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41 results for “T cell receptor repertoire”

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zenodo40/100

Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities: discovery cohort meta data and parsed TCR repertoire data

<p>Meta data corresponding the the discovery cohort for the paper, &quot;Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities&quot;&nbsp;by Magdalena L Russell, Aisha Souquette, David M Levine, Stefan A Schattgen, E Kaitlynn Allen, Guillermina Kuan, Noah Simon, Angel Balmaseda, Aubree Gordon, Paul G Thomas, Frederick A Matsen IV, and Philip Bradley. These meta data include:&nbsp;</p> <p>(1) a file mapping the SNP data subject IDs&nbsp;to the TCR repertoire data&nbsp;subject IDs (gwas_id_mapping.tsv)<br> (2) a file including the PCAir PCs, self-reported ancestry, and genomic ancestry for each subject (all_pc_air.txt)<br> (3) a file including the PCAir variance explained by each PC (all_pc_air_variance.txt)<br> (3)&nbsp;a file including the SNP ID, chromosome, hg19 position, allele, rsid, and quality control metrics&nbsp;for each SNP in the SNP array (emerson_snp_rs_data.tsv)<br> (4) a file including IMGT genes and sequences used for parsing TCRB repertoire data (human_vj_allele_cdr3_nucseqs.tsv)<br> (5) a file including predicted TRBD2 allele genotypes for each subject (emerson_trbd2_alleles.tsv)<br> (6)&nbsp;Parsed TCRB repertoire data.&nbsp;These raw data were&nbsp;first published in Emerson et. al,&nbsp;<em>Nature Genetics&nbsp;</em>2017. (emerson_parsed_tcrb.tgz)</p> <p><strong>Corresponding discovery&nbsp;cohort raw TCR repertoire data is available here:&nbsp;</strong>https: //doi.org/10.21417/B7001Z (ImmuneACCESS database)<br> <strong>Corresponding discovery cohort SNP data is available here:</strong>&nbsp;https: //www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001918.v1.p1 (The database of Genotypes and Phenotypes,&nbsp;accession number: phs001918)<br> <br> <strong>Software tools designed to work with these data are available here:</strong>&nbsp;https://github.com/phbradley/tcr-gwas</p>

opencc-by-4.0Dec 2021View details →
zenodo36/100

Pre-processed B cell receptor repertoire sequencing data from BioProject PRJNA527941

