Study Design & Data Audit
Define phenotype, contrast, unit of replication and decision threshold; review sample size, controls, repeated measures, missingness, batch–group confounding and platform-specific quality before analysis.
Target Discovery from Omics Data
Connect genomic, transcriptomic, epigenomic, proteomic, metabolomic, single-cell and spatial evidence to prioritize disease-relevant, testable target candidates.
Start Your ProjectOVERVIEW
From molecular association to a defensible target hypothesis
Target discovery from omics data asks which genes, proteins or regulatory programs are consistently linked to a defined disease phenotype across complementary molecular layers—and which candidates merit experimental perturbation. We build a traceable evidence chain from study design and data quality through differential signals, cross-omics integration, network context and target ranking.
Projects may use a single rich omics layer or matched multi-omics cohorts. Candidate scores summarize convergent evidence and uncertainty; they do not establish causality, clinical validity or druggability. Those questions require dedicated genetics, perturbation, pharmacology and validation studies.
Our Bioinformatics Services support human and model-organism programs across discovery and translational research.
Data and metadata readiness
- Species: human, mouse, rat and other well-annotated organisms; non-model species assessed case by case.
- Samples: tissue, blood, biofluids, organoids, cell lines, sorted cells, single cells/nuclei and spatial sections.
- Platforms: Illumina, PacBio/ONT, microarray, LC–MS/MS proteomics/metabolomics, methylation arrays, ATAC/ChIP-seq, single-cell and spatial platforms.
- Formats: FASTQ/BAM/CRAM/VCF, counts or abundance matrices, mzML/vendor exports, H5AD/Seurat objects, images and processed tables.
- Required metadata: phenotype, endpoint, group, replicate/subject IDs, tissue/cell type, collection time, treatment, batch, platform, library preparation and relevant covariates.
CORE SERVICES
Evidence layers designed around the target question
Modality-Specific Processing
Perform appropriate QC, alignment or feature extraction, normalization and annotation for variants, RNA, chromatin, methylation, proteins and metabolites while preserving reference genome and database provenance.
Differential & Association Analysis
Estimate effect sizes with confidence intervals and multiplicity-controlled significance using models matched to counts, intensities, survival, longitudinal, paired or multi-factor designs.
Cross-Omics Integration
Integrate matched or partially matched layers through concordance analysis, latent-factor models, supervised multiblock models or pathway-level synthesis, selected according to sample overlap and endpoint.
Network & Genetic Evidence
Place candidates in co-expression, protein-interaction, regulatory and pathway networks; where appropriate, overlay GWAS/eQTL colocalization or causal-inference evidence without treating network proximity as proof of mechanism.
Target Prioritization
Rank genes or proteins by direction, magnitude, reproducibility, cell/tissue specificity, cross-layer support, network position and available perturbation evidence, with explicit score components and uncertainty.
INTEGRATED WORKFLOW
Six stages from question definition to validation plan
| Stage | Key Activities | Decision Output |
|---|---|---|
| 1. Study & Endpoint Scoping | Define disease context, phenotype, comparison groups, biological unit, target class, inclusion criteria and acceptance thresholds. | Analysis plan, estimand, contrast matrix and feasibility assumptions. |
| 2. Data & Metadata Audit | Check sample identity, replication, coverage, missingness, outliers, batch, covariate balance and whether batch is inseparable from biology. | QC decision log, exclusions, data gaps and usable cohort. |
| 3. Processing & Harmonization | Modality-specific processing, identifier mapping, reference/annotation control, normalization and within-layer covariate handling. | Traceable feature matrices and harmonized sample map. |
| 4. Statistical & Integrative Analysis | Fit endpoint-appropriate models; report effect size and uncertainty; control FDR; integrate layers with training-only preprocessing when prediction is used. | Robust molecular associations, factors and cross-layer modules. |
| 5. Biological Interpretation & Ranking | Pathway and network analysis, tissue/cell localization, genetic support, perturbation evidence and transparent multi-criteria ranking. | Prioritized candidates with evidence cards and limitations. |
| 6. Validation & Reporting | Sensitivity analyses, holdout or nested cross-validation, external-cohort assessment and orthogonal or wet-lab experiment design. | Reproducible report, validation roadmap and go/no-go criteria. |
Statistical design: Sample-size adequacy depends on endpoint, heterogeneity, effect size and modality. Biological replicates and appropriate controls are required for confirmatory inference. For predictive ranking, feature selection, scaling and tuning remain inside training folds; grouped or nested cross-validation is used where appropriate, followed by external validation. Performance is reported with uncertainty and only within the represented disease, tissue, platform and population domain.
