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78 results for “reference sequence”
MMSEQS meets AntiRef: reference clusters of human antibody sequences
<p>This data set contains pre-computed mmseqs databases for the antiref fasta files created by <em>Briney et al.</em> </p> <p>Please cite the original work if you use any of the databases provided here.</p> <p>Sources:</p> <ul> <li><a href="https://github.com/brineylab/antiref">Antiref GitHub</a></li> <li><a href="../records/7474336">Antiref Zenodo</a></li> <li><a href="https://academic.oup.com/bioinformaticsadvances/article/3/1/vbad109/7247530?login=true">Antiref Paper</a></li> </ul> <p> </p> <p>The mmseqs databases were created as follows:</p> <p> </p> <p>```</p> <p>aria2x -x16 -s16 --input-file antiref_links.txt<br>snakemake -s antiref_mmseqs.smk --jobs 1 --cores 1 --local-cores 250</p> <p>```</p>
Protein haplotype sequences obtained by ProHap from the Haplotype Reference Consortium Release 1.1 dataset
<p>Database of protein sequences obtained using ProHap (<a href="https://github.com/ProGenNo/ProHap">https://github.com/ProGenNo/ProHap</a>) on the data set of phased genotypes published by the Haplotype Reference Consortium, Release 1.1 (<a href="https://ega-archive.org/datasets/EGAD00001002729" target="_blank" rel="noopener">https://ega-archive.org/datasets/EGAD00001002729</a>). We used Ensembl v.110 for the mapping of coordinates between genes, exons, and transcripts.</p> <p>Release 1.1 of the HRC is provided aligned with the GRCh37 reference genome. We have performed a liftover to the GRCh38 reference using GeneBe (https://genebe.net/tools/liftover). Variants for which the reported alternative allele is considered as reference in GRCh38 were removed. A threshold of 1% minor allele frequency was applied to filter the remaining variants. After translation, a frequency threshold of 0.5% was applied to filter the resulting unique non-canonical sequences. The complete configuration file for the ProHap run is attached to this repository.</p> <p>This dataset contains one compressed directory, contains the following files:</p> <ul> <li>F1: The concatenated fasta file ready to be used with search engines, contains the following: <ul> <li>Protein haplotype sequences obtained by ProHap</li> <li>Reference proteome as per Ensembl v. 110</li> <li>Contaminant sequences from the cRAP project (<a href="https://www.thegpm.org/crap/">https://www.thegpm.org/crap/</a>)</li> <li>The file is provided in two formats - full and simplified. The simplified fasta contains only the artificial protein identifier and the matching gene name, and is optimised for compatibility with a wide range of tools. For annotation of peptides using the PeptideAnnotator, please provide the header (F1.2) in addition to the fasta file. </li> </ul> </li> <li>F2: Additional information about the haplotype sequences, to be used for mapping identified peptides to the original haplotypes</li> <li>F3: Translations of haplotype cDNA sequences, before merging with the reference proteome</li> </ul> <p>For further description of the files, please refer to <a href="https://github.com/ProGenNo/ProHap/wiki/Output-files">https://github.com/ProGenNo/ProHap/wiki/Output-files</a>.</p> <p>For the usage of these databases with search engines, and downstream anaylsis of identified peptides, please refer to the project's wiki page: <a href="https://github.com/ProGenNo/ProHap/wiki/Using-the-database-for-proteomic-searches">https://github.com/ProGenNo/ProHap/wiki/Using-the-database-for-proteomic-searches</a>.</p> <p>When using these databases in your publication, please cite: Vašíček, J., Kuznetsova, K.G., Skiadopoulou, D. <em>et al.</em> ProHap enables human proteomic database generation accounting for population diversity. <em>Nat Methods</em> (2024). <a href="https://doi.org/10.1038/s41592-024-02506-0">https://doi.org/10.1038/s41592-024-02506-0</a></p>
Protein haplotype sequences obtained by ProHap from the Human Pangenome Reference Consotruim dataset
