Comparison of evolutionary algorithms in gene regulatory network model inference

Background  

The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient.     

GeNESiS: gene network evolution simulation software

Background  

There has been a lot of interest in recent years focusing on the modeling and simulation of Gene Regulatory Networks (GRNs). However, the evolutionary mechanisms that give rise to GRNs in the first place are still largely unknown. In an earlier work, we developed a framework to analyze the effect of objective functions, input types and starting populations on the evolution of GRNs with a specific emphasis on the robustness of evolved GRNs.     

A Kalman-Filter Based Approach to Identification of Time-Varying Gene Regulatory Networks

Motivation

Conventional identification methods for gene regulatory networks (GRNs) have overwhelmingly adopted static topology models, which remains unchanged over time to represent the underlying molecular interactions of a biological system. However, GRNs are dynamic in response to physiological and environmental changes. Although there is a rich literature in modeling static or temporally invariant networks, how to systematically recover these temporally changing networks remains a major and significant pressing challenge. The purpose of this study is to suggest a two-step strategy that recovers time-varying GRNs.

Results

It is suggested in this paper to utilize a switching auto-regressive model to describe the dynamics of time-varying GRNs, and a two-step strategy is proposed to recover the structure of time-varying GRNs. In the first step, the change points are detected by a Kalman-filter based method. The observed time series are divided into several segments using these detection results; and each time series segment belonging to two successive demarcating change points is associated with an individual static regulatory network. In the second step, conditional network structure identification methods are used to reconstruct the topology for each time interval. This two-step strategy efficiently decouples the change point detection problem and the topology inference problem. Simulation results show that the proposed strategy can detect the change points precisely and recover each individual topology structure effectively. Moreover, computation results with the developmental data of Drosophila Melanogaster show that the proposed change point detection procedure is also able to work effectively in real world applications and the change point estimation accuracy exceeds other existing approaches, which means the suggested strategy may also be helpful in solving actual GRN reconstruction problem.     

Systematic analysis of mRNA 5' coding sequence incompleteness in Danio rerio: an automated EST-based approach

Background  

All standard methods for cDNA cloning are affected by a potential inability to effectively clone the 5' region of mRNA. The aim of this work was to estimate mRNA open reading frame (ORF) 5' region sequence completeness in the model organism Danio rerio (zebrafish).     

Cross-platform microarray data normalisation for regulatory network inference

Background

Inferring Gene Regulatory Networks (GRNs) from time course microarray data suffers from the dimensionality problem created by the short length of available time series compared to the large number of genes in the network. To overcome this, data integration from diverse sources is mandatory. Microarray data from different sources and platforms are publicly available, but integration is not straightforward, due to platform and experimental differences.

Methods

We analyse here different normalisation approaches for microarray data integration, in the context of reverse engineering of GRN quantitative models. We introduce two preprocessing approaches based on existing normalisation techniques and provide a comprehensive comparison of normalised datasets.

Conclusions

Results identify a method based on a combination of Loess normalisation and iterative K-means as best for time series normalisation for this problem.     

Causal inference and large-scale expert validation shed light on the drivers of SDM accuracy and variance

Aim

To develop a causal understanding of the drivers of Species distribution model (SDM) performance.

Location

United Kingdom (UK).

Methods

We measured the accuracy and variance of SDMs fitted for 518 species of invertebrate and plant in the UK. Our measure of variance reflects variation among replicate model fits, and taxon experts assessed model accuracy. Using directed acyclic graphs, we developed a causal model depicting plausible effects of explanatory variables (e.g. species' prevalence, sample size) on SDM accuracy and variance and quantified those effects using a multilevel piecewise path model.

Results

According to our model, sample size and niche completeness (proportion of a species' niche covered by sampling) directly affect SDM accuracy and variance. Prevalence and range completeness have indirect effects mediated by sample size. Challenging conventional wisdom, we found that the effect of prevalence on SDM accuracy is positive. This reflects the facts that sample size has a positive effect on accuracy and larger sample sizes are possible for widespread species. It is possible, however, that the omission of an unobserved confounder biased this effect. Previous studies, which reported negative correlations between prevalence and SDM accuracy, conditioned on sample size.

