A powerful web-based system for integrative functional genomics that helps researchers find convergent signals across multiple species and experimental paradigms.
Explore GeneWeaverIn the early 21st century, biology entered an era of unprecedented data generation. 1 High-throughput genome technologies began producing a wealth of information, with researchers worldwide conducting thousands of experiments linking genes to biological functions. Genome-wide association studies, quantitative trait locus mapping, microarray experiments, and RNA-sequencing data poured into databases and publication supplements. Yet this embarrassment of riches created a formidable challenge: how could scientists make sense of these disparate, non-computable data stores scattered across incompatible formats and multiple species? 1
The problem was particularly acute for complex traits and diseases. Conditions like alcohol use disorder, which affects millions globally, clearly had genetic components, but these were distributed across many molecular entities and biological pathways. Each study offered valuable insights, but no single experiment could capture the full picture. Biologists faced the complex, nearly impossible task of manually integrating relevant studies, reanalyzing primary data, unifying gene identifiers, and applying computational analysis to the integrated set. 1
This is the problem GeneWeaver was designed to solve. Developed as a freely available web-based system, GeneWeaver serves as both a curated repository of genomic experimental results and an analytical toolkit that enables researchers to dynamically integrate these datasets. It allows scientists to interactively address fundamental questions about biological functions and their relations to genes, organizing independently published genomic results into new conceptual frameworks driven by underlying biological relationships rather than pre-existing semantic structures. 1
At its core, GeneWeaver is a powerful web-based data and analysis software system specifically designed to find convergent signals in noisy functional genomics data. 5
GeneWeaver operates on a fundamental unit called a "gene set" – a list of genomic features along with descriptive content, ontology annotations, and gene association scores. These gene sets come from diverse sources including expression microarray data, RNA-seq experiments, QTL mapping, GWAS studies, mutation screens, and biological pathway databases. 7
What makes GeneWeaver particularly powerful is its ability to map these genomic features across multiple species, currently supporting ten model organisms including humans, mice, rats, zebrafish, fruit flies, and yeast. 7
130,000+
Gene Sets
1.9M+
Genomic Features
10
Supported Species
Free
Access
GeneWeaver provides researchers with a flexible suite of analytical tools to develop customized workflows for answering biological questions. 4
This tool groups experimentally derived gene sets based on the genes they contain, creating a graph of hierarchical relationships determined solely from gene set intersections.
This visualization generates a bipartite graph showing both genes and gene sets, allowing researchers to identify highly connected genes that appear across multiple experiments.
These enable complex set operations including unions, intersections, and identification of high-degree genes within groups of gene sets.
Through homology mapping and identifier translation tables, GeneWeaver can harmonize gene identifiers across species. 7
| Supported Species | Primary Research Applications |
|---|---|
| Mus musculus (mouse) | Disease modeling, therapeutic development |
| Homo sapiens (human) | Disease gene discovery, biomarker identification |
| Rattus norvegicus (rat) | Physiological studies, behavioral genetics |
| Danio rerio (zebrafish) | Developmental genetics, high-throughput screening |
| Drosophila melanogaster (fruit fly) | Genetic screening, neurogenetics |
| Macaca mulatta (macaque) | Translational research, neuroscience |
| Caenorhabditis elegans (roundworm) | Neurobiology, developmental biology |
| Saccharaomyces cerevisiae (yeast) | Molecular pathways, functional genomics |
A compelling demonstration of GeneWeaver's power comes from a study that identified a previously unrecognized gene involved in alcohol response using convergent evidence across multiple species and experimental paradigms.
The bioinformatic discovery was followed by experimental validation using a Pafah1b1 conditional knock-out mouse model. The results demonstrated that this gene indeed was associated with increased alcohol preference and an altered thermoregulatory response to alcohol.
This successful identification and validation demonstrated how diverse cross-species functional genomics data could be made readily computable to generate testable hypotheses. The GeneWeaver platform enabled researchers to find consensus among highly heterogeneous experiments, effectively separating biological signal from experimental noise.
| Analysis Component | Finding | Significance |
|---|---|---|
| Number of alcohol-related gene sets | 32 datasets | Substantial existing evidence across studies |
| Previously known alcohol genes | 52 gene sets from curated resources | Established baseline of knowledge |
| Most highly connected novel gene | Pafah1b1 | Strong convergent evidence across studies |
| Experimental validation | Increased alcohol preference in knock-outs | Confirmed bioinformatic prediction |
| Research Reagent | Function in Genomics Research |
|---|---|
| Heterogeneous Stock (HS) Rats | Genetically diverse outbred population for mapping complex traits |
| Microarrays | High-throughput measurement of gene expression levels |
| RNA-sequencing Platforms | Comprehensive transcriptome profiling |
| Conditional Knock-out Mice | Tissue-specific and temporal gene deletion for functional testing |
| Genotype Imputation | Predicting unobserved genotypes from reference panels |
| Linear Mixed Models | Statistical approach for detecting genetic associations |
As the volume of biological data continues to grow exponentially, platforms like GeneWeaver become increasingly essential for making sense of this information. The system continues to evolve, with recent updates including enhanced visualization tools, new features, and a fully programmable interface for reproducible analysis. 5
GeneWeaver's open-source nature and availability through The Jackson Laboratory ensure it will remain accessible to the broader research community. The platform represents a paradigm shift in how we approach complex biological questions, moving from single-experiment interpretations to integrative, data-driven approaches that leverage the collective knowledge of the scientific community. 7
By enabling researchers to readily ask and answer questions about which genes are reliably associated with biological processes, which candidates from genetic experiments have functional relevance, and what common molecular mechanisms underlie related biological processes, GeneWeaver provides a powerful framework for 21st century biological discovery. 5
The fox may know many things, but in the era of big data biology, it's platforms like GeneWeaver that help the scientific community know that one big thing: how to find meaningful patterns in the complexity of living systems. 3