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15 results for “Protein classification”
PhasAGE Training School 1 -Overview of bioinformatics tools for the life sciences & Classification and evolution of non-globular proteins- LECTUREs
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</p>
DPCstruct Classification of AlphaFold2-Predicted Protein Structures
<p>This dataset contains DPCstruct domain classifications for protein structures predicted by AlphaFold2, as presented in the paper "Unsupervised Domain Classification of AlphaFold2-Predicted Protein Structures."</p> <p>DPCstruct was applied to a non-redundant set of the AlphaFold Database v4.0, known as Foldseek Clusters, which includes approximately 15 million representative proteins, as described in the work by <a href="https://doi.org/10.1038/s41586-023-06510-w">Barrio-Hernandez et al.</a></p> <p>This repository provides the results of our classification, along with all the data related to the analyses presented in our study. DPCstruct algorithm can be found at <a href="https://github.com/RitAreaSciencePark/DPCstruct">https://github.com/RitAreaSciencePark/DPCstruct</a> together with examples on how to use it.</p> <p><strong>FILES DESCRIPTION:</strong></p> <ul> <li><strong>dpcstruct_classification.tsv: </strong>List of domains identified by DPCstruct and their corresponding metacluster. Columns: Metacluster ID, Protein Uniprot ID, domain start, domain end.</li> <li><strong>mcs_reps.fasta:</strong> For each metacluster, two representative domains were selected: one representing the center of the cluster and the other being the domain with the highest pLDDT score. If these are the same, only one domain is included as the representative. This file contains the list of representative domains and their sequences in FASTA format.</li> <li><strong><span>mcs_reps_pdbs.zip: </span></strong>Contains a PDB file for each representative domain. The filename is structured as 'proteinID_metacluster.pdb'.</li> <li><strong>mcs_properties.tsv:</strong> Set of properties per metacluster, including: <ul> <li><strong>mcID:</strong> Metacluster ID.</li> <li><strong>size:</strong> Number of domains.</li> <li><strong>len_aa:</strong> Average length of domains (number of amino acids).</li> <li><strong>len_std:</strong> Standard deviation of domain lengths.</li> <li><strong>len_ratio:</strong> Ratio of len_std to len_aa.</li> <li><strong>plddt:</strong> Average predicted LDDT as reported by AlphaFold2.</li> <li><strong>disorder:</strong> Average intrinsic disorder score calculated with AIUPred.</li> <li><strong>alntmscore:</strong> Pairwise alignment TM-score between domains, averaged over all pairs.</li> <li><strong>tmscore:</strong> Pairwise alignment TM-score between domains, averaged over all pairs, using the maximum between TM-score normalized by query or target.</li> <li><strong>lddt:</strong> Pairwise LDDT score, averaged over all pairs.</li> <li><strong>prob:</strong> Pairwise probability of homology according to SCOPe, as reported by Foldseek.</li> <li><strong>pident:</strong> Pairwise percentage identity, averaged over all pairs.</li> </ul> </li> <li><span><strong>annotated_[cath|scop]_qc[x]_t[x]_l[x].tsv:</strong> </span>For each fold in [CATH|SCOP], we provide the best matching DPCstruct domain, if available, along with the structural alignment information as reported by Foldseek. A fold is considered annotated if its alignment values meet or exceed the following thresholds: <ul> <li>qc: query coverage.</li> <li>t: template modelling score of the alignment.</li> <li>l: lddt score of the alignment.</li> </ul> </li> <li><strong>dpcstruct_consistency.tsv:</strong> Consistency of DPCstruct metaclusters with respect to Pfam 36.0 labels. Note that we consider a Pfam label to overlap with a DPCstruct domain even if it shares just one amino acid, which is why some metaclusters have many labels. In such cases, we only display 5 representative labels.</li> <li><strong>pfam_consistency.tsv:</strong> Consistency of Pfam Clans with respecto to DPCstruct labels.</li> </ul> <p><strong>Note:</strong> All 'tsv' files contain a header as the first row.</p> <p>If there is any doubt regarding the data or there is something missing please contact us: </p> <p>federico.barone@areasciencepark.it</p>
Accurate annotation of protein coding sequences with IDTAXA - classification results
<p>Raw classification results generated by IDTAXA, BLAST, and HMMER on a training set scraped from KEGG of > 1.5M sequences. HMMER and BLAST results are also converted into IDTAXA like objects for ease of comparison.</p>
