Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
87
datasets available to search
ShareScore release 0.9.0
Dataset results
87 results for “human assemblies”
Eigen scores for human genome assembly GRCh37 Part 4 (Chr17 - Chr22)
<p>Eigen is a spectral approach to the functional annotation of genetic variants in coding and noncoding regions. Eigen makes use of a variety of functional annotations in both coding and noncoding regions (such as protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics projects), and combines them into one single measure of functional importance. Eigen is an unsupervised approach, and, unlike many existing methods, is not based on any labelled training data. Eigen produces estimates of predictive accuracy for each functional annotation score, and subsequently uses these estimates of accuracy to derive the aggregate functional score for variants of interest as a weighted linear combination of individual annotations.</p>
de novo genome assembly of the LNCaP human prostate cancer cell line
<p>Whole-genome sequencing reads from the LNCaP human prostate cancer cell line were used to generate a <em>de novo </em>assembly with SGA v0.10.15. Please see https://github.com/sciseim/PCaWGS for associated scripts. Library preparation was performed using a TruSeq Nano DNA kit (Illumina) with a target insert size of 350bp. Paired-end libraries (150bp) were sequenced using a HiSeqX sequencer (Illumina).</p>
de novo genome assembly of the PC3 human prostate cancer cell line
<p>Whole-genome sequencing reads from the PC3 human prostate cancer cell line were used to generate a <em>de novo </em>assembly with SGA v0.10.15. Please see https://github.com/sciseim/PCaWGS for associated scripts. Library preparation was performed using a TruSeq Nano DNA kit (Illumina) with a target insert size of 350bp. Paired-end libraries (150bp) were sequenced using a HiSeqX sequencer (Illumina).</p> <p> </p> <p> </p> <p> </p>
Experimental Validation video for paper "The Critical Role of Effective Communication in Human-Robot Collaborative Assembly"
<p>Experimental Validation video for paper "The Critical Role of Effective Communication in Human-Robot Collaborative Assembly".</p><p>The video shows a collaborative manipulator executing a collaborative assembly job with the user using a natural vocal communication architecture. The experiment compares the proposed framework with the state-of-art interaction and highlight the differences.</p>
Assembly of human HG002 (GM24385) ONT Q20+ Simplex dataset generated by phasebook
<p>This is assembly of human HG002 (GM24385) dataset generated by phasebook. The raw sequencing data is from : October 2021 GM24385 Q20+ Simplex Dataset Release. Reads from 4 flowcells were used. https://labs.epi2me.io/gm24385_q20_2021.10/?</p>
MACIE scores for human genome assembly GRCh37 Part 1 (Chr1 - Chr3)
<p>MACIE (Multi-dimensional Annotation Class Integrative Estimation) is an unsupervised multivariate mixed model framework to assess multi-dimensional functional impacts for both coding and non-coding variants in the human genome. MACIE integrates a variety of functional annotations, including protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics, and estimates the joint posterior probabilities of each genetic variant being functional.</p> <p>For each non-synonymous coding variant, the MACIE score is a vector of length 4, representing the estimated joint posterior probabilities of “not damaging protein functional and evolutionarily conserved” (MACIE01); “damaging protein functional and not evolutionarily conserved” (MACIE10); “not damaging protein functional and not evolutionarily conserved” (MACIE00); “both damaging protein functional and evolutionarily conserved” (MACIE11). MACIE_protein is the estimated posterior probability of “damaging protein functional”, which is the sum of MACIE10 and MACIE11; MACIE_conserved is the estimated posterior probability of “evolutionarily conserved”, which is the sum of MACIE01 and MACIE11; MACIE_anyclass is the estimated posterior probability of “damaging protein functional” or “evolutionarily conserved”, which is the sum of MACIE01, MACIE10, and MACIE11.</p> <p>For each non-coding and synonymous coding variant, the MACIE score is a vector of length 4, representing the estimated joint posterior probabilities of “not evolutionarily conserved and regulatory functional” (MACIE01); “evolutionarily conserved and not regulatory functional” (MACIE10); “not evolutionarily conserved and not regulatory functional” (MACIE00); “both evolutionarily conserved and regulatory functional (MACIE11). MACIE_conserved is the estimated posterior probability of “evolutionarily conserved”, which is the sum of MACIE10 and MACIE11; MACIE_regulatory is the estimated posterior probability of “regulatory functional”, which is the sum of MACIE01 and MACIE11; MACIE_anyclass is the estimated posterior probability of “evolutionarily conserved” or “regulatory functional”, which is the sum of MACIE01, MACIE10, and MACIE11.</p>
