pyTCR: A comprehensive and scalable solution for TCR-Seq data analysis to facilitate reproducibility and rigor of immunogenomics research
T cell receptor (TCR) studies have grown substantially with the advancement in the sequencing techniques of T cell receptor repertoire sequencing (TCR-Seq). The analysis of the TCR-Seq data requires computational skills to run the computational analysis of TCR repertoire tools. However biomedical re...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Immunology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2022.954078/full |
| _version_ | 1828098761580085248 |
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| author | Kerui Peng Jaden Moore Jaden Moore Mohammad Vahed Jaqueline Brito Guoyun Kao Amanda M. Burkhardt Houda Alachkar Serghei Mangul |
| author_facet | Kerui Peng Jaden Moore Jaden Moore Mohammad Vahed Jaqueline Brito Guoyun Kao Amanda M. Burkhardt Houda Alachkar Serghei Mangul |
| author_sort | Kerui Peng |
| collection | DOAJ |
| description | T cell receptor (TCR) studies have grown substantially with the advancement in the sequencing techniques of T cell receptor repertoire sequencing (TCR-Seq). The analysis of the TCR-Seq data requires computational skills to run the computational analysis of TCR repertoire tools. However biomedical researchers with limited computational backgrounds face numerous obstacles to properly and efficiently utilizing bioinformatics tools for analyzing TCR-Seq data. Here we report pyTCR, a computational notebook-based solution for comprehensive and scalable TCR-Seq data analysis. Computational notebooks, which combine code, calculations, and visualization, are able to provide users with a high level of flexibility and transparency for the analysis. Additionally, computational notebooks are demonstrated to be user-friendly and suitable for researchers with limited computational skills. Our tool has a rich set of functionalities including various TCR metrics, statistical analysis, and customizable visualizations. The application of pyTCR on large and diverse TCR-Seq datasets will enable the effective analysis of large-scale TCR-Seq data with flexibility, and eventually facilitate new discoveries. |
| first_indexed | 2024-04-11T08:07:04Z |
| format | Article |
| id | doaj.art-8c0bd513c19c4c74817e45f9114aae3f |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-11T08:07:04Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-8c0bd513c19c4c74817e45f9114aae3f2022-12-22T04:35:29ZengFrontiers Media S.A.Frontiers in Immunology1664-32242022-10-011310.3389/fimmu.2022.954078954078pyTCR: A comprehensive and scalable solution for TCR-Seq data analysis to facilitate reproducibility and rigor of immunogenomics researchKerui Peng0Jaden Moore1Jaden Moore2Mohammad Vahed3Jaqueline Brito4Guoyun Kao5Amanda M. Burkhardt6Houda Alachkar7Serghei Mangul8Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, United StatesDepartment of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, United StatesComputer Science Department, Orange Coast College, Costa Mesa, CA, United StatesDepartment of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, United StatesDepartment of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, United StatesDepartment of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, United StatesDepartment of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, United StatesDepartment of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, United StatesDepartment of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, United StatesT cell receptor (TCR) studies have grown substantially with the advancement in the sequencing techniques of T cell receptor repertoire sequencing (TCR-Seq). The analysis of the TCR-Seq data requires computational skills to run the computational analysis of TCR repertoire tools. However biomedical researchers with limited computational backgrounds face numerous obstacles to properly and efficiently utilizing bioinformatics tools for analyzing TCR-Seq data. Here we report pyTCR, a computational notebook-based solution for comprehensive and scalable TCR-Seq data analysis. Computational notebooks, which combine code, calculations, and visualization, are able to provide users with a high level of flexibility and transparency for the analysis. Additionally, computational notebooks are demonstrated to be user-friendly and suitable for researchers with limited computational skills. Our tool has a rich set of functionalities including various TCR metrics, statistical analysis, and customizable visualizations. The application of pyTCR on large and diverse TCR-Seq datasets will enable the effective analysis of large-scale TCR-Seq data with flexibility, and eventually facilitate new discoveries.https://www.frontiersin.org/articles/10.3389/fimmu.2022.954078/fullTCR - T cell receptorTCR-seqimmunogenomicscomputational notebooksTCR characterizationreproducibility |
| spellingShingle | Kerui Peng Jaden Moore Jaden Moore Mohammad Vahed Jaqueline Brito Guoyun Kao Amanda M. Burkhardt Houda Alachkar Serghei Mangul pyTCR: A comprehensive and scalable solution for TCR-Seq data analysis to facilitate reproducibility and rigor of immunogenomics research Frontiers in Immunology TCR - T cell receptor TCR-seq immunogenomics computational notebooks TCR characterization reproducibility |
| title | pyTCR: A comprehensive and scalable solution for TCR-Seq data analysis to facilitate reproducibility and rigor of immunogenomics research |
| title_full | pyTCR: A comprehensive and scalable solution for TCR-Seq data analysis to facilitate reproducibility and rigor of immunogenomics research |
| title_fullStr | pyTCR: A comprehensive and scalable solution for TCR-Seq data analysis to facilitate reproducibility and rigor of immunogenomics research |
| title_full_unstemmed | pyTCR: A comprehensive and scalable solution for TCR-Seq data analysis to facilitate reproducibility and rigor of immunogenomics research |
| title_short | pyTCR: A comprehensive and scalable solution for TCR-Seq data analysis to facilitate reproducibility and rigor of immunogenomics research |
| title_sort | pytcr a comprehensive and scalable solution for tcr seq data analysis to facilitate reproducibility and rigor of immunogenomics research |
| topic | TCR - T cell receptor TCR-seq immunogenomics computational notebooks TCR characterization reproducibility |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2022.954078/full |
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