<p><strong>Data Processing</strong></p> <p>&nbsp;</p> <p>Samples were demultiplexed via their Illumina indices, and processed using the Immcantation toolkit(1,2).&nbsp;Raw fastq files were filtered based on a quality score threshold of 20. Paired reads were joined if they had a minimum length of 10 nt, maximum error rate of 0.3 and a significance threshold of 0.0001. Reads with identical UMI were collapsed to a consensus sequence. Reads with identical full-length sequence and identical constant primer but differing UMI were further collapsed. Sequences were then submitted to IgBlast (3) for VDJ assignment and sequence annotation. Constant region sequences were mapped to germline using Stampy(4). The number and type of V gene mutations was calculated using the shazam R package.(2)</p> <p>&nbsp;</p> <p><strong>software_versions</strong>&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;pRESTO:0.5.3,Change-O:0.3.4,IgBlast 1.6.1, stampy1.0.21. shazam0.1.8</p> <p><strong>quality_thresholds</strong>&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;FilterSeq.py pRESTO Q&gt;20</p> <p><strong>paired_reads_assembly</strong>&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;AssemblePairs.py pRESTO minlen 10 maxerror 0.3 alpha 0.0001</p> <p><strong>primer_match_cutoffs</strong>&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;MaskPrimers.py pRESTO C primer &amp; V primer maxerror 0.2</p> <p><strong>consensus_building</strong>&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;BuildConsensus.py pRESTO maxerror 0.1 maxgap 0.5</p> <p><strong>collapsing_method</strong>&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;CollapseSeq.py pRESTO</p> <p><strong>germline_database&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>IMGT</p> <p>&nbsp;</p> <p><strong>Format</strong></p> <p>&nbsp;</p> <p>Processed sequences are provided in a tab delimited file format, including the following annotations:</p> <p>&nbsp;</p> <p><strong>C_CALL&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Isotype subclass</p> <p><strong>SEQUENCE_ID&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Sequence identifier</p> <p><strong>V_CALL&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>V segment gene and allele</p> <p><strong>D_CALL&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>D segment gene and allele</p> <p><strong>J_CALL&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>J segment gene and allele</p> <p><strong>JUNCTION_LENGTH&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Junction length</p> <p><strong>CONSCOUNT&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Raw read count from which UMI consensus sequences were generated, summed over all UMIs for the given unique sequence.</p> <p><strong>DUPCOUNT&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>UMI count for the given unique sequence</p> <p><strong>ISOTYPE&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Constant region primer (isotype)</p> <p><strong>MU_COUNT_CDR_R&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Number of replacement mutations in CDR region</p> <p><strong>MU_COUNT_CDR_S&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Number of silent mutations in CDR region</p> <p><strong>MU_COUNT_FWR_R&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Number of replacement mutations in FWR region</p> <p><strong>MU_COUNT_FWR_S&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Number of silent mutations in FWR region</p> <p><strong>MUT_TOTAL&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Total number of mutations in V gene&nbsp;</p> <p><strong>SEQUENCE_INPUT&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Full length sequence</p> <p><strong>SEQUENCE_IMGT&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Gapped IMGT sequence</p> <p><strong>V_GERM_START_VDJ&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>position of the first nucleotide in ungapped V germline sequence alignment</p> <p><strong>JUNCTION&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Junction nucleotide sequence</p> <p><strong>GERMLINE_IMGT_D_MASK&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>IMGT-gapped germline nucleotide sequence with ns masking the NP1-D-NP2 regions</p> <p><strong>Run&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>ID of sequencing run</p> <p><strong>Sample_type&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>The tissue sampled (e.g Peripheral Blood, bone marrow, ..)</p> <p><strong>Sex&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Sex of the Subject</p> <p><strong>Age&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Age of the subject</p> <p><strong>UNIQUE_ID&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Subject identifier&nbsp;</p> <p><strong>SAMPLE_ID&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Sample identifier, linking back to raw data</p> <p><strong>Subset&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Defined B cell subset&nbsp;</p> <p><strong>Repertoire&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Defined B cell repertoire (Naive, Memory IgM/IgD, IgA, IgG)</p> <p><strong>R_SCDR&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>R/S ratio in CDR region</p> <p><strong>R_SFWR&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>R/S ratio in FWR region</p> <p><strong>V_FAM&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>V family gene</p> <p><strong>V_GENE&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>V segment gene</p> <p><strong>D_GENE&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>D segment gene</p> <p><strong>J_GENE&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>J segment gene</p> <p><strong>Clust_Rank&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Cluster rank</p> <p><strong>Clust_REPRES&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Cluster representative</p> <p><strong>Clust_SIZE&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Cluster size</p> <p><strong>Clust_MAXFREQ&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Cluster maximum frequency</p> <p><strong>Clust_SHAREDNESS&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>Cluster sharedness</p> <p><strong>CDR3_AA_GRAVY&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>CDR3 hydrophobicity index</p> <p><strong>CDR3_AA_CHARGE&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>CDR3 charge</p> <p><strong>CDRH3PDB&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>CDRH3 PDB (Structure) code</p> <p><strong>H1Canon&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>H1 Canonical class</p> <p><strong>H2Canon&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>H2 Canonical class</p> <p><strong>H1_GERMLINE&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>H1 Germline Canonical class</p> <p><strong>H2_GERMLINE&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</strong>H2 Germline Canonical class</p> <p>&nbsp;</p> <p><strong>References</strong></p> <p>1.&nbsp;Vander Heiden, J. A., G. Yaari, M. Uduman, J. N. H. Stern, K. C. O&rsquo;Connor, D. A. Hafler, F. Vigneault, and S. H. Kleinstein.&nbsp;2014. PRESTO: A toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires.&nbsp;<em>Bioinformatics</em>30: 1930&ndash;1932.</p> <p>2. Gupta, N. T., J. A. Vander Heiden, M. Uduman, D. Gadala-Maria, G. Yaari, and S. H. Kleinstein.&nbsp;2015. Change-O: A toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data.&nbsp;<em>Bioinformatics</em>31: 3356&ndash;3358.</p> <p>3. Ye, J., N. Ma, T. L. Madden, and J. M. Ostell. 2013. IgBLAST: an immunoglobulin variable domain sequence analysis tool.&nbsp;<em>Nucleic Acids Res.</em>41.</p> <p>4. Lunter, G., and M. Goodson. 2011. Stampy: A statistical algorithm for sensitive and fast mapping of Illumina sequence reads.&nbsp;<em>Genome Res.</em>21: 936&ndash;939.</p>

