever a more accurate full recombination code becomes available. Once trained, RICO computes the cosmological ionization history with negligible fitting error in {approx}10 ms, a speedup of at least 10{sup 6} over the full recombination code that was used here. Also RICO is able to reproduce the ionization history of the full code to a level well below 0.1%, thereby ensuring that the theoretical power spectra of cosmic microwave background (CMB) fluctuations can be computed to sufficient accuracy and speed for analysis from upcoming CMB experiments like Planck. Furthermore, it will enable cross-checking different recombination codes across cosmological parameter space, a comparison that will be very important in order to assure the accurate interpretation of future CMB data.« less

Fast and Accurate Approximation to Significance Tests in Genome-Wide Association Studies

PubMed Central

Zhang, Yu; Liu, Jun S.

2011-01-01

Genome-wide association studies commonly involve simultaneous tests of millions of single nucleotide polymorphisms (SNP) for disease association. The SNPs in nearby genomic regions, however, are often highly correlated due to linkage disequilibrium (LD, a genetic term for correlation). Simple Bonferonni correction for multiple comparisons is therefore too conservative. Permutation tests, which are often employed in practice, are both computationally expensive for genome-wide studies and limited in their scopes. We present an accurate and computationally efficient method, based on Poisson de-clumping heuristics, for approximating genome-wide significance of SNP associations. Compared with permutation tests and other multiple comparison adjustment approaches, our method computes the most accurate and robust p-value adjustments for millions of correlated comparisons within seconds. We demonstrate analytically that the accuracy and the efficiency of our method are nearly independent of the sample size, the number of SNPs, and the scale of p-values to be adjusted. In addition, our method can be easily adopted to estimate false discovery rate. When applied to genome-wide SNP datasets, we observed highly variable p-value adjustment results evaluated from different genomic regions. The variation in adjustments along the genome, however, are well conserved between the European and the African populations. The p-value adjustments are significantly correlated with LD among SNPs, recombination rates, and SNP densities. Given the large variability of sequence features in the genome, we further discuss a novel approach of using SNP-specific (local) thresholds to detect genome-wide significant associations. This article has supplementary material online. PMID:22140288

Computational analysis of TRAPPC9: candidate gene for autosomal recessive non-syndromic mental retardation.

PubMed

Khattak, Naureen Aslam; Mir, Asif

2014-01-01

Mental retardation (MR)/ intellectual disability (ID) is a neuro-developmental disorder characterized by a low intellectual quotient (IQ) and deficits in adaptive behavior related to everyday life tasks such as delayed language acquisition, social skills or self-help skills with onset before age 18. To date, a few genes (PRSS12, CRBN, CC2D1A, GRIK2, TUSC3, TRAPPC9, TECR, ST3GAL3, MED23, MAN1B1, NSUN1) for autosomal-recessive forms of non syndromic MR (NS-ARMR) have been identified and established in various families with ID. The recently reported candidate gene TRAPPC9 was selected for computational analysis to explore its potentially important role in pathology as it is the only gene for ID reported in more than five different familial cases worldwide. YASARA (12.4.1) was utilized to generate three dimensional structures of the candidate gene TRAPPC9. Hybrid structure prediction was employed. Crystal Structure of a Conserved Metalloprotein From Bacillus Cereus (3D19-C) was selected as best suitable template using position-specific iteration-BLAST. Template (3D19-C) parameters were based on E-value, Z-score and resolution and quality score of 0.32, -1.152, 2.30°A and 0.684 respectively. Model reliability showed 93.1% residues placed in the most favored region with 96.684 quality factor, and overall 0.20 G-factor (dihedrals 0.06 and covalent 0.39 respectively). Protein-Protein docking analysis demonstrated that TRAPPC9 showed strong interactions of the amino acid residues S(253), S(251), Y(256), G(243), D(131) with R(105), Q(425), W(226), N(255), S(233), its functional partner 1KBKB. Protein-protein interacting residues could facilitate the exploration of structural and functional outcomes of wild type and mutated TRAPCC9 protein. Actively involved residues can be used to elucidate the binding properties of the protein, and to develop drug therapy for NS-ARMR patients.

Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin, Katherine J.; Patrick, Denis R.; Bissell, Mina J.

2008-10-20

One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasetsmore » having 295, 286, and 118 samples, respectively. Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome. The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds

Multiclass cancer diagnosis using tumor gene expression signatures

DOE PAGES

Ramaswamy, S.; Tamayo, P.; Rifkin, R.; ...

2001-12-11

The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data. In some instances, this task is difficult or impossible because of atypical clinical presentation or histopathology. To determine whether the diagnosis of multiple common adult malignancies could be achieved purely by molecular classification, we subjected 218 tumor samples, spanning 14 common tumor types, and 90 normal tissue samples to oligonucleotide microarray gene expression analysis. The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a supportmore » vector machine algorithm. Overall classification accuracy was 78%, far exceeding the accuracy of random classification (9%). Poorly differentiated cancers resulted in low-confidence predictions and could not be accurately classified according to their tissue of origin, indicating that they are molecularly distinct entities with dramatically different gene expression patterns compared with their well differentiated counterparts. Taken together, these results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.« less

RNA-seq reveals more consistent reference genes for gene expression studies in human non-melanoma skin cancers