<p>Database of protein sequences obtained using ProHap (<a href="https://github.com/ProGenNo/ProHap">https://github.com/ProGenNo/ProHap</a>) on the data set of phased genotypes published by the Human Pangenome Reference Consotruim (HPRC), first release (<a href="https://github.com/human-pangenomics/hpp_pangenome_resources">https://github.com/human-pangenomics/hpp_pangenome_resources</a>), 44 samples. We used Ensembl v.110 for the mapping of coordinates between genes, exons, and transcripts.</p> <p>This repository contains one database created using all 43 samples of the HPRC release (the haplotypes of the sample NA21309 did not encode any non-canonical sequences), and then a database for each of the 43 samples separately. No filtering on allele frequency or haplotype frequency was applied in any of the databases. The complete configuration file for the ProHap run is attached to this repository.</p> <p>There is one compressed directory for each of the databases, containing the following files:</p> <ul> <li>F1: The concatenated fasta file ready to be used with search engines, contains the following: <ul> <li>Protein haplotype sequences obtained by ProHap</li> <li>Reference proteome as per Ensembl v. 110</li> <li>Contaminant sequences from the cRAP project (<a href="https://www.thegpm.org/crap/">https://www.thegpm.org/crap/</a>)</li> <li>For this dataset, only the simplified format is provided. The simplified fasta contains only the artificial protein identifier and the matching gene name, and is optimised for compatibility with a wide range of tools. For annotation of peptides using the PeptideAnnotator, please provide the header (F1.2) in addition to the fasta file. </li> </ul> </li> <li>F2: Additional information about the haplotype sequences, to be used for mapping identified peptides to the original haplotypes</li> <li>F3: Translations of haplotype cDNA sequences, before merging with the reference proteome</li> </ul> <p>For further description of the files, please refer to <a href="https://github.com/ProGenNo/ProHap/wiki/Output-files">https://github.com/ProGenNo/ProHap/wiki/Output-files</a>.</p> <p>For the usage of these databases with search engines, and downstream anaylsis of identified peptides, please refer to the project's wiki page: <a href="https://github.com/ProGenNo/ProHap/wiki/Using-the-database-for-proteomic-searches">https://github.com/ProGenNo/ProHap/wiki/Using-the-database-for-proteomic-searches</a>.</p> <p>When using these databases in your publication, please cite: Vašíček, J., Kuznetsova, K.G., Skiadopoulou, D. <em>et al.</em> ProHap enables human proteomic database generation accounting for population diversity. <em>Nat Methods</em> (2024). <a href="https://doi.org/10.1038/s41592-024-02506-0">https://doi.org/10.1038/s41592-024-02506-0</a></p>
Virus+ Sequence Masked Mouse Reference Genome (GRCm38)
<p>A version of the mouse genome (<a href="https://www.ncbi.nlm.nih.gov/assembly/327618">GRCm38</a>) masked for all possible viral sequences.</p> <p>See <a href="https://zenodo.org/record/4116107#.X5B7ti9h3UI">Virus+ Masked Human Genome</a> for a masked human reference database.</p> <p>The following commands were used to generate the additional virus sequence masked reference database:</p> <p><strong>1) Download all RefSeq and Neighbor nucleotide records:</strong></p> <p><a href="https://www.ncbi.nlm.nih.gov/nuccore/?term=Viruses[Organism]%20NOT%20cellular%20organisms[ORGN]%20NOT%20wgs[PROP]%20NOT%20gbdiv%20syn[prop]%20AND%20(srcdb_refseq[PROP]%20OR%20nuccore%20genome%20samespecies[Filter])">https://www.ncbi.nlm.nih.gov/nuccore/?term=Viruses[Organism]%20NOT%20cellular%20organisms[ORGN]%20NOT%20wgs[PROP]%20NOT%20gbdiv%20syn[prop]%20AND%20(srcdb_refseq[PROP]%20OR%20nuccore%20genome%20samespecies[Filter])</a></p> <p><strong>2) Shred the downloaded viral genomes using shred.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>shred.sh in=refseq_virus_reformated.fasta out=virus_shred.fasta.gz length=85 minlength=75 overlap=30</p> <p><strong>3) Map shredded virus sequence to the GRCm38</strong><strong> genome using bbmap.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>bbmap.sh ref=GRCm38.fa.gz in=virus_shred.fasta.gz outm=map_mouse_all_viruses.sam minid=0.90</p> <p><strong>4) Mask virus sequenced mapped regions from the GRCm38 genome using bbmask.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>bbmask.sh in=GRCm38.fa.gz out=GRCm38_virus_masked.fasta.gz sam=map_mouse_all_viruses.sam</p> <p><strong>5) Remove all N's to further reduce file size using <a href="https://bioinf.shenwei.me/seqkit/">seqkit</a></strong><br> seqkit -is replace -p "n" -r "" GRCm38_virus_masked.fasta.gz > mouse_virus_masked.fasta_Ns_removed.gz</p> <p><strong>Additional References:</strong></p> <ol> <li><a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a></li> <li><a href="https://bioinf.shenwei.me/seqkit/">seqkit</a></li> <li><a href="https://www.ncbi.nlm.nih.gov/genome/viruses/">NCBI Virus Genome RefSeq</a></li> </ol>
