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49 results for “protein database”
LukProt - an animal evolution-centric eukaryotic protein database
<p>LukProt is the EukProt database with additional species added, mostly the undersampled animal and some holozoan taxa. The database is composed of sequences translated from annotated genomes, transcriptomes or ESTs. <strong>The main purposes of the database are to consolidate sequences from undersampled animal taxa</strong> and provide usable search tools. The publication associated with LukProt can be found here: <a href="https://doi.org/10.1093/gbe/evae231">https://doi.org/10.1093/gbe/evae231</a>.</p> <p>The current version of the database (v1.5.1) is based on <a href="https://doi.org/10.24072/pcjournal.173">EukProt v3</a>. The home of all public versions of LukProt is this page (Zenodo).</p> <p>Proteomes that are novel in LukProt are denoted as LPXXXXX and those coming from AniProtDB are called APXXXXX. The sequence IDs from EukProt are conserved in LukProt. This means that each sequence is assigned an ID in the following format:</p> <pre><code>(A/E/L)PXXXXX_Species_epithet_(strain)_PYYYYYY</code></pre> <p>where XXXXX is a number from 00001 to 99999 and YYYYYY is a number from 000001 to 999999. Each sequence is assigned a unique number YYYYYY, and each taxon XXXXXX. All the IDs are compatible with BLAST v5 "-parse_seqids" option and the database can be readily deployed, for example on a server running <a href="https://doi.org/10.1093/molbev/msz185">SequenceServer</a>. Within each of the source fasta files, the source sequence identifier was kept after a blank space, so that it can still be retrieved if needed.</p> <p>A publicly available BLAST server providing LukProt search is available at: <a title="LukProt BLAST server" href="https://lukprot.hirszfeld.pl/" target="_blank" rel="noopener">https://lukprot.hirszfeld.pl/</a>.</p> <p>Comparison of EukProt v2/v3, LukProt 1.4.1 and LukProt v1.5.1 in their main areas of difference:</p> <table> <tbody> <tr> <th>Taxogroup</th> <th>EukProt v2</th> <th>EukProt v3</th> <th>LukProt v1.4.1</th> <th>LukProt v1.5.1</th> </tr> <tr> <th> <p>Holozoa</p> <p>(excluding Metazoa)</p> </th> <td>31</td> <td>40</td> <td>39</td> <td>43</td> </tr> <tr> <th>Ctenophora</th> <td>2</td> <td>2</td> <td>35</td> <td>38</td> </tr> <tr> <th>Porifera</th> <td>4</td> <td>5</td> <td>30</td> <td>47</td> </tr> <tr> <th>Placozoa</th> <td>2</td> <td>2</td> <td>3</td> <td>6</td> </tr> <tr> <th>Cnidaria</th> <td>3</td> <td>5</td> <td>65</td> <td>88</td> </tr> <tr> <th>Bilateria</th> <td>51</td> <td>51</td> <td>94</td> <td>142</td> </tr> </tbody> </table> <p>Included with the database are:</p> <ul> <li>ready to use main database files: <ul> <li><em>LukProt_v1.5.1_single_species_FASTA.7z</em> – a FASTA file with the sequences - <a href="https://en.wikipedia.org/wiki/7z">7-zipped</a>, <strong>uncompressed size: 17.6 GB</strong><br> <ul> <li>to concatenate all into one file, run this in the parent directory: <code>for file in $(find . -type f -name "*.fasta"); do awk 'FNR==1{print ""}1' $file >> LukProt_v1.5.1.fa; done</code>. This will create single FASTA file with all the sequences in the parent directory. <code>awk</code> is used to insert a new line after every file because <code>cat</code> would sometimes merge the last sequence with the header of the first sequence.</li> </ul> </li> <li><em>LukProt_v1.5.1_full_BLAST_db.7z</em> – a preformatted, full BLAST database (NCBI BLAST database format version: v5, masked with segmasker), <strong>uncompressed size: 28.3 GB</strong></li> <li><em>LukProt_v1.5.1_taxogroup_BLAST_db.7z</em> – a collection of BLAST databases where each proteome is one taxogroup and is placed within the eukaryotic tree of life directory structure, <strong>uncompressed size: 26.3 GB</strong></li> <li><em>LukProt_v1.5.1_single_species_BLAST_db.7z</em> – a collection of BLAST databases where each proteome is one BLAST database and is placed within the eukaryotic tree of life directory structure, <strong>uncompressed size: 26.4 GB</strong></li> </ul> </li> <li>auxiliary database files: <ul> <li><em>LukProt_v1.5.1.cdhit70.7z</em> – the full database