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1,973 results for “T cell receptor”
Control T-cell receptor (TCR) alpha and beta chain nucleotide and amino acid sequences from human and mouse
<p>A dataset of pooled T-cell receptor (TCR) sequences for TCR alpha and beta chains of human and mouse.</p> <p>Sequences are obtained from various samples of healthy individuals/mice using our conventional protocols: see for example [Britanova et al "Dynamics of individual T cell repertoires: from cord blood to centenarians" The Journal of Immunology 2016] and [Izraelson et al. "Comparative analysis of murine T‐cell receptor repertoires." Immunology 2018].</p> <p>The sequences are stored as gzipped clonotype tables in VDJtools format, see [https://vdjtools-doc.readthedocs.io/en/master/input.html#vdjtools-format].</p> <p>This control dataset can be used as a proxy for a generative VDJ rearrangement model to estimate the expected frequency distribution of TCRs and check for enrichment of rare TCR clonotypes and groups of similar TCR sequences. For the implementation of the enrichment analysis, please see CalcDegreeStats routine from VDJtools software, see [https://vdjtools-doc.readthedocs.io/en/master/annotate.html#calcdegreestats].</p> <p>Files named "human.tra.strict.txt.gz", etc are pools of random/naive TCR clonotypes containing unique V/J/CDR3 nucleotide sequence combinations observed in data. The pools.zip file is used for TCR motif inference in VDJdb database [https://github.com/antigenomics/vdjdb-motifs], it contains human.tra.aa.txt, etc files that contain random/naive TCR clonotypes grouped by CDR3 amino acid sequence with the most frequent representative V and J.</p>
Genome-wide identification of cell-surface and intracellular immune receptors in 350 plant species
<p>Here we identified cell-surface (LRR-RLKs, LRR-RLPs, LysM-RLKs and LysM-RLPs) and intracellular immune receptors (NB-ARCs) from the genomes of 350 plant species. </p> <p> </p> <p>Zip file contains:</p> <p>Folder 'Immune_receptor_sequences' - FASTA files of the identified LRR-RLPs, Lys-RLKs, LysM-RLPs and NB-ARCs.</p> <p>Folder 'RLK_sequences' - FASTA files of the identified LRR-RLKs (all and 20 individual subgroups).</p> <p>Folder 'RLK_trees' - Phylogenetic TREE files of the identified LRR-RLKs (all and 20 individual subgroups); classified according to their kinase domains.</p> <p>238.species - Phylogenetic tree of the 238 plant species used in the analyses (taken from <a href="https://doi.org/10.1093/jpe/rtv047">https://doi.org/10.1093/jpe/rtv047</a>).</p> <p>350.species - Phylogenetic tree of the 350 plant species used in the analyses.</p> <p>simple.to.original.ids- Translator file for the original ID of each gene. </p> <p> </p>
Code for: MHC Heterozygosity Prunes the Numbers of Different T Cell Receptors Expressed in CD4 T Cells
<p>Contains source data file and code for publication "MHC Heterozygosity Prunes the Numbers of Different T Cell Receptors Expressed in CD4 T Cells".<br>Associated FASTQ files are deposited on the NIH SRA under accession: PRJNA1106276</p>
Sparsification of AP firing in adult-born hippocampal granule cells via voltage-dependent alpha5-GABAA receptors
<p>The ZIP file consists of folders, containing RAW data used for analysis and used to prepare the Figures 1-6 of the paper published in Cell Reports 2021, with the title mentioned above.</p> <p>All recordings were made from acute hippocampal brain slices, obtained from adult C57BL6 mice. Whole-cell voltage-clamp and current-clamp recordings of mature and adult-born young hippocampal granule cells were performed as outlined in methods and recordings were digitized using a power1401 interface from CED (Cambridge Electronic Design, UK), and saved to file using the CFS library support from CED. The contents of the different files is described in File_Description.pdf.</p>
Spatial transcriptomics of B and T cell receptors uncovers lymphocyte clonal dynamics.
