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GenomeOne

The Next Generation of AI-Driven Genomic Intelligence for Modern Plant Breeding

Predict your hybrids before you make them.

Genomic-Based Prediction for smarter, faster breeding decisions
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Plant breeding is evolving. GenomeOne is built to lead the revolution.

GenomeOne is a powerful, AI-driven SaaS platform designed to transform how commercial breeding programs, universities and research institutes leverage genomic intelligence.
By unifying genotypic and phenotypic data within a single cloud-based ecosystem, GenomeOne enables R&D teams to convert complex biological data into precise, data-driven breeding decisions with unmatched speed and confidence.

From Static Reports to Dynamic SaaS

Shifting the Paradigm

Traditionally, genomic services have been delivered on a rigid, project-by-project basis. This conventional approach is notoriously expensive, slow, and ultimately produces a static report which may quickly become outdated.
GenomeOne changes the process by replacing outsourcing bottlenecks with a continuous SaaS (Software as a Service) solution.

Insight in Clicks, Not Months

Complex genomic analysis results are displayed with just a few mouse clicks.

On-the-Fly Adjustments

Users can independently adjust input parameters at any time to test different hypotheses and obtain immediate answers to evolving breeding questions.

Unified Workspace

Analytical results are displayed directly adjacent to the raw breeding data, rather than being isolated in a separate system or a forgotten PDF.

Core Capabilities

Driving the Prediction-Era Workflow

GenomeOne delivers a modular suite of high-performance applications engineered to answer critical breeding questions and accelerate genetic gain.
1

High-Volume Genotype Data Management and Filtering

GenomeOne effortlessly handles ultra-large-scale datasets without performance degradation. Powered by a dedicated big-data database, GenomeOne supports the loading and storage of massive volumes of markers (including SNPs and other marker types) and samples.
Using an intuitive, user-friendly interface, teams can instantly visualize datasets and apply advanced multi-dimensional filtering options, including:
Specific chromosome regions.
Minor Allele Frequency (MAF) thresholds.
Maximum percentage of missing data per marker or per sample.
Heterozygosity percentages and more.
Crucially, the platform includes built-in imputation tools to automatically fill in missing scores, ensuring data completeness for downstream pipelines. Crucially, this big-data environment is natively connected to your phenotypic data, allowing you to seamlessly layer any phenotypic traits onto your genomic samples to form a complete, multi-omic view.
Grid visualization of marker scores for a given genotype study.
Grid visualization of marker scores for a given genotype study.
List of genotypic studies and their derived analyses.
List of genotypic studies and their derived analyses.
2

Heterotic Pools Identification Analysis

Unlock the true power of hybrid vigor. GenomeOne introduces advanced heterotic groups identification analysis to maximize the efficiency of your crosses. By sorting germplasm into distinct heterotic pools based on genetic distance, the platform allows breeders to deliberately exploit dominance variance, predict hybrid performance, and streamline crossing strategies without wasting resources on incompatible lines.
Samples are partitioned into 5 inferred ancestral populations. This population breakdown serves as a framework for parental selection. Crosses between distinct ancestral groups (such as Cluster 1 and Cluster 5) can be targeted to maximize heterotic response and capture hybrid vigor, thus outperforming standard intra-cluster crosses.
Samples are partitioned into 5 inferred ancestral populations. This population breakdown serves as a framework for parental selection. Crosses between distinct ancestral groups (such as Cluster 1 and Cluster 5) can be targeted to maximize heterotic response and capture hybrid vigor, thus outperforming standard intra-cluster crosses.
Projection of the high-dimensional SNP genotype data into a two-dimensional space using the first two principal components (PC1 and PC2). Samples are colored according their heterotic pools. For parental selection, crosses designed between samples from opposing extremes of the plot (e.g., a red cluster sample vs. a red orange sample) represent the most genetically divergent pairings, which are the prime candidates for maximizing heterosis and hybrid vigor.
Projection of the high-dimensional SNP genotype data into a two-dimensional space using the first two principal components (PC1 and PC2). Samples are colored according their heterotic pools. For parental selection, crosses designed between samples from opposing extremes of the plot (e.g., a red cluster sample vs. a red orange sample) represent the most genetically divergent pairings, which are the prime candidates for maximizing heterosis and hybrid vigor.
Dendrogram visualization of the samples across the 5 heterotic pools.
Dendrogram visualization of the samples across the 5 heterotic pools.
Displaying multiple PCA analysis using various numbers of heterotic pools (K parameter).
Displaying multiple PCA analysis using various numbers of heterotic pools (K parameter).
How groups split and merge as K grows: each ribbon represents samples flowing from a group at one K to a group at the next.
How groups split and merge as K grows: each ribbon represents samples flowing from a group at one K to a group at the next.
3

Genomic Prediction and In-Silico Crosses

In the modern agricultural era, leading commercial seed companies are rapidly transitioning toward prediction-driven workflows. GenomeOne supports advanced Genomic Prediction, allowing your team to estimate the breeding value of unphenotyped individuals using comprehensive genotypic profiles. By simulating crosses in silico, you can dramatically compress breeding timelines, minimize costly field trials, and confidently advance the highest-performing candidates.
Genomic prediction results for yield, with model accuracy metrics and an observed vs. predicted plot.
Genomic prediction results for yield, with model accuracy metrics and an observed vs. predicted plot.
4

