license: mit
task_categories:
- text-generation
language:
- en
tags:
- biology
- antibodies
size_categories:
- 100M<n<1B
CoSiNE Training Dataset
Training data for CoSiNE model, which simulates antibody affinity maturation. Please read our paper for more details!
Dataset Summary
This dataset consists of approximately 2 million B-cell receptor (BCR) parent-child sequence transitions derived from 120,000 clonal families across 555 individual donors. The data was processed using a rigorous phylogenetic inference pipeline to capture the nuances of somatic hypermutation:
- Clonal Inference: Sequences were clustered into clonal families and naive germlines were inferred using
partis. - Quality Filtering: We retained only productive sequences (no stop codons, conserved CDR3 anchors) and excluded sequences with mutations in conserved signature cysteines.
- Phylogenetic Reconstruction: Phylogenetic trees and ancestral sequences were inferred using IQ-TREE under a K80 substitution model.
- Paired-Chain Modeling: For paired heavy and light chain data, we utilized an edge-linked-proportional partition model to account for distinct evolutionary rates across chains.
The final training set consists of Parent-Child Pairs (PCPs) extracted from the edges of these phylogenetic trees, representing a comprehensive map of the evolutionary trajectories within the adaptive immune system. For a detailed description of the processing pipeline, please refer to https://elifesciences.org/reviewed-preprints/109644v1.
Dataset Sources
The dataset was compiled using B-cell receptor (BCR) sequencing datasets from five sources:
Citation
Consider citing our paper if you use CoSiNE in your research!
@article{Lu2026ConditionallySN,
title={Conditionally Site-Independent Neural Evolution of Antibody Sequences},
author={Stephen Zhewen Lu and Aakarsh Vermani and Kohei Sanno and Jiarui Lu and IV FrederickA.Matsen and Milind Jagota and Yun S. Song},
journal={ArXiv},
year={2026},
url={https://api.semanticscholar.org/CorpusID:285973749}
}
Dataset Card Contact
For questions or issues, please contact:
- Stephen Z. Lu (stephen.lu@berkeley.edu)
- Aakarsh Vermani (aakarshv@berkeley.edu)