clustered at 70% identity using CD-HIT with the following command: <code>cd-hit -g 1 -d 0 -T 20 -M 90000 -c 0.7 -uL 0.2 -uS 0.9 -s 0.2</code>, <strong>uncompressed sizes: fasta file - 11 GB, clstr file - 2.5 GB</strong></li> <li><em>LukProt_IDs_mapped.txt.gz</em> – a text file mapping the LukProt IDs to the AniProtDB IDs and EukProt IDs that are different</li> <li><em>BUSCO_tables.ods</em> – a spreadsheet with full result tables generated by BUSCO analysis</li> <li><em>OMAmer_output.zip</em> – a folder with full results of OMAmer analyses (includes per-sequence taxonomy classification)</li> <li><em>OMArk_output.zip</em> – a folder with the results of all OMArk analyses</li> </ul> </li> <li>metadata: <ul> <li><em>README.md</em> – a README file describing the metadata</li> <li><strong><em>LukProt_metadata_sheet.ods</em> – main metadata file. A spreadsheet with information about each proteome (in an open .ods format, most compatible with <a href="https://www.libreoffice.org/">LibreOffice</a>)</strong></li> <li><em>LukProt_metadata_other.zip</em> – an archive with other metadata files, documented in the README. Contents include:<br> <ul> <li>the LukProt taxonomy in various formats</li> <li>supporting scripts for data manipulation and visualization</li> </ul> </li> <li>a recoloring script (modified by LFS, originally by Dr. Celine Petitjean). The script is in <a title="formatFigtree2" href="https://doi.org/10.5281/zenodo.10654583">public domain</a> and reuploaded here only for convenience. </li> <li>other files - see README</li> </ul> </li> <li><em>changelog.md</em> – database changelog</li> </ul> <p>Words of caution:</p> <ul> <li>The database has been synchronized to EukProt v3 in version v1.5.1. This means that identifiers were modified in comparison to LukProt v1.4.1. The convention is not expected to change any more in future updates.</li> <li>Many proteomes, especially those transcriptome-based, may contain contamination from different species. In addition, the translation algorithms often introduce errors (e.g. the transcript may not represent a full length protein). For this reason, to get accurate sequences from each organism, users are directed to source data and to the included OMAmer, OMArk and BUSCO data for details.</li> <li>The taxonomy is different to UniEuk/EukMap, but UniEuk data were integrated where possible.</li> <li>A few NCBI taxids are missing and will be added in due course.</li> <li>Proteomes from NCBI and UniProt will be updated to current versions.</li> <li>A number of proteomes present in some metadata, are unpublished and were held back.</li> <li>While the database contains metadata that present a particular phylogeny of animals, holozoans and other eukaryotes, no particular claims or hypotheses are made by the author(s). However, in the future efforts will be made to name clades officially, once they are more firmly established.</li> </ul> <p><strong>Please report any problems or suggestions to Lukasz Sobala: lukasz.sobala (at) hirszfeld.pl.</strong></p> <p> </p> <p>Acknowledgements:</p> <ul> <li> <p>Andrew E. Allen Lab for creating the original <a href="https://allenlab.ucsd.edu/data/" target="_blank" rel="noopener">PhyloDB</a>.</p> </li> <li> <p>Daniel Richter <em>et al.</em> for creating <a href="https://doi.org/10.6084/m9.figshare.12417881">EukProt</a> and keeping it updated.</p> </li> <li> <p>Members of <a href="https://multicellgenome.com/">the Multicellgenome Lab</a>, especially Michelle Leger (for donating her database), for the bioinformatics support and for doing great science.</p> </li> <li> <p>All the authors of the original data.</p> </li> <li> <p>National Science Centre of Poland for funding of the project 2020/36/C/NZ8/00081, "The role of glycosylation in the emergence of animal multicellularity", which enabled the creation of this database.</p> </li> </ul>
SeMRA Protein Complex Mappings Database
<p>Analyze the landscape of protein complex nomenclature resources, species-agnostic. See instructions for reproduction and usage in the attached README.md.</p>
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>
EukZoo, an aquatic protistan protein database for meta-omics studies.
