Local Perturbation Analysis: A Computational Tool for Biophysical Reaction-Diffusion Models

Diffusion and interaction of molecular regulators in cells is often modeled using reaction-diffusion partial differential equations. Analysis of such models and exploration of their parameter space is challenging, particularly for systems of high dimensionality. Here, we present a relatively simple and straightforward analysis, the local perturbation analysis, that reveals how parameter variations affect model behavior. This computational tool, which greatly aids exploration of the behavior of a model, exploits a structural feature common to many cellular regulatory systems: regulators are typically either bound to a membrane or freely diffusing in the interior of the cell. Using well-documented, readily available bifurcation software, the local perturbation analysis tracks the approximate early evolution of an arbitrarily large perturbation of a homogeneous steady state. In doing so, it provides a bifurcation diagram that concisely describes various regimes of the model’s behavior, reducing the need for exhaustive simulations to explore parameter space. We explain the method and provide detailed step-by-step guides to its use and application.     

Lucid Phoenix: 交互式多媒体网络检索表工具

Lucid Phoenix是一个编写和演示二叉检索表的计算机工具,本文详细介绍了它的组件、功能和特色。其生成的Phoenix检索表整合文本、超文本、图片等多媒体信息,采用最新的跨平台技术可直接嵌入浏览器网上传播和交互使用。它对于促进检索表资源的网络共享与利用十分有意义。     

CONAN: A Tool to Decode Dynamical Information from Molecular Interaction Maps

The analysis of contacts is a powerful tool to understand biomolecular function in a series of contexts, from the investigation of dynamical behavior at equilibrium to the study of nonequilibrium dynamics in which the system moves between multiple states. We thus propose a tool called CONtact ANalysis (CONAN) that, from molecular dynamics (MD) trajectories, analyzes interresidue contacts, creates videos of time-resolved contact maps, and performs correlation, principal component, and cluster analysis, revealing how specific contacts relate to functionally relevant states sampled by MD. We present how CONAN can identify features describing the dynamics of ubiquitin both at equilibrium and during mechanical unfolding. Additionally, we show the analysis of MD trajectories of an α-synuclein mutant peptide that undergoes an α-β conformational transition that can be easily monitored using CONAN, which identifies the multiple states that the peptide explores along its conformational dynamics. The high versatility and ease of use of the software make CONAN a tool that can significantly facilitate the understanding of the complex dynamical behavior of proteins or other biomolecules. CONAN and its documentation are freely available for download on GitHub.     

Traditional Chinese Medicine Explanatory Models of Depressive Disorders: A Qualitative Study

Culture, Medicine, and Psychiatry - Traditional Chinese medicine (TCM) is an alternative medical system utilised by many Chinese. However, the knowledge of TCM concepts of depression is limited...     

Co-authorship Network Analysis: A Powerful Tool for Strategic Planning of Research,Development and Capacity Building Programs on Neglected Diseases

Background

New approaches and tools were needed to support the strategic planning, implementation and management of a Program launched by the Brazilian Government to fund research, development and capacity building on neglected tropical diseases with strong focus on the North, Northeast and Center-West regions of the country where these diseases are prevalent.

Methodology/Principal Findings

Based on demographic, epidemiological and burden of disease data, seven diseases were selected by the Ministry of Health as targets of the initiative. Publications on these diseases by Brazilian researchers were retrieved from international databases, analyzed and processed with text-mining tools in order to standardize author- and institution''s names and addresses. Co-authorship networks based on these publications were assembled, visualized and analyzed with social network analysis software packages. Network visualization and analysis generated new information, allowing better design and strategic planning of the Program, enabling decision makers to characterize network components by area of work, identify institutions as well as authors playing major roles as central hubs or located at critical network cut-points and readily detect authors or institutions participating in large international scientific collaborating networks.

