Connect genetic perturbations, regulatory measurements, and cellular phenotypes to interpretable gene functions and testable mechanisms.
Start Your ProjectFunctional genomics converts systematic genetic or regulatory perturbations into evidence about gene function, cellular programs, and phenotype control.
Our service interprets pooled and arrayed CRISPR knockout, CRISPRi, CRISPRa, base-editing, saturation mutagenesis, Perturb-seq, reporter, MPRA, RNAi, overexpression, ATAC-seq, ChIP-seq, CUT&RUN, transcriptomic, proteomic, imaging, and quantitative phenotype datasets. We link guide- or element-level measurements to genes, molecular responses, cell states, regulatory programs, and experimentally testable hypotheses.
The central question is design-specific: which genes control viability under treatment, which perturbations shift a cell state, which enhancers regulate a target gene, which alleles alter function, or which regulators explain a molecular program? A screen hit is not automatically a causal disease gene or therapeutic target. Results are reported as perturbation-associated effects within the assayed model, with confidence, context, and validation needs visible.
Accepted formats include FASTQ, BAM/CRAM, count matrices, guide-count tables, H5AD/RDS/MTX single-cell objects, VCF, BED, peak files, reporter counts, protein or imaging feature matrices, and processed statistical results. Essential metadata include organism and reference build, library design, guide or element sequence, perturbation modality, controls, replicate and batch, cell line or tissue, treatment, time point, sequencing platform, selection scheme, phenotype definition, and sample or donor identity.
We review library representation, read depth, guide mapping, control behavior, bottlenecks, copy-number effects, dropout, replicate concordance, and gene-level aggregation. Positive selection, negative selection, drug interaction, FACS, and reporter designs require different null models and effect interpretations.
Guide assignment, multiplicity, ambient guide signal, cell quality, doublets, perturbation efficiency, donor structure, cell-state composition, and within-state responses are evaluated before differential or latent-effect modeling.
ATAC, ChIP, CUT&RUN, MPRA, CRISPRi enhancer screens, and chromatin-contact evidence are integrated with genomic distance, accessibility, motif, QTL, and expression response. Element-to-gene links remain hypotheses unless experimentally resolved.
Saturation editing, reporter assays, allele-specific measurements, and molecular QTL can be used to compare alleles. Reference orientation, editing outcomes, coverage, local sequence context, and calibration with known controls are retained.
RNA-seq, proteomics, phosphoproteomics, metabolomics, or chromatin data can reveal proximal and downstream responses. Layer-specific missingness, normalization, timing, and cellular composition are handled before integration.
Fitness, morphology, imaging, secretion, differentiation, electrophysiology, or reporter endpoints are modeled according to scale and experimental unit. Technical wells or cells are not mistaken for independent biological replicates.
The workflow preserves the link between perturbation identity, measured response, statistical effect, biological interpretation, and the experiment needed to confirm it.
Define perturbation, phenotype, experimental unit, contrasts, positive and negative controls, expected direction, and acceptance criteria.
Verify sample, guide, allele, cell, feature, reference, batch, metadata, and library representation.
Evaluate read depth, mapping, guide assignment, off-target risk, cell quality, missingness, replicate concordance, and bottlenecks.
Estimate guide-, gene-, element-, allele-, cell-state-, or phenotype-level effects with appropriate uncertainty and multiple-testing control.
Connect effects to regulatory programs, pathways, cell states, network modules, upstream regulators, and alternative explanations.
Prioritize orthogonal reagents, rescue, dose response, temporal assays, independent models, and proximal plus functional readouts.
| Question | Analysis focus | Interpretation boundary |
|---|---|---|
| Which perturbations change abundance or fitness? | Guide quality, count models, control-based normalization, gene aggregation, effect size, essentiality and treatment interaction | Depletion may reflect growth, editing toxicity, copy number, or general fitness rather than a specific disease mechanism |
| Which perturbations alter cell state? | Donor-aware pseudobulk or cell-level models, compositional change, state-specific response, perturbation efficiency | Cell numbers do not replace independent donors; inferred states depend on sampling and annotation |
| Which regulatory element controls a gene? | Element activity, chromatin accessibility, contacts, expression response, distance, motif and QTL support | Proximity and correlation do not prove enhancer–gene regulation |
| Which molecular program mediates a phenotype? | Differential response, modules, trajectories, mediation-oriented hypotheses, pathway and regulator analysis | Temporal order and perturbation support strengthen causality, but indirect effects and adaptation remain possible |
Sample size is assessed at the level of the true experimental unit: independent cultures, animals, donors, or batches—not reads or cells. Controls and biological replicates are required to estimate variation and identify technical drift. Models account for pairing, batch, treatment, time, donor, library composition, and other estimable confounders. Effect sizes, confidence or uncertainty, and false-discovery control accompany ranked hits.