opencc-by-4.0Apr 2019View details →
zenodo36/100

Enhancing comparative T-cell receptor repertoire analysis in small biological samples through pooling homologous cell samples from multiple mice

<p>All data files used to generate the figures in the paper are shared in this project.</p> <p>Scripts are available on <a href="https://github.com/i3-unit/CRM_24" target="_blank" rel="noopener">GitHub</a>.</p>

opencc-by-4.0Mar 2024View details →
zenodo36/100

Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities: validation cohort meta data and parsed TCR repertoire data

<p>Meta data corresponding the the validation cohort for the paper,&nbsp;&quot;Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities&quot;&nbsp;by Magdalena L Russell, Aisha Souquette, David M Levine, Stefan A Schattgen, E Kaitlynn Allen, Guillermina Kuan, Noah Simon, Angel Balmaseda, Aubree Gordon, Paul G Thomas, Frederick A Matsen IV, and Philip Bradley. These meta data include:&nbsp;</p> <p>(1) SNP genotypes for the two SNPs which overlap with the discovery cohort<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;- (nicaragua_snp_genotypes_ints.tsv) -- SNP genotypes as integers<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;- (nicaragua_snp_genotypes_strings.tsv) -- SNP genotypes as allele strings&nbsp;<br> (2) the ancestry PCs for each individual in the validation cohort (nicaragua_snp_ancestry_PCA.tsv)<br> (3) a file including IMGT genes and sequences used for parsing TCRB repertoire data (human_vj_allele_cdr3_nucseqs.tsv)<br> (4) a file including IMGT genes&nbsp;used for parsing TCRA&nbsp;repertoire data (human_vj_alleles_alpha.tsv)<br> (5)&nbsp;Parsed TCRA repertoire data (nicaragua_parsed_TCRA.tgz)<br> (6) Parsed TCRB repertoire data (nicaragua_parsed_TCRB.tgz)&nbsp;</p> <p><strong>Corresponding raw validation cohort TCR repertoire data is available here:</strong>&nbsp;https://www. ncbi.nlm.nih.gov/bioproject/PRJNA762269 (The BioProject database,&nbsp;accession number: PRJNA762269)</p> <p><strong>Software tools designed to work with these data are available here:</strong>&nbsp;https://github.com/phbradley/tcr-gwas</p>

opencc-by-4.0Dec 2021View details →
zenodo36/100

Partis post-processed B cell receptor repertoires from BioProject PRJNA349143

<p>These files correspond to partis annotations of several datasets found in&nbsp;BioProject PRJNA349143. (DOI:&nbsp;10.5281/zenodo.821659).</p>

opencc-by-4.0Sep 2019View details →
zenodo32/100

AIRRSHIP: Example synthetic B cell receptor repertoire data

<p>Example repertoire data generated by AIRRSHIP (https://github.com/Cowanlab/airrship). Four repertoires are available (two with SHM, two without), each of which contains 100,000 sequences produced using the default AIRRSHIP parameters. Sequence data is contained in the FASTA files, TSV files give details of each step in the generation process, summary file shows the command given to AIRRSHIP and the locus file contains the alleles used in the repertoire. See https://airrship.readthedocs.io/en/latest/output/ for more information on file format.</p> <p>Repertoires were created using version 0.1.2 of AIRRSHIP.</p>

opencc-by-4.0Jan 2023View details →
ClinicalTrials.gov32/100

ALLoreactive T-Cell receptOr RePertoire in kidnEy tranSplantation

ClinicalTrials.gov study NCT03422224. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.