Conclusions/Significance

Traditional criteria used to monitor and evaluate research proposals or R&D Programs, such as researchers'' productivity and impact factor of scientific publications, are of limited value when addressing research areas of low productivity or involving institutions from endemic regions where human resources are limited. Network analysis was found to generate new and valuable information relevant to the strategic planning, implementation and monitoring of the Program. It afforded a more proactive role of the funding agencies in relation to public health and equity goals, to scientific capacity building objectives and a more consistent engagement of institutions and authors from endemic regions based on innovative criteria and parameters anchored on objective scientific data.     

Melting of DNA Oligomers: Dynamical Models and Comparison with Experimental Results

We compare experimental melting curves of short heterogeneous DNA oligomers with theoretical curves derived from statistical mechanics. Partition functions are computed with the one-dimensional Peyrard-Bishop (PB) Hamiltonian, already used in the study of the melting of long DNA chains. Working with short chains we take into account, in the computations, not only the breaking of the interstrand hydrogen bonds, but also the complete dissociation of the double helix into separate single strands. Since this dissociation equilibrium is of general relevance, independent of the particular microscopic model, we give some details of its treatment. We discuss how the non bonded three-dimensional interactions, not explicitly considered in the one-dimensional PB model, are taken into account through the treatment of the dissociation equilibrium. We also evaluate the relevance of the dissociation as a function of the chain length.     

StochPy: A Comprehensive,User-Friendly Tool for Simulating Stochastic Biological Processes

Single-cell and single-molecule measurements indicate the importance of stochastic phenomena in cell biology. Stochasticity creates spontaneous differences in the copy numbers of key macromolecules and the timing of reaction events between genetically-identical cells. Mathematical models are indispensable for the study of phenotypic stochasticity in cellular decision-making and cell survival. There is a demand for versatile, stochastic modeling environments with extensive, preprogrammed statistics functions and plotting capabilities that hide the mathematics from the novice users and offers low-level programming access to the experienced user. Here we present StochPy (Stochastic modeling in Python), which is a flexible software tool for stochastic simulation in cell biology. It provides various stochastic simulation algorithms, SBML support, analyses of the probability distributions of molecule copy numbers and event waiting times, analyses of stochastic time series, and a range of additional statistical functions and plotting facilities for stochastic simulations. We illustrate the functionality of StochPy with stochastic models of gene expression, cell division, and single-molecule enzyme kinetics. StochPy has been successfully tested against the SBML stochastic test suite, passing all tests. StochPy is a comprehensive software package for stochastic simulation of the molecular control networks of living cells. It allows novice and experienced users to study stochastic phenomena in cell biology. The integration with other Python software makes StochPy both a user-friendly and easily extendible simulation tool.     

rMVP: A Memory-efficient,Visualization-enhanced,and Parallel-accelerated Tool for Genome-wide Association Study

Along with the development of high-throughput sequencing technologies, both sample size and SNP number are increasing rapidly in genome-wide association studies (GWAS), and the associated computation is more challenging than ever. Here, we present a memory-efficient, visualization-enhanced, and parallel-accelerated R package called “rMVP” to address the need for improved GWAS computation. rMVP can 1) effectively process large GWAS data, 2) rapidly evaluate population structure, 3) efficiently estimate variance components by Efficient Mixed-Model Association eXpedited (EMMAX), Factored Spectrally Transformed Linear Mixed Models (FaST-LMM), and Haseman-Elston (HE) regression algorithms, 4) implement parallel-accelerated association tests of markers using general linear model (GLM), mixed linear model (MLM), and fixed and random model circulating probability unification (FarmCPU) methods, 5) compute fast with a globally efficient design in the GWAS processes, and 6) generate various visualizations of GWAS-related information. Accelerated by block matrix multiplication strategy and multiple threads, the association test methods embedded in rMVP are significantly faster than PLINK, GEMMA, and FarmCPU_pkg. rMVP is freely available at https://github.com/xiaolei-lab/rMVP.     

