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53 results for “Receptor Repertoire”
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, "Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities" 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: </p> <p>(1) a file mapping the SNP data subject IDs to the TCR repertoire data 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) a file including the SNP ID, chromosome, hg19 position, allele, rsid, and quality control metrics 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) Parsed TCRB repertoire data. These raw data were first published in Emerson et. al, <em>Nature Genetics </em>2017. (emerson_parsed_tcrb.tgz)</p> <p><strong>Corresponding discovery cohort raw TCR repertoire data is available here: </strong>https: //doi.org/10.21417/B7001Z (ImmuneACCESS database)<br> <strong>Corresponding discovery cohort SNP data is available here:</strong> https: //www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001918.v1.p1 (The database of Genotypes and Phenotypes, accession number: phs001918)<br> <br> <strong>Software tools designed to work with these data are available here:</strong> https://github.com/phbradley/tcr-gwas</p>
Pre-processed B cell receptor repertoire sequencing data from BioProject PRJNA527941
<p><strong>Data Processing</strong></p> <p> </p> <p>Samples were demultiplexed via their Illumina indices, and processed using the Immcantation toolkit(1,2). 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> </p> <p><strong>software_versions</strong> 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> FilterSeq.py pRESTO Q>20</p> <p><strong>paired_reads_assembly</strong> AssemblePairs.py pRESTO minlen 10 maxerror 0.3 alpha 0.0001</p> <p><strong>primer_match_cutoffs</strong> MaskPrimers.py pRESTO C primer & V primer maxerror 0.2</p> <p><strong>consensus_building</strong> BuildConsensus.py pRESTO maxerror 0.1 maxgap 0.5</p> <p><strong>collapsing_method</strong> CollapseSeq.py pRESTO</p> <p><strong>germline_database </strong>IMGT</p> <p> </p> <p><strong>Format</strong></p> <p> </p> <p>Processed sequences are provided in a tab delimited file format, including the following annotations:</p> <p> </p> <p><strong>C_CALL </strong>Isotype subclass</p> <p><strong>SEQUENCE_ID </strong>Sequence identifier</p> <p><strong>V_CALL </strong>V segment gene and allele</p> <p><strong>D_CALL </strong>D segment gene and allele</p> <p><strong>J_CALL </strong>J segment gene and allele</p> <p><strong>JUNCTION_LENGTH </strong>Junction length</p> <p><strong>CONSCOUNT </strong>Raw read count from which UMI consensus sequences were generated, summed over all UMIs for the given unique sequence.</p> <p><strong>DUPCOUNT </strong>UMI count for the given unique sequence</p> <p><strong>ISOTYPE </strong>Constant region primer (isotype)</p> <p><strong>MU_COUNT_CDR_R </strong>Number of replacement mutations in CDR region</p> <p><strong>MU_COUNT_CDR_S </strong>Number of silent mutations in CDR region</p> <p><strong>MU_COUNT_FWR_R </strong>Number of replacement mutations in FWR region</p> <p><strong>MU_COUNT_FWR_S </strong>Number of silent mutations in FWR region</p> <p><strong>MUT_TOTAL </strong>Total number of mutations in V gene </p> <p><strong>SEQUENCE_INPUT </strong>Full length sequence</p> <p><strong>SEQUENCE_IMGT </strong>Gapped IMGT sequence</p> <p><strong>V_GERM_START_VDJ </strong>position of the first nucleotide in ungapped V germline sequence alignment</p> <p><strong>JUNCTION </strong>Junction nucleotide sequence</p> <p><strong>GERMLINE_IMGT_D_MASK </strong>IMGT-gapped germline nucleotide sequence with ns masking the NP1-D-NP2 regions</p> <p><strong>Run </strong>ID of sequencing run</p> <p><strong>Sample_type </strong>The tissue sampled (e.g Peripheral Blood, bone marrow, ..)</p> <p><strong>Sex </strong>Sex of the Subject</p> <p><strong>Age </strong>Age of the subject</p> <p><strong>UNIQUE_ID </strong>Subject identifier </p> <p><strong>SAMPLE_ID </strong>Sample identifier, linking back to raw data</p> <p><strong>Subset </strong>Defined B cell subset </p> <p><strong>Repertoire </strong>Defined B cell repertoire (Naive, Memory IgM/IgD, IgA, IgG)</p> <p><strong>R_SCDR </strong>R/S ratio in CDR region</p> <p><strong>R_SFWR </strong>R/S ratio in FWR region</p> <p><strong>V_FAM </strong>V family gene</p> <p><strong>V_GENE </strong>V segment gene</p> <p><strong>D_GENE </strong>D segment gene</p> <p><strong>J_GENE </strong>J segment gene</p> <p><strong>Clust_Rank </strong>Cluster rank</p> <p><strong>Clust_REPRES </strong>Cluster representative</p> <p><strong>Clust_SIZE </strong>Cluster size</p> <p><strong>Clust_MAXFREQ </strong>Cluster maximum frequency</p> <p><strong>Clust_SHAREDNESS </strong>Cluster sharedness</p> <p><strong>CDR3_AA_GRAVY </strong>CDR3 hydrophobicity index</p> <p><strong>CDR3_AA_CHARGE </strong>CDR3 charge</p> <p><strong>CDRH3PDB </strong>CDRH3 PDB (Structure) code</p> <p><strong>H1Canon </strong>H1 Canonical class</p> <p><strong>H2Canon </strong>H2 Canonical class</p> <p><strong>H1_GERMLINE </strong>H1 Germline Canonical class</p> <p><strong>H2_GERMLINE </strong>H2 Germline Canonical class</p> <p> </p> <p><strong>References</strong></p> <p>1. Vander Heiden, J. A., G. Yaari, M. Uduman, J. N. H. Stern, K. C. O’Connor, D. A. Hafler, F. Vigneault, and S. H. Kleinstein. 2014. PRESTO: A toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. <em>Bioinformatics</em>30: 1930–1932.</p> <p>2. Gupta, N. T., J. A. Vander Heiden, M. Uduman, D. Gadala-Maria, G. Yaari, and S. H. Kleinstein. 2015. Change-O: A toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. <em>Bioinformatics</em>31: 3356–3358.</p> <p>3. Ye, J., N. Ma, T. L. Madden, and J. M. Ostell. 2013. IgBLAST: an immunoglobulin variable domain sequence analysis tool. <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. <em>Genome Res.</em>21: 936–939.</p>
Alignment free identification of clones in Bcell receptor repertoires
<p>The data sets are used for the analysis of the Alignment free [1] clonal identification approach.</p> <p>[1] "Alignment free identification of clones in B cell receptor repertoires", Ofir Lindenbaum, Nima Nouri, Yuval Kluger, Steven H. Kleinstein .</p> <p>Preprint available at: https://www.biorxiv.org/content/10.1101/2020.03.30.017384v1</p>
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>
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, "Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities" 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: </p> <p>(1) SNP genotypes for the two SNPs which overlap with the discovery cohort<br> - (nicaragua_snp_genotypes_ints.tsv) -- SNP genotypes as integers<br> - (nicaragua_snp_genotypes_strings.tsv) -- SNP genotypes as allele strings <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 used for parsing TCRA repertoire data (human_vj_alleles_alpha.tsv)<br> (5) Parsed TCRA repertoire data (nicaragua_parsed_TCRA.tgz)<br> (6) Parsed TCRB repertoire data (nicaragua_parsed_TCRB.tgz) </p> <p><strong>Corresponding raw validation cohort TCR repertoire data is available here:</strong> https://www. ncbi.nlm.nih.gov/bioproject/PRJNA762269 (The BioProject database, accession number: PRJNA762269)</p> <p><strong>Software tools designed to work with these data are available here:</strong> https://github.com/phbradley/tcr-gwas</p>
Data from: Olfaction written in bone: cribriform plate size parallels olfactory receptor gene repertoires in Mammalia
The evolution of mammalian olfaction is manifested in a remarkable diversity of gene repertoires, neuroanatomy, and skull morphology across living species. Olfactory receptor genes (ORG), which initiate the conversion of odorant molecules into odor perceptions and help an animal resolve the olfactory world, range in number from a mere handful to several thousand genes across species. Within the snout, each of these ORGs is exclusively expressed by a discrete population of olfactory sensory neurons (OSN), suggesting that newly evolved ORGs may be coupled with new OSN populations in the nasal epithelium. Because OSNs axon bundles leave high-fidelity perforations (foramina) in the bone as they traverse the cribriform plate (CP) to reach the brain, we predicted that taxa with larger ORG repertoires would have proportionately expanded footprints in the CP foramina. Previous work found a correlation between ORG number and absolute CP size that disappeared when body size effects were accounted for. Using updated, digital measurement data from high-resolution CT scans and reexamining the relationship between CP and body size, we report a striking linear correlation between relative CP area and number of functional ORGs across species from all mammalian superorders. This correlation suggests strong developmental links in the olfactory pathway between genes, neurons, and skull morphology. Furthermore, because ORG number is linked to olfactory discriminatory function, this correlation supports relative CP size as a viable metric for inferring olfactory capacity across modern and extinct species. By quantifying CP area from a fossil sabertooth cat (Smilodon fatalis) we predicted a likely ORG repertoire for this extinct felid.
