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19,342 results for “Sequences”
PSSH2 - database of protein sequence-to-structure homologies (including Sars-CoV-2 structures)
<p><strong>Protein sequence and structure data</strong></p> <p>This data set contains data from Uniprot (in the files called protein_sequence, protein_synonyms, protein_names, organism_synonyms) and PDB (in the files called PDB and PDB_chain) as used by the <a href="https://github.com/ODonoghueLab/Aquaria">Aquaria web resource</a> at the time of download (2022-02-08).</p> <p> </p> <p><strong>The PSSH2 data set</strong><br> <br> PSSH2 is a database of protein sequence-to-structure homologies based on HHblits, an alignment method employing iterative comparisons of hidden Markov models (HMMs). To ensure the highest possible final alignment quality for matches in Aquaria using HHblits, we first calculate HMM profiles for each unique PDB sequence (PDB_full) and also for each unique Swiss-Prot sequence. We generated PSSH2 using HHblits to find similarities between HMMs from PDB and HMMs from UniProt sequences.</p> <p> </p> <p><strong>Calculating PSSH2</strong></p> <p>The Swissprot and PDB data was downloaded in November 2021.<br> Generating PSSH2: We used <a href="https://gwdu111.gwdg.de/~compbiol/uniclust/2021_03/UniRef30_2021_03.tar.gz">UniRef30_2021_03</a> (originally called UniRef30_2021_06) from HH-suite, a database of non-redundant UniProt sequence clusters in which the highest pairwise sequence identity between clusters was 30%. The HHblits code and the code for running the calculations was retrieved from git (https://github.com/soedinglab/hh-suite.git and https://github.com/aschafu/PSSH2.git respectively) at the respective time of calculation in the timeframe until December 2021. <br> </p> <p><strong>PDB based sequence-to-structure alignments</strong></p> <p>In addition to the PSSH2 data, new PDB structures were retrieved based on the primary accession of the proteins, by querying for all chains in all PDB entries with exact matches using the sequence cross references records given in PDB. Sequence-to-structure alignments were then created, again based on information provided in each PDB entry. These are contained in the PDBchain data.</p> <p>This data covers sequences and PDB structures in the timeframe until February 2022. </p> <p> </p> <p><strong>Evaluating PSSH2</strong></p> <p>The resulting alignment data was analysed using CATH domain assignments downloaded from /cath/releases/all-releases/v4_2_0/cath-classification-data/ to define correct hits and false hits: </p> <ul> <li>The set of query sequences is defined by the CATH non-redundant S40_overlap_60 dataset (ftp://orengoftp.biochem.ucl.ac.uk/cath/releases/all-releases/v4_2_0/non-redundant-data-sets/)</li> <li>The set of all expected hits are all pdb structures containing a domain with the same CATH code if contained in the set of processed sequences (-> all) or only if also contained in the set of non redundant sequences (-> nr40).</li> <li>The set of true positives is defined by sharing the same CATH code up to the level of homology ("CATH") or up to the level of topology ("CAT").</li> </ul> <p>The data was evaluated with respect to false discovery rate (FDR) and recall (true positive rate TPR) by cumulatively considering all hits with an E-value below the threshold ("C") or in bins with an E-value between the threshold and one tenth of the threshold ("B"). This evaluation was carried out for the data obtained in November 2021 (202111) as well as previous data from October 2020 (202010), February 2020 (202002) and September 2017 (201709). The results are collected in <a href="https://zenodo.org/api/files/445add84-fcf1-4dfe-b8a1-63dc55f378ee/PSSH%20CATH%20validation.csv?versionId=a5df7473-6efd-442b-b422-3944e9452003">PSSH CATH validation.csv</a>. </p> <p> </p> <p><strong>Known errors</strong></p> <p>Due to processing error, the profile of pdb structure 5fia A / B (sequence md5 052667679fc644184f40063c7602c9e1) is incomplete in the pdb_full hhblits database which led to further errors in generating sequence based alignments for sequences for 1vtm P (sequence md5 c844aff103449363cb8489c78c58ebf1) and 434t A / B (sequence md5 d67aa1c3a36492c719cb48b5e7ecc624).<br> <br> </p>