PubMed Central

Tan, Jean-Marie; Payne, Elizabeth J.; Lin, Lynlee L.; Sinnya, Sudipta; Raphael, Anthony P.; Lambie, Duncan; Frazer, Ian H.; Dinger, Marcel E.; Soyer, H. Peter

2017-01-01

Identification of appropriate reference genes (RGs) is critical to accurate data interpretation in quantitative real-time PCR (qPCR) experiments. In this study, we have utilised next generation RNA sequencing (RNA-seq) to analyse the transcriptome of a panel of non-melanoma skin cancer lesions, identifying genes that are consistently expressed across all samples. Genes encoding ribosomal proteins were amongst the most stable in this dataset. Validation of this RNA-seq data was examined using qPCR to confirm the suitability of a set of highly stable genes for use as qPCR RGs. These genes will provide a valuable resource for the normalisation of qPCR data for the analysis of non-melanoma skin cancer. PMID:28852586

Reranking candidate gene models with cross-species comparison for improved gene prediction

PubMed Central

Liu, Qian; Crammer, Koby; Pereira, Fernando CN; Roos, David S

2008-01-01

Background Most gene finders score candidate gene models with state-based methods, typically HMMs, by combining local properties (coding potential, splice donor and acceptor patterns, etc). Competing models with similar state-based scores may be distinguishable with additional information. In particular, functional and comparative genomics datasets may help to select among competing models of comparable probability by exploiting features likely to be associated with the correct gene models, such as conserved exon/intron structure or protein sequence features. Results We have investigated the utility of a simple post-processing step for selecting among a set of alternative gene models, using global scoring rules to rerank competing models for more accurate prediction. For each gene locus, we first generate the K best candidate gene models using the gene finder Evigan, and then rerank these models using comparisons with putative orthologous genes from closely-related species. Candidate gene models with lower scores in the original gene finder may be selected if they exhibit strong similarity to probable orthologs in coding sequence, splice site location, or signal peptide occurrence. Experiments on Drosophila melanogaster demonstrate that reranking based on cross-species comparison outperforms the best gene models identified by Evigan alone, and also outperforms the comparative gene finders GeneWise and Augustus+. Conclusion Reranking gene models with cross-species comparison improves gene prediction accuracy. This straightforward method can be readily adapted to incorporate additional lines of evidence, as it requires only a ranked source of candidate gene models. PMID:18854050

Accurate structural and spectroscopic characterization of prebiotic molecules: The neutral and cationic acetyl cyanide and their related species.

PubMed

Bellili, A; Linguerri, R; Hochlaf, M; Puzzarini, C

2015-11-14

In an effort to provide an accurate structural and spectroscopic characterization of acetyl cyanide, its two enolic isomers and the corresponding cationic species, state-of-the-art computational methods, and approaches have been employed. The coupled-cluster theory including single and double excitations together with a perturbative treatment of triples has been used as starting point in composite schemes accounting for extrapolation to the complete basis-set limit as well as core-valence correlation effects to determine highly accurate molecular structures, fundamental vibrational frequencies, and rotational parameters. The available experimental data for acetyl cyanide allowed us to assess the reliability of our computations: structural, energetic, and spectroscopic properties have been obtained with an overall accuracy of about, or better than, 0.001 Å, 2 kcal/mol, 1-10 MHz, and 11 cm(-1) for bond distances, adiabatic ionization potentials, rotational constants, and fundamental vibrational frequencies, respectively. We are therefore confident that the highly accurate spectroscopic data provided herein can be useful for guiding future experimental investigations and/or astronomical observations.

Reverse engineering gene regulatory networks from measurement with missing values.

PubMed

Ogundijo, Oyetunji E; Elmas, Abdulkadir; Wang, Xiaodong

2016-12-01

Gene expression time series data are usually in the form of high-dimensional arrays. Unfortunately, the data may sometimes contain missing values: for either the expression values of some genes at some time points or the entire expression values of a single time point or some sets of consecutive time points. This significantly affects the performance of many algorithms for gene expression analysis that take as an input, the complete matrix of gene expression measurement. For instance, previous works have shown that gene regulatory interactions can be estimated from the complete matrix of gene expression measurement. Yet, till date, few algorithms have been proposed for the inference of gene regulatory network from gene expression data with missing values. We describe a nonlinear dynamic stochastic model for the evolution of gene expression. The model captures the structural, dynamical, and the nonlinear natures of the underlying biomolecular systems. We present point-based Gaussian approximation (PBGA) filters for joint state and parameter estimation of the system with one-step or two-step missing measurements . The PBGA filters use Gaussian approximation and various quadrature rules, such as the unscented transform (UT), the third-degree cubature rule and the central difference rule for computing the related posteriors. The proposed algorithm is evaluated with satisfying results for synthetic networks, in silico networks released as a part of the DREAM project, and the real biological network, the in vivo reverse engineering and modeling assessment (IRMA) network of yeast Saccharomyces cerevisiae . PBGA filters are proposed to elucidate the underlying gene regulatory network (GRN) from time series gene expression data that contain missing values. In our state-space model, we proposed a measurement model that incorporates the effect of the missing data points into the sequential algorithm. This approach produces a better inference of the model parameters and hence

Time-Accurate Simulations and Acoustic Analysis of Slat Free-Shear-Layer. Part 2