restrictedIPD-UNDECIDEDFeb 2026View details →
zenodo28/100

Systematic profiling of full-length immunoglobulin and T-cell receptor repertoire diversity in rhesus macaque through long read transcriptome sequencing

<p>Using long read sequencing, we sequenced four Indian-origin rhesus macaque tissues. From raw full-length, non-chimeric circular consensus sequencing (CCS) reads, we&nbsp;obtained high quality, full-length sequences for over 6,000 unique immunoglobulin and T-cell receptor transcripts, without the need for sequence assembly.</p>

opencc-by-4.0Feb 2020View details →
dryad28/100

Data from: Spatial clustering of receptors and signaling molecules regulates NK cell response to peptide repertoire changes

Open the record for dataset details and reuse information.

publicApr 2019View details →
geo24/100

Longitudinal analysis of T-cell receptor repertoires reveals persistence of antigen-driven CD4+ and CD8+ T-cell clusters in Systemic Sclerosis patients

GEO Series GSE156980. Homo sapiens. 24 samples. Type: Other.

openGEO-OpenJan 2021View details →
geo24/100

T-cell receptor repertoire in uterine and ovarian carcinosarcoma

GEO Series GSE155276. Homo sapiens. 8 samples. Type: Other.

openGEO-OpenDec 2020View details →
geo24/100

Resistance Potential of the HLA-A2-restricted Immunodominant SARS-CoV-2 Specific CD8+ T Cell Receptor Repertoire to Antigenic Drift

GEO Series GSE246491. Homo sapiens. 30 samples. Type: Expression profiling by high throughput sequencing; Other.

openGEO-OpenJan 2026View details →
geo24/100

T-cell receptor repertoire alterations drive pathological gut immunological and microbial signatures in humans and mice with hypomorphic RAG deficiency

GEO Series GSE277151. Mus musculus. 18 samples. Type: Expression profiling by high throughput sequencing; Other.

openGEO-OpenApr 2025View details →
geo24/100

T-cell receptor repertoire in metastatic colorectal cancer during chemotherapy

GEO Series GSE182031. Homo sapiens. 103 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenJan 2022View details →
geo24/100

FB5P-seq: FACS-based 5-prime end single-cell RNAseq for integrative analysis of transcriptome and antigen receptor repertoire in B and T cells

GEO Series GSE137275. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenOct 2019View details →
geo24/100

Perturbations in the T cell receptor β repertoire during malaria infection in children: a preliminary study.

GEO Series GSE212894. Homo sapiens. 28 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenSep 2022View details →
geo24/100

Machine learning analysis of the T cell receptor repertoire identifies sequence features that predict self-reactivity

GEO Series GSE221703. Mus musculus. 20 samples. Type: Other.

openGEO-OpenJan 2023View details →
geo24/100

T cell receptor repertoire analysis of tumor-infiltrating T cells in non-small cell lung cancer [tumor_T_cells]

GEO Series GSE151537. Homo sapiens. 2950 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenMar 2021View details →
geo24/100

The Glioma-Infiltrating T Cell Receptor Repertoire

GEO Series GSE79338. Homo sapiens. 75 samples. Type: Expression profiling by high throughput sequencing; Other.

openGEO-OpenMay 2016View details →
geo24/100

T-cell receptor repertoire analysis of advanced solid tumors in first-in-human phase 1 study of IT1208, a defucosylated humanized anti-CD4 depleting antibody [TCR-seq]

GEO Series GSE120101. Homo sapiens. 105 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenAug 2019View details →

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