Simulation Data for "Community-Driven Code Comparisons for Three-Dimensional Dynamic Modeling of Sequences of Earthquakes and Aseismic Slip"
<p>Simulation data from Jiang et al. (2022), "Community-Driven Code Comparisons for Three-Dimensional Dynamic Modeling of Sequences of Earthquakes and Aseismic Slip," <em>Journal of Geophysical Research: Solid Earth</em><em>.</em></p> <p>The archive includes simulation data for 3D SEAS benchmarks BP4-QD and BP5-QD that are analyzed in our paper (descriptions in NOTES.txt) </p> <p><strong>BP4-QD Benchmark Simulations:</strong><br>1000 m: jiang.5, lambert.8, barbot.3, barbot.2, dliu.2, li.4<br>500 m: jiang.3, lambert.3, barbot.5, barbot.7, ozawa</p> <p><strong>BP5-QD Benchmark Simulations:</strong><br>2000 m: jiang.6, lambert.8, liu.4, cattania.5, dli.7, barbot.3, dliu.10, li.3<br>1000 m: jiang.2, lambert.7, liu.5, cattania.3, ozawa, dli.5, barbot, dliu.6, li.2<br>500 m: jiang.4, lambert.9, liu.6, cattania.4, ozawa.2, dli.6, barbot.2, dliu.8<br>250 m: lambert.10, liu.7</p> <p><strong>BP5-QD with Off-Fault Data:</strong><br>1000 m: lambert.7, dli.5, barbot, dliu.6, li.2<br>500 m: lambert.9, dli.6, barbot.2, dliu.8</p> <p>Tables 2–4 in our paper summarizes details of numerical codes and selected simulations.</p> <p>The benchmark descriptions and the full suite of simulation data are available at SEAS online platform https://strike.scec.org/cvws/seas/.</p>
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>
bulk-tumour-api: a programmatically accessible dataset of pre-processed bulk tumour sequencing data
<p><strong>This repository, including the API, are currently under development.</strong></p> <p><strong>bulk-tumour-api</strong>: A programmatically accessible dataset of pre-processed bulk tumour sequencing data. The python API can be found at https://github.com/tomouellette/bulk-tumour-api. All data stored in this repository have been collected from open access online sources. Original references and sources are provided in database.tsv (for empirical patient data) and synthetic.tsv (for simulated data).</p> <p><strong>A note on datasets: </strong></p> <ul> <li>All <em>empirical patient sequencing </em>samples have been processed into pseudo-VCF files which at minimum contain the following columns: sample identifier (sample), patient identifier (patient), chromosome (chr), position (pos), variant allele frequency (VAF), alternate read counts (t_alt_count), depth (DP), and total copy number (total_cn). However, if more data is required, unprocessed data including copy number segments or gene-level calls, clinical, and/or biopsy level information can be found in the /raw/. </li> <li>All <em>synthetic datasets </em>have also been processed in pseudo-VCF files. In some cases, all ground truth information (e.g. subclone frequency) is contained within the pseudo-VCF. In other cases, additional meta/ground-truth information are in separate files; any simulated sample with a column marked has_meta = True will have multiple files that will be downloaded together.</li> </ul>
Raw sequencing data PhD Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea
<p>Raw sequencing data PhD Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea</p>
Summary statistics for "Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer's Disease"
<p>These are the burden test results (summary statistics) for the publication:</p> <p>"Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer’s Disease",</p> <p>Nature Genetics, 2022.</p> <p> </p> <p><em>Format: tab-separated-value.</em></p> <p><em>Fields:</em></p> <ul> <li><em>gene_stable_id: Ensembl gene id</em></li> <li><em>gene_name: standard gene name</em></li> <li><em>pvalue: burden test significance (likelihood ratio test, population structure correction based on 6 PCA components)</em></li> <li><em>cmac_all: sum of minor allele dosages across all contributing samples and variants</em></li> <li><em>group: variant group (LOF, LOF+REVEL>=75, LOF+REVEL>=50, LOF+REVEL>=25, see publication methods for further selection criteria).</em></li> <li><em>beta/se: beta/se of logistic ordinal regression (see publication methods). Positive = risk-increasing. Negative = risk-decreasing.</em></li> </ul> <p> </p>
