antibody-discovery

Imports your raw data — sequencing reads, count matrices, single-cell objects, tabular results — organizes it into named datasets, and attaches the sample metadata every downstream analysis groups and compares by

百奥赛图（Biocytogen）Antibody Vita：支撑 RenSuper™ Workstation AI 驱动抗体发现与高通量自动化的实验流程��数据和协作基础平台。Workflow foundation for AI-powered antibody discovery across RenMice® and Project Integrum.

Build somatic hypermutation (SHM) lineage trees from antibody repertoire sequencing data. Reconstruct clonal relationships between antibody sequences and visualize how B-cell receptor (BCR) clones diversify during somatic hypermutation and affinity maturation.

Extract TCR and BCR clonotypes from raw sequencing data. Alignment against germline gene databases, error correction, receptor assembly, and clonotype grouping — turning FASTQ into a quantified repertoire with full QC.

Explore and annotate your sequences interactively, lets you write rule-based annotations that tag sequences by any criteria you choose

Screen antibody, TCR, and peptide sequences for developability liabilities — the motifs that cause deamidation, isomerization, fragmentation, unwanted glycosylation, oxidation, and cysteine problems.

Select and rank the most promising antibody, peptide, or TCR candidates from a discovery campaign

Compute learned per-sequence vector representations for peptide and antibody/TCR datasets. Runs protein language models over your dataset so you can compare, cluster, and map a repertoire by learned similarity

Compare repertoire diversity fairly across samples sequenced to different depths. Subsamples each library at a series of depths and plots how many unique sequences appear, producing rarefaction curves whose shape tells you whether you have captured a library's diversity or only scratched it.

Compares clonotype or peptide frequencies across the conditions of a selection experiment — phage display rounds, yeast display sorts, in vivo timepoints — scores how strongly each sequence enriched, and ranks the candidates that came up.

Groups similar sequences (clonotypes or peptides) into clusters based on similarity using MMseqs2.

Find B-cell receptors that independent lineages converged on to identify likely antigen-specific BCRs from repertoire data alone

Predict 3D structures of antibody and nanobody variable domains directly from sequence.

Group antibody candidates by the shape of their binding site, surfacing candidates whose CDR loops adopt the same conformation even when their sequences diverge — and reports a centroid structure, cluster radius, and per-member structural

Group sequences by learned similarity rather than shared letters. Clusters clonotypes or peptides by their distance in protein language model embedding space using HDBSCAN, finding related sequences that identity-based clustering separates because their amino acids differ.

Process scFv sequencing data, separating each construct into its VH chain, VL chain, and synthetic linker, then assembling and quantifying unique scFv clonotypes

Rank clonotypes as antibody leads on all the evidence at once - maturation, abundance, convergence, and generation-probability signals into a single tunable score between 0 and 1

TenSixty Bio — independent third-party profile of a public API surface, by API Evangelist. TenSixty Bio is an oncology-focused biotechnology company based in Cambridge, Massachusetts, working to revolutionize the treatment of cancer. The company combines biology, machine learning, and high-throughput technologies to build an atlas of antibody targe

Adimab — independent third-party profile of a public API surface, by API Evangelist. Adimab is a private biotechnology company founded in 2007, headquartered in Lebanon, New Hampshire, with a west coast office in Mountain View, California. It operates a proprietary yeast-based antibody discovery and engineering platform that combines fully human sy