Code and data for: Network-based protein structural classification
<p>Code and data related to the research article titled, "Network-based protein structural classification".</p> <p>More information about the code and the data is available at https://nd.edu/~cone/NETPCLASS/</p>
VMXi Classification Dataset: Micrographs of Protein Crystallisation Experiments with Labels of Experimental Outcomes
<p>The VMXi Classification Dataset consists of images of protein crystallisation experiments collected on a Rock Imager 1000 (Formulatrix, USA) automated microplate imager at the VMXi experimental facility at Diamond Light Source, UK. These images were used to train the CHiMP (Crystal Hits in My Plate) Classifier network that classifies images into categories of experimental outcome.</p> <ul> <li>The directory named "VMXi_Classification_Images", consists of 18,782 JPEG images with a resolution of 3376 × 2704 pixels. </li> <li>13,951 of these images are associated with a label describing the experimental outcome depicted in the images. The labels are Clear, Crystals, Precipitate or Other.</li> <li>The file "VMXi_Classification_Train.csv" contains filenames and labels for the 11,161 images in the training set used for the CHiMP Classifier network.</li> <li>The file "VMXi_Classification_Validation.csv" contains filenames and labels for the 2,790 images in the validation set used for the CHiMP Classifier network.</li> </ul> <p>In addition, an independent test set of images are included in the directory named "VMXI_Classification_Test_Dataset". Within this direcectory:</p> <ul> <li>The subdirectory named "VMXi_Classification_Test_Images" contains 1000 JPEG images with a resolution of 3376 × 2704 pixels.</li> <li>Each image is associated with a label describing the experimental outcome depicted in the images. The labels are Clear, Crystals, Precipitate or Other.</li> <li>The file "unambiguous_test_dataset.csv" contains filenames and labels for the 632 images in the test set where three experts independently agreed on a label.</li> <li>The file "mostly_unambiguous_test_dataset.csv" contains filenames and labels for the 949 images in the test set where at least two experts independently agreed on a label.</li> <li>The file "original_expert_labels.csv" contains filenames for all 1000 images and the labels given by three experts independently. The column headed "expert_1_1" refers to labels given by expert number 1 at a timepoint 6 months prior to categorising the images again, given in the column "expert_1_2". The columns "expert_2" and "expert_3" contain the labels given by experts 2 and 3 respectively.</li> <li>The <em>unambiguous</em> and <em>mostly unambiguous</em> test sets were created from the categories chosen by "expert_1_2", "expert_2" and "expert_3"</li> </ul>
Protein Subcellular localization prediction data used in the article entitled "MSclassifier: Median-Supplement model-based Classification tool for automated knowledge discovery"
<p>This repository contains data used to obtain results from a 5-fold cross-validation testing of how MSclassifier and other packages accurately predict protein subcellular localization in the software article entitled "MSclassifier: median-supplement model-based classification tool for automated knowledge discovery." The data used in the software article is derived from data generated in "G. K. Acquaah-Mensah, S. M. Leach, and C. Guda, Predicting the subcellular localization of human proteins using machine learning and exploratory data analysis, Genomics Proteomics Bioinformatics, 4(2):120-133, 2006, <a href="https://doi.org/10.1016/S1672-0229(06)60023-5">https://doi.org/10.1016/S1672-0229(06)60023-5</a>"</p>
Dataset for Ha and Aylward 'Automated classification of giant virus genomes using a random forest model built on trademark protein families'
<ul><li>Genome sets used for model training and testing</li><li>Custom Python script that generated fragmented genomes at random completeness levels</li></ul>
DPAM Domain Classification of Human Proteins against ECOD Reference
<p>Domain definitions of AlphaFold classifications of the human proteome (v1) from the AlphaFold Database. Also included are classifications of <em>Danio rerio</em>, <em>Mus musculus</em>, <em>Pan paniscus</em>, <em>Drosophila melanogaster</em>, <em>Caenorhabditis elegans </em>used for comparative analysis to human. See README file for descriptions of file formats.</p>
Evaluating the use of paralogous protein domains to increase data availability for missense variant classification [dataset]
Open the record for dataset details and reuse information.