DATASET: Genotyping by sequencing of the common bean Spanish Diversity Panel
<p>Genotyping by sequencing of 308 common bean lines included in the Spanish Diversity Panel. The ApeKI restriction enzyme was used. The sequencing reads were aligned using the reference genome V2.1 (https://phytozome.jgi.doe.gov/pz/portal.html#!info?alias=Org_Pvulgaris). A total of 11,763 SNP markers are included in this dataset after filtering for missing values (< 10%) and minor allele frequency (MAF> 0.05). </p>
MALDI-TOF-MS reference spectra and sequence data for domesticated equids (horse and donkey) collagen for Zooarchaeology by Mass Spectrometry (ZooMS)
<p>MALDI-TOF-MS spectra of extracted collagen from modern reference and archaeological bone samples to develop markers for Zooarchaeology by Mass Spectrometry (ZooMS) to distinguish between Equus species. For each sample digestions were done in both trypsin and chymotrypsin separately. Information about the species of the samples can be found in 'sample metadata.csv' file. Information on the extraction and digestion protocol can be found in the associated manuscript. The sequence data contains alignments of the proteins COL1A1 and COL1A2 for available Equus collagen protein sequences. More information on these files can be found in the corresponding manuscript to this dataset.<br> </p>
Control panel created from 30 Nanopore sequencing data from the Human Pangenome Reference Consortium
<p>This is control panel for <a href="https://github.com/friend1ws/nanomonsv">nanomonsv</a> software, which is expected to exclude many false positives as well as improve computational cost. This is made by aligning 30 Nanopore sequencing data from Human Pangenome Reference Consortium to the GRCh38 reference genome (obtained from <a href="https://console.cloud.google.com/storage/browser/genomics-public-data/resources/broad/hg38/v0;tab=objects">here</a>) with <a href="https://github.com/lh3/minimap2">minimap2</a> version 2.24. <strong>When you use these control panels and publish, do not forget to credit to <a href="https://humanpangenome.org/data-use-protocol/">HPRC</a>!</strong></p>
Twist Whole-Exome Sequencing Dataset - High Coverage - WGGC SIG4 Benchmarking
<p>GIAB Reference Genome for Benchmarking Initiatives in the West German Genome Center (WGGC) - SIG4. </p> <p>Twist Whole-Exome Sequencing Dataset - High Coverage - 200M Reads.</p> <p> </p> <p> </p>
InSAR stack of the 2019 Ridgecrest, California earthquake sequence from Sentinel-1 descending track 71 processed with ASF HyP3
<p>A stack of unwrapped interferograms on Owens Valley, California for the <a href="https://earthquake.usgs.gov/earthquakes/eventpage/ci38457511/executive">2019 Ridgecrest earthquake sequence</a>.</p> <p>Sensor: Sentinel-1 descending track 71</p> <p>Time: 2019.06.10 - 2019.08.15, 7 acquisitions, 11 interferograms</p> <p>Processor: <a href="https://hyp3-docs.asf.alaska.edu/guides/insar_product_guide/">ASF HyP3</a> (GAMMA)</p> <p>Tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy">MintPy</a>.</p>
Aligned DNA sequence matrix for phylogenetic analyses in the article "A new glassfrog of the genus Centrolene (Amphibia: Centrolenidae) from the Subandean Kutukú Cordillera, eastern Ecuador"
<p>Aligned DNA sequence matrix for phylogenetic analyses of the article "A new glassfrog of the genus Centrolene (Amphibia: Centrolenidae) from the Subandean Kutukú Cordillera, eastern Ecuador"</p> <p>The matrix is in NEXUS format and has 6626 bp and 239 terminals.