Function2Gene: A gene selection tool to increase the power of genetic association studies by utilizing public databases and expert knowledge

Background  

Many common disorders have multiple genetic components which convey increased susceptibility. SNPs have been used to identify genetic components which are associated with a disease. Unfortunately, many studies using these methods suffer from low reproducibility due to lack of power.     

Extracting key information from historical data to quantify the transmission dynamics of smallpox

Background  

Quantification of the transmission dynamics of smallpox is crucial for optimizing intervention strategies in the event of a bioterrorist attack. This article reviews basic methods and findings in mathematical and statistical studies of smallpox which estimate key transmission parameters from historical data.     

Core Hunter: an algorithm for sampling genetic resources based on multiple genetic measures

Background  

Existing algorithms and methods for forming diverse core subsets currently address either allele representativeness (breeder's preference) or allele richness (taxonomist's preference). The main objective of this paper is to propose a powerful yet flexible algorithm capable of selecting core subsets that have high average genetic distance between accessions, or rich genetic diversity overall, or a combination of both.     

OptFlux: an open-source software platform for <Emphasis Type="Italic">in silico</Emphasis> metabolic engineering

Background  

Over the last few years a number of methods have been proposed for the phenotype simulation of microorganisms under different environmental and genetic conditions. These have been used as the basis to support the discovery of successful genetic modifications of the microbial metabolism to address industrial goals. However, the use of these methods has been restricted to bioinformaticians or other expert researchers. The main aim of this work is, therefore, to provide a user-friendly computational tool for Metabolic Engineering applications.     

Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations

Background  

During the most recent decade many Bayesian statistical models and software for answering questions related to the genetic structure underlying population samples have appeared in the scientific literature. Most of these methods utilize molecular markers for the inferences, while some are also capable of handling DNA sequence data. In a number of earlier works, we have introduced an array of statistical methods for population genetic inference that are implemented in the software BAPS. However, the complexity of biological problems related to genetic structure analysis keeps increasing such that in many cases the current methods may provide either inappropriate or insufficient solutions.     

An information-flow-based model with dissipation,saturation and direction for active pathway inference

Background  

Biological systems process the genetic information and environmental signals through pathways. How to map the pathways systematically and efficiently from high-throughput genomic and proteomic data is a challenging open problem. Previous methods design different heuristics but do not describe explicitly the behaviours of the information flow.     

Molecular mechanistic associations of human diseases

Background  

The study of relationships between human diseases provides new possibilities for biomedical research. Recent achievements on human genetic diseases have stimulated interest to derive methods to identify disease associations in order to gain further insight into the network of human diseases and to predict disease genes.     

Optimal Step Length EM Algorithm (OSLEM) for the estimation of haplotype frequency and its application in lipoprotein lipase genotyping

Background  

Haplotype based linkage disequilibrium (LD) mapping has become a powerful and cost-effective method for performing genetic association studies, particularly in the search for genetic markers in linkage disequilibrium with complex disease loci. Various methods (e.g. Monte-Carlo (Gibbs sampling); EM (expectation maximization); and Clark's method) have been used to estimate haplotype frequencies from routine genotyping data.     

Genetic transformation of Vitis vinifera via organogenesis

Background  

Efficient transformation and regeneration methods are a priority for successful application of genetic engineering to vegetative propagated plants such as grape. The current methods for the production of transgenic grape plants are based on Agrobacterium-mediated transformation followed by regeneration from embryogenic callus. However, grape embryogenic calli are laborious to establish and the phenotype of the regenerated plants can be altered.     

Rapid self-assembly of DNA on a microfluidic chip

Background  

DNA self-assembly methods have played a major role in enabling methods for acquiring genetic information without having to resort to sequencing, a relatively slow and costly procedure. However, even self-assembly processes tend to be very slow when they rely upon diffusion on a large scale. Miniaturisation and integration therefore hold the promise of greatly increasing this speed of operation.     

UVPAR: fast detection of functional shifts in duplicate genes

Background  

The imprint of natural selection on gene sequences is often difficult to detect. A plethora of methods have been devised to detect genetic changes due to selective processes. However, many of those methods depend heavily on underlying assumptions regarding the mode of change of DNA sequences and often require sophisticated mathematical treatments that made them computationally slow. The development of fast and effective methods to detect modifications in the selective constraints of genes is therefore of great interest.     

Continuous-time modeling of cell fate determination in Arabidopsis flowers

Background  

The genetic control of floral organ specification is currently being investigated by various approaches, both experimentally and through modeling. Models and simulations have mostly involved boolean or related methods, and so far a quantitative, continuous-time approach has not been explored.     

Gelatinases, endonuclease and Vascular Endothelial Growth Factor during development and regression of swine luteal tissue

Background  

Zebrafish (D. rerio) has become a powerful and widely used model system for the analysis of vertebrate embryogenesis and organ development. While genetic methods are readily available in zebrafish, protocols for two dimensional (2D) gel electrophoresis and proteomics have yet to be developed.     

Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models

Background  

Growing interest on biological pathways has called for new statistical methods for modeling and testing a genetic pathway effect on a health outcome. The fact that genes within a pathway tend to interact with each other and relate to the outcome in a complicated way makes nonparametric methods more desirable. The kernel machine method provides a convenient, powerful and unified method for multi-dimensional parametric and nonparametric modeling of the pathway effect.     

Performance of a genetic algorithm for mass spectrometry proteomics

Background  

Recently, mass spectrometry data have been mined using a genetic algorithm to produce discriminatory models that distinguish healthy individuals from those with cancer. This algorithm is the basis for claims of 100% sensitivity and specificity in two related publicly available datasets. To date, no detailed attempts have been made to explore the properties of this genetic algorithm within proteomic applications. Here the algorithm's performance on these datasets is evaluated relative to other methods.     

Theoretical basis to measure the impact of short-lasting control of an infectious disease on the epidemic peak

Background  

While many pandemic preparedness plans have promoted disease control effort to lower and delay an epidemic peak, analytical methods for determining the required control effort and making statistical inferences have yet to be sought. As a first step to address this issue, we present a theoretical basis on which to assess the impact of an early intervention on the epidemic peak, employing a simple epidemic model.     

ResBoost: characterizing and predicting catalytic residues in enzymes

Background  

Identifying the catalytic residues in enzymes can aid in understanding the molecular basis of an enzyme's function and has significant implications for designing new drugs, identifying genetic disorders, and engineering proteins with novel functions. Since experimentally determining catalytic sites is expensive, better computational methods for identifying catalytic residues are needed.     

PSMIX: an R package for population structure inference via maximum likelihood method

Background  

Inference of population stratification and individual admixture from genetic markers is an integrative part of a study in diverse situations, such as association mapping and evolutionary studies. Bayesian methods have been proposed for population stratification and admixture inference using multilocus genotypes and widely used in practice. However, these Bayesian methods demand intensive computation resources and may run into convergence problem in Markov Chain Monte Carlo based posterior samplings.     

An imaging system for standardized quantitative analysis of <Emphasis Type="Italic">C. elegans</Emphasis> behavior

Background  

The nematode Caenorhabditis elegans is widely used for the genetic analysis of neuronal cell biology, development, and behavior. Because traditional methods for evaluating behavioral phenotypes are qualitative and imprecise, there is a need for tools that allow quantitation and standardization of C. elegans behavioral assays.