<p>This database contain protein sequences of aquatic microbial eukaryotes, or protists. The purpose of this is to make a database that is of reasonable quality to serve as resource for both taxonomy and functional interpretation of metagenomic and metatranscriptomic studies of protists. The source of the sequences were mainly from Marine Microbial Eukaryotes Transcriptome Sequencing Project (MMETSP), and supplemented with various genomes and transcriptomes of organisms that were not a part of MMETSP.</p> <p>To use this database, one has to understand the main function of the three files here.</p> <p>(1) The protein sequences are stored in .faa file. You can build an alignment/search database out of that and search your meta-omics sequences against it. Each sequence in the FASTA file has an ID which always consists of two parts like this: "MMETSP0004_1234567". The text before the first underscore is the source ID of that sequence.</p> <p>(2) Taxonomy information of each source ID are stored in "EukZoo_taxonomy_table_v_0.2.tsv". One can use the information within in conjunction with database search results to assign taxonomy to sequences.</p> <p>(3) KEGG annotation of each sequence are stored in "EukZoo_KEGG_annotation_v_0.2.tsv". One can use the information within in conjunction with database search results to assign KEGG functional annotation (KO ID) to sequences.</p> <p>I also provide scripts to assign taxonomy and KEGG annotation from database search results. You can also find the scripts and explanations on how to use them on the <a href="https://github.com/zxl124/EukZoo-database">EukZoo GitHub page</a>. You will find details on how the database was created and curated on there as well.</p> <p>Please contact me at zhenfeng.liu1@gmail.com if you have any questions or requests. Thank you for your interest in EukZoo.</p>
UPS2 standard in solution converted to mzML with entrapment and decoy appended protein databases
<p>RAW data downloaded from: <a href="http://data.marcottelab.org/MSdata/Data_13/DATA/Marcotte_0909/">http://data.marcottelab.org/MSdata/Data_13/</a></p> <p>Converted to mzML with ThermoRawFileConverter.</p> <p>It is one of the datasets used in the EPIFANY publication.</p>
PhasAGE Training School 1 - Computational prediction and databases of protein phase separation - PRACTICAL
<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>
PhasAGE Training School 1 - Computational prediction and databases of protein phase separation Overview- LECTURE
<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>
DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction
<p>This dataset contains replication data for the paper titled "DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction". The dataset consists of pickled Pandas DataFrame files, along with training, validation, and (for DB5-Plus) test filename lists for cross-validation, that can be used to develop and evaluate protein interface prediction models. This dataset also contains the externally generated residue-level PSAIA and HH-suite3 features for users' convenience (e.g. raw MSAs and profile HMMs for each protein complex). Our GitHub repository linked in the "Additional notes" metadata section below provides more details on how we parsed through these files to create our cross-validation datasets. The GitHub repository for DIPS-Plus also includes scripts that can be used to impute missing feature values and convert the final "raw" complexes into DGL-compatible graph objects. Since our final DGL graph representation for each complex uses PyTorch tensors in its construction of residue embeddings, the final representation of each complex can easily be adapted to fit the users' needs (e.g. feeding a complex's 2D residue feature tensors into a convolutional neural network).</p>
DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction (Supplementary Data)
<p>This dataset contains supplementary replication data for the paper titled "DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction". In particular, it contains a new version of our `final_raw_dips.tar.gz` protein pair representations which now contain (1) residue-level annotations for intrinsic disorder regions (IDRs) as well as (2) a copy of each protein pair representation in the HDF5 file format for programming language-agnostic read capabilities. In addition, this record also contains (3) raw MSAs (in HDF5 file format) generated for each protein pair using Jackhmmer and AlphaFold's small version of the Big Fantastic Database (BFD). Lastly, this record contains (4) PDB metadata derived for each DIPS-Plus complex using Graphein's PDBManager API as well as (5) structure-based (i.e., FoldSeek-based) training and validation splits of the dataset's complexes in the form of respective text files containing the file paths of complexes assigned to each split.</p>
MMseqs2 virus protein database with ICTV taxonomy
<p>MMseqs2 virus protein database decorated with ICTV taxonomy. Proteins originally retrieved from NCBI NR in 2025-02-08.</p> <p>Steps for reproduction can be found at https://github.com/apcamargo/ictv-mmseqs2-protein-database</p>
Towards a reproducible interactome: semantic-based detection of redundancies to unify protein-protein interaction databases