</p> <p>Partitions are as follows:</p> <div> <div>charset 12S = 1-967;</div> <div>charset 16S = 968-2130;</div> <div> </div> <div>charset BNDFcodonPos1 = 2133-2829\3;</div> <div>charset BNDFcodonPos2 = 2131-2830\3;</div> <div>charset BNDFcodonPos3 = 2132-2828\3;</div> <div> </div> <div> </div> <div>charset ND1codonPos1 = 2832-3786\3;</div> <div>charset ND1codonPos2 = 2833-3787\3;</div> <div>charset ND1codonPos3 = 2831-3788\3;</div> <div> </div> <div> </div> <div>charset CXCR4codonPos1 = 3790-4144\3;</div> <div>charset CXCR4codonPos2 = 3791-4142\3;</div> <div>charset CXCR4codonPos3 = 3789-4143\3;</div> <div> </div> <div> </div> <div>charset cmyccodonPos1 = 4145-4547\3;</div> <div>charset cmyccodonPos2 = 4146-4548\3;</div> <div>charset cmyccodonPos3 = 4147-4549\3;</div> <div> </div> <div> </div> <div>charset POMCcodonPos1 = 4551-5160\3;</div> <div>charset POMCcodonPos2 = 4552-5161\3;</div> <div>charset POMCcodonPos3 = 4550-5162\3;</div> <div> </div> <div> </div> <div>charset RAG1codonPos1 = 5163-5616\3;</div> <div>charset RAG1codonPos2 = 5164-5617\3;</div> <div>charset RAG1codonPos3 = 5165-5618\3;</div> <div> </div> <div> </div> <div>charset SLC8A1codonPos1 = 5620-6160\3;</div> <div>charset SLC8A1codonPos2 = 5621-6158\3;</div> <div>charset SLC8A1codonPos3 = 5619-6159\3;</div> <div> </div> <div> </div> <div> </div> <div>charset SLC8A3codonPos1 = 6162-6627\3;</div> <div>charset SLC8A3codonPos2 = 6163-6625\3;</div> <div>charset SLC8A3codonPos3 = 6161-6626\3;</div> </div> <p> </p>
TCRb sequencing of CD4+ and CD8+ T cells from hematological patients (part 1)
<p>This dataset contains TCRb sequencing of 177 samples from patients with aplastic anemia, myelodysplastic syndrome, immune thrombocytopenia, immunodeficiency or graft-versus-host disease and healthy controls. Samples are separated CD4+ or CD8+ cells from peripheral blood or bone marrow. The data has been produced with immunoSEQ platform (Adaptive Biotechnologies). The details regarding sample processing, sequencing and metadata can be found from the publication <em>Somatic mutations associate with clonal expansion of CD8+ T cells</em> (Lundgren et al, Science Advances, in press).</p> <p>The dataset is divided in 2 parts containing 91 (part 1) and 86 (part 2) files. Data is in immunoSEQ format (v2).</p>
Research compendium for 'A pre-Campanian Ignimbrite techno-cultural shift in the Aurignacian sequence of Grotta di Castelcivita, southern Italy'
<h2><strong>Research compendium for 'A pre-Campanian Ignimbrite techno-cultural shift in the Aurignacian sequence of Grotta di Castelcivita, southern Italy' </strong></h2> <p><strong>Compendium DOI: </strong></p> <p><a href="https://doi.org/10.5281/zenodo.10639553">https://doi.org/</a><a href="../doi/10.5281/zenodo.10639552">10.5281/zenodo.10639552</a></p> <p>The content available at the above provided URL will reproduce the results as documented in the first paper's submission. Instead, the files hosted at <a href="https://github.com/ArmandoFalcucci/Castelcivita-Aur-Techno">https://github.com/ArmandoFalcucci/Castelcivita-Aur-Techno</a> represent the developmental versions and might have undergone modifications since the paper's publication.</p> <p><strong>Maintainer of this repository: </strong></p> <p>Armando Falcucci (<a href="mailto:armando.falcucci@uni-tuebingen.de">armando.falcucci@uni-tuebingen.de</a>; <a href="https://orcid.org/0000-0002-3255-1005">https://orcid.org/0000-0002-3255-1005</a>) </p> <p><strong>Published paper:</strong></p> <p>Armando Falcucci, Simona Arrighi, Vincenzo Spagnolo, Matteo Rossini, Owen Higgins, Brunella Muttillo, Ivan Martini, Jacopo Crezzini, Francesco Boschin, Annamaria Ronchitelli, Adriana Moroni. A pre-Campanian Ignimbrite techno-cultural shift in the Aurignacian sequence of Grotta di Castelcivita, southern Italy. <em>Scientific Reports</em>, 14: 12783. doi:10.1038/s41598-024-59896-6 (2024)</p> <p><strong>Abstract:</strong></p> <p>The Aurignacian is the first European technocomplex assigned to Homo sapiens recognized across a wide geographic extent. Although archaeologists have identified marked chrono-cultural shifts within the Aurignacian mostly by examining the techno-typological variations of stone and osseous tools, unraveling the underlying processes driving these changes remains a significant scientific challenge. Scholars have, for instance, hypothesized that the Campanian Ignimbrite (CI) super-eruption and the climatic deterioration associated with the onset of Heinrich Event 4 had a substantial impact on European foraging groups. The technological shift from the Protoaurignacian