1/ [immunarch](https://immunarch.com/index.html)
2/ [scRepertoire](https://github.com/ncborcherding/scRepertoire)
3/ [dandelion](https://sc-dandelion.readthedocs.io/en/latest/) python package for analyzing single cell BCR/TCR data from 10x Genomics 5’ solution!
4/ [TRUST4](https://www.nature.com/articles/s41592-021-01142-2) developed in Shirley Liu's group. Use it to extract TCR/BCR information from bulk RNAseq or 5' scRNAseq data.
5/ 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 [CompAIRR](https://github.com/uio-bmi/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
6/ [ClusTCR](https://svalkiers.github.io/clusTCR/): a Python interface for rapid clustering of large sets of CDR3 sequences with unknown antigen specificity;
7/ [GLIPH2](https://www.nature.com/articles/s41587-020-0505-4)
8/ [GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation](https://www.nature.com/articles/s41467-021-25006-7) from Bo Li.
9/ [tcrdist3](https://github.com/kmayerb/tcrdist3) is a python API-enabled toolkit for analyzing T-cell receptor repertoires
10/ [TCRex](https://tcrex.biodatamining.be/): a web tool for the prediction of TCR–epitope recognition
11/ [ImRex](https://github.com/pmoris/ImRex) TCR-epitope recognition prediction using combined sequence input represention for convolutional neural networks.
12/ [NetTCR - 2.0](https://services.healthtech.dtu.dk/service.php?NetTCR-2.0) Sequence-based prediction of peptide-TCR binding
13/ [CellaRepertorium](https://github.com/amcdavid/CellaRepertorium)
14/ [enclone](https://10xgenomics.github.io/enclone/) from 10x. we should give this a try if we want to cluster TCR and BCR clonotypes.
15/ [migec](https://github.com/mikessh/migec):A RepSeq processing swiss-knife.
16/ [MiXCR](https://github.com/milaboratory/mixcr) is a universal software for fast and accurate analysis of T- and B- cell receptor repertoire sequencing data.
17/ [ImReP](https://sergheimangul.wordpress.com/imrep/) is a computational method for rapid and accurate profiling of the adaptive immune repertoire from regular RNA-Seq data.
18/ [TcellMatch](https://github.com/theislab/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
19/ [scirpy](https://github.com/icbi-lab/scirpy): A scanpy extension for single-cell TCR analysis.
20/ [Tessa](https://github.com/jcao89757/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.
21/ [DeepTCR](https://github.com/sidhomj/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
22/ [Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA)](https://www.nature.com/articles/s41587-021-00989-2)
23/ [Echidna: Integrated simulations of single-cell immune receptor repertoires and transcriptomes](https://academic.oup.com/bioinformaticsadvances/advance-article/doi/10.1093/bioadv/vbac062/6687122?login=false)
