Target Universe Definition
Resolve disease synonyms and ontologies; standardize Ensembl, HGNC, UniProt, NCBI Gene, compound, and trial identifiers; define inclusion rules and evidence dates.
Target Landscape
Mapping Service
Build a disease-centered view of target biology, evidence strength, pathway position, tractability, therapeutic modalities, and clinical competition.
Start Your ProjectOverview
See the target field before committing to one target
Target landscape mapping asks a broader question than target discovery: within a defined disease, phenotype, pathway, cell state, or therapeutic hypothesis, which targets are supported, crowded, emerging, modality-accessible, or comparatively underexplored? We harmonize target identities and disease concepts, then connect genetics, omics, functional perturbation, pathways, tissue and cell context, bioactivity, safety, approved drugs, and clinical programs.
Projects may start from a disease area, a pathway, a client target list, or outputs from our Bioinformatics Services. Human projects are most comprehensively covered; mouse, rat, non-human primate, livestock, plant, microbial, and other supported species can be analyzed where reference genomes, orthology mappings, annotations, and evidence resources are sufficient.
Accepted inputs: FASTQ/BAM/CRAM, count or abundance matrices, VCF, proteomics and metabolomics tables, single-cell or spatial matrices, functional genetic screening results, target lists, assay tables, literature sets, and public-accession lists. Required metadata include species, genome/annotation build, tissue or cell type, disease definition, platform, sample identifiers, groups, replicates, batch, covariates, endpoints, and processing history.
Disease and phenotypeOntology, subtypes, patient context
Biological evidenceGenetics, omics, perturbation, pathways
Intervention feasibilityTractability, modality, safety, biomarkers
Competitive positionCompounds, trials, sponsors, whitespace
Core Services
A multi-axis map built for portfolio decisions
Evidence Architecture
Integrate human genetics, expression, proteomics, functional screens, model systems, literature, and curated target–disease evidence with source-level provenance.
Pathway and Network Position
Map targets to pathways, protein associations, regulatory modules, cell types, tissues, and disease processes; identify hubs, modules, redundancy, and pathway coverage.
Tractability and Modality Map
Compare small-molecule, antibody, protein-degrader, oligonucleotide, gene-editing, and cell-therapy feasibility using localization, structure, ligandability, precedent, and delivery context.
Pipeline and Competition Mapping
Connect targets to approved drugs, bioactivity records, development stage, trial status, indication, modality, sponsor, and intervention mechanism using date-stamped records.
Whitespace and Scenario Analysis
Rank evidence-rich, lower-crowding or differentiated opportunities under explicit weights; test how rankings change with indication, modality, safety, novelty, and evidence assumptions.
Six-Stage Workflow
From scope definition to a versioned decision landscape
| Stage | Key Activities | Decision Output |
|---|---|---|
| 1. Question and endpoint scoping | Define disease ontology, subtype, biology, target universe, intervention intent, modality, geography, time cut-off, and decision criteria. | Landscape charter and inclusion rules |
| 2. Data and metadata audit | Audit internal/public omics, genetics, perturbation, literature, bioactivity, drug, and trial data; inspect missingness, identifier drift, duplicates, batch, and confounders. | Evidence inventory and gap report |
| 3. Harmonization and QC | Process raw data where included; map builds and identifiers; normalize comparable measurements; retain species, tissue, assay, direction, provenance, database version, and retrieval date. | Analysis-ready evidence graph |
| 4. Statistical and network analysis | Estimate effect sizes and uncertainty; model covariates and batch; control false discovery rate; perform pathway enrichment, module detection, and sensitivity analyses. | Context-resolved evidence layers |
| 5. Landscape scoring | Combine evidence, tractability, safety, modality, novelty, pipeline crowding, and strategic fit using transparent weights; quantify missing evidence and rank stability. | Target quadrants, clusters, and scenarios |
| 6. Validation and reporting | Review high-value targets, compare independent cohorts, define orthogonal assays, set experimental gates, and deliver reproducible tables, figures, methods, and version records. | Decision package and validation roadmap |
Deliverables
Outputs that preserve evidence, context, and uncertainty
Landscape Evidence Matrix
Target-by-evidence table covering source, direction, disease subtype, tissue or cell context, assay, effect size, confidence, and version.
