DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires.

Autor: Sidhom JW; Bloomberg Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA. jsidhom1@jhmi.edu.; The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA. jsidhom1@jhmi.edu.; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA. jsidhom1@jhmi.edu., Larman HB; Bloomberg Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA.; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Pardoll DM; Bloomberg Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA.; The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Baras AS; Bloomberg Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA.; The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2021 Mar 11; Vol. 12 (1), pp. 1605. Date of Electronic Publication: 2021 Mar 11.
DOI: 10.1038/s41467-021-21879-w
Abstrakt: Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks. The ability to learn complex patterns in data has tremendous implications in immunogenomics. T-cell receptor (TCR) sequencing assesses the diversity of the adaptive immune system and allows for modeling its sequence determinants of antigenicity. We present DeepTCR, a suite of unsupervised and supervised deep learning methods able to model highly complex TCR sequencing data by learning a joint representation of a TCR by its CDR3 sequences and V/D/J gene usage. We demonstrate the utility of deep learning to provide an improved 'featurization' of the TCR across multiple human and murine datasets, including improved classification of antigen-specific TCRs and extraction of antigen-specific TCRs from noisy single-cell RNA-Seq and T-cell culture-based assays. Our results highlight the flexibility and capacity for deep neural networks to extract meaningful information from complex immunogenomic data for both descriptive and predictive purposes.
Databáze: MEDLINE
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