Guide-level concordance, alternative aggregation rules, leave-one-guide-out analyses, control-set choices, thresholds, and reference annotations are tested when they could alter a conclusion. Perfect confounding cannot be removed computationally. Small or unreplicated studies are treated as exploratory and prioritized for confirmation.
Machine learning may support phenotype prediction, response embeddings, perturbation similarity, regulatory inference, or candidate ranking. Preprocessing, feature selection, and tuning occur inside training folds; cells from the same donor or perturbation batch are grouped to prevent leakage. Nested cross-validation and external model systems are preferred. Applicability is limited by organism, cell type, assay, perturbation modality, dose, time, library, phenotype, and training distribution.
Contrast definitions, controls, library and mapping metrics, replicate behavior, exclusions, confounding, and data gaps.
Guide, gene, element, allele, cell state, or phenotype estimates with uncertainty, adjusted significance, and control calibration.
Perturbation-to-response links, regulatory modules, cell-state effects, pathways, driver features, and evidence provenance.
Robust hits, context-specific effects, contradictory evidence, alternative explanations, and confidence tiers.
Methods, software and database versions, parameters, reference files, analysis-ready matrices, scripts or notebooks as agreed.
Independent reagents, rescue, orthogonal assays, model selection, readouts, milestones, and go/no-go criteria.
Identify context-specific genes controlling viability, treatment response, or resistance.
Find perturbations that shift differentiation, activation, stress, or disease-associated programs.
Prioritize enhancers, promoters, motifs, and candidate target genes for validation.
Compare allele effects and connect molecular changes to measurable functional outcomes.
CRISPR screens can measure survival, reporter signal, sorted phenotypes, molecular profiles, or single-cell responses. Each design creates different count structures, controls, biases, and interpretations; analysis must therefore be matched to the assay rather than reduced to a generic hit-calling procedure.1
Dropout, enrichment, FACS, imaging, reporter, and single-cell screens differ in what constitutes a unit, a null effect, and a biological hit. Guide efficiency, off-target activity, variable multiplicity, copy-number effects, bottlenecks, selection intensity, cell composition, and sequencing depth can all change observed effects. We preserve these design features in the statistical model and report which evidence directly supports a function.
High-value findings should reproduce with independent guides or alleles, an orthogonal perturbation modality, a rescue construct where feasible, and a proximal molecular readout paired with the functional phenotype. Replication in another model or donor cohort helps establish the applicability domain.
1 Zhao, Y.; Zhang, M.; Yang, D. Bioinformatics approaches to analyzing CRISPR screen data: from dropout screens to single-cell CRISPR screens. Quantitative Biology 2022, 10, 307–318. Distributed under Open Access license CC BY 4.0.
We first define the functional claim that the data can support, the controls that calibrate it, and the next experiment that could falsify it. A focused project may begin with a guide-count or differential-result table; an end-to-end project may include raw sequencing QC, mapping, statistical modeling, multi-omic integration, functional reporting, and validation design. For human data, de-identification, access expectations, secure transfer, and permitted outputs are agreed before analysis.
Interpretation tiers separate reproducible primary effects from indirect responses and context-limited observations. A high-confidence finding should be supported by multiple effective guides, alleles, or elements; show a coherent effect relative to matched controls; and remain stable across reasonable filtering and modeling choices. Secondary findings may be biologically plausible but depend on a particular cell state, treatment, time point, or analysis assumption. Conflicting results are not averaged away: they are traced to differences in perturbation efficiency, assay sensitivity, cellular composition, genetic background, or model context. This structure helps teams choose between immediate validation, targeted data generation, and deliberate deprioritization.
We favor findings that show consistent perturbation effects, appropriate control behavior, credible molecular direction, relevant cell or tissue context, and a practical orthogonal validation route. Negative results and context restrictions are retained because they determine whether a function is general, conditional, or unsupported. Validation should pair a proximal readout of the intended molecular change with a disease- or phenotype-relevant functional endpoint, ideally using an independent reagent and rescue strategy. To discuss a screen, perturbation dataset, regulatory question, or validation plan, please Contact Us or submit the Online Inquiry below.
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