Partis post-processed B cell receptor repertoires from BioProject PRJNA349143
<p>These files correspond to partis annotations of several datasets found in BioProject PRJNA349143. (DOI: 10.5281/zenodo.821659).</p>
Pre-processed IgH receptor repertoire data from MS patients after aHSCT from BioProject PRJNA763367
<p><strong>Data Processing</strong></p> <p>Samples were demultiplexed via their Illumina indices, and processed using the Immcantation toolkit(1,2). 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> </p> <p><strong>software_versions</strong> 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> FilterSeq.py pRESTO Q>20</p> <p><strong>paired_reads_assembly</strong> AssemblePairs.py pRESTO minlen 10 maxerror 0.3 alpha 0.0001</p> <p><strong>primer_match_cutoffs</strong> MaskPrimers.py pRESTO C primer & V primer maxerror 0.2</p> <p><strong>consensus_building</strong> BuildConsensus.py pRESTO maxerror 0.1 maxgap 0.5</p> <p><strong>collapsing_method</strong> CollapseSeq.py pRESTO</p> <p><strong>germline_database </strong>IMGT</p> <p> </p> <p><strong>Format</strong></p> <p>Processed sequences are provided in a tab delimited file format, including the following annotations:</p> <p> </p> <p><strong>ISOTYPE_SUBCLASS </strong>Isotype subclass</p> <p><strong>SEQUENCE_ID </strong>Sequence identifier</p> <p><strong>JUNCTION_LENGTH </strong>Junction length</p> <p><strong>CONSCOUNT </strong>Raw read count from which UMI consensus sequences were generated, summed over all UMIs for the given unique sequence.</p> <p><strong>DUPCOUNT </strong>UMI count for the given unique sequence</p> <p><strong>ISOTYPE </strong>Constant region primer (isotype)</p> <p><strong>MUT_TOTAL </strong>Total number of mutations in V gene </p> <p><strong>SAMPLE </strong>Sample identifier, linking back to raw data</p> <p><strong>JUNCTION </strong>Junction nucleotide sequence</p> <p><strong>Protein_seq </strong>Amino acid sequence</p> <p><strong>CDR3_AA_GRAVY </strong>CDR3 hydrophobicity index</p> <p><strong>CDR3_AA_BULK </strong>CDR3 bulkiness</p> <p><strong>CDR3_AA_ALIPHATIC </strong>CDR3 aliphatic index</p> <p><strong>CDR3_AA_POLARITY </strong>CDR3 polarity</p> <p><strong>CDR3_AA_CHARGE </strong>CDR3 normalized net charge</p> <p><strong>CDR3_AA_BASIC </strong>CDR3 basic side chain residue content</p> <p><strong>CDR3_AA_ACIDIC </strong>CDR3 acidic side chain residue content</p> <p><strong>CDR3_AA_AROMATIC </strong>CDR3 aromatic side chain content</p> <p><strong>Subset </strong>Defined B cell subset </p> <p><strong>Repertoire </strong>Defined B cell repertoire (Naive, Memory IgM/IgD, IgA, IgG)</p> <p><strong>R_SCDR </strong>R/S ratio in CDR region</p> <p><strong>R_SFWR </strong>R/S ratio in FWR region</p> <p><strong>V_GENE </strong>V segment gene</p> <p><strong>D_GENE </strong>D segment gene</p> <p><strong>J_GENE </strong>J segment gene</p> <p><strong>V_FAM </strong>V family gene</p> <p><strong>Clust_REPRES </strong>Cluster representative</p> <p><strong>Clust_SIZE </strong>Cluster size</p> <p><strong>Sex </strong>Sex of the Subject</p> <p><strong>UNIQUE_ID </strong>Sample identifier </p> <p><strong>Bcellno </strong>Input B cell number</p> <p><strong>Days_posttx </strong>Sampling time point relative to transplantation</p> <p><strong>Age_at_tx </strong>Age of the subject (at aHSCT)</p> <p><strong>Disease </strong>MS subtype</p> <p><strong>Last_therapy </strong>Last therapy prior to aHSCT</p> <p><strong>Disease_duration </strong>Disease duration</p> <p><strong>CMV_reactivation </strong>Cytomegalovirus reactivation</p> <p><strong>Month_label </strong>Month post-aHSCT inverval bin</p> <p><strong>Patient_label </strong>Subject identifier</p> <pre> </pre> <p><strong>References</strong></p> <p>1. Vander Heiden, J. A., G. Yaari, M. Uduman, J. N. H. Stern, K. C. O’Connor, D. A. Hafler, F. Vigneault, and S. H. Kleinstein. 