GWAS (Genome-Wide Association Study) Analysis

Empower your molecular researchers to scan entire genomes with precision. GenomeOne features robust GWAS Analysis support, enabling teams to discover statistically significant associations between specific genetic variants (SNPs) and critical agronomic traits. These permit pinpointing the exact genetic drivers behind yield, disease resistance, and climate resilience.
This figure displays the results of a Genome-Wide Association Study (GWAS) for a single agronomic trait, combining a Manhattan plot (top) to pinpoint genetic loci and a Quantile-Quantile (Q-Q) plot (bottom) to validate the statistical model.
This figure displays the results of a Genome-Wide Association Study (GWAS) for a single agronomic trait, combining a Manhattan plot (top) to pinpoint genetic loci and a Quantile-Quantile (Q-Q) plot (bottom) to validate the statistical model.
Compare results of GWAS between two traits or between two different models used to calculate the same trait.
Compare results of GWAS between two traits or between two different models used to calculate the same trait.
Compare markers significance (LOD score) between two traits or between two different models used to calculate the same trait.
Compare markers significance (LOD score) between two traits or between two different models used to calculate the same trait.
Compare markers significance (LOD score) and effects between two traits or between two different models used to calculate the same trait.
Compare markers significance (LOD score) and effects between two traits or between two different models used to calculate the same trait.
5

QTL (Quantitative Trait Loci) Analysis

Accelerate your marker-assisted selection through built-in QTL Analysis. GenomeOne maps quantitative trait loci to identify the specific genomic regions responsible for complex, polygenic agronomic traits. Understanding the architecture of these vital traits gives your R&D directors a distinct competitive advantage when fixing favorable alleles within elite populations.
Interactive chromosome map showing QTL regions and trait associations along each chromosome.
Interactive chromosome map showing QTL regions and trait associations along each chromosome.
Genome-wide LOD profile highlighting QTL peaks above the significance threshold.
Genome-wide LOD profile highlighting QTL peaks above the significance threshold.
6

Hidden Value Feature: Automatic SNP Marker Inheritance

Maximize your existing data assets with zero additional sequencing costs. GenomeOne features an intelligent Automatic SNP Marker Inheritance engine that automatically transfers known SNP marker data from parents to offspring. This substantially increases the volume of your germplasm with usable marker scores, rapidly expanding the training sets available for genomic prediction. You can unlock, map, and leverage “hidden” germplasm material your company already owns but hasn’t yet fully exploited.
7

Enterprise-Grade Visualization and Data Retention

Turn complex bioinformatics into board-ready corporate assets. All current and future genomic analyses run within GenomeOne are securely and permanently saved in the system, ensuring long-term data preservation across breeding cycles. The platform generates stunning, presentation-ready visualizations, such as interactive Manhattan plots, PCA scatters, and population structure charts, enabling effortless sharing and collaboration among R&D directors, field breeders, and executive stakeholders.

Built to Work where Breeders Work and Fully Connected via Breeding API (BrAPI)

GenomeOne is not simply another standalone genomic tool. It is natively integrated with PhenomeOne, creating a seamless digital environment where field observations, pedigrees, and trial structures directly inform genomic predictions.
No manual exports.
No fragmented workflows.
No data silos.
To ensure total interoperability across your entire enterprise architecture, GenomeOne fully supports BrAPI, the open-source, international standard for breeding data exchange. This means your data is never trapped in a siloed, disconnected system. You can programmatically send, retrieve, and process genomic data between GenomeOne and external tools, permitting your data science teams absolute freedom and flexibility.

Who Uses GenomeOne

GenomeOne is built for plant and crop breeding companies, universities and research institutes that are ready to meld genomic intelligence into their core operations. It serves cross-functional R&D teams across three primary roles:

Plant Breeders

Make faster, more confident selection decisions supported by GWAS results, heterotic grouping, and genomic predictions, without needing to rely on external pipelines or bioinformatics support for every query.

Quantitative Geneticists & Research Scientists

Run and compare complex genomic analyses (GWAS, QTL mapping, and population structure) directly within the platform using multi-study comparison views. Crucially, scientists can evaluate and compare various statistical and predictive models for each of these genomic analyses side-by-side to identify the most successful and accurate model for their specific crop.

Bioinformaticians & Data Managers

Benefit from a centralized, structured environment for uploading, organizing, and quality-controlling genomic datasets, reducing data wrangling and ensuring clean data lineage. Bioinformaticians can utilize full BrAPI integration to securely send and retrieve high-volume genomics data to and from GenomeOne via open-source, international standard APIs, thereby streamlining custom downstream pipelines.

Key Business Benefits

Faster Breeding Cycles

Compress timelines without sacrificing genetic gain by integrating genomic selection and marker-assisted decision-making directly into the operational breeding workflow.

One Platform for All Your Data

Eliminate the need to reconcile spreadsheets or merge fragmented exports. Genotypic data, phenotypic records, trial structures, and pedigrees co-exist in one connected, versioned ecosystem.

Self-Improving Intelligence

As new phenotypic data is added and new genotypic data uploaded, GenomeOne’s predictive models continuously refine and improve, increasing accuracy year over year.

Built for Breeders, Not Just Bioinformaticians

Advanced genomic analysis is translated into clear, actionable insights through interactive visualizations, democratizing data access across the full R&D team.

Zero Infrastructure Overhead

As a fully cloud-based SaaS platform, GenomeOne requires no on-premise servers or dedicated IT maintenance. New analytical models, parameters, and features roll out automatically.

Bridging the Lab and the Field

GenomeOne bridges the gap between genomic research and breeding operations. It creates a unified, scalable environment where genomic intelligence and field performance operate in true alignment.
The result: faster selection decisions, stronger genetic gains, and accelerated commercialization of superior germplasm.

Explore GenomeOne

If you are ready to move past the limitations of static reports, fully transition your program into a prediction-driven workflow, and maximize the commercial value of your germplasm, let’s connect.
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