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.
107
datasets available to search
ShareScore release 0.9.0
Dataset results
107 results for “protein localization”
Fig. 4 in Analysis of major sperm proteins in two nematode species from two classes, Enoplus brevis (Enoplea, Enoplida) and Panagrellus redivivus (Chromadorea, Rhabditida), reveals similar localization, but less homology of protein sequences than expected for Nematoda phylum
Fig. 4 Immunolocalization of MSP in P. redivivus sperm. a Immature spermatozoa extracted from male. MSP localizes in granules. In some cells, MSP has strongest signals in the periphery (arrowheads) (scale bar 10 µm). b Chain of mature spermatozoa extracted from female.
Disorder predictions for all reviewed UniProt proteins with "membrane" subcellular localization
<h1>IDRs in membrane proteins</h1> <h3>About</h3> <p>The writeup below walks through how to answer the question "what fraction of membrane proteins have IDRs".</p> <p>The tl/dr: on average <strong>55% of membrane proteins [as defined here] have IDRs</strong>.</p> <h3>Data</h3> <p>This directory (WITH the associated sequences), which are not included on GitHub, are deposited on Zenodo.</p> <h2>Steps</h2> <h4>Install software</h4> <p>Install metapredict</p> <div> <pre><code>pip install metapredict</code></pre> </div> <p>Done.</p> <h4>Find the data</h4> <p>Using the "Advanced Search", I downloaded all reviewed proteins on <a href="https://uniprot.org/">UniProt</a> tagged as having a "membrane" subcellular localization.</p> <p>There are, of course, much more detailed ways one could get these data (e.g., requiring transmembrane helices, integral membrane protein filtering etc, etc), but I figured for purposes here, I'd just do this...</p> <h4>Download the data</h4> <p>From this, I downloaded all the FASTA sequences. This triggers the following file to be downloaded:</p> <div> <pre><code>uniprotkb_cc_scl_term_SL_0162_AND_revie_2024_11_09.fasta</code></pre> </div> <h4>Preprocessing the data</h4> <p>Next I wanted to fix any sequences with non-standard amino acids by converting those residues to "standard" amino acids. I did this using <code>pfasta</code>, a commandline tool that's part of <a href="https://protfasta.readthedocs.io/en/latest/">protfasta</a> which comes with metapredict.</p> <p>Did this simply with</p> <div> <pre><code>pfasta --invalid-sequence convert-all -o membrane_proteins.fasta uniprotkb_cc_scl_term_SL_0162_AND_revie_2024_11_08.fasta</code></pre> </div> <ul> <li>The <code>--invalid-sequence</code> flag tells pfasta what to with an invalid sequence, and <code>-o</code> tells it what the ouput file should be called.</li> </ul> <p>Running this (~5 seconds) generates the following output:</p> <div> <pre><code>........................ pfasta version 0.1.13+0.gc1786bd.dirty Please report bugs to: https://github.com/holehouse-lab/protfasta ........................ [INFO]: Reading in the file uniprotkb_cc_scl_term_SL_0162_AND_revie_2024_11_08.fasta [INFO]: Read in file with 1004330 lines [INFO]: Parsed file to recover 117691 sequences [INFO]: Converted 697 sequences to valid sequences [INFO]: Writing new sequence file [membrane_proteins.fasta]...</code></pre> </div> <h4>Predicting all IDRs</h4> <p>Run one of metapredict's command-line utilities to predict all IDRs in these proteins.</p> <div> <pre><code>metapredict-predict-idrs membrane_proteins.fasta -o membrane_protein_idrs.fasta</code></pre> </div> <p>This takes ~20 seconds on my desktop with an A4500 GPU or ~5 min with my Macbook Pro. We're then left with a file called <code>membrane_protein_idrs.fasta</code>. This is a FASTA file that the header format:</p> <div> <pre><code>><HEADER FOR ORIGINAL PROTEIN> IDR_START=<start position> IDR_END=<end_position></code></pre> </div> <p>e.g.</p> <div> <pre><code>>sp|B4R1D8|HH_DROSI Protein hedgehog OS=Drosophila simulans OX=7240 GN=hh PE=3 SV=1 IDR_START=0 IDR_END=94 MDNHSSVPWASAASVTCLSLDAKCHSSSSSCSSKSTASSISASPETQTMRHIAHTQRCLS RLTSLVALLLIVLPMMFSPAHSCGPGRGLGRHRA</code></pre> </div> <h3>Hacky way to get number of proteins with IDRs</h3> <p>Get the number of unique proteins in the main file:</p> <div> <pre><code>cat membrane_proteins.fasta | grep ">" | awk -F "|" {' print $2 '} | sort | uniq |wc</code></pre> </div> <p>Which returns</p> <div> <pre><code>117691 117691 829609</code></pre> </div> <p>So we have 117,691 unique proteins (NB: we could also just have gotten this without the <code>awk -F "|" {' print $2 '} | sort | uniq</code> part BUT we do need that for the number of unique proteins with IDRs so good to be consistent.</p> <div> <pre><code>cat membrane_protein_idrs.fasta | grep ">" | awk -F "|" {' print $2 '} | sort | uniq |wc</code></pre> </div> <p>Which returns</p> <div> <pre><code>64971 64971 458829 </code></pre> </div> <p>So on average <strong>55% of membrane proteins [as defined here] have IDRs</strong>.</p> <h3>Bonus!</h3> <p>In addition we can predict all disorder scores rather than discrete IDRs using</p> <div> <pre><code>metapredict-predict-disorder membrane_proteins.fasta -o membrane_protein_disorder_scores.csv</code></pre> </div> <p>This takes ~5 min on my Macbook Pro. The data for these are included here for completeness/convenience.</p>