to the Early Aurignacian is regarded as an archaeological manifestation of adaptation to changing environments. However, some of the most crucial regions and stratigraphic sequences for testing these scenarios have been overlooked. In this study, we delve into the high-resolution stratigraphic sequence of Grotta di Castelcivita in southern Italy. Here, the Uluzzian is followed by three Aurignacian layers, sealed by the eruptive units of the CI. Employing a comprehensive range of quantitative methods—encompassing attribute analysis, 3D model analysis, and geometric morphometrics—we demonstrate that the key technological feature commonly associated with the Early Aurignacian developed well before the deposition of the CI tephra. Our study provides thus the first direct evidence that the volcanic super-eruption played no role in this cultural process. Furthermore, we show that local paleo-environmental proxies do not correlate with the identified patterns of cultural continuity and discontinuity. Consequently, we propose alternative research paths to explore the role of demography and regional trajectories in the development of the Upper Paleolithic.</p> <p><strong>Keywords:</strong></p> <p>Early Upper Paleolithic; Italy; Aurignacian; lithic technology; geometric morphometrics; 3D model analysis; cultural evolution; human-environment interaction; open science.</p> <p><strong>Overview of contents and how to reproduce:</strong></p> <p>Within this repository, various folders house data (<code>data</code>), code (<code>script</code>), and output files (<code>output</code>) pertinent to the paper. The data folder encompasses the complete dataset, the core dataset, and 2D outline coordinates utilized for the geometric morphometrics study. To replicate the results, download the entire repository and employ <code>Castelcivita-Aur-Techno.Rproj</code> and open the folder <code>script</code>, following the numbered folder structure. For ensuring reproducibility, the <code>renv</code> package (v. 1.0.3) was utilized, following the procedures detailed in its vignette. All analyses and visualizations in the paper were conducted using R 4.3.1 on Microsoft Windows 10.0.19045 (64-bit). As the necessary packages are available in the <code>renv</code> folder, they are not explicitly listed here.</p> <p><strong>Licenses:</strong></p> <p>Code: <strong>MIT </strong>(<a href="http://opensource.org/licenses/MIT">http://opensource.org/licenses/MIT),</a> copyright holder: Armando Falcucci (2024).</p> <p><strong>Data and intellectual work:</strong> Creative Commons Attribution 4.0 International License (<a href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</a>), copyright holder: the authors (2024).</p>
Data supporting "Transformer Model Generated Bacteriophage Genomes are Compositionally Distinct from Natural Sequences"
<p>Sequence and composition data supporting doi: <a href="https://doi.org/10.1101/2024.03.19.585716" target="_blank" rel="noopener">10.1101/2024.03.19.585716</a>. Uncompressed file size is ~5.8GB.</p> <p>Data in zip files is organized by sequence provenance (generRNA, natural, or transformer (megaDNA)). Common file types between folders include:</p> <ul> <li>Multi-record fasta file: Sequence data for all sequences of a given provenance. For generRNA sequences, these are found within the `seq` column of file "MFE_distribution_Fig4a.csv"</li> <li>Composition files: Individual sequence level compositional metrics for sliding 120 bp windows. Only structural metrics were used in this study.</li> <li>Genomad: Results from the genomad pipeline (https://portal.nersc.gov/genomad/)</li> <li>Stats: Aggregate statistics for all sequences of a given provenance.</li> </ul> <p>The natural folder also has a metadata file detailing the taxonomy for all natural sequences.<br><br>Figure datasets are the cleaned (sometimes aggregated) datasets that underly specific figures in the manuscript. The figure designations are based on the order in: https://www.biorxiv.org/content/10.1101/2024.03.19.585716v1.</p>
A map of canine sequence variation relative to a Greenland wolf outgroup