<p>Protein-protein interactions (PPIs) play an ubiquitous and fundamental role in all biological processes. Information on PPIs described in the literature is annotated and made available by several protein-interaction databases. Because most databases have their own curation rules and priorities, they often annotate overlapping sets of publications, which leads to redundancies. We developed a semantic-based approach which enables to accurately detect redundancies within PPI datasets from multiple databases. We applied this approach to assemble a "reproducible interactome", with PPIs supported by at least two methods or publications.</p>
Cave Ecosystem Protein Database
<p>The concatenated library of predicted protein sequences from protein level assembly of cave shotgun metagenomes using PLASS and some basic metadata of the metagenomes included.</p>
Protein database for SAGApipeline
<p>Proteins from multiple databases were used in algal genome annotation. We first downloaded the protein data of green algae (Chlorophyta) and red algae (Rhodophyta) on UniProt for 10 higher-quality assemblies gene predictions. We refer to these protein sequences for RefSeq genomes gene prediction as seed_algae_mix, whose role is to complete gene prediction of higher quality genomes quickly and accurately. These predicted results obtained in the RefSeq genomes will be used as the query sequences to query the NR (RefSeq non-redundant proteins) database, Then the searched target sequence is extracted as the NR_extract part. Subsequently, we retrieved the protein sequences on Uniprot of all lineages of algae, please note that we did not select only the protein sequences of the 17 lineages to be annotated, but all algae from the 21 lineages searched on NCBI taxonomy, a total of 2,432,633 (1.17GB) algae protein sequences named total_algae_mix were for future predictions. The plants part of OrthoDB V10.1 and BUSCO proteins of 7 lineages were also merged into pr_total_mix.</p>
A structural database of chain-chain and domain-domain interfaces of proteins
<p>Library of protein-protein and domain-domain interfaces from the protein data bank. The data also contains the structural clusters of protein-protein and domain-domain interfaces.</p>
PSnpBind: A database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow
<p>A key concept in drug design is how natural variants, especially the ones occurring in the binding site of drug targets, affect the inter-individual drug response and efficacy by altering binding affinity. These effects have been studied on very limited and small datasets while, ideally, a large dataset of binding affinity changes due to binding site single-nucleotide polymorphisms (SNPs) is needed for evaluation. However, to the best of our knowledge, such a dataset does not exist. Thus, a reference dataset of ligands binding affinities to proteins with all their reported binding sites’ variants was constructed using a molecular docking approach. Having a large database of protein-ligand complexes covering a wide range of binding pocket mutations and a large small molecules’ landscape is of great importance for several types of studies. For example, developing machine learning algorithms to predict protein-ligand affinity or a SNP effect on it requires an extensive amount of data. In this work, we present PSnpBind: A large database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow. It provides a web interface to explore and visualize the protein-ligand complexes and a REST API to programmatically access the different aspects of the database contents. PSnpBind is freely available at <a href="https://psnpbind.org">https://psnpbind.org</a>.<strong> </strong>The source code of the tools used in constructing PSnpBind is available on <a href="https://github.com/ammar257ammar/PSnpBind-Build">GitHub</a>.</p>
PROTEIN NAP Database
<p>A dataset of expert-validated Nutrition and Physical Activity Plans (NAP), i.e., plans created and reviewed by experts in nutrition and physcial activity, as well as, medical experts. The dataset contains foods, meals, recipes, and physcial activities.</p> <p>The following databases have been used as input:</p> <ul> <li>USDA Food Composition Database, April 2018 (United States)</li> <li>Portuguese Food Composition Table (TCAP)</li> <li>Brazilian Food Composition Table (TACO)</li> <li>Peso, Uma Questão de Peso (Portugal)*</li> <li>Base de Datos del Centro de Enseñanza Superior de Nutrición y Dietética (CESNID Foundation, Spain)*</li> <li>Luján University Data Base (Argentina)</li> <li>Spanish Food Composition Database (BEDCA, UGR)</li> <li>French food composition (CIQUAL, ANSES)</li> <li>Composition tables of foods and Greek dishes (CTFG, HHF)*</li> <li>Food Composition Database for Epidemiological Studies in Italy (FCDBE, IEO)*</li> <li>Irish Food Composition Database (IFCD, UCC)</li> <li>Polish Foods Composition Database (PFCT, NFNI)*</li> <li>Tables of the Nutritional Composition of Food Consumed in Brazil of IBGE (TCNA, IBGE)</li> <li>McCance and Widdowson’s The Composition of Foods integrated dataset (Co FID)</li> <li>Mexican Food Equivalents System (Mexico)*</li> </ul> <p><em>* Items originating from these databases where not included in the offered file due to copyright issues; you may contact the authors directly for further inquiries regarding these items.</em></p> <p>Funding: The research leading to these results has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 817732 (PROTEIN: PeRsOnalized nutriTion for hEalthy living).</p> <p>© 2022 by the authors.</p>