<p>This dataset contains a single zipped folder containing:</p> <ul> <li> <p>data</p> </li> <li> <p>scripts</p> </li> </ul> <p>needed to reproduce the manuscript entitled "Spatial transcriptomics of B and T cell receptors uncovers lymphocyte clonal dynamics". Each folder is organized by tissue type, methodology, and analysis. A readme file accompanies each folder with details on the files/scripts within that folder. Alongside the paper and supplementary materials, it should be possible to reproduce all the figures in the manuscript.</p>
Supplements for "Understanding the interaction between a human transferrin receptor aptamer-short double stranded RNA conjugate and its cell membrane target by in silico methods"
<p>Supplements for "Understanding the interaction between a human transferrin receptor aptamer-short double stranded RNA conjugate and its cell membrane target by in silico methods". </p> <p>This supplement includes the following files:</p> <p> </p> <p>1. Structures of the most stable Protein-Aptamer complexes predicted from HADDOCK</p> <p>Haddock_Cluster1.pdb <br> Haddock_Cluster2.pdb <br> Haddock_Cluster3.pdb </p> <p>2. Structure of the most stable conformation aligned with Protein-transferring complex PDB</p> <p>cluster1_aligned.pdb <br> transferrin_aligned.pdb </p> <p>3. MM-GBSA decomposition analysis of the three replicas for Protein-Aptamer</p> <p>aptamer_new_rep01_Decomp.dat <br> aptamer_new_rep02_Decomp.dat <br> aptamer_new_rep03_Decomp.dat </p> <p>4. MM-GBSA decomposition analysis of the three replicas for Protein-Aptamer-Conjugate<br> conjugate_new_rep01_Decomp.dat <br> conjugate_new_rep02_Decomp.dat <br> conjugate_new_rep03_Decomp.dat <br> </p> <p><br> <br> </p>
Data from: MET receptor activation by stromal cells serves as promising target in melanoma brain metastases
<p>Supplementary tables to our recent manuscript: MET receptor activation by stromal cells serves as promising target in melanoma brain metastases</p>
Phylogenomic analysis of cell-surface receptors and downstream signaling components in the plant lineage
<p>Here we identified cell-surface receptors and downstream signaling components from the genomes of 350 plant species. </p><p>Zip file contains:</p><p>Folder 'seqeunces for downstream signaling components' - FASTA and TREE files of the identified downstream signaling components.</p><p>Folder 'sequences for cell-surface receptors' - FASTA and TREE files of the identified cell-surface receptors.</p><p>Folder 'Specific analysis' - Contains specific analysis for the identified cell-surface receptors.</p><p>Subfolder 'ID analysis' - Contains information on ID clusters and motifs analysis in IDs.</p><p>Subfolder 'LRR motif gap analysis' - Contains information on small (10-29 aa) and large (30-90) gaps between LRR motifs in RLPs and RLKs.</p><p>Subfolder 'LRR-RLK & LRR-RLP phylogenetic analysis' - Contains FASTA and TREE files of the specific domain/regions (C3, C3-F, eJM-TM-cJM, and all) in LRR-RLPs and LRR-RLKs. This subfolder also contains the specific amino acid, charge and motif analysis in this region (see C3F-end features.xlsx).</p><p>Protein counts per species file - Contains the total number of each protein family/subfamily in each of the 350 species.</p><p>simpleToFullNames (translator file)- Translator file for the original ID of each gene. </p>
ATAC-seq dataset: Chromatin accessibility landscapes activated by cell-surface and intracellular immune receptors
<p>The dataset encompasses raw sequencing reads, identified peaks, and regions of differential accessibility derived from ATAC-seq experiments conducted under various immune activation conditions. For additional technical details regarding data collection, please refer to the published source at https://doi.org/10.1093/jxb/erab373.</p>
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>
Computationally profiling peptide:MHC recognition by T-cell receptors and T-cell receptor-mimetic antibodies
<p>Supporting datasets for preprint version of "Computationally profiling peptide:MHC recognition by T-cell receptors and T-cell receptor-mimetic antibodies".</p>
Sclerostin blockade inhibits bone resorption through PDGF receptor signaling in osteoblast lineage cells
<p>While sclerostin-neutralizing antibodies (Scl-Ab) transiently stimulate bone formation by activating Wnt signaling in osteoblast lineage cells, they exert sustained inhibition of bone resorption, suggesting an alternate signaling pathway by which Scl-Ab control osteoclast activity. Since sclerostin can activate platelet-derived growth factor receptors (PDGFRs) in osteoblast lineage cells in vitro and PDGFR signaling in these cells induces bone resorption through M-CSF secretion, we hypothesized that the prolonged anti-catabolic effect of Scl-Ab could result from PDGFR inhibition. We show here that inhibition of PDGFR signaling in osteoblast lineage cells is sufficient and necessary to mediate prolonged Scl-Ab effect on M-CSF secretion and osteoclast activity in mice. Indeed, sclerostin co-activates PDGFRs independently of Wnt/β-catenin signaling inhibition, by forming a ternary complex with LRP6 and PDGFRs in pre-osteoblasts. In turn, Scl-Ab prevents sclerostin-mediated co-activation of PDGFR signaling and consequent M-CSF up-regulation in pre-osteoblast cultures, thereby inhibiting osteoclast activity in pre-osteoblast/osteoclast co-culture assays. These results provide a new potential mechanism explaining the dissociation between anabolic and anti-resorptive effects of long-term Scl-Ab.</p>
Profiling phage-host interactions between Skunavirus receptor binding proteins and lactococcal cell wall polysaccharide structures
Open the record for dataset details and reuse information.
DS-1205c With Gefitinib for Metastatic or Unresectable Epidermal Growth Factor Receptor (EGFR)-Mutant Non-Small Cell Lung Cancer
ClinicalTrials.gov study NCT03599518. IPD Sharing: YES. Countries: 1. Publications: 1.
DS-8201a in Human Epidermal Growth Factor Receptor 2 (HER2)-Expressing or -Mutated Non-Small Cell Lung Cancer
ClinicalTrials.gov study NCT03505710. IPD Sharing: YES. Countries: 5. Publications: 3.
DS-1205c With Osimertinib for Metastatic or Unresectable Epidermal Growth Factor Receptor (EGFR)-Mutant Non-Small Cell Lung Cancer
ClinicalTrials.gov study NCT03255083. IPD Sharing: YES. Countries: 1. Publications: 2.
Data from: The brassinosteroid receptor gene BRI1 safeguards cell-autonomous brassinosteroid signaling across tissues
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Data for: Antigen footprint governs activation of the B cell receptor
Open the record for dataset details and reuse information.
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>
A Bayesian Phylogenetic Hidden Markov Model for B Cell Receptor Sequence Analysis
<p>simulation and PC64/VRC01 input/output data files</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.