MACIE scores for human genome assembly GRCh37 Part 4 (Chr14 - Chr22)
<p>MACIE (Multi-dimensional Annotation Class Integrative Estimation) is an unsupervised multivariate mixed model framework to assess multi-dimensional functional impacts for both coding and non-coding variants in the human genome. MACIE integrates a variety of functional annotations, including protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics, and estimates the joint posterior probabilities of each genetic variant being functional.</p> <p>For each non-coding and synonymous coding variant, the MACIE score is a vector of length 4, representing the estimated joint posterior probabilities of “not evolutionarily conserved and regulatory functional” (MACIE01); “evolutionarily conserved and not regulatory functional” (MACIE10); “not evolutionarily conserved and not regulatory functional” (MACIE00); “both evolutionarily conserved and regulatory functional (MACIE11). MACIE_conserved is the estimated posterior probability of “evolutionarily conserved”, which is the sum of MACIE10 and MACIE11; MACIE_regulatory is the estimated posterior probability of “regulatory functional”, which is the sum of MACIE01 and MACIE11; MACIE_anyclass is the estimated posterior probability of “evolutionarily conserved” or “regulatory functional”, which is the sum of MACIE01, MACIE10, and MACIE11.</p>
MACIE scores for human genome assembly GRCh37 Part 3 (Chr8 - Chr13)
<p>MACIE (Multi-dimensional Annotation Class Integrative Estimation) is an unsupervised multivariate mixed model framework to assess multi-dimensional functional impacts for both coding and non-coding variants in the human genome. MACIE integrates a variety of functional annotations, including protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics, and estimates the joint posterior probabilities of each genetic variant being functional.</p> <p>For each non-coding and synonymous coding variant, the MACIE score is a vector of length 4, representing the estimated joint posterior probabilities of “not evolutionarily conserved and regulatory functional” (MACIE01); “evolutionarily conserved and not regulatory functional” (MACIE10); “not evolutionarily conserved and not regulatory functional” (MACIE00); “both evolutionarily conserved and regulatory functional (MACIE11). MACIE_conserved is the estimated posterior probability of “evolutionarily conserved”, which is the sum of MACIE10 and MACIE11; MACIE_regulatory is the estimated posterior probability of “regulatory functional”, which is the sum of MACIE01 and MACIE11; MACIE_anyclass is the estimated posterior probability of “evolutionarily conserved” or “regulatory functional”, which is the sum of MACIE01, MACIE10, and MACIE11.</p>
Human assemblies evaluated in the hifiasm (Hi-C) paper
<p>This repository contains all human assemblies evaluated in the paper titled: <strong>Haplotype-resolved assembly of diploid genomes without parental data</strong>. Non-human assemblies are available at <a href="https://zenodo.org/record/5953248">10.5281/zenodo.5953248</a>. "*.DipAsm*" by <a href="https://www.nature.com/articles/s41587-020-0711-0">Garg et al (2020)</a> and "*.PAGS*" by <a href="https://www.nature.com/articles/s41587-020-0719-5">Porubsky et al (2020)</a>. The rest of the assemblies were generated by Cheng et al.</p>
HiFi Metagenomic Sequencing Enables Assembly of Accurate and Complete Genomes from Human Gut Microbiota.
<p>We reported 102 complete metagenome assembled genomes (cMAGs) from five human fecal HiFi sequencing samples.</p> <p>102_cMAGs_fna.tar.gz: Fasta sequence files of 102 cMAGs.</p> <p>gc_skew_figures.tar.gz: GC-skew pattern figures of 102 cMAGs. (SVG format)</p> <p>coverage_plots.tar.gz: Genome coverage plot of 102 cMAGs.</p>
Genomic, transcriptomic and proteomic comparison of MRSA CC398 isolates collected from human and wild animal samples (Genome assembly and annotation dataset)
<p>This dataset includes the assembled contigs (.fasta and .gbk files), the nucleotide sequences of the prediction transcripts (CDS, rRNA, tRNA, tmRNA, misc_RNA) (.ffn files) and the respective amino acid sequences of the translated CDS sequences (.faa files) for the following methicillin-resistant <em>Staphylococcus aureus</em> (MRSA) strains: MRSA CC398 isolates recovered from humans, namely C5621 and C9017, and from a wild boar, namely OR418.</p> <p>All raw sequence reads used in this study were deposited in the European Nucleotide Archive (ENA) (BioProject PRJEB35102).</p>
