David Gfeller
@dgfeller.bsky.social
Fascinated by computational biology applied to cancer immune cell recognition
Happy the share our latest perspective on T-cell epitope recognition predictions for cancer immunotherapy: rdcu.be/fdKu6
Advances in predicting T cell epitope recognition for cancer immunotherapy
Nature Cancer - Gfeller et al. discuss the recent technological and computational advances in prediction of TCR–epitope recognition and the therapeutic applications in cancer immunotherapy.
rdcu.be
Happy to share the work of Aisha Shah about training predictors of TCR-epitope recognition with unpaired TCRa + TCRb sequences: www.biorxiv.org/content/10.6....
biorxiv.org
Interested in understanding how epitope recognition specificity is encoded in your TCR sequences? Try our new interactive page for building TCR specificity profiles on the TCR Motif Atlas: tcrmotifatlas.unil.ch/Building_mot....
Building Motifs
tcrmotifatlas.unil.ch
Happy to share the beautiful manuscript of Leonard Herault: www.biorxiv.org/content/10.6...
biorxiv.org
Very proud of the work of Dana Moreno about statistical modelling of CDR3 sequences in TCR repertoires: www.biorxiv.org/content/10.6....
Statistical modelling of CDR3 sequences provides robust quality control for TCR repertoire datasets
T-Cell Receptors (TCRs) show extensive sequence diversity across T cells. This diversity arises from different choices of V and J genes and from insertions and deletions at the V(D)J junction within t...
biorxiv.org
Nice results: rdcu.be/eTNVr. Looks like the decision to develop specific ML models of TCR-epitope interactions for each epitope was a reasonable choice.
Assessing data size requirements for training generalizable sequence-based TCR specificity models via pan-allelic MHC-I point-mutation ligandome evaluation
Scientific Reports - Assessing data size requirements for training generalizable sequence-based TCR specificity models via pan-allelic MHC-I point-mutation ligandome evaluation
rdcu.be
For those who followed the IMMREP25 competition and are interested in understanding how specificity was encoded for each epitope, check our TCR Motif Atlas IMMREP25 page: tcrmotifatlas.unil.ch/Browse_epito...
Model Motif Atlas
tcrmotifatlas.unil.ch
Very happy to share our latest manuscript about TCR-epitope recognition specificity: www.biorxiv.org/content/10.1...
biorxiv.org
Cool paper on scRNA T cell annotation. However, a note for ProjecTILs users: I wouldn’t rely on the ProjecTILs benchmark in this work, as it was run using a mouse reference map to classify human T cells (even though human CD4/CD8 T cell maps are available github.com/carmonalab/P...)
GitHub - carmonalab/ProjecTILs: Interpretation of cell states using reference single-cell maps
Interpretation of cell states using reference single-cell maps - carmonalab/ProjecTILs
github.com
Nature Methods@natmethods.nature.com · 11mo agoIntroducing TCAT, a pipeline to simultaneously capture gene expression programs related to T cell subsets and activation states. www.nature.com/articles/s41...
Interested in unraveling specificity in epitope recognition by TCRs and KIRs in the context of graft-versus-host diseases. We have an open post-doc position in the group. Do not hesitate to share the word.
Happy to share the great work of Giancarlo Croce: Phage display enables machine learning discovery of cancer antigen–specific TCRs | Science Advances www.science.org/doi/10.1126/...
Phage display enables machine learning discovery of cancer antigen–specific TCRs
Phage display and machine learning join forces to identify cancer-specific TCRs.
science.org
Matei Teleman's SuperSpot paper out in Bioinformatics: academic.oup.com/bioinformati...
SuperSpot: Coarse Graining Spatial Transcriptomics Data into Metaspots
AbstractSummary. Spatial Transcriptomics is revolutionizing our ability to phenotypically characterize complex biological tissues and decipher cellular nic
academic.oup.com