Virus+ Sequence Masked Human Reference Genome (hg19)
<p>A version of the human genome (hg19) originally masked for ribosomal, plant, animal, fungal and low-entropy sequences by Brian Bushnell (<a href="https://zenodo.org/record/1208052#.X5BuTy9h3UI">Bushnell Masked Human Genome</a>) additionally masked for all possible viral sequences.</p> <p>The following commands were used to generate the additional virus sequence masked reference database:</p> <p><strong>1) Download all RefSeq and Neighbor nucleotide records:</strong></p> <p><a href="https://www.ncbi.nlm.nih.gov/nuccore/?term=Viruses[Organism]%20NOT%20cellular%20organisms[ORGN]%20NOT%20wgs[PROP]%20NOT%20gbdiv%20syn[prop]%20AND%20(srcdb_refseq[PROP]%20OR%20nuccore%20genome%20samespecies[Filter])">https://www.ncbi.nlm.nih.gov/nuccore/?term=Viruses[Organism]%20NOT%20cellular%20organisms[ORGN]%20NOT%20wgs[PROP]%20NOT%20gbdiv%20syn[prop]%20AND%20(srcdb_refseq[PROP]%20OR%20nuccore%20genome%20samespecies[Filter])</a></p> <p><strong>2) Shred the downloaded viral genomes using shred.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>shred.sh in=refseq_virus_reformated.fasta out=virus_shred.fasta.gz length=85 minlength=75 overlap=30</p> <p><strong>3) Map shredded virus sequence to the hg19-masked human genome using bbmap.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>bbmap.sh ref=hg19_main_mask_ribo_animal_allplant_allfungus.fa.gz in=virus_shred.fasta.gz outm=map_human_all_viruses.sam minid=0.90</p> <p><strong>4) Mask virus sequenced mapped regions from the hg19-masked human genome using bbmask.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>bbmask.sh in=hg19_main_mask_ribo_animal_allplant_allfungus.fa.gz out=human_virus_masked.fasta.gz sam=map_human_all_viruses<br> .sam</p> <p><strong>5) Remove all N's to further reduce file size using <a href="https://bioinf.shenwei.me/seqkit/">seqkit</a></strong><br> seqkit -is replace -p "n" -r "" human_virus_masked.fasta.gz > human_virus_masked.fasta_Ns_removed.gz</p> <p><strong>Additional References:</strong></p> <ol> <li><a href="http://seqanswers.com/forums/showthread.php?t=42552">http://seqanswers.com/forums/showthread.php?t=42552</a> for additional information on the original masking of hg19</li> <li><a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a></li> <li><a href="https://bioinf.shenwei.me/seqkit/">seqkit</a></li> <li><a href="https://www.ncbi.nlm.nih.gov/genome/viruses/">NCBI Virus Genome RefSeq</a></li> </ol>
Genomes and full-length 16S reference sequences for 27 Alpha- and Gamma-Proteobacterial isolates from Red Sea Acropora corals
<p>Coral-associated bacteria contribute to the biology of their host, but the underlying molecular interactions are largely unknown. To further our functional understanding, we obtained 27 alpha- and gamma-proteobacterial isolates, many of which are Rhodobacteraceae, from three coral species of the genus <em>Acropora </em>and assembled/annotated their genomes as a resource for further functional studies. Our results reveal the immense taxonomic and genetic diversity of common alpha- and gamma-proteobacterial coral-associated bacteria. We hope these data provide a framework to study the function of specific bacteria in the coral holobiont. Isolates are available upon request.</p>
Reference sequences and analyses
<p>Reference sequences used to train PlasX. Includes contig sequences, gene calls, and gene functional annotations.</p>
DNA sequence and taxonomic gap analyses to quantify the coverage of aquatic cyanobacteria and eukaryotic microalgae in reference databases: Results of a survey in the Alpine region
<p>This dataset has been prepared as part of the Interreg Alpine Space project Eco-AlpsWater (ASP569) - <em>Innovative Ecological Assessment and Water Management Strategy for the Protection of Ecosystem Services in Alpine Lakes and Rivers</em>, <a href="https://www.alpine-space.eu/projects/eco-alpswater/en/home">https://www.alpine-space.eu/projects/eco-alpswater/en/home</a></p> <p>Individual archives include 16S rRNA (cyanobacteria) and 18S rRNA (microalgae) FASTA sequences and associated blastn results obtained from the high throughput sequencing of plankton and biofilm bulk/eDNA samples collected in 2019 in 37 lakes and 22 rivers across the Alpine region. These are supporting files for the paper by Salmaso et al., 2022. DNA sequence and taxonomic gap analyses to quantify the coverage of aquatic cyanobacteria and eukaryotic microalgae in reference databases: Results of a survey in the Alpine region. Science of the Total Environment, in press.</p>