DELIVERABLES
Decision-ready outputs with full analytical provenance
Data & Metadata QC Report
Sample inventory, quality metrics, exclusion rationale, batch/confounder assessment and analysis-readiness summary.
Processed Omics Matrices
Normalized, annotated feature tables with sample mapping, reference genome, annotation source and transformation records.
Statistical Results Package
Contrasts, effect sizes, confidence intervals, raw and adjusted P values, diagnostics and sensitivity analyses.
Integrated Evidence Atlas
Cross-layer factors, pathway/module results, networks and cell- or tissue-context visualizations.
Prioritized Target Portfolio
Ranked candidates with score components, evidence strength, conflicts, applicability domain and uncertainty.
Reproducible Report & Validation Plan
Methods, software/database versions, parameters, scripts or workflow records, plus independent and experimental validation recommendations.
APPLICATIONS
Target hypotheses for distinct discovery decisions
Disease Mechanism Programs
Identify convergent genes and pathways across diseased versus reference tissues, molecular layers and cell states.
Patient or Disease Subtypes
Resolve subtype-specific target programs while separating phenotype from treatment, ancestry, site and technical effects.
Drug Response & Resistance
Connect baseline or longitudinal multi-omics changes with response phenotypes to nominate testable resistance or sensitization nodes.
Rare & Data-Limited Diseases
Combine patient omics with matched public cohorts, ortholog evidence and pathway context, clearly separating exploration from confirmation.
SCIENTIFIC EVIDENCE
Methods selected for the structure of the data
Multi-omics integration is not one algorithm. MOFA provides an unsupervised latent-factor framework for heterogeneous views and can expose biological and technical sources of variation.1 DIABLO uses supervised multiblock integration to identify correlated features associated with a defined outcome, but tuning and validation must be isolated from evaluation data.2 Translational multi-omics guidance emphasizes study design, sample matching, modality-specific preprocessing and independent validation as prerequisites for interpretable integration.3 We choose an approach according to the endpoint, sample overlap, dimensionality, missingness and intended claim rather than forcing every project into a single model.
1 Argelaguet, R.; et al. Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets. Molecular Systems Biology 2018, 14, e8124. https://doi.org/10.15252/msb.20178124. Open Access under CC BY 4.0.
2 Singh, A.; et al. DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics 2019, 35, 3055–3062. https://doi.org/10.1093/bioinformatics/bty1054. Open Access under CC BY-NC 4.0.
3 Athieniti, E.; et al. A guide to multi-omics data collection and integration for translational medicine. Computational and Structural Biotechnology Journal 2022, 20, 6472–6484. https://doi.org/10.1016/j.csbj.2022.11.050. Open Access under CC BY 4.0.
PROJECT STRATEGY
Transparent ranking built for the next experiment
Design-aware analysis
We flag underpowered comparisons, pseudoreplication and complete batch–group confounding early; computation cannot recover information absent from the design.
Traceable evidence
Every candidate links back to samples, contrasts, effect estimates, molecular layers, network sources, database versions and scoring rules.
Validation-oriented decisions
High-value candidates are paired with independent cohorts, qPCR/immunoassay or targeted MS, genetic perturbation or RNAi studies, rescue studies and disease-relevant functional assays as appropriate.
A computationally prioritized candidate remains a hypothesis. Association, enrichment, network centrality and predictive importance do not by themselves demonstrate causal disease biology, therapeutic efficacy or clinical validity. To discuss your cohort and decision criteria, please Contact Us or use the Online Inquiry below.
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