Panning complexity outpaces the tools
Multi-round, multi-condition campaigns generate datasets that need flexible, user-defined comparisons. Static tools force you to flatten that complexity or work around it manually.
Data analysis pipelines, end to end
From in vitro display libraries to in vivo immune repertoires — process raw NGS into a ranked set of developable candidates, all in one platform.
In vitro antibody discovery
In vitro antibody discovery
From raw NGS to ranked candidates. Find enriched clones without liabilities.
- 4×screening throughput
- 60%less time on manual steps
- Lower riskof late-stage failures
Challenges of in vitro antibody discovery
Enrichment doesn't equal affinity
High-frequency clones are fast growers, not necessarily strong binders. Without the right enrichment analysis, the best candidates stay buried in your data.
Liabilities found late cost the most
Sequence liabilities that slip through early selection cause failures deep in development. Catching them at the clone selection stage is where it matters.
Too many parameters for static tools
Antigen concentration, blocking conditions, round number, assay results — panning campaigns generate rich metadata. Cross-comparing all of it in a spreadsheet isn't just slow, it's practically impossible.
Simply select better antibodies
Build your panning campaign in a flexible analysis environment — multiple rounds, targets, and counter-selections, no matter how complex the design. Overlay enrichment scores, assay results, and liability predictions in interactive visualizations, and make decisions on promising candidates from a single screen.
End-to-end in vitro clone selection
Clone annotation
- Specify and analyze any amplicon configuration with UMIs, barcodes, or linkers.
- Annotate CDRs, frameworks, barcodes, and UMIs across any amplicon configuration.
- Process millions of sequences within an hour.
Quality control
- Interactive visualizations for FASTQ quality control, including Read Length Distribution and Base/Sample Quality.
- Inspect read fate and detect sequencing biases.
Clonotype clustering
- Group clones by any combination of regions & genes: CDRs, framework regions, VDJ genes, single or paired chains.
- Sort and filter clusters by any metadata, like gene usage, somatic hypermutation levels, and CDR3 length.
Enrichment analysis
- Track clones across panning rounds to identify highly enriched candidates through interactive, user-defined visualizations.
- Freely join, intersect, and subtract your datasets to match your panning campaign design.
- Overlay any metadata — assay results, liability scores, antigen conditions — directly onto enrichment plots.
- Include negative controls to track antigen-specific clones, and subtract irrelevant antibodies found in control conditions.
Developability analysis
- Run ML-based liability predictions or upload your own, and overlay them onto your enrichment data.
- Compare candidates against molecules that have reached clinical development.
Candidate prioritization
- Make informed decisions by visualizing all your data in one view: in-silico predictions, experimental results, and enrichment scores.
- Access Heavy & Light chain information at any stage.
- Set flexible thresholds to make clone selection faster, consistent, and reproducible.
In vivo antibody discovery
In vivo antibody discovery
Explore lineages and track somatic hypermutation through interactive phylogenetic trees. Integrate bulk and single-cell data, overlay assay results, and select a diverse set of developable leads.
- 5×faster lead clone selection
- 50%faster timelines with automation
- Lower riskof late-stage failures
Challenges of in vivo antibody discovery
Lineage complexity hides your best candidates
Tracing affinity maturation across animals and timepoints is hard. Without clear lineage tracking, picking the right branch is guesswork.
Immunization generates enormous diversity
Thousands of clones across multiple animals, timepoints, and immunization conditions. Finding the signal in that noise is hard.
Liabilities found late cost the most
Sequence liabilities caught late are expensive. Overlay liability predictions during clone selection to cut that risk early.
Frequency doesn't equal quality
Dominant clones in a repertoire aren't necessarily the best binders or the most developable.
Diversity is hard to guarantee
Selecting candidates that are truly structurally diverse requires more than just picking top clones by abundance or affinity.
Metadata fragmentation
Assay data, animal metadata, and immunization conditions all live separately from the sequence data, making informed decisions hard.
Simply select better antibodies
With the ENPICOM Platform, you can integrate any assay data from wet-lab experiments and compute structure-based developability profiles in silico. This information is seamlessly incorporated into your analysis workflow, creating information-rich visualizations in clustering, phylogeny, and the final selection of the optimal mix of diverse and developable hits.
End-to-end in vivo clone selection
Clone annotation
- Specify and analyze any amplicon configuration with UMIs, barcodes, or linkers.
- Annotate CDRs, frameworks, barcodes, and UMIs across any amplicon configuration.
- Process millions of sequences within an hour.
Quality control
- Interactive visualizations for FASTQ quality control, including Read Length Distribution and Base/Sample Quality.
- Inspect read fate and detect sequencing biases.
Repertoire overview
- Visualize V/J gene usage, diversity measures, and CDR3 length distribution.
- Stratify data by any metadata, with toggles, sliders, and tooltips for intuitive control.
Clonotype clustering
- Group clones by any combination of regions & genes: CDRs, framework regions, VDJ genes, single or paired chains.
- Sort and filter clusters by any metadata, like gene usage, somatic hypermutation levels, and CDR3 length.
Phylogeny
- Explore phylogenetic trees for clusters of interest and trace affinity maturation across lineages.
- Overlay any metadata — assay results, SHM levels, liability predictions — onto interactive tree visualizations to identify the best candidates.
Developability analysis
- Run ML-based liability predictions or upload your own, and overlay them onto your phylogenetic trees.
- Benchmark candidates against antibodies that have reached clinical development.
Candidate prioritization
- Visualize all your data in one view: in-silico predictions, experimental results, and SHM levels.
- Access heavy & light chain information at any stage.
- Set flexible thresholds to make clone selection faster, consistent, and reproducible.
- Select a structurally diverse panel, not just the most abundant clones.
Ready to accelerate your data analysis pipelines?