InSatDb: A Genomic Tool for Insect Geneticists

Microsatellites show tremendous variation between genomes in terms of their occurrence and composition. Availability of whole genome sequences allows us to study microsatellite characteristics of fully sequenced insect genomes to understand the evolution and biological significance of microsatellites. InSatDb is an insect microsatellite database that provides an interactive interface to query information on microsatellites annotated with size (in base pairs and repeat units); genomic location (exon, intron, up-stream or transposon); nature (perfect or imperfect); and sequence composition (repeat motif and GC%). Here, we present a snap shot of the distribution and composition of microsatellites in introns and exons of insect genomes. The data present interesting observations regarding the microsatellite life-cycle and genome flux.     

基因定位修复技术——嵌合修复术（chimeraplasty）

传统的以病毒DNA为载体的基因治疗方法存在病毒导入的靶向性差、整合位点的特异性差、基因剂量的可控性差以及病毒载体具有一定的免疫原性的缺点,而实际上许多单基因遗传病只要将突变的单个碱基校正就能达到基因治疗的目的,不必置换或整合入整个基因.嵌合修复术(chimerplasty)是近年来迅速发展起来的一种基因定位修复技术,该技术利用RNA/DNA嵌合体分子与要修复的宿主染色体DNA碱基序列互补而精确定位并原位修复,也可用于人工定点突变,已在单基因遗传病的基因治疗及植物遗传改良等方面获得了成功的应用.介绍了该技术的原理、应用举例及前景.     

MAFCO: A Compression Tool for MAF Files

In the last decade, the cost of genomic sequencing has been decreasing so much that researchers all over the world accumulate huge amounts of data for present and future use. These genomic data need to be efficiently stored, because storage cost is not decreasing as fast as the cost of sequencing. In order to overcome this problem, the most popular general-purpose compression tool, gzip, is usually used. However, these tools were not specifically designed to compress this kind of data, and often fall short when the intention is to reduce the data size as much as possible. There are several compression algorithms available, even for genomic data, but very few have been designed to deal with Whole Genome Alignments, containing alignments between entire genomes of several species. In this paper, we present a lossless compression tool, MAFCO, specifically designed to compress MAF (Multiple Alignment Format) files. Compared to gzip, the proposed tool attains a compression gain from 34% to 57%, depending on the data set. When compared to a recent dedicated method, which is not compatible with some data sets, the compression gain of MAFCO is about 9%. Both source-code and binaries for several operating systems are freely available for non-commercial use at: http://bioinformatics.ua.pt/software/mafco.     

GIV: A Tool for Genomic Islands Visualization

A Genomic Islands (GI) is a chunk of DNA sequence in a genome whose origin can be traced back to other organisms or viruses. The detection of GIs plays an indispensable role in biomedical research, due to the fact that GIs are highly related to special functionalities such as disease-causing GIs - pathogenicity islands. It is also very important to visualize genomic islands, as well as the supporting features corresponding to the genomic islands in the genome. We have developed a program, Genomic Island Visualization (GIV), which displays the locations of genomic islands in a genome, as well as the corresponding supportive feature information for GIs. GIV was implemented in C++, and was compiled and executed on Linux/Unix operating systems.

Availability

GIV is freely available for non-commercial use at http://www5.esu.edu/cpsc/bioinfo/software/GIV     

Marginal Models Via GLS: A Convenient Yet Neglected Tool for the Analysis of Correlated Data in the Behavioural Sciences

Behavioural research often produces data that have a complicated structure. For instance, data can represent repeated observations of the same individual and suffer from heteroscedasticity as well as other technical snags. The regression analysis of such data is often complicated by the fact that the observations (response variables) are mutually correlated. The correlation structure can be quite complex and might or might not be of direct interest to the user. In any case, one needs to take correlations into account (e.g. by means of random‐effect specification) in order to arrive at correct statistical inference (e.g. for construction of the appropriate test or confidence intervals). Over the last decade, such data have been more and more frequently analysed using repeated‐measures ANOVA and mixed‐effects models. Some researchers invoke the heavy machinery of mixed‐effects modelling to obtain the desired population‐level (marginal) inference, which can be achieved by using simpler tools – namely marginal models. This paper highlights marginal modelling (using generalized least squares [GLS] regression) as an alternative method. In various concrete situations, such marginal models can be based on fewer assumptions and directly generate estimates (population‐level parameters) which are of immediate interest to the behavioural researcher (such as population mean). Sometimes, they might be not only easier to interpret but also easier to specify than their competitors (e.g. mixed‐effects models). Using five examples from behavioural research, we demonstrate the use, advantages, limits and pitfalls of marginal and mixed‐effects models implemented within the functions of the ‘nlme’ package in R.     