<p><span>For over 15 years, canine genetics research relied on a reference assembly from a Boxer breed dog named Tasha (i.e., canFam3.1). Recent advances in long-read sequencing and genome assembly have led to the development of numerous high-quality assemblies from diverse canines. These assemblies represent notable improvements in completeness, contiguity, and the representation of gene promoters and gene models. Although genome graph and pan-genome approaches have promise, most genetic analyses in canines rely upon the mapping of Illumina sequencing reads to a single reference. The Dog10K consortium, and others, have generated deep catalogs of genetic variation through an alignment of Illumina sequencing reads to a reference genome obtained from a German Shepherd Dog named Mischka (i.e, canFam4, UU_Cfam_GSD_1.0). However, alignment to a breed-derived genome may introduce bias in genotype calling across samples. Since the use of an outgroup reference genome may remove this effect, we have reprocessed 1,929 samples analyzed by the Dog10K consortium using a Greenland wolf (mCanLor1.2) as the reference. We efficiently performed remapping and variant calling using a GPU-implementation of common analysis tools. The resulting call set removes the variability in genetic differences seen across samples while maintaining general patterns of breed relationships. Using this sequence data, we inferred the history of population sizes and found that village dog populations experienced a 9-13 fold reduction in historic effective population size relative to wolves. </span></p>
Catalog of relocated seismic sequences in Irpinia
<p>This catalog contains the hypocenter coordinates and the source parameters estimations for the enhanced catalogs of seismic sequences in Southern Apennines obtained in Scotto di Uccio et al. (2023)</p>
Human intestinal Bacteria Collection (HiBC): 16S rRNA gene sequences
<p>The <a href="https://hibc.rwth-aachen.de/" target="_blank" rel="noopener">Human intestinal Bacteria Collection (HiBC)</a> is a collection of bacterial strains, isolated from the human gut for which 16S rRNA gene sequences, genome sequences and culture conditions are made available to the research community. In addition to previously described bacteria, we include strains that represent novel species which have been taxonomically described and validly named, or will be in the future. This collection will be updated regularly.</p> <p>This dataset includes the sequences of the 16S rRNA gene sequences of the isolates in the FASTA nucleotide format. Sequences ending in Sanger were obtained using the Sanger dideoxy sequencing technology. Sequences ending in Genome were obtained from the genome sequence using barrnap.</p>
Supplementary data - Simultaneous polyclonal antibody sequencing and epitope mapping by cryo electron microscopy and mass spectrometry – a perspective
<p>Analysis files and scripts for <a href="https://doi.org/10.1101/2024.06.21.600107" target="_blank" rel="noopener">associated manuscript</a>. </p> <ul> <li>CR3022.zip: script (in Rust) and necessary data to run said script for CR3022 analysis with the results from running the script.</li> <li>MA-analysis-script.zip: script (in Rust) and necessary data to run said script for automated analysis of MA benchmark results.</li> <li>MA-analysis-data.zip: data from running the MA-analysis-script, containing all MA and Stitch output files.</li> <li>MA-analysis-data-EMPEM.zip: data from running MA and Stitch on the EMPEM benchmark.</li> </ul>
Variant dataset and code for "Population-level whole genome sequencing of Ascochyta rabiei identifies genomic loci associated with isolate aggressiveness"
<p>This dataset contains genetic variants (SNPs) of <em>Ascochyta rabiei</em> isolates and the R code used in their analysis to generate the results and figures described in the manuscript "<strong>Population-level whole genome sequencing of <em>Ascochyta rabiei</em> identifies genomic loci associated with isolate aggressiveness</strong>".</p> <div> <div> </div> </div>
ScienceDex guides
Understand access before you commit
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.