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40 results for “protein annotation”
Cross-phyla protein annotation by structural prediction and alignment
<p><strong>Background:</strong> Protein annotation is a major goal in molecular biology, yet experimentally determined knowledge is typically limited to a few model organisms. In non-model species, the sequence-based prediction of gene orthology can be used to infer protein identity, however this approach loses predictive power at longer evolutionary distances. Here we propose a workflow for protein annotation using structural similarity, exploiting the fact that similar protein structures often reflect homology and are more conserved than protein sequences.</p> <p><strong>Results:</strong> We propose a workflow of openly available tools for the functional annotation of proteins via structural similarity (MorF: <strong>Mor</strong>pholog<strong>F</strong>inder) and use it to annotate the complete proteome of a sponge. Sponges are highly relevant for inferring the early history of animals, yet their proteomes remain sparsely annotated. MorF accurately predicts the functions of proteins with known homology in >90% cases, and annotates an additional 50% of the proteome beyond standard sequence-based methods. We uncover new functions for sponge cell types, including extensive FGF, TGF and Ephrin signalling in sponge epithelia, and redox metabolism and control in myopeptidocytes. Notably, we also annotate genes specific to the enigmatic sponge mesocytes, proposing they function to digest cell walls.</p> <p><strong>Conclusions:</strong> Our work demonstrates that structural similarity is a powerful approach that complements and extends sequence similarity searches to identify homologous proteins over long evolutionary distances. We anticipate this to be a powerful approach that boosts discovery in numerous -omics datasets, especially for non-model organisms.</p>
Annotation of Phytozome V12 protein plant sequences using the ragp pipeline for hydroxyproline-rich glycoprotein mining
<p>Hydroxyproline aware annotation of hydroxyproline-rich glycoprotein (HRGP) sequences was performed on sequence data from 62 plant proteomes obtained from Phytozome database (<a href="https://phytozome.jgi.doe.gov/pz/portal.html">https://phytozome.jgi.doe.gov/pz/portal.html</a>, version 12) using the ragp R package (<a href="https://github.com/missuse/ragp">https://github.com/missuse/ragp</a>, version 0.3.0.0001). </p> <p>In each archive a single comma separated value table (.csv) is present along with a README.txt file describing the contents of the corresponding .csv file. The archives are:</p> <p>- phytozome_V12.tar.gz - sequences from 62 plant proteomes (phytozome V12) with a total of 2797062 protein sequences.</p> <p>- phytozome_V12_phobius.tar.gz -<strong> </strong> Signal peptide prediction using Phobius (<a href="http://phobius.sbc.su.se/">http://phobius.sbc.su.se/</a>) on sequences present in phytozome_V12.tar.gz.</p> <p>- phytozome_V12_signalp.tar.gz -<strong> </strong> Signal peptide prediction using SignalP 4.1 (<a href="http://www.cbs.dtu.dk/services/SignalP-4.1/">http://www.cbs.dtu.dk/services/SignalP-4.1/</a>) on sequences present in phytozome_V12.tar.gz.</p> <p>- phytozome_V12_targetp.tar.gz - Signal peptide prediction using TargetP 1.1 (<a href="http://www.cbs.dtu.dk/services/TargetP/">http://www.cbs.dtu.dk/services/TargetP/</a>) on sequences present in phytozome_V12.tar.gz.</p> <p>- phytozome_V12_predict_hyp.tar.gz - Probability of proline hydroxylation for each proline from 266135 protein sequences which were predicted to be secreted by a majority vote (using Phobius, SignalP 4.1 and TargetP 1.1).</p> <p>- phytozome_V12_maab.tar.gz - Motif and amino acid bias (MAAB) classification of hydroxyproline-rich glycoproteins performed on 266135 protein sequences which were predicted to be secreted by a majority vote (using Phobius, SignalP 4.1 and TargetP 1.1). The number of predicted hydroxyprolines in each sequence is also indicated (based on predictions provided in phytozome_V12_predict_hyp.tar.gz).</p> <p>- phytozome_V12_scan_ag.tar.gz. - Hydroxyproline aware arabinogalactan motif scan performed on 266135 protein sequences which were predicted to be secreted by a majority vote (using Phobius, SignalP 4.1 and TargetP 1.1). Hydroxyproline predictions are provided in phytozome_V12_predict_hyp.tar.gz. </p> <p>- phytozome_V12_scan_ag_hmmscan.tar.gz - Detection of domains in a subset of protein sequences which were found to contain arabinogalactan motifs (a subset of phytozome_V12_scan_ag.tar.gz).</p> <p>The list of the 62 plant species is provided in phytozome_V12.tar.gz README.txt.</p> <p>For questions contact mdragicevic@ibiss.bg.ac.rs.</p> <p> </p> <p> </p>
