Research application
Immune profiling
Integrate somatic variation, expression, HLA and experimentally observed peptides; evidence-weighted candidate ranking, presentation/escape context. Scope is agreed after reviewing laboratory-generated data, study design and feasibility.
Analyses are tailored to your experimental design, data quality and research question. The workflow and deliverables are agreed following project review.
Analysis modules
Input concordance: review tumour-normal variation, RNA and HLA compatibility.
Candidate integration: combine somatic variants with expression evidence.
Peptide evidence: incorporate experimentally observed peptides where available.
Evidence-weighted ranking: report candidate support and explicit uncertainty.
Escape context: examine supported presentation changes without conflating binding, recognition and benefit.
Data and metadata
Suitable tumour-normal sequencing, RNA, HLA and optional immunopeptidomics. Include assay and processing provenance, quality-control summaries, sample identifiers without patient identifiers, species/tissue, groups, controls, batch and relevant donor/time-point metadata.
Proposed deliverables
Candidate evidence table and explicit uncertainty. Proposed outputs include documented methods, quality-control summaries and explicit limitations; deliverables are tailored after review.
Interpretation and feasibility
Binding, presentation, T-cell recognition and clinical benefit are distinct; experimental validation is needed.
Selected scientific references
Selected methodological and research references. Methods and software are chosen for each project after feasibility and licence review. Citations do not imply affiliation or validation of an ImmunLattice pipeline.
Mutanome-guided immunopeptidomics (Nature Communications 2025). Integration of variation and experimentally observed peptides.
NetMHCpan-4.1 and NetMHCIIpan-4.0 (Nucleic Acids Research 2020). Peptide–HLA binding methods; not proof of recognition.
Key parameters of tumor epitope immunogenicity revealed through a consortium approach improve neoantigen prediction (Cell 2020). Benchmark: separates peptide binding, presentation and T-cell recognition when prioritising candidate neoantigens.
NetMHCpan-4.2: improved prediction of CD8+ epitopes by use of transfer learning and structural features (Frontiers in Immunology 2025). Method: updated HLA class I epitope prediction; predicted scores require experimental interpretation.
Immunopeptidomics-based identification of naturally presented non-canonical circRNA-derived peptides (Nature Communications 2024). Research application: extends antigen discovery to non-canonical peptides supported by immunopeptidomics.
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