ENAapplication/xmlftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR494/SRR494402/SRR494402.fastq.gzftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR494/SRR494400/SRR494400.fastq.gzftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR494/SRR494401/SRR494401.fastq.gzftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR494/SRR494403/SRR494403.fastq.gzftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR494/SRR494399/SRR494399.fastq.gzftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR494/SRR494405/SRR494405.fastq.gzftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR494/SRR494404/SRR494404.fastq.gzprimary200OKGenomicsShemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Scienceshttps://www.ebi.ac.uk/ena/browser/view/PRJNA163911Homo sapiensThe T-cell receptor (TCR) repertoire is a mirror of the human immune system that reflects processes caused by infections, cancer, autoimmunity, and aging. Next generation sequencing (NGS) is becoming a powerful tool for deep TCR profiling yet, questions abound regarding the methodological approaches for sample preparation and correct data interpretation. Accumulated PCR and sequencing errors along with library preparation bottlenecks and uneven PCR efficiencies lead to information loss, biased quantification, and generation of huge artificial TCR diversity. Here, we compare Illumina, 454, and Ion Torrent platforms for individual TCR profiling, evaluate the rate and character of errors, and propose advanced platform-specific algorithms to correct massive sequencing data.ENAT Cells, Thymus-Dependent Lymphocyte., T-cell, T cell, Thymus-Dependent Lymphocytes, T Lymphocyte, T-Cell, mature T cell, T-Cells, Cells, Thymus-Dependent, T, T Cell, Lymphocytes, T-Lymphocyte, T-lymphocyte, immature T cell, T Lymphocytes, T lymphocyte, Thymus Dependent Lymphocytes, Cell, LymphocyteHuman, Modern., human being, Man (Taxonomy), Homo sapiens, man, Man, human, Modern ManfalseHomo sapiensComparison of three platforms for T-cell receptor repertoire sequencing2025-09-242013-05-31PRJNA1639119606