PharmaCoNER corpus: gold standard annotations of Pharmacological Substances, Compounds and proteins in Spanish clinical case reports
<p><strong>Intro:</strong></p><p>The PharmaCoNER corpus (divided into train, dev and test) is a Gold Standard manually annotated dataset used for the the PharmaCoNER shared task posed at BIONLP-ST (at EMNLP). In addition, we include here the PharmaCoNER background set. It contains the train, development and test sets of the two subtasks (subtask-1 and subtask-2) with Gold Standard annotations. In addition, it contains the documents of the background set, without annotations.</p><p>The PharmaCoNER corpus consists of:</p><ul><li>Manually classified clinical case sections derived from Open access Spanish medical publications, named the Spanish Clinical Case Corpus (SPACCC).</li><li>It was manually selected by a practicing oncologist and revised by a clinical documentalist to assure that records were relevant/representative and resembled structure and content relevant to process clinical records.</li><li>The final corpus: 1000 clinical cases 16,504 sentences</li><li>The corpus contains a total of 396,988 words, with an average of 396.2 words per clinical case.</li><li>It covers a range of medical disciplines including oncology, urology, cardiology, pneumology or infections diseases, etc.</li><li>The corpus has been annotated at the mention level by experts in medicinal chemistry and pharmacology following a granular annotation scheme covering four mention types:<ul><li><i>Entity type 1 (NORMALIZABLES)</i>: mentions of chemicals that can be manually normalized to a unique concept identifier (primarily SNOMED-CT).</li><li><i>Entity type 2 (NO_NORMALIZABLES)</i>: mentions of chemicals that could not be normalized manually to a unique concept identifier.</li><li><i>Entity type 3 (PROTEINAS)</i>: mentions of proteins/genes following an adaptation of the BioCreative GPRO track annotation guidelines (includes peptides, peptide hormones & antibodies).</li><li><i>Entity type 4 (UNCLEAR )</i>: cases of general substance class mentions of clinical relevance, including certain pharmaceutical formulations, general treatments, chemotherapy programs, and vaccines.</li><li>Mentions class "<i>UNCLEAR</i>" (not evaluated for the PharmaCoNER track)</li></ul></li></ul><p> </p><p> </p><p><strong>Please, cite: </strong></p><p>A. G. Agirre, M. Marimon, A. Intxaurrondo, O. Rabal, M. Villegas, M. Krallinger, Pharmaconer: Pharmacological substances, compounds and proteins named entity recognition track, in: Proceedings of The 5th Workshop on BioNLP Open Shared Tasks, 2019, pp. 1–10.</p><p> </p><p><strong>Annotation quality</strong></p><p>Inter-annotator agreement: 93% for annotation, 73% for mapping.</p><p>For more information, see the <a href="https://paperswithcode.com/paper/pharmaconer-pharmacological-substances">paper</a>.</p><p> </p><p><strong>Format</strong></p><p>For subtask 1 annotations are distributed in <i>Brat</i> format. (More info at Brat webpage https://brat.nlplab.org/standoff.html)</p><p>For subtask-2, codes are associated with each document are given in a <i>TSV</i> file with the following columns: </p><blockquote><p>filename code</p></blockquote><p> </p><p><strong>Shared task goal:</strong></p><p>In the two subtasks, the goal is to predict the annotations of the test files (either the ANN files or the TSV with the codes) given only the plain text files. </p><p> </p><p><strong>Resources:</strong></p><ul><li><a href="https://temu.bsc.es/pharmaconer/"><strong>Web</strong></a></li><li><a href="https://www.aclweb.org/anthology/D19-5701.pdf"><strong>Citation</strong></a><strong>: </strong>A. G. Agirre, M. Marimon, A. Intxaurrondo, O. Rabal, M. Villegas, M. Krallinger, Pharmaconer: Pharmacological substances, compounds and proteins named entity recognition track, in: Proceedings of The 5th Workshop on BioNLP Open Shared Tasks, 2019, pp. 1–10.</li><li><a href="https://doi.org/10.5281/zenodo.4271908"><strong>Silver Standard corpus</strong></a></li><li><a href="https://doi.org/10.5281/zenodo.3763276"><strong>Annotation guidelines</strong></a></li><li><a href="https://github.com/TeMU-BSC/PharmaCoNER-Tagger"><strong>PharmaCoNER tagger</strong></a></li><li><a href="https://www.youtube.com/watch?v=B3ZzJl5OMkY"><strong>Youtube video(general setting)</strong></a></li><li><a href="https://www.slideshare.net/MartinKrallinger/pharmaconer-pharmacological-substances-compounds-and-proteins-named-entity-recognition-track-at-bionlpost-workshop-november-4-skycity-rm-2-hong-kong-emnlp2019"><strong>Slides PharmacoNER overview talk at BIONLP-ST / EMNLP </strong></a></li></ul><p>For further information, please visit <a href="https://temu.bsc.es/pharmaconer/">https://temu.bsc.es/pharmaconer/</a> or