2014. PRESTO: A toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. <em>Bioinformatics</em>30: 1930–1932.</p> <p>2. Gupta, N. T., J. A. Vander Heiden, M. Uduman, D. Gadala-Maria, G. Yaari, and S. H. Kleinstein. 2015. Change-O: A toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. <em>Bioinformatics</em>31: 3356–3358.</p> <p>3. Ye, J., N. Ma, T. L. Madden, and J. M. Ostell. 2013. IgBLAST: an immunoglobulin variable domain sequence analysis tool. <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. <em>Genome Res.</em>21: 936–939.</p>
Data from: Olfaction written in bone: cribriform plate size parallels olfactory receptor gene repertoires in Mammalia
Open the record for dataset details and reuse information.
Evolution of odorant receptor repertoires across Hymenoptera is not linked to the evolution of eusociality
Open the record for dataset details and reuse information.
GMPipe: A gene mining pipeline for identifying olfactory receptor repertoires and other multicopy gene families from genomes
<p>This repository contains the code and tutorial for the GMPipe algorithm. GMPipe is a gene mining pipeline designed to maximize the identification of multicopy gene family homologs in genome assemblies (for intronless genes) or lists of protein sequences. GMPipe has been validated for mining olfactory receptor genes from vertebrate genomes. This algorithm uses custom ingroup and outgroup sequence lists to distinguish between members of a gene family of interest from close off-target relatives. Overall, GMPipe uses HMMER as an initial screening step, paired with phylogenetic clustering to test whether passing sequences are more closly related to "ingroup" than "outgroup" sequences. The result is a pipeline with a low false negative rate, with minimal impact on the false positive rate, especially when paired with additional family-specific filtering steps.</p>
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>
ALLoreactive T-Cell receptOr RePertoire in kidnEy tranSplantation
ClinicalTrials.gov study NCT03422224. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
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 obtained high quality, full-length sequences for over 6,000 unique immunoglobulin and T-cell receptor transcripts, without the need for sequence assembly.</p>
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.
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.
Seven chain adaptive immune receptor repertoire analysis in rheumatoid arthritis reveals novel features associated with disease and clinically relevant phenotypes
GEO Series GSE256256. Homo sapiens. 112 samples. Type: Other.
T-cell receptor repertoire in uterine and ovarian carcinosarcoma
GEO Series GSE155276. Homo sapiens. 8 samples. Type: Other.
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.
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.
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