PLBD (Protein Ligand Binding Database) table description XML file
<p>PLBD (Protein Ligand Binding Database) table description XML file<br> =================================================================</p> <p>General<br> -------</p> <p>The provided ZIP archive contains an XML file "main-database-description.xml" with the description of all tables (VIEWS) that are exposed publicly at the PLBD server (https://plbd.org/). In the XML file, all columns of the visible tables are described, specifying their SQL types, measurement units, semantics, calculation formulae, SQL statements that can be used to generate values in these columns, and publications of the formulae derivations.</p> <p>The XML file conforms to the published XSD schema created for descriptions of relational databases for specifications of scientific measurement data. The XSD schema ("relational-database_v2.0.0-rc.18.xsd") and all included sub-schemas are provided in the same archive for convenience. All XSD schemas are validated against the "XMLSchema.xsd" schema from the W3C consortium.</p> <p>The ZIP file contains the excerpt from the files hosted in the https://plbd.org/ at the moment of submission of the PLBD database in the Scientific Data journal, and is provided to conform the journal policies. The current data and schemas should be fetched from the published URIs:</p> <p> https://plbd.org/<br> https://plbd.org/doc/db/schemas<br> https://plbd.org/doc/xml/schemas</p> <p>Software that is used to generate SQL schemas, RestfulDB metadata and the RestfulDB middleware that allows to publish the databases generated from the XML description on the Web are available at public Subversion repositories:</p> <p> svn://www.crystallography.net/solsa-database-scripts<br> svn://saulius-grazulis.lt/restfuldb</p> <p>Usage<br> -----</p> <p>The unpacked ZIP file will create the "db/" directory with the tree layout given below. In addition to the database description file "main-database-description.xml", all XSD schemas necessary for validation of the XML file are provided. On a GNU/Linux operating system with a GNU Make package installed, the XML file validity can be checked by unpacking the ZIP file, entering the unpacked directory, and running 'make distclean; make'. For example, on a Linux Mint distribution, the following commands should work:</p> <p> unzip main-database-description.zip<br> cd db/release/v0.10.0/tables/<br> sh -x dependencies/Linuxmint-20.1/install.sh<br> make distclean<br> make</p> <p>If necessary, additional packages can be installed using the 'install.sh' script in the 'dependencies/' subdirectory corresponding to your operating system. As of the moment of writing, Debian-10 and Linuxmint-20.1 OSes are supported out of the box; similar OSes might work with the same 'install.sh' scripts. The installation scripts require to run package installation command under system administrator privileges, but they use *only* the standard system package manager, thus they should not put your system at risk. For validation and syntax checking, the 'rxp' and 'xmllint' programs are used.</p> <p>The log files provided in the "outputs/validation" subdirectory contain validation logs obtained on the system where the XML files were last checked and should indicate validity of the provided XML file against the references schemas.</p> <p>Layout of the archived file tree<br> --------------------------------</p> <p> db/<br> └── release<br> └── v0.10.0<br> └── tables<br> ├── Makeconfig-validate-xml<br> ├── Makefile<br> ├── Makelocal-validate-xml<br> ├── dependencies<br> ├── main-database-description.xml<br> ├── outputs<br> └── schema</p>
CusVarDB: A tool for building customized sample-specific variant protein database from Next-generation sequencing datasets
<p>CusVarDB is a windows based tool for creating a variant protein database from Next-generation sequencing datasets. The program supports variant calling for Genome, RNA-Seq and exome datasets.</p> <p>This repository will provide the resultant variant peptides identified in our study and its corresponding information. The detailed information of the table is given below.</p> <p>Supplementary Table 1. This table contains the resultant variant peptides along with its wild-type peptides from BT474, MDMAB157, MFM223, and HCC38 datasets. Along with mutant peptides, this section also provides additional information such as peptide-spectrum match (PSM), Protein accession, cross-correlation value from the search (Xcorr), and retention time (RT).</p> <p>Supplementary Table 2. This table provides the complete details of the resultant peptides. Here the mutant and corresponding wild-type peptides are mentioned in different sheets. For a given mutant peptide its wild-type peptide and corresponding information can be mapped using the VLOOKUP function in Excel by keeping column A (Sl.No) as lookup parameter.</p> <p>Supplementary Table 3. This table briefs about the variants which are already reported in other cancers.</p>
Uncovering hundreds of RNA viral RdRps amongst uncharacterised sequences in public protein databases.