Main conclusions

Our model explicates the causal basis of previously reported correlations between SDM performance and species/data characteristics. It also suggests that niche completeness has similarly large effects on SDM accuracy and variance as sample size. Analysts should consider niche completeness, or proxies thereof, in addition to sample size when deciding whether modelling is worthwhile.     

Validation of computerized diagnostic information in a clinical database from a national equine clinic network

Background  

Computerized diagnostic information offers potential for epidemiological research; however data accuracy must be addressed. The principal aim of this study was to evaluate the completeness and correctness of diagnostic information in a computerized equine clinical database compared to corresponding hand written veterinary clinical records, used as gold standard, and to assess factors related to correctness. Further, the aim was to investigate completeness (epidemiologic sensitivity), correctness (positive predictive value), specificity and prevalence for diagnoses for four body systems and correctness for affected limb information for four joint diseases.     

Commentary on issues in data quality analysis in life cycle assessment

Purpose  

For compliance with the ISO standard 14044, comparative life cycle assessments are required to address data quality for time-related coverage, geographic coverage, technology coverage, precision, completeness, representativeness, consistency, reproducibility, sources of the data and uncertainty of the information. As the community of practitioners and data developers grows, the purpose of this commentary is to initiate discussion of current issues and opportunities for improvement in data quality analysis.     

New insights into protein-protein interaction data lead to increased estimates of the <Emphasis Type="Italic">S. cerevisiae</Emphasis> interactome size

Background  

As protein interactions mediate most cellular mechanisms, protein-protein interaction networks are essential in the study of cellular processes. Consequently, several large-scale interactome mapping projects have been undertaken, and protein-protein interactions are being distilled into databases through literature curation; yet protein-protein interaction data are still far from comprehensive, even in the model organism Saccharomyces cerevisiae. Estimating the interactome size is important for evaluating the completeness of current datasets, in order to measure the remaining efforts that are required.     

SciDBMaker: new software for computer-aided design of specialized biological databases

Background  

The exponential growth of research in molecular biology has brought concomitant proliferation of databases for stocking its findings. A variety of protein sequence databases exist. While all of these strive for completeness, the range of user interests is often beyond their scope. Large databases covering a broad range of domains tend to offer less detailed information than smaller, more specialized resources, often creating a need to combine data from many sources in order to obtain a complete picture. Scientific researchers are continually developing new specific databases to enhance their understanding of biological processes.     

Practice of ST-segment elevation myocardial infarction care in the Netherlands during four snapshot weeks with the National Cardiovascular Database Registry for Acute Coronary Syndrome

Background

Clinical registries provide information on the process of care and patient outcomes, with the potential to improve the quality of patient care. A large Dutch national acute coronary syndrome (ACS) registry is currently lacking. Recently, we initiated the National Cardiovascular Database Registry (NCDR) for ACS in the Netherlands. The purpose of this study was to assess the NCDR ACS registry on feasibility and data completeness during a pilot phase of four snapshot weeks.

Methods

Between 2013 and 2015, we invited all hospitals in the Netherlands to record a predefined dataset for every patient that was admitted to their hospital with ST-segment elevation myocardial infarction (STEMI). Data were entered in an online case report form. All patient-specific data were encrypted to ensure privacy.

Results

A total of 392 patients were registered in 35 centres. The mean age of the patients was 64 years (SD 13); 8% of patients presented with signs of cardiogenic shock and 11% with an out-of-hospital cardiac arrest. The median time from first medical contact to percutaneous coronary intervention (PCI) was 75 min (IQR 51–108) and this was significantly longer for patients who presented at a non-PCI centre or to a primary care physician. In-hospital and 30-day mortality rates were 5.2% and 7.8%, respectively. The amount of completeness varied, with improved completeness over time.

Conclusion

This report shows that a Dutch ACS registry is feasible with respect to STEMI patients. Data completeness, however, was suboptimal. Improved data completeness is warranted for the future.

     

Sparse canonical correlation analysis for identifying, connecting and completing gene-expression networks

Background  

We generalized penalized canonical correlation analysis for analyzing microarray gene-expression measurements for checking completeness of known metabolic pathways and identifying candidate genes for incorporation in the pathway. We used Wold's method for calculation of the canonical variates, and we applied ridge penalization to the regression of pathway genes on canonical variates of the non-pathway genes, and the elastic net to the regression of non-pathway genes on the canonical variates of the pathway genes.