T/BCR-seq-analysis

T/B cell receptor sequencing analysis notes

Please check awesome vdj too!

tutorials

• A clonotype nomenclature for T cell receptors
• T Cell Clonal Analysis Using Single-cell RNA Sequencing and Reference Maps
• biostar post on integration scTCR with Seurat
• https://repseq-tutorial.readthedocs.io/en/latest/prerequisites.html
• Welcome to the Immcantation Portal Use the docker version of Immcantation if you have installation problems. 10x scBCR tutorial using Immcantation https://immcantation.readthedocs.io/en/stable/tutorials/10x_tutorial.html
• scirpy "getting started" tutorial and case study reanalysing 140k T-cells from Wu et al. (2020).
• Tutorial: a statistical genetics guide to identifying HLA alleles driving complex disease

papers

• Can we predict T cell specificity with digital biology and machine learning?
• reivew High-throughput and single-cell T cell receptor sequencing technologies
• Disease diagnostics using machine learning of immune receptors
• Rep-Seq: uncovering the immunological repertoire through next-generation sequencing
• Single Cell T Cell Receptor Sequencing: Techniques and Future Challenges
• T-cell repertoire analysis and metrics of diversity and clonality
• TCR-Vγδ usage distinguishes protumor from antitumor intestinal γδ T cell subsets
• De novo prediction of cancer-associated T cell receptors for noninvasive cancer detection
• TCR-engineered T cell therapy in solid tumors: State of the art and perspectives

simulation

Echidna: Integrated simulations of single-cell immune receptor repertoires and transcriptomes

Tools

"Cool! I would start with immunarch, VDJTools, and the new scRepertoire package" -- Wʏᴀᴛᴛ MᴄDᴏɴɴᴇʟʟ from 10x genomcis

• dandelion python package for analyzing single cell BCR/TCR data from 10x Genomics 5’ solution!

• TRUST4 developed in Shirley Liu's group. Use it to extract TCR/BCR information from bulk RNAseq or 5' scRNAseq data.

• Benchmarking computational methods for B-cell receptor reconstruction from single-cell RNA-seq data

• We are happy to report a dramatic speedup for one of the core computations for adaptive immune receptor repertoire (AIRR) analysis - the discovery and counting of receptors that overlap between repertoires! Check out our CompAIRR. With 10^4 repertoires of 10^5 sequences each, CompAIRR ran in 17 minutes while the fastest existing tool took 10 days, amounting to a ~1000x speedup

• ClusTCR: a Python interface for rapid clustering of large sets of CDR3 sequences with unknown antigen specificity

• GLIPH2

• GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation from Bo Li.

• tcrdist3 is a python API-enabled toolkit for analyzing T-cell receptor repertoires

• TCRex: a web tool for the prediction of TCR–epitope recognition

• ImRex TCR-epitope recognition prediction using combined sequence input represention for convolutional neural networks.

• NetTCR - 2.0 Sequence-based prediction of peptide-TCR binding

• GraphMHC: neoantigen prediction model applying the graph neural network to molecular structure" A hybrid graph attention network + CNN to predict peptides that bind MHC proteins

• CellaRepertorium

• enclone from 10x. we should give this a try if we want to cluster TCR and BCR clonotypes.

• migec:A RepSeq processing swiss-knife.

• MiXCR is a universal software for fast and accurate analysis of T- and B- cell receptor repertoire sequencing data.

• tcR: an R package for T cell receptor repertoire advanced data analysis

• ImReP is a computational method for rapid and accurate profiling of the adaptive immune repertoire from regular RNA-Seq data.

• Grouping of Lymphocyte Interactions by Paratope Hotspots paper: https://www.nature.com/nature/journal/v547/n7661/full/nature22976.html

• TcellMatch: Predicting T-cell to epitope specificity. cellMatch is a collection of models to predict antigen specificity of single T cells based on CDR3 sequences and other single cell modalities, such as RNA counts and surface protein counts

• scirpy: A scanpy extension for single-cell TCR analysis.