Processed and additional data for our publication titled "Endoplasmic reticulum stress activates human IRE1α through reversible assembly of inactive dimers into small oligomers"
<p>This is an updated version of our original data archive (which can be found under the doi 10.5281/zenodo.5513025) that reflects changes we've made to the manuscript over the course of the review process and incorporates the new data we've collected since the time of the initial bioRxiv submission.</p> <p>This data archive contains all raw data EXCEPT for single-particle microscopy movies (which are deposited separately due to their size) for our paper titled "Endoplasmic reticulum stress activates human IRE1a through reversible assembly of inactive dimers into small oligomers". These raw data are stored in "non_SPT_data_final_v2.zip". Additionally, full plasmid sequences for all plasmids used in this paper are stored in GenBank format in the file "plasmid_sequences_v2.zip". Finally, this repository contains processed single-particle movies in the form of dual-color tracks from the TrackMate ImageJ plugin in XML format (file: "SPT_processed_data_and_settings_final_v2.zip").</p> <p>The processed XML tracks are organized in the same way as the raw data files in the separate repository. They are sorted into subfolders by date of acquisition first, followed by experimental conditions. To recreate the figures from the paper, follow instructions in the README.md file included with the source code repository and use the JSON settings files saved here under "analysis_settings".</p>
Early-life human gut metagenome-assembled genomes and proteins catalogs
<p>The description of the files:</p> <p>(1) The 32,277 genomes include six parts: ELGG_part_1.zip, ELGG_part_2.zip, ELGG_part_3.zip, ELGG_part_4.zip, ELGG_part_5.zip, ELGG_part_6.zip.</p> <p>(2) The 2,172 representative species: ELGG_representatives_2172.zip.</p> <p>(3) The ELGP catalog clustered at 95% amino acid identity: ELGP_95.faa.gz.</p> <p> </p>
Virulence and antibiotic resistance plasticity of Arcobacter butzleri: insights on the genomic diversity of an emerging human pathogen (genome assembly, annotation dataset, core- and pan-genome loci)
<p>This dataset refers to the analysis of 49 <em>Arcobacter butzleri</em> genomes and includes the assembled contigs (.fasta and .gbk files), the nucleotide sequences of the predicted transcripts (CDS, rRNA, tRNA, tmRNA, misc_RNA) (.ffn files), the respective amino acid sequences of the translated CDS sequences (.faa files), the nucleotide alignments of all the 1165 core-genome loci, the nucleotide alignments of the genes <em>hecA</em>, <em>tetR </em>and <em>porA</em>, the categorized amino acid sequences of the six hypervariable regions of PorA, and the nucleotide sequences of the first allele of each of the 7474 pan-genome loci with the respective complete allelic profile matrix.</p> <p>All raw sequence reads used in this study were deposited in the European Nucleotide Archive (ENA) (BioProject PRJEB34441).</p>
Metagenome-assembled genomes(MAGs) generated from CRC human gut (PRJEB27928).
<p>MAGs generated from CRC human gut (PRJEB27928) with Maxbin2, VAMB, Metabat2, SemiBin(single-sample binning) and VAMB, SemiBin(multi-sample binning).</p> <p>Single-sample binning: Maxbin2.tar.gz, Metabat2.tar.gz, VAMB.tar.gz and SemiBin(_pretrain).tar.gz. </p> <p>Multi-sample binning: VAMB_multi.tar.gz and SemiBin_multi.tar.gz.</p>
Data for: Endo-lysosomal assembly variations among Human Leukocyte Antigen class I (HLA-I) allotypes
<p>The extreme polymorphisms of HLA-I proteins enable the presentation of diverse peptides to cytotoxic T lymphocytes (CTL). The canonical endoplasmic reticulum (ER) HLA-I assembly pathway enables presentation of cytosolic peptides, but effective intracellular surveillance requires multi-compartmental antigen sampling. Endo-lysosomes are generally sites of HLA class II assembly, but human monocytes and monocyte-derived dendritic cells (moDCs) also contain significant reserves of endo-lysosomal HLA-I molecules. We hypothesized variable influences of HLA-I polymorphisms upon outcomes of endo-lysosomal trafficking, as the stabilities and peptide occupancies of cell surface HLA-I are variable. Consistent with this model, when the endo-lysosomal pH of moDCs is disrupted, HLA-B allotypes display varying propensities for reductions in surface expression, with HLA-B*08:01 or HLA-B*35:01 being among the most resistant or sensitive respectively, among eight tested HLA-B allotypes. Perturbations of moDC endo-lysosomal pH result in redistribution of HLA-B*35:01, but not HLA-B*08:01, to LAMP1+ compartments and increase HLA-B*35:01 peptide receptivity. These findings reveal the intersection of the vacuolar cross-presentation pathway with a constitutive assembly pathway for some HLA-B allotypes. Notably, cross-presentation of epitopes derived from two soluble antigens was also more efficient for B*35:01 compared to B*08:01, even when matched for T cell response sensitivity, and more affected by cathepsin inhibition. Thus, HLA-I polymorphisms dictate the degree of endo-lysosomal assembly, which can supplement ER assembly for constitutive HLA-I expression and increase the efficiency of cross-presentation.</p>