Dynamical Systems and Depression: A Framework for Theoretical Perspectives

The theory of dynamical systems allows one to describe the change in a system's macroscopic behavior as a bifurcation in the underlying dynamics. We show here, from the example of depressive syndrome, the existence of a correspondence between clinical and electro-physiological dimensions and the association between clinical remission and brain dynamics reorganization (i.e. bifurcation). On the basis of this experimental study, we discuss the interest of such results concerning the question of normality versus pathology in psychiatry and the relationship between mind and brain.     

Comparing Artificial Neural Networks,General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs

Background

Exogenous short interfering RNAs (siRNAs) induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models.

Principal Findings

Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs), General Linear Models (GLMs) and Support Vector Machines (SVMs). Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3×5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation.

Conclusions

The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features found to result in maximally predictive models are not consistent across learning techniques, suggesting care should be taken in the interpretation of feature relevance. In the models developed here, there are statistically differentiable combinations of learning techniques and feature mapping methods where the SVM technique under a specific combination of features significantly outperforms all the best combinations of features within the ANN and GLM techniques.     

LIBS: A Quality Control Tool for Food Supplements

In the present paper the ability of calibration free laser induced breakdown spectroscopy (CF-LIBS) as a quality control tool to monitor the composition of different minerals present in food supplement samples belonging to Indian brands (brand-A and brand-B) has been demonstrated. LIBS spectra of these two food supplements (brand-A and brand-B) available in the form of tablet have been recorded. As reported by manufacturers of these two food supplements, LIBS spectra of brand-A contains the spectral signatures of minerals like Ca, Mg, C, P, Zn, Fe, Cu, and Cr whereas LIBS spectra of brand-B shows the presence of spectral lines like Ca, Mg and C. The spectral signatures of Na and K are also found in both brands whereas spectral signature of Ti is observed only in brand-B but these elements are not mentioned on the nutritional label of the brands. The quantitative analysis of mineral contents in food supplements has been done using CF-LIBS for brand A and brand B to verify the content of the minerals reported by the manufacturer of the food supplements. Our results show that Ca and Mg are the main matrix elements of these brands. The concentration of minor and trace elements estimated using CF-LIBS technique is found in agreement with the reported nutritional values of both the brands. The concentration of major elements Ca and Mg are also estimated from Atomic Absorption Spectroscopy which is in close agreement with CF-LIBS result.     

galign: A Tool for Rapid Genome Polymorphism Discovery

Background

Highly parallel sequencing technologies have become important tools in the analysis of sequence polymorphisms on a genomic scale. However, the development of customized software to analyze data produced by these methods has lagged behind.

Methods/Principal Findings

Here I describe a tool, ‘galign’, designed to identify polymorphisms between sequence reads obtained using Illumina/Solexa technology and a reference genome. The ‘galign’ alignment tool does not use Smith-Waterman matrices for sequence comparisons. Instead, a simple algorithm comparing parsed sequence reads to parsed reference genome sequences is used. ‘galign’ output is geared towards immediate user application, displaying polymorphism locations, nucleotide changes, and relevant predicted amino-acid changes for ease of information processing. To do so, ‘galign’ requires several accessory files easily derived from an annotated reference genome. Direct sequencing as well as in silico studies demonstrate that ‘galign’ provides lesion predictions comparable in accuracy to available prediction programs, accompanied by greater processing speed and more user-friendly output. We demonstrate the use of ‘galign’ to identify mutations leading to phenotypic consequences in C. elegans.

Conclusion/Significance

Our studies suggest that ‘galign’ is a useful tool for polymorphism discovery, and is of immediate utility for sequence mining in C. elegans.