<p>These data are associated with the following manuscript:</p> <p>Brown, K., Firth, A. E. (2025)<br>Uncovering hundreds of RNA viral RdRps amongst uncharacterised sequences in public protein databases.<br><br></p>
VEBA Microeukaryotic Protein Database (MicroEuk100/90/50, Version 3)
<p>Microeukaryotic protein database consisting of protists and fungi for <a href="https://github.com/jolespin/veba">VEBA</a>.</p><p> </p><p><strong>Number of sequences:</strong></p><p> * MicroEuk100 = 79,920,431 (19 GB)</p><p> * MicroEuk90 = 51,767,730 (13 GB)</p><p> * MicroEuk50 = 29,898,853 (6.5 GB)</p><p> </p><p><strong>Number of source organisms per dataset:</strong></p><p>* MycoCosm = 2503</p><p>* PhycoCosm = 174</p><p>* EnsemblProtists = 233</p><p>* MMETSP = 759</p><p>* TARA_SAGv1 = 8</p><p>* EukProt = 366</p><p>* EukZoo = 27</p><p>* TARA_SMAGv1 = 389</p><p>* NR_Protists-Fungi = 48217</p><p> </p><p><strong>Files:</strong></p><p>MicroEuk_v3.tar.gz = 25 GB</p><p>-rw-rw---- 1 jespinoz jcl110 19G Nov 15 14:57 MicroEuk100.faa.gz - Main fasta file with 79,920,431 protein sequences from 52,676 source organisms. Uses md5 hash identifiers.</p><p>-rw-rw---- 1 jespinoz jcl110 2.0G Nov 15 14:59 identifier_mapping.proteins.tsv.gz - Protein identifier mappings between datasets, original identifiers, source organisms, and md5 hash identifiers.</p><p>-rw-rw---- 1 jespinoz jcl110 1.7G Nov 15 16:10 MicroEuk90_clusters.tsv.gz - MMSEQS2 clustering MicroEuk100</p><p>-rw-rw---- 1 jespinoz jcl110 1.5G Nov 15 14:57 MicroEuk100.list.gz - List of md5 hash protein identifiers in MicroEuk100</p><p>-rw-rw---- 1 jespinoz jcl110 1.1G Nov 15 16:10 MicroEuk50_clusters.tsv.gz - MMSEQS2 clustering MicroEuk90</p><p>-rw-rw---- 1 jespinoz jcl110 13M Nov 15 23:39 MicroEuk100.eukaryota_odb10.list.gz - MicroEuk100 protein identifier hits to BUSCO's eukaryota_odb10 marker using the provided score thresholds</p><p>-rw-rw---- 1 jespinoz jcl110 1.5M Nov 15 14:58 source_taxonomy.tsv.gz - Source taxonomy, lineage, dataset, and notes for each source organism</p><p> </p><p><strong>For more information and citations, please visit the main GitHub repository: </strong></p><p><a href="https://github.com/jolespin/veba">https://github.com/jolespin/veba</a></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
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DANDI Archive for NWB datasets
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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.