FUN-LDA scores for human genome assembly GRCh37
<p>FUN-LDA is based on a Latent Dirichlet Allocation (LDA) model for predicting functional effects of non-coding genetic variants in a cell type and tissue-specific way by integrating diverse epigenetic annotations for specific cell types and tissues from large-scale genomics projects such as ENCODE and Roadmap Epigenomics. Using this unsupervised approach, we predict tissue-specific functional effects for every position in the human genome for 127 tissues and cell types in ENCODE and Roadmap Epigenomics. This <a href="https://docs.google.com/spreadsheet/ccc?key=0Am6FxqAtrFDwdHU1UC13ZUxKYy1XVEJPUzV6MEtQOXc&usp=sharing">online spreadsheet</a> includes the information about the 127 Roadmap tissues in detail.</p> <p><strong>Format</strong></p> <p>The FUN-LDA scores are stored in the UCSC Genome Browser bigWig Track Format. </p> <p>To extract FUN-LDA scores, the bigWigAverageOverBed utility is required. It can be downloaded from the Genome Browser website at <a href="https://hgdownload.cse.ucsc.edu/admin/exe">https://hgdownload.cse.ucsc.edu/admin/exe</a>.</p> <p>User should prepare a UCSC Genome Browser bed file, a tab-separated four-column file. The first column is the chromosome; the second is the zero-based coordinate of the position of interest; the third is that zero-based coordinate plus one; and the fourth is a unique identifier for the position. The command below is an example of extracting FUN-LDA scores in tissue E007 for the positions defined in a bed file, "input.bed".</p> <pre><code class="language-bash">bigWigAverageOverBed E007.valley9.c89.bigwig input.bed output.tab</code></pre> <p>It produces a six-column file, output.tab. The first column includes the unique position id defined in the input bed file. The last column, average over the covered bases, is the score for this position.</p> <p><strong>Reference</strong></p> <p>Daniel Backenroth, Zihuai He, Krzysztof Kiryluk, Valentina Boeva, Lynn Pethukova, Ekta Khurana, Angela Christiano, Joseph Buxbaum, Iuliana Ionita-Laza. FUN-LDA: A latent Dirichlet allocation model for predicting tissue-specific functional effects of noncoding variation: Methods and applications. American Journal of Human Genetics, 2018.<br> </p> <p> </p>
Complete bacterial assemblies for 'Enterobacterales plasmid sharing amongst human bloodstream infections, livestock, wastewater, and waterway niches in Oxfordshire, UK'
<p>Complete bacterial assemblies from the journal article 'Matlock, William, et al. "<em>Enterobacterales</em> plasmid sharing amongst human bloodstream infections, livestock, wastewater, and waterway niches in Oxfordshire, UK." <em>Elife</em> 12 (2023): e85302.'</p> <p>'assemblies.zip' contains assemblies for <em>n</em>=1,458 isolates with circularised chromosomes and <em>n</em>=3,697 circularised plasmids.</p> <p><strong>If you use this data please cite the journal article <em>and</em> the Zenodo DOI. </strong></p>
Human genome assemblies enhanced by LOCLA
<p>This is a data repository for the genome assemblies of three human samples enhanced by LOCLA (DOI: 10.5281/zenodo.8280853 ). LOCLA is a novel genome assembly optimization tool, LOCLA, that iteratively improves the quality of an assembly by locating sequencing reads on partially assembled scaffolds and thus enable gap filling and further scaffolding. </p> <p>The three human genome assemblies and the assembly statistics are compressed into one single zip file. File names are explained as follows:</p> <ol> <li>LLD0021C_locla.fasta : Whole genome assembly of a Taiwanese male individual generated by LOCLA</li> <li>LLD0021C_locla_quality.txt : Assembly statistics of LLD0021C_locla.fasta</li> <li>chm13_locla.fasta : Whole genome assembly of the CHM13 cell line generated by LOCLA</li> <li>chm13_locla_quality.txt : Assembly statistics of chm13_locla.fasta</li> <li>hg002_gma_locla.fasta : Whole genome assembly of the HG002 sample generated by LOCLA </li> <li>hg002_gma_locla_quality.txt : Assembly statistics of hg002_gma_locla.fasta</li> </ol> <p> </p>
Sequential human assembly and disassembly motions in human-robot coexisting environments
Open the record for dataset details and reuse information.
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.