See what we took part in and how we judge what fits
For public projects you can check the site and the paper, and we separately state which parts GeneSight took part in. The project scenarios on this page are examples of judgment; they are not presented as completed projects.
For these projects, we can publicly state which parts we took part in
The project site and paper confirm that the project exists publicly; our scope of involvement is stated separately. Third-party sites may change over time, and the overall results of each project remain with the original research teams.
EDomics
How can evo-devo research across animal species compare multiple types of omics evidence within one resource?
Integrates animal genome, transcriptome, single-cell and spatial data to support evo-devo research.
The parts we can publicly describe are data architecture, field design, visualization and front-end pages.
What you can learn from it: How cross-species multi-omics data can be structured into a searchable, comparable resource around a clear scientific question.
SilkMeta
How can pan-genome and population data be integrated into a shared entry point that serves trait, comparative and breeding research?
A platform for sharing and using pan-genome and multi-omics data in silkworm research.
The parts we can publicly describe are data architecture, the search system, visualization and the maintenance process.
What you can learn from it: How pan-genome and population data can be integrated into a data-sharing platform for the research community.
ISwine
How can pig genome, transcriptome and quantitative trait evidence support the search and prioritization of trait-related candidate genes from one entry point?
Integrates pig genomic variation, gene expression, QTX and annotation information, and provides an entry point for candidate gene prioritization analysis based on multi-omics data.
GeneSight can publicly state that its role was database platform development, including the search pages and data visualization. The modeling methods, data and paper conclusions belong to the original research team.
What you can learn from it: Place candidate gene prioritization on the main path, then connect variation, expression, QTX, gene details and analysis tools.
IFmut
How can large volumes of livestock variants be linked to regulatory elements, chromatin interactions, related genes and trait evidence to support functional variant screening?
Integrates epigenome, 3D genome and single-cell data across livestock species, used to browse regulatory region annotations and assess candidate functional variants.
GeneSight can publicly state that its role was database platform development, including the variant search pages and data visualization. The variant classification methods, data and paper conclusions belong to the original research team.
What you can learn from it: Use variants as the entry point, structuring functional classification, epigenetic regulation, 3D interactions, genes, traits and cell-type evidence into a traceable explanation path.
Different data calls for a different approach
The three scenarios below illustrate how we judge. Before any real start, each case is confirmed against data samples, the intended public scope and how the team will keep using the result.
Plant stress multi-omics resource
A research group has accumulated multiple batches of RNA-seq data, phenotype data, differential analysis results and paper figures around plant stress, traits, treatment conditions or candidate genes.
Sample browsing, gene details, expression plots, enrichment results, figure export and citation notes.
Can serve as a supplementary entry point for a paper and as material for collaboration, and can lay the groundwork for a later resource paper.
Data basis and use cases
RNA-seq data, phenotype data, differential analysis results, candidate gene lists, figures from published papers and supplementary tables.
New students look up earlier data, PIs prepare grant materials and collaborators review candidate gene evidence.
Cross-project candidate gene evidence base
Several students or projects all work on the same set of candidate genes, pathways, traits or disease models, but the evidence is scattered across different projects and papers.
Candidate gene lists, gene detail pages, evidence chain cards, literature sources, figure downloads and version records.
Turns a research group's long-term accumulation around one direction into a stable, citable and updatable evidence entry point.
Data basis and use cases
Candidate gene tables, expression results, differential analyses, phenotype associations, literature evidence, figures and supplementary materials.
PIs quickly judge how strong the evidence for a candidate gene is, students continue earlier work and evidence chains can be shown during collaboration discussions.
Thematic data resource entry point
A research group has a steady accumulation around a particular species, disease, regional samples, a long-term observation system or a distinctive data type, and wants a clear resource entry point.
Resource home page, data browsing, field descriptions, data download, citation notes and a release log.
Forms a clear, continuously updated resource entry point around what the group already does best; you can then assess whether to prepare a resource paper.
Data basis and use cases
Datasets for a specific research subject, sample metadata, omics data, analysis results, downloadable files and documentation.
Collaboration display, grant applications, citations from peers and assessing whether to further organize the work into a resource paper.
Resources peers have already built are worth careful study
These public databases help us understand how different fields organize data, provide citations and sustain long-term updates. They are peer references only and not part of the GeneSight project experience.
SoIR
NAR 2025 / Solanaceae information resourceHow a resource for related species organizes genomes, functional annotations and comparative analysis entry points.
View referenceAMIR
NAR 2025 / Plant multi-omics and breeding platformHow plant multi-omics data serves breeding, trait and population research.
View referenceMouseOmics
NAR 2026 / Model organism multi-omics databaseHow a model organism database organizes samples, omics data, genes and biological questions.
View referenceOmniPath
NAR 2026 / Multi-omics analysis knowledge baseHow a database extends from data storage to analysis support, evidence integration and knowledge organization.
View referencescCancerExplorer
NAR 2025 / Pan-cancer single-cell multi-omics databaseHow a disease-focused resource organizes data around scientific questions, cell types and evidence chains.
View referenceGenome Variation Map
NAR 2026 / Genome variation integration platformHow variants, samples, annotations, search and analysis entry points are organized in one place.
View referenceThe external resources listed here are for learning and comparison only; listing them does not mean GeneSight took part in building them or is endorsed by the project teams.