Localization files for "Imaging proteins sensitive to direct fusions using transient peptide-peptide interactions"
<p>Localization csv files for super-resolution images taken and analysed for "Imaging proteins sensitive to direct fusions using transient peptide-peptide interactions". Single localizations were processed using the Peak Fit function of the Fiji (Java 8 2017 release) GDSC SMLM plugin, using a signal strength threshold of 30, a minimum photon threshold of 100, and a precision threshold of 15-30 nm (precision threshold used is indicated in the file name). </p>
Endotoxin & Cytokines. Do Protein Loss and Metabolic Effects Depend on Central Nervous System (CNS) Activation of Stress Hormones or on Local Mechanisms in Muscle and Fat?
ClinicalTrials.gov study NCT01452958. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Safety Study of BJ-001, an IL-15 Fusion Protein, for Locally Advanced/Metastatic Solid Tumors
ClinicalTrials.gov study NCT04294576. IPD Sharing: Not stated. Countries: 1. Publications: 18.
Arabidopsis SAUR63 seedling phenotypes and fluorescent fusion protein localization
Open the record for dataset details and reuse information.
Global analysis of cell behavior and protein localization dynamics reveals region-specific functions for Shroom3 and N-cadherin during neural tube closure
Open the record for dataset details and reuse information.
Data from: Determining the subcellular location of new proteins from microscope images using local features
Open the record for dataset details and reuse information.
Data from: Separating golgi proteins from cis to trans reveals underlying properties of cisternal localization
Open the record for dataset details and reuse information.
Genome-wide CRISPR screen for comprehensive identification of human factors involved in alternative polyadenylation based on differential localization of CD47 protein
GEO Series GSE288919. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Stress modulates Ahi1-dependent nuclear localization of Ten-Eleven Translocation Protein 2
GEO Series GSE167344. Mus musculus. 7 samples. Type: Methylation profiling by high throughput sequencing.
Codon usage influences the local rate of translation elongation to regulate co-translational protein folding
GEO Series GSE71032. Neurospora crassa. 8 samples. Type: Expression profiling by high throughput sequencing; Other.
NuRD-interacting protein ZFP296 regulates genome-wide NuRD localization and differentiation of mouse embryonic stem cells (ChIP-seq)
GEO Series GSE117287. Mus musculus. 36 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Xist-seeded nucleation sites form local concentration gradients of silencing proteins to inactivate the X-chromosome
GEO Series GSE181236. Mus musculus musculus x Mus musculus castaneus; Mus musculus. 47 samples. Type: Other; Expression profiling by high throughput sequencing.
Tissue-specific transcriptomics, chromosomal localization, and phylogeny of chemosensory and odorant binding proteins from the red flour beetle Tribolium castaneum reveal subgroup specificities for ol
GEO Series GSE63162. Tribolium castaneum. 16 samples. Type: Expression profiling by high throughput sequencing.
The C. elegans ortholog of TDP-43 regulates the chromatin localization of the heterochromatin protein 1 homolog, HPL-2
GEO Series GSE100829. Caenorhabditis elegans. 21 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Other.
Local regulation of the Srs2 helicase by the SUMO-like domain protein Esc2 promotes recombination at sites of stalled replication
GEO Series GSE65701. Saccharomyces cerevisiae. 9 samples. Type: Genome binding/occupancy profiling by genome tiling array.
Highly parallel quantification and compartment localization of transcription factors and nuclear proteins
GEO Series GSE140325. Homo sapiens. 9 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
RAB protein controls Argonaute localization for proper miRNA function in C. elegans
GEO Series GSE141719. Caenorhabditis elegans. 9 samples. Type: Non-coding RNA profiling by high throughput sequencing.
The Smc5/6 Complex Restricts HBV when Localized to ND10 without Inducing an Innate Immune Response and is Counteracted by the HBV X Protein Shortly after Infection
GEO Series GSE93153. Homo sapiens. 56 samples. Type: Expression profiling by high throughput sequencing.
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.