Homology-based classification of accessory proteins in coronavirus genomes uncovers extremely dynamic evolution of gene content
<p>Fasta files of ORF sequences for all orthogroups generated in the study.</p>
HiPHD: Hierarchical Classification for Protein Remote Homology Detection using Graph Neural Networks and Language Models
Open the record for dataset details and reuse information.
CATH-KinFams: CATH Protein Kinase classification alignments and Hidden Markov Models
<p>CATH KinFams are protein kinase domain families classified according to functional similarity based on SDP. In this deposition we make available 2,210 KinFams sequence alignments alongside Hidden Markov Models built from them to be used with HMMER3.</p> <p>A concatenated library 'kinases_4.3-FF-seed.hmm' is also available to scan against the whole KinFams dataset.</p> <p>The Zenodo deposition contains:</p> <p>kinfams-cath-4.3-seed-alignments.tar.gz - KinFams FASTA file alignments with headers 'UniProt_ID/start-stop' i.e. A8XMX4/281-587</p> <p>kinfams-cath-4.3-seed-hmms.tar.gz - HMMs for each individual KinFam and concatenated in a HMM library.</p> <p>kinfams-cath-4.3-seed-mda-strings - Multi-Domain-Architecture string assignment for each sequence in the KinFams dataset.</p> <p>human_kinfams_af2_models_cif.tar.gz - Chopped mmCIF files containing Human Kinases AlphaFold2 Models.</p>
Pecan kinome: classification and expression analysis of all protein kinases in Carya illinoinensis
GEO Series GSE179336. Carya illinoinensis. 18 samples. Type: Expression profiling by high throughput sequencing.
Diamond formatted protein database for taxonomic classification
<p>This is a diamond formatted database (diamond version 0.9.22) built on December 14th 2018.</p> <p>The database contains a total of 17,694,143 sequences:</p> <ul> <li>2,708,401 protein sequences from the <a href="https://bitbucket.org/dbeisser/taxmapper_supplement/src/master/">taxmapper database</a> (commit 450d337), containing 121 unique taxa.</li> <li>14,976,193 protein sequences from 1055 unique fungal taxa (downloaded from <a href="https://genome.jgi.doe.gov/1000_fungi_project">JGI 1000 fungi project</a> on November 23 2018).</li> <li>9,549 protein sequences from the <em>Hygrophorus russula</em> genome obtained from Genbank (accession GCA_003314125.1) on November 28 2018.</li> </ul> <p>The <em>Hygrophorus russula</em> protein sequences were obtained by running Augustus (v. 3.2.3) gene caller on the genomic fasta file using the laccaria_bicolor gene model.</p> <p>Taxonomic information was built into the diamond database by running:</p> <pre><code class="language-bash">zcat fasta.gz | diamond makedb -d diamond -p 4 --taxonmap taxonmap.gz --taxonnodes nodes.dmp</code></pre> <p>The nodes.dmp file was obtained from the <a>taxdump.tar.gz</a> file on December 11 2018.</p>
Search of Protein Biomarkers in Order to Achieve a Molecular Classification of Gliomas
ClinicalTrials.gov study NCT02473484. IPD Sharing: NO. Countries: 1. Publications: 0.
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.