• Tessa is a Bayesian model to integrate T cell receptor (TCR) sequence profiling with transcriptomes of T cells. Enabled by the recently developed single cell sequencing techniques, which provide both TCR sequences and RNA sequences of each T cell concurrently, Tessa maps the functional landscape of the TCR repertoire, and generates insights into understanding human immune response to diseases.

• DeepTCR Deep Learning Methods for Parsing T-Cell Receptor Sequencing (TCRSeq) Data https://twitter.com/John_Will_I_Am/status/1570837756787691527 https://www.science.org/doi/10.1126/sciadv.abq5089

• Efficient and accurate KIR and HLA genotyping with massively parallel sequencing data

• Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA)

machine learning

• Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes

• DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis

• STAPLER: Efficient learning of TCR-peptide specificity prediction from full-length TCR-peptide data

• Structure-based prediction of T cell receptor:peptide-MHC interactions Preprint from Philip Bradley where he creates a version of AlphaFold to model TCR:peptide-MHC interactions. Benchmark is far from perfect, but the paper shows that deep learning-based structural modelling is a possible strategy to predict TCR specificity.

• Uni-Fold: an open-source platform for developing protein models beyond AlphaFold. https://github.com/dptech-corp/Uni-Fold

• Equidock: docking protein receptor and ligand https://github.com/octavian-ganea/equidock_public news https://news.mit.edu/2022/ai-predicts-protein-docking-0201

• AlphaFill: enriching AlphaFold models with ligands and cofactors

• ATRAP - Accurate T cell Receptor Antigen Pairing through data-driven filtering of sequencing information from single-cells

• gget alphafold: Predict the 3D structure of a protein from its amino acid sequence using @DeepMind ’s AlphaFold v2.0 from a Python or command-line environment in 3 lines of code. Runs on any laptop and requires only ~4 GB of disk space. Simply ‘pip install gget’.

• ColabFold: making protein folding accessible to all

• We have trained ESMFold to predict full atomic protein structure directly from language model representations of a single sequence. Accuracy is competitive with AlphaFold on most proteins with order of magnitude faster inference. By @MetaAI Protein Team.

• DeepMind AlphaFold for antibody discovery: What's the status?

• Why AlphaFold won’t revolutionise drug discovery We made AlphaFold dream of new protein assemblies, used #ProteinMPNN to bring it back to reality. https://twitter.com/BasileWicky/status/1570564831522213888

• Here, we introduce OmegaFold, the first computational method to successfully predict high-resolution protein structure from a single primary sequence alone. Using a new combination of a protein language model that allows us to make predictions from single sequences and a geometry-inspired transformer model trained on protein structures, OmegaFold outperforms RoseTTAFold and achieves similar prediction accuracy to AlphaFold2 on recently released structures.

• Hallucinating symmetric protein assemblies

• Learning inverse folding from millions of predicted structures https://twitter.com/alexrives/status/1513603415959556096

• [PSP: Million-level Protein Sequence Dataset for Protein Structure Prediction}(https://arxiv.org/abs/2206.12240)

• Fast, accurate ranking of engineered proteins by receptor binding propensity using structural modeling https://twitter.com/DingXiaozhe/status/1618257727515676672

database

• The IPD-IMGT/HLA Database provides a specialist database for sequences of the human major histocompatibility complex (MHC) and includes the official sequences named by the WHO Nomenclature Committee For Factors of the HLA System. The IPD-IMGT/HLA Database is part of the international ImMunoGeneTics project (IMGT).

• hlabud provides methods to retrieve sequence alignment data from IMGTHLA and convert the data into convenient R matrices ready for downstream analysis.

• TCRdb A comprehensive database of human T-cell receptor (TCR) sequences

• Immuno-Navigator A database for gene coexpression in the immune system

• McPAS-TCR A manually curated catalogue of pathology associated T-cell receptor sequences

• Vdjdb

• @OPIGlets has built lots of lovely stuff including SAbPred, OAS, TAP http://opig.stats.ox.ac.uk/resources