email us at encargo-pln-life@bsc.es</p><p>Copyright (c) 2018 Secretaría de Estado para el Avance Digital (SEAD)</p><p> </p><p><strong>License</strong></p><p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p><p> </p><p><strong>Contact</strong></p><p>If you have any questions or suggestions, please contact us at:</p><p><br>- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p><p><strong>Additional resources and corpora</strong></p><p>If you are interested in PharmaCoNER, you might want to check out these corpora and resources:</p><ul><li><a href="https://zenodo.org/records/7614764">DisTEMIST</a> (Corpus of disease mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/8224056">MedProcNER </a>(Corpus of clinical procedure mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/8413866">SympTEMIST</a> (Corpus of symptoms, signs and findings mentions and normalization, same document collection)</li><li><a href="https://zenodo.org/records/7116201">MEDDOPROF</a> (Corpus of mentions of professions, occupations and working status and normalization, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/8403498">MEDDOPLACE</a> (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/4279323">MEDDOCAN</a> (Corpus of mentions of Personal Health Identifiers (PHI), modified synthetic verions of the document collection)</li><li><a href="https://zenodo.org/records/3978041">CANTEMIST</a> (Corpus of cancer tumor morphology mentions and normalization, different document collection)</li><li><a href="https://zenodo.org/records/3837305">CodiESp</a> (Corpus of clinical case reportes with assigned clinical codes from ICD10, Spanish version, same document collection)</li><li><a href="https://zenodo.org/records/7684093">LivingNER</a> (Corpus of mentions of species, including human/family members, pathogens, food, etc.. and normalization to NCBI Taxonomy, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/2560344">SPACCC-POS</a> (Corpus of clinical case reports in Spanish annotated with POS-tags, same document collection)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-TOKEN</a> (Corpus of clinical case reports in Spanish annotated with token-tags (word mention boundaries), same document collection)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-SPLIT</a> (Corpus of clinical case reports in Spanish annotated with sentence boundary-tags, same document collection)</li><li><a href="https://zenodo.org/records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)</li></ul>
Training data for 'Functional annotation of protein sequences' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial for functional annotation of protein sequences.</p>
FAPM: Functional annotation of proteins using multi-modal models beyond structural modeling
<p>Assigning accurate property labels to proteins, like functional terms and catalytic activity, is challenging, especially for proteins without homologs and "tail labels" with few known examples. Unlike previous methods that mainly focused on protein sequence features, we use a pretrained large natural language model to understand the semantic meaning of protein labels. Specifically, we introduce FAPM, a contrastive multi-modal model that links natural language with protein sequence language. This model combines a pretrained protein sequence model with a pretrained large language model to generate labels, such as Gene Ontology (GO) functional terms and catalytic activity predictions, in natural language. Our results show that FAPM excels in understanding protein properties, outperforming models based solely on protein sequences or structures. It achieves state-of-the-art performance on public benchmarks and in-house experimentally annotated phage proteins, which often have few known homologs. Additionally, FAPM's flexibility allows it to incorporate extra text prompts, like taxonomy information, enhancing both its predictive performance and explainability. This novel approach offers a promising alternative to current methods that rely on multiple sequence alignment for protein annotation.</p>
The North Pacific Eukaryotic Gene Catalog: KOfam protein function annotations
