Richtlinien zur Einreichung von Proben
Why Target Lists Need More Than One Type of Evidence
A strong expression signal or screen result can be a useful lead. It is rarely enough to choose a target on its own.
Before a target advances, the research team needs to know why it may affect the disease. The team also needs to know where it acts, what happens when it is changed, and which risks remain.
We bring these questions into one review. AI helps us find and organize evidence across large datasets. Our scientists then check the sources, interpret conflicts, and connect each important gap to a suitable experiment.
If public evidence is not enough, we can generate new omics data. This allows the study to move beyond database review and test the target in a relevant tissue, cell type, or model.
Questions we help you answer
- Why is this target linked to the disease?
- Which cells or tissue regions carry the signal?
- Does changing the target alter a useful phenotype?
- Is the target practical to study or modulate?
- What safety or biology risks could stop the program?
- Which experiment would reduce uncertainty?
How We Support Common Target Discovery Decisions
The study design depends on where the target list came from and which decision must be made next.
Find the Most Likely Target Gene at a Disease-Associated Locus
Your question: A genetic study found a disease-associated region. Which gene in that region is the best target candidate?
Data and experiments: We can combine GWAS results, fine-mapped variants, QTL data, coding variants, disease traits, and tissue expression. New exome or RNA data can be added when the existing evidence does not resolve the gene.
How AI helps: AI-assisted methods connect variants to possible genes across several evidence sources. Our scientists separate regional association from direct gene support and flag results that disagree.
What we deliver: You receive a ranked gene list, a source-linked evidence card for each candidate, and a clear account of remaining uncertainty.
How to validate: Strong candidates can move to expression or perturbation studies. Unclear candidates may need tissue-specific RNA, epigenetic, or functional evidence first.
Published evidence: Nelson et al. found that relevant human genetic support was more common among successful drug mechanisms. Genetics can strengthen a target choice, but it does not prove that one gene will work in a new program.
Locate a Target in the Right Cell Type and Tissue Region
Your question: Is the target active in the cells driving the disease, or does it only look important in bulk tissue?
Data and experiments: We can compare disease and control tissue, treatment-sensitive and resistant regions, or lesions and nearby tissue. The design may combine bulk RNA sequencing, single-cell RNA sequencing, and spatial profiling. Chromatin data can add information about gene regulation.
How AI helps: AI-assisted analysis connects cell types, cell states, tissue regions, pathways, and cell-cell signals. It also helps identify signals caused mainly by changes in cell numbers.
What we deliver: You receive a target ranking with cell and tissue maps, disease-region evidence, nearby cell interactions, and pathway context.
How to validate: Selected targets can be checked in more tissues with targeted in situ assays, expression tests, or cell-specific perturbation studies.
Published evidence: Katzenelenbogen et al. used single-cell data to identify a TREM2-positive myeloid cell program in tumors. Follow-up experiments in mice tested the biological role of that program.
Prioritize Dependencies Linked to a Subtype or Resistance State
Your question: Which screen hits are specific to the disease model, mutation, or resistance state of interest?
Data and experiments: We can combine perturbation screens with genomic background, RNA expression, pathway activity, and drug-response data. RNA or single-cell profiling after perturbation can show how the model responds.
How AI helps: AI-assisted analysis helps separate broadly essential genes from selective dependencies. It can also link dependencies to mutations, expression states, pathways, and known research compounds.
What we deliver: You receive a ranked dependency list, possible response markers, pathway evidence, and a staged validation plan.
How to validate: Important hits should be repeated with independent reagents and additional disease-relevant models. Rescue or another form of target modulation can add stronger support.
Published evidence: Behan et al. combined genome-scale screens in 324 human cancer cell lines from 30 cancer types with genomic and target-feasibility data. Manguso et al. used an in vivo screen and follow-up experiments to study regulators of tumor immunity.
Choose Which Targets Should Advance to Validation
Your question: Several targets look reasonable. Which should move forward, and which need more evidence first?
Data and experiments: We review disease evidence, genetics, tissue and cell expression, spatial location, functional results, pathways, known research compounds, practical feasibility, and safety risks. A focused omics experiment can fill a high-value gap.
How AI helps: AI-assisted methods organize the evidence and apply agreed scoring rules. We show which sources drive the rank and how the list changes when uncertain evidence receives less weight.
What we deliver: Each target is placed into one of four groups: advance, resolve, hold, or deprioritize. You also receive an evidence table, target cards, risk summary, and next-step plan.
How to validate: Each unresolved target receives a specific recommendation, such as a perturbation study, another cohort, targeted RNA testing, or cell and spatial confirmation.
Published evidence: The Open Targets framework shows how genetics, functional data, expression, feasibility, and safety information can support target ranking. It also keeps the original evidence available for review.
