GeneNetwork is a group of linked data sets and tools used to study complex networks of genes, molecules, and higher order gene function and phenotypes. GeneNetwork combines more than 25 years of legacy data generated by hundreds of scientists together with sequence data (SNPs) and massive transcriptome data sets (expression genetic or eQTL data sets). The quantitative trait locus (QTL) mapping module that is built into GN is optimized for fast on-line analysis of traits that are controlled by combinations of gene variants and environmental factors. GeneNetwork can be used to study humans, mice (BXD, AXB, LXS, etc.), rats (HXB), Drosophila, and plant species (barley and Arabidopsis). Most of these population data sets are linked with dense genetic maps (genotypes) that can be used to locate the genetic modifiers that cause differences in expression and phenotypes, including disease susceptibility.
Our laboratory has developed an online interactive resource called PhenoGen which provides an archive of brain and other organ gene expression data from a panel of 20 common inbred mouse strains, and three recombinant inbred (RI) panels (two mouse and one rat). DNA microarray data can also be uploaded to the site where numerous analytical tools can be implemented. An important advantage to the archived data is that each array represents data from a single animal and each strain was sampled 4–7 times, providing an estimate of genetic variance (heritability) of individual transcript levels. These panels also allow genetic mapping of expression QTLs. Overlap of eQTLs with phenotypic QTLs provides a powerful approach to candidate gene identification. These methods are briefly described here and we encourage the use of our site for both scientific discovery and as a teaching tool in quantitative genetics.
The Rat Genome Database (RGD) was established in 1999 and is the premier site for genetic, genomic, phenotype, and disease data generated from rat research. In addition, it provides easy access to corresponding human and mouse data for cross-species comparisons. RGD’s comprehensive data and innovative software tools make it a valuable resource for researchers worldwide.
GeneWeaver is a web application for the integrated cross-species analysis of functional genomics data from heterogeneous sources. The application consists of a large database of gene sets curated from multiple public data resources and curated submissions, along with a suite of analysis tools designed to allow flexible, customized workflows through web-based interactive analysis or scripted API driven analysis. Gene sets come from multiple widely studied species and include ontology annotations, brain gene expression atlases, systems genetic study results, gene regulatory information, pathway databases, drug interaction databases and many other sources. Users can retrieve, store, analyze and share gene sets through a graded access system. Gene sets and analysis results can be stored, shared and compared privately, among user defined groups of investigators, and across all users. Analysis tools are based on combinatorics and statistical methods for comparing, contrasting and classifying gene sets based on their members.
There are few effective therapies to treat drug abuse and dependence, despite the urgent need to develop more effective treatments. A major impediment to the development of such treatments is our extremely limited understanding of the biological basis of drug abuse. While human genome-wide association studies (GWAS) have begun to elucidate genes that influence various traits relevant to drug abuse, they are still unable to attribute more than a small fraction of the heritable variance to specific genes. NIDA center for GWAS in outbred rats (P50DA037844, “Integrated GWAS of complex behavioral and gene expression traits in outbred rats”) was created in 2014 to perform GWAS on numerous behavioral traits that have well-established relevance to drug abuse using outbred rats. We expect to discover new genes that can influence drug abuse-related behaviors in rats, therefore improving our understanding of genetic susceptibility to drug abuse in humans. Our results will help to identify new opportunities to treat psychiatric disorders, including addiction.
Chilibot is a specialized search software for the PubMed literature database. It is designed for rapidly identifying relationships between genes, proteins, or any keywords that the user might be interested. In contrast to the PubMed interface where results are organized based on articles, Chilibot directly presents the key information user is seeking, i.e. sentences containing both of the terms. These sentences are organized into different relationship types based on linguistic analysis of the text. In addition, Chilibot is especially suited to batch process large number of terms (e.g. microarray results). The relationships are summarized into as a graph, with links to sentences describing the relationships, as well as the terms themselves.
The Bayesian Network Web Server (BNW) is a comprehensive web server for Bayesian network modeling of biological data sets. It is designed so that users can quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data, and use the model to understand and make predictions about relationships between the variables in the model. Many real world data sets, including those used to create genetic network models, contain both discrete (e.g., genotypes) and continuous (e.g., gene expression traits) variables, and BNW allows for modeling of these hybrid data sets.