<p><strong>KEGG functional annotation using KofamScan v1.3.0</strong></p> <p>These tables are larger alternative versions to the KOfam tables included in the North Pacific Eukaryotic Gene Catalog protein data repository here: <a href="../records/12630398">https://zenodo.org/records/12630398</a><br><br>A full description of this data is published in Scientific Data, available here: <a href="https://www.nature.com/articles/s41597-024-04005-5" target="_blank" rel="noopener">The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations</a>. Please cite this publication if your research uses this data:<br><br>Groussman, R. D., Coesel, S. N., Durham, B. P., Schatz, M. J., & Armbrust, E. V. (2024). The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations. <em>Scientific Data</em>, <em>11</em>(1), 1161.<br><br>Clustered protein sequences were annotated against the KEGG collection (release 104.0) of 20,819 protein family Hidden Markov Models (HMMs) using <a href="https://github.com/takaram/kofam_scan" target="_blank" rel="noopener">KofamScan </a>and KofamKOALA. Kofam annotation code is documented in the project github repository here: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.kofamscan_function.sh">NPEGC.kofamscan_function.sh</a></p> <p>Excerpt of core NPEGC_kofam function:</p> <p><code># Define input FASTA</code><br><code>local INPUT_FASTA="NPac.${STUDY}.bf100.id99.aa.fasta"</code></p> <p><code># KofamScan call</code><br><code>${KOFAM_DIR}/kofam_scan-1.3.0/exec_annotation -f detail-tsv -E ${EVALUE} -o ${ANNOTATION_DIR}/NPac.${STUDY}.bf100.id99.aa.tsv ${FASTA_DIR}/${INPUT_FASTA}</code></p> <p>Unprocessed annotation results were filtered with a minimum score of 30 to remove low-scoring matches:<br><br><code>zcat NPac.<em><u>NPacID</u></em>.kofam.tsv.gz | awk -F'\t' '{ gsub(/"/, "", $5); $5 = $5 + 0; if ($5 >= 30) print }' | gzip > NPac.<em><u>NPacID</u></em>.UW.bf100.id99.aa.incT30.tsv.gz</code></p>
ProtNote: a multimodal method for protein-function annotation
<div> <div>Understanding protein sequence-function relationships is essential for advancing protein biology and engineering. However, fewer than 1% of known protein sequences have human-verified functions, and scientists continually update the set of possible functions. While deep learning methods have demonstrated promise for protein function prediction, current models are limited to predicting only those functions on which they were trained. Here, we introduce ProtNote, a multimodal deep learning model that leverages free-form text to enable both supervised and zero-shot protein function prediction. ProtNote not only maintains near state-of-the-art performance for annotations in its train set, but also generalizes to unseen and novel functions in zero-shot test settings. We envision that ProtNote will enhance protein function discovery by enabling scientists to use free text inputs, without restriction to predefined labels – a necessary capability for navigating the dynamic landscape of protein biology.</div> </div>
Accurate annotation of protein coding sequences with IDTAXA - Training Data
<p>Training data used to test IDTAXA, HMMER, and BLAST performance of classification of amino acid and nucleotide sequences.</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>
Phylogenetic profile of 100 annotated low complexity proteins against the Uniprot Reference Proteome dataset
<p>Phylogenetic profile of 100 human proteins with characteristic compositional bias, previously recorded by Mier et al (2020) against the Uniprot Reference Proteome, containing a total of 11297 proteomes, excluding viruses. The counts for each protein correspond to homologs found in each proteome. </p> <p>Detailed description of included columns:</p> <p><strong>ref_proteome_identifier</strong>: The<strong> </strong>Uniprot Reference Proteome identifier</p> <p><strong>ncbi_taxid</strong>: The NCBI taxonomy ID</p> <p><strong>species_name</strong>: NCBI common name corresponding to taxonomy ID</p> <p><strong>species_code</strong>: internal species code composed of 9 characters</p> <p><strong>taxonomic domain</strong>: E/B/A for Eukaryota/Bacteria/Archaea classification of proteome</p> <p> </p>
KEGG Orthology annotation of Avena sativa (cv. Sang) proteins
<p>KofamScan v1.3.0 was used to assign KEGG Orthologs (KOs) to the <em>Avena sativa</em> (cv. Sang) proteins.</p> <p>exec_annotation Asativa_sang.v1.1.aa.fa -o Asativa_sang.v1.1.aa.ko.txt --profile profiles/eukaryote.hal --ko-list ko_list</p> <p>The Oat proteins (Asativa_sang.v1.1.aa.fa) were downloaded from https://doi.org/10.5447/ipk/2022/2<br> The KOfam database was downloaded from https://www.genome.jp/ftp/db/kofam/archives/2021-12-01/</p>
Supplementary dataset for Enzyme promiscuous profiles for protein sequence and reaction annotation
<p>The data and scripts used to produce, analyze, and visualize the results of the manuscript Enzyme promiscuous profiles for protein sequence and reaction annotation by Homa MohammadiPeyhani, Anastasia Sveshnikova, Ljubisa Miskovic, and Vassily Hatzimanikatis. The detailed description of the datafiles and scripts is provided in the accompanying README.rtf file.</p>
FAPM: Functional annotation of proteins using multi-modal models beyond structural modeling
Open the record for dataset details and reuse information.