Choose the Experiment That Answers the Missing Question
More data is not always better. We select the technology that can change the target decision.
| Service Technology | Question It Can Answer | Common Use in Target Discovery |
|---|---|---|
| Whole-Exome Sequencing | Are coding variants or model genotypes linked to the target? | Gene support, disease subtype, model selection, and variant confirmation |
| RNA Sequencing | Where is the target expressed, and which pathways change with it? | Disease comparison, mechanism, resistance, and perturbation response |
| Single-Cell RNA Sequencing | Which cell type or cell state carries the target signal? | Rare cells, immune states, tissue heterogeneity, and cell-specific response |
| Spatial Multi-Omics Sequencing Services | Where is the target located inside the tissue? | Disease regions, cell neighborhoods, resistant niches, and local interactions |
| ATAC-Seq | Which regulatory regions may control the target program? | Chromatin state, upstream regulators, and state-specific control |
| Multi-Omics Services | Do several molecular layers support the same target? | Cross-layer confirmation, conflict review, and mechanism refinement |
| Bioinformatics Services | Can existing data be processed and compared reliably? | Data review, harmonization, public-data integration, and reproducible reporting |
Spatial profiling is especially useful when bulk data cannot show where a signal comes from. Depending on the tissue and research question, we can consider Visium, Visium HD, Stereo-seq, or Xenium in situ profiling.
Start With Data, Samples, or a Target Shortlist
| Project Type | What You Provide | How We Support the Study |
|---|---|---|
| Data-to-Insight | Omics or screen data, metadata, public datasets, and a research question | We review data quality, add relevant public evidence, rank targets, and recommend validation studies. |
| Hybrid Study | Existing data plus biospecimens or models for a missing evidence layer | We design a focused experiment, generate the data, and update the target ranking. |
| Shortlist Review | A defined set of targets and the criteria for moving them forward | We compare each target, identify risks, test ranking stability, and define the next decision. |
See Why Every Target Receives Its Rank
The final score is only useful when the evidence behind it is easy to inspect.
- Disease evidence: association with the disease, trait, or phenotype
- Genetic support: variants, QTL evidence, and links from a locus to a gene
- Biological location: tissue, cell type, cell state, and spatial region
- Functional support: perturbation results, dependency, rescue, and mechanism
- Pathway role: network position, redundancy, and possible escape routes
- Practical feasibility: how accessible the target is to research tools or a chosen modality
- Safety risks: normal-tissue expression, human phenotypes, and essential functions
- Evidence quality: study design, replication, model relevance, and conflicting results
For each target, we record the source, result, direction, confidence, and scoring effect. We also test whether a target remains highly ranked when uncertain evidence or scoring weights change.
From a Broad Target List to a Testable Shortlist
One connected workflow keeps data generation, evidence review, ranking, and validation focused on the same decision.
Step 1 - Define the decision: We clarify the disease, biological context, preferred modality, target criteria, and main reasons to stop or advance.
Step 2 - Review current evidence: We assess your data, public studies, target list, compounds, sample information, and known evidence gaps.
Step 3 - Design focused experiments: If needed, we select the omics study most likely to resolve an important gap.
Step 4 - Generate and process data: We perform the agreed experiment, quality control, bioinformatics, and comparison with compatible evidence.
Step 5 - Build the evidence table: We organize disease, genetic, cell, spatial, functional, pathway, feasibility, and safety information.
Step 6 - Rank and test stability: We apply clear rules, inspect conflicts, and check whether different assumptions change the shortlist.
Step 7 - Review each target: Our scientists classify candidates as advance, resolve, hold, or deprioritize.
Step 8 - Plan validation: We recommend the experiment, model, cohort, or tissue study needed for the next decision.
What We Need to Plan the Study
The project can begin with a broad disease question or a defined shortlist.
- Disease, phenotype, population, stage, and proposed mechanism
- Current target list and reasons each target was selected
- Raw or processed omics, screening, or response data
- Sample, model, batch, treatment, and outcome information
- Public cohorts, known compounds, prior findings, and internal hypotheses
- Available biospecimens or models for new experiments
- Preferred modality, tissue access, and reasons to exclude a target
Deliverables Built for the Next Research Decision
- Data and evidence-source review
- Quality-controlled omics data when experiments are included
- Target longlist and prioritized shortlist
- Target-by-evidence table
- Individual target summary cards
- Cell, tissue, spatial, pathway, and network context
- Clear ranking rules and reasons
- Conflicts, uncertainty, and safety-risk flags
- Ranking stability analysis
- Advance, resolve, hold, or deprioritize decisions
- Target-specific validation recommendations
- Reproducible tables, figures, methods, and limitations
Close Evidence Gaps With New Omics Data
A database review can show what is already known. It cannot always answer whether the target is active in your samples, your models, or the disease region that matters.