Interactive-Ready Network Files
Node and edge tables for target–disease, pathway, protein association, drug, modality, trial, and sponsor relationships, plus Cytoscape-compatible exports.
Competitive Pipeline Atlas
Date-stamped target, asset, mechanism, modality, phase, status, indication, sponsor, and trial-source records with normalized identifiers.
Target Quadrants and Shortlist
Evidence-versus-crowding, tractability-versus-risk, and customizable portfolio views with explicit weights, thresholds, missing-data flags, and rank sensitivity.
Reproducible Analysis Package
QC summary, processed tables, parameters, database and genome versions, retrieval dates, scripts or notebooks as scoped, and method documentation.
Validation Roadmap
Independent datasets, orthogonal measurements, perturbation assays, disease-relevant models, acceptance criteria, and evidence gaps for prioritized targets.
Applications
Landscape intelligence across discovery and portfolio planning
Disease-Area Entry
Compare the breadth, maturity, and crowding of targets before defining a new discovery program.
Target-Class Strategy
Map paralogs, pathway neighbors, selectivity liabilities, existing ligands, and modality options across a family.
Indication Expansion
Assess whether target evidence, tissue context, biomarkers, and clinical precedent support exploration in another indication.
Portfolio Differentiation
Compare internal candidates with competing mechanisms, trial maturity, modality choices, and biological whitespace.
Combination Hypotheses
Identify complementary pathway nodes or resistance mechanisms for experimentally testable combination strategies.
Landscape Refresh
Update a prior map with new publications, database releases, clinical status changes, and internal evidence.
Scientific Evidence
Integrated evidence supports clearer target prioritization
Target landscape mapping becomes more informative when disease biology, genetics, functional studies, pathways, known drugs, clinical evidence, tractability, safety, and development feasibility are evaluated together. The Open Targets framework illustrates how these complementary evidence layers can be organized from target–disease association assessment through target prioritization and therapeutic hypothesis generation.1
Our service applies this integrated perspective to the disease area and portfolio question defined for each project. We combine target-specific evidence with pathway and network context, therapeutic modality, clinical precedence, pipeline activity, and strategic differentiation to generate a structured landscape, comparison matrix, and prioritized target set. High-value candidates can then be advanced through independent cohort analysis, orthogonal molecular assays, or disease-relevant functional studies.
1 Buniello, A. et al. Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Research 2025, 53, D1467–D1475. https://doi.org/10.1093/nar/gkae1128. Open Access, CC BY 4.0.
2 Szklarczyk, D. et al. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Research 2023, 51, D638–D646. https://doi.org/10.1093/nar/gkac1000. Open Access, CC BY 4.0.
3 Merico, D. et al. Enrichment Map: a network-based method for gene-set enrichment visualization and interpretation. PLoS ONE 2010, 5, e13984. https://doi.org/10.1371/journal.pone.0013984. Open Access, CC BY.
Project Strategy
Scope the map around the decision—not around every available database
Each project begins with a target decision: enter or avoid a disease space, compare mechanisms, select a tractable target class, identify whitespace, refresh a portfolio, or design validation. We agree on evidence cut-offs, time horizon, geography, modalities, competing programs, weighting scheme, and review checkpoints before building the map. Database releases, ontology mappings, genome and annotation builds, software versions, parameters, retrieval dates, and manual curation decisions remain traceable in the final package.
For a tailored landscape based on your indication, target list, internal omics, functional screens, or portfolio criteria, please Contact Us or submit the Online Inquiry below.
Related Services
Connected target discovery and validation capabilities
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