ProGene - A Large-scale, High-Quality Protein-Gene Annotated Benchmark Corpus
<p>The Pro(tein)/Gene corpus was developed at the JULIE Lab Jena under supervision of Prof. Udo Hahn.</p> <p>The goals of the annotation project were</p> <ul> <li>to construct a consistent and (as far as possible) subdomain-independent/-comprehensive protein-annotated corpus</li> <li>to differentiate between protein families and groups, protein complexes, protein molecules, protein variants (e.g. alleles) and elliptic enumerations of proteins.</li> </ul> <p>The corpus has the following annotation levels / entity types:</p> <ul> <li>protein</li> <li>protein_familiy_or_group</li> <li>protein_complex</li> <li>protein_variant</li> <li>protein_enum</li> </ul> <p>For definitions of the annotation levels, please refer to the Proteins-guidelines-final.doc file that is found in the download package.</p> <p>To achieve a large coverage of biological subdomains, document from multiple other protein / gene corpora were reannotated. For further coverage, new document sets were created. All documents are abstracts from PubMed/MEDLINE. The corpus is made up of the union of all the documents in the different subcorpora.<br> All document are delivered as MMAX2 (http://mmax2.net/) annotation projects.</p>
Data from: Genome-scale annotation of protein binding sites via language model and geometric deep learning
<p>The dataset contains the training and test sets of protein binding sites with DNA, RNA, peptide, protein, ATP, HEM, Zn2+, Ca2+, Mg2+ and Mn2+. Each protein is associated with 3 lines indicating the protein name (PDB accession code and chain), sequence and residue labels (0 for non-binding and 1 for binding), respectively. The ESMFold-predicted structures are also provided.</p>
Shaw Lab Protein Coding Gene Annotation Table
<p>1. Original Table was downloaded from https://ftp.ncbi.nih.gov/gene/DATA/GENE_INFO/Mammalia/Homo_sapiens.gene_info.gz</p> <p>2. Table was then merged with https://ftp.ncbi.nih.gov/gene/DATA/gene2ensembl.gz on April 8, 2024</p> <p>3. The table was appended with CellMark 2.0 https://pubmed.ncbi.nlm.nih.gov/36300619/</p> <p>4. Appended genes encoding surfaceome proteins GESPs from the supplementary table S1-S40 https://pubmed.ncbi.nlm.nih.gov/35121907/</p> <p>5. Appended mouse homology genes from https://www.informatics.jax.org/downloads/reports/HOM_MouseHumanSequence.rpt source code used in the processing is https://github.com/shawlab-moffitt/Human2MouseGeneSymbolConversion</p>
Taxonomic and functional annotations of transcripts and proteins derived from a Metatranscriptomic study of microbial eukaryotes from Lake Pavin
<p>These data were obtain as part of a metatranscriptomic study (Monjot <em>et al.,</em> 2023, 2024). All scripts to obtain these annotations are available at https://github.com/amonjot/SSN_Monjot_2024. The sequencing data (i.e. metatranscriptomic) used to obtain this taxonomic and functional information are archived at ENA under accession number PRJEB61515.</p> <p>This repository also contains various protein sequence similarity networks (Lagoon_output.zip). As these files are very time-consuming to produce, we have provided them to complete all the steps in Monjot <em>et al,</em> 2024. All procedures to produce them are present on the following github repository : https://github.com/amonjot/SSN_Monjot_2024.</p> <p> </p>
Dataset - Functional annotation for protein sequences
<p>These are the input and output files for the functional annotation of protein sequences workflow tests.</p>
Annotated Protein files of 120 Phytophthora genomes
<p>These are the annotated protein files of 120 Phytophthora genomes</p>
UniProt subset about proteins and annotations generated using Shape Expressions
<p>Subset of Uniprot obtained from Shape Expression</p> <p>Link to Shape expression: https://github.com/shex-consolidator/subsetting-examples/blob/master/protein/protein.shex</p> <p>Dumps from Uniprot downloaded on 26-June-2023</p> <p>Tool employed in the creation of the subset: Pschea-rs (https://github.com/angelip2303/pschema-rs)</p>
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.