CD Genomics combines wet-lab omics with bioinformatics, single-cell analysis, spatial profiling, and evidence review. When the ranking depends on missing information, our team can design and perform the experiment needed to obtain it.
You receive more than a score. The final report shows the supporting evidence, the main risks, the limits of the current data, and the most useful next study.
Research boundary
Target discovery remains uncertain. We do not guarantee that a target will be practical to modulate, pass validation, or succeed in later development.
References
- Nelson MR, Tipney H, Painter JL, et al. The support of human genetic evidence for approved drug indications. Nature Genetics. 2015.
- Katzenelenbogen Y, Sheban F, Yalin A, et al. Coupled scRNA-Seq and Intracellular Protein Activity Reveal an Immunosuppressive Role of TREM2 in Cancer. Cell. 2020.
- Behan FM, Iorio F, Picco G, et al. Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens. Nature. 2019.
- Manguso RT, Pope HW, Zimmer MD, et al. In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target. Nature. 2017.
- Ochoa D, Hercules A, Carmona M, et al. Open Targets Platform: supporting systematic drug-target identification and prioritisation. Nucleic Acids Research. 2021.
- Ghoussaini M, Mountjoy E, Carmona M, et al. Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics. Nucleic Acids Research. 2021.
For Research Use Only. Not for use in diagnostic or clinical procedures.
Example Target Prioritization Report
The report is designed for target review meetings. It shows the score, the evidence behind it, the main risks, and the next action for each target.
The report can include an evidence heatmap, category scores, cell and spatial context, score contributions, risk flags, ranking stability, and recommended experiments. Evidence categories and weights are selected for the project.
AI-Assisted Target Discovery and Prioritization FAQs
1. Can the project start with an existing target list?
Yes. We can review a longlist or shortlist against agreed biology, feasibility, safety-risk, and validation criteria. The report explains why each target advances, needs more evidence, remains on hold, or is deprioritized.
2. Can CD Genomics generate new experimental data?
Yes. We can add exome, RNA, epigenetic, single-cell, spatial, or multi-omics data when an important evidence gap prevents a clear decision.
3. How can spatial multi-omics improve target selection?
Spatial profiling shows where a target is active inside tissue. It can reveal disease regions, cell neighborhoods, invasive boundaries, immune compartments, or resistant niches that bulk data cannot separate.
4. Does the service rely only on public databases?
No. We can combine public evidence with your own data and newly generated experiments. We review study quality and biological relevance before combining sources.
5. What does AI do in the project?
AI helps retrieve, organize, compare, and summarize large amounts of evidence. Our scientists check the sources, set the scoring rules, interpret conflicts, and review the final ranking.
6. Can the scoring rules be changed?
Yes. We select weights for the disease, preferred modality, and research decision. We also show whether the ranking changes when uncertain evidence receives less weight.
7. Can the service guarantee a successful target?
No. Target discovery and validation are uncertain. We help identify stronger evidence, important risks, and the most useful next experiments.
Published Case Study
Independent Research Highlight
Using Functional Screens to Rank Context-Specific Cancer Dependencies
This publication is an independent research example. It is not a CD Genomics customer project.
Background
A strong screen signal does not always identify a selective or practical target. The researchers asked whether functional results could be combined with genomic and feasibility evidence to improve target ranking.
Methods
Behan et al. performed genome-scale screens in 324 human cancer cell lines from 30 cancer types. They combined cell-fitness effects with genomic markers and information about how practical each target might be to study or modulate.
Results
The analysis identified dependencies linked to specific tissue and genetic settings. WRN was highlighted in microsatellite-instability cancer models and was studied further in additional experiments.
Conclusion
The study shows why screen strength should not be used alone. Selectivity, genomic context, practical feasibility, reproducibility, and follow-up experiments all affect the target decision. Results from one study do not predict the outcome of another project.
Reference
- Behan FM, Iorio F, Picco G, et al. Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens. Nature. 2019.
Selected Publications
These independent publications provide research foundations for genetics-led, cell-specific, functional, and evidence-based target prioritization. They are not presented as CD Genomics customer projects.
- Nelson MR, Tipney H, Painter JL, et al. The support of human genetic evidence for approved drug indications. Nature Genetics. 2015.
- Katzenelenbogen Y, Sheban F, Yalin A, et al. Coupled scRNA-Seq and Intracellular Protein Activity Reveal an Immunosuppressive Role of TREM2 in Cancer. Cell. 2020.
- Manguso RT, Pope HW, Zimmer MD, et al. In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target. Nature. 2017.
- Ochoa D, Hercules A, Carmona M, et al. Open Targets Platform: supporting systematic drug-target identification and prioritisation. Nucleic Acids Research. 2021.
- Ghoussaini M, Mountjoy E, Carmona M, et al. Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics. Nucleic Acids Research. 2021.
