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19,342 results for “Sequences”
Data from: Hippocampal sequences represent working memory and implicit timing
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Whole genome sequence data of Mycobacterium tuberculosis and Mycolicibacterium smegmatis mutants of the riboflavin biosynthetic pathway- Part 2
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Optimized SMRT-UMI protocol produces highly accurate sequence datasets from diverse populations – application to HIV-1 quasispecies
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Comparative seagulls of gut microbiota by using metagenomics and 16S rDNA sequencing
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Whole-genome sequencing reveals asymmetric introgression between two sister species of cold-resistant leaf beetles
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Single-cell RNA sequencing of the testis of drive and standard Teleopsis dalmanni males
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Data for: Human atlastin-3 is a constitutive ER membrane fusion catalyst (phylogenetic and sequence analysis)
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Fragmentary gene sequences negatively impact gene tree and species tree reconstruction
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Whole genome sequences of 23 species from the Drosophila montium species group (Diptera: Drosophilidae): a resource for testing evolutionary hypotheses
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Single and half-cell RNA-sequencing in Stentor coeruleus control and beta-tubulin knockdown cells
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Hippocampal ensembles represent sequential relationships among an extended sequence of nonspatial events
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Genome sequence and characterization of a freshwater photoarsenotroph, Cereibacter azotoformans strain ORIO, isolated from sediments capable of cyclic light-dark arsenic oxidation and reduction
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Lab Standards for Benthic Macroinvertebrate Sequencing (repackaging of occurrences published by the NEON Biorepository Data Portal)
These DNA extracts are community standard or mock standard of macroinvertebrate DNA created at NEON headquarters, and used as a positive control for macroinvertebrate and zooplankton metabarcoding data.
Examples of CARE-related Activities Carried out by Repositories, in Sequences or Groups
This dataset is designed to accompany the paper submitted to Data Science Journal: O'Brien et al, "Earth Science Data Repositories: Implementing the CARE Principles". This dataset shows examples of activities that data repositories are likely to undertake as they implement the CARE principles. These examples were constructed as part of a discussion about the challenges faced by data repositories when acquiring, curating, and disseminating data and other information about Indigenous Peoples, communities, and lands. For clarity, individual repository activities were very specific. However, in practice, repository activities are not carried out singly, but are more likely to be performed in groups or in sequence. This dataset shows examples of how activities are likely to be combined in response to certain triggers. See related dataset O'Brien, M., R. Duerr, R. Taitingfong, A. Martinez, L. Vera, L. Jennings, R. Downs, E. Antognoli, T. ten Brink, N. Halmai, S.R. Carroll, D. David-Chavez, M. Hudson, and P. Buttigieg. 2024. Alignment between CARE Principles and Data Repository Activities. Environmental Data Initiative. https://doi.org/10.6073/pasta/23e699ad00f74a178031904129e78e93 (Accessed 2024-03-13), and the paper for more information about development of the activities and their categorization, raw data of relationships between specific activities and a discussion of the implementation of CARE Principles by data repositories. Data in this table are organized into groups delineated by a triggering event in the first column. For example, the first group consists of 9 rows; while the second group has 7 rows. The first row of each group contains the event that triggers the set of actions described in the last 4 columns of the spreadsheet. Within each group, the associated rows in each column are given in numerical not temporal order, since activities will likely vary widely from repository to repository. For example, the first group of rows is about what likely needs to h
Development of Illumina oligos for fungal ITS sequencing
New PCR primers were designed with the following objectives: 1) targetting the ITS2 region to minimize bias due to introns at the 3' end of the SSU, 2) have the broadest possible coverage of Fungi, 3) minimize amplification of plant, protist and other Eukaryote sequences, 4) compatibility with Illumina sequencing adaptors and GOLAY barcodes. Primer regions were selected by eye from sequence alignments then further analyzed using PrimerProspector. At present, several mock communities and hundreds of soil DNA extracts have been successfully sequenced on the Illumina MiSeq platform using the oligos that were developed.
Sequence data from viral assembly graph analysis of the SERC virome
<p>Various sequence data and details that were used in publishing the manuscript describing assembly graph binning in the SERC viral metagenome. Including: assembled contigs, FASTG, predicted ORFs, two tables describing the assembled contigs and graphs.</p> <p>Also PolA and RNR sequences that were mined from the SERC assembly.</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>
Chloroplast genome sequencing reads from snow gum
<p>Tutorial data for chloroplast genome assembly: fastq reads from illumina and nanopore sequencing for the snow gum, <em>Eucalyptus pauciflora</em>.</p> <p>Data from: Wang, W., Schalamun, M., Morales-Suarez, A. et al. Assembly of chloroplast genomes with long- and short-read data: a comparison of approaches using Eucalyptus pauciflora as a test case. BMC Genomics 19, 977 (2018) doi:10.1186/s12864-018-5348-8</p> <p>Data hosted at NCBI under accession numbers: illumina (SRR7153063) and nanopore (SRR7153095). Additional illumina file SRR7153071 not used here. </p> <p>This is how the files have been changed from the original datasets: </p> <p>Using the Galaxy platform (usegalaxy.org): </p> <ul> <li> <p>Each dataset was separately mapped to the NCBI Reference Sequence for <em>Eucalyptus pauciflora </em>chloroplast NC_039597.1, using BWA-MEM. </p> </li> <li> <p>Unmapped reads were filtered out using a SAMtools flag. </p> </li> <li> <p>Bam files were converted to fastq files.</p> </li> <li> <p>Each fastq file was then reduced in size:</p> </li> <li> <p>snow-gum-illumina-cp-reduced: has the first 62,500 reads only. Note that original pairing of reads has not been preserved so consider these to be unpaired reads for this tutorial.</p> </li> <li> <p>snow-gum-nanopore-cp-reduced: has only reads that are longer than 90,000 bp.</p> </li> </ul>
Mithocondrial sequences for fly species identification
<p>These sequences were generated from flies and leaf swabs collected in Tai National Park in 2017 and 2018.</p>
Aligned DNA sequence matrix for phylogenetic analyses in the article "New species of fossorial salamanders of the genus Oedipina (Plethodontidae) from the northwestern Ecuador"
<p>Aligned DNA sequence matrix for phylogenetic analyses of the article "New species of fossorial salamanders of the genus Oedipina (Plethodontidae) from the northwestern Ecuador". The matrix is in NEXUS format.</p> <p>Gene partitions are arranged as follows (tRNAs are included as part of larger adjacent genes):</p> <p>16S = 4- 789 1761- 1891 ;<br> ND1-codonPos1 = 790-1759\3;<br> ND1-codonPos2 = 791-1760\3;<br> ND1-codonPos3 = 792-1758\3;<br> CytB-codonPos1 = 1893-2274\3;<br> CytB-codonPos2 = 1894-2275\3;<br> CytB-codonPos3 = 1892-2276\3;</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.