Resources

ToxPi Standalone GUI

ToxPi GUI (Toxicological Priority Index graphical user interface) is a flexible prioritization support software tool based on the "Endocrine Profiling and Prioritization of Environmental Chemicals Using ToxCast Data” article by David M. Reif et al." that incorporates chemical’s bioactivity profiles, inferred toxicity pathways, dose estimates, exposure data, chemical structural descriptors, etc.

FastMap 2.0

Gene expression association mapping involves the calculation of millions of genotype to phenotype correlations, which requires considerable computational resources. We have developed FastMap 2.0, which has a user friendly graphical interface, to perform fast association mapping in heterozygous populations on a standard desktop computer.

SAFEGUI

Summary: A large number of websites and applications perform significance testing for gene categories/pathways in microarray data. Many of these packages fail to account for expression correlation between transcripts, with a resultant inflation in Type I error. Array permutation and other resampling-based approaches have been proposed as solutions to this problem. SAFEGUI provides a user-friendly graphical interface for the assessment of categorical significance in microarray studies, while properly accounting for the effects of correlations among genes. SAFEGUI incorporates both permutation and more recently proposed bootstrap algorithms that are demonstrated to be more powerful in detecting differential expression across categories of genes.

DRPATHWAY

Motivation: Gene expression data is widely used for evaluation of disease- or treatment-induced adverse biological effects. A common analysis step is aggregation of individual transcripts into pathways or gene sets. While both qualitative (e.g., which pathways are affected) and quantitative (e.g., fold-changes for individual genes) data analysis strategies are used, such approaches only partly meet the needs for human health risk assessment. Traditionally, quantitative risk assessment relies on apical endpoints (e.g., tumor incidence) collected from dose-response studies in humans or animal models. However, transcriptional profiling is now common in chemical safety testing, and is an important source of information for evaluation of health risks. To enable the quantitative pathway-based analysis of perturbations by potentially hazardous chemicals, it is necessary to integrate dose-response information. Unfortunately, existing methods are not well-suited to dose-response designs, where it is desirable to make a global summary of transcriptional response for each pathway while maintaining appropriate error control.

Solution: An interactive DR Pathway graphical user interface (GUI) application has been implemented for dose-response analysis of gene expression data at the pathway level, connected to annotation for the three Gene Ontology (GO) domains. Pathways enriched for dose-responsive genes are detected using a fast and valid testing procedure, controlled for multiple comparisons. Pathway-level dose-response profiles are generated with bootstrap-derived confidence intervals for quantitative assessment.