MALDI-TOF-MS reference spectra and sequence data for domesticated equids (horse and donkey) collagen for Zooarchaeology by Mass Spectrometry (ZooMS)
<p>MALDI-TOF-MS spectra of extracted collagen from modern reference and archaeological bone samples to develop markers for Zooarchaeology by Mass Spectrometry (ZooMS) to distinguish between Equus species. For each sample digestions were done in both trypsin and chymotrypsin separately. Information about the species of the samples can be found in 'sample metadata.csv' file. Information on the extraction and digestion protocol can be found in the associated manuscript. The sequence data contains alignments of the proteins COL1A1 and COL1A2 for available Equus collagen protein sequences. More information on these files can be found in the corresponding manuscript to this dataset.<br> </p>
Control panel created from 30 Nanopore sequencing data from the Human Pangenome Reference Consortium
<p>This is control panel for <a href="https://github.com/friend1ws/nanomonsv">nanomonsv</a> software, which is expected to exclude many false positives as well as improve computational cost. This is made by aligning 30 Nanopore sequencing data from Human Pangenome Reference Consortium to the GRCh38 reference genome (obtained from <a href="https://console.cloud.google.com/storage/browser/genomics-public-data/resources/broad/hg38/v0;tab=objects">here</a>) with <a href="https://github.com/lh3/minimap2">minimap2</a> version 2.24. <strong>When you use these control panels and publish, do not forget to credit to <a href="https://humanpangenome.org/data-use-protocol/">HPRC</a>!</strong></p>
gapseq reference sequence databases for Bacteria and Archaea
<p>The repository contains the protein sequences used by <a href="https://github.com/jotech/gapseq">gapseq</a> to predict the presence of metabolic reactions and to construct metabolic models.</p> <p>The workflow using gapseq to generate this set of reference protein sequences:</p> <p> </p> <p>```sh</p> <p># delete all "old" data<br>rm dat/seq/Bacteria/rev/*.fasta<br>rm dat/seq/Bacteria/unrev/*.fasta<br>rm dat/seq/Bacteria/rxn/*.fasta<br>rm dat/seq/Archaea/rev/*.fasta<br>rm dat/seq/Archaea/unrev/*.fasta<br>rm dat/seq/Archaea/rxn/*.fasta</p> <p># run gapseq find to re-download everything#<br># the genome is irrelevant as no blasting is performed ('-x')<br>gapseq find -p all -t Bacteria -n -x -U toy/ecoli.faa.gz > bac_update.log 2>&1<br>gapseq find -p all -t Archaea -n -x -U toy/ecoli.faa.gz > ar_update.log 2>&1</p> <p># create all sequence .tar.gz archives (rev/unrev/rxn)<br>cd dat/seq/Bacteria/rev/ && tar -czvf sequences.tar.gz ./*.fasta && cd ../../../../<br>cd dat/seq/Bacteria/unrev/ && tar -czvf sequences.tar.gz ./*.fasta && cd ../../../../<br>cd dat/seq/Bacteria/rxn/ && tar -czvf sequences.tar.gz ./*.fasta && cd ../../../../<br>cd dat/seq/Archaea/rev/ && tar -czvf sequences.tar.gz ./*.fasta && cd ../../../../<br>cd dat/seq/Archaea/unrev/ && tar -czvf sequences.tar.gz ./*.fasta && cd ../../../../<br>cd dat/seq/Archaea/rxn/ && tar -czvf sequences.tar.gz ./*.fasta && cd ../../../../</p> <p># create md5sum table for all tar.gz archives<br>cd dat/seq/<br>find -mindepth 2 -type f -name "*.tar.gz" -exec md5sum {} \; > md5sums.txt</p> <p># create taxon-specific final archive for Zenodo upload<br>tar -czvf Bacteria.tar.gz Bacteria/*/*.tar.gz<br>tar -czvf Archaea.tar.gz Archaea/*/*.tar.gz</p> <p># Upload Bacteria.tar.gz, Archaea.tar.gz, and md5sums.txt to Zenodo via the web-interface</p> <p>```</p>
Lepidoptera genomics based on 88 chromosomal reference sequences informs population genetic parameters for conservation
<p>This repository contains (1) germline mutations called by the DeepVariant (v1.1.0) pipeline in VCF format; (2) rejected substitution scores calculated by the Genomic Evolutionary Rate Profiling (GERP++) software on each species and chromosome; and (3) the phylogenetic tree used as guide tree in the Cactus alignment.</p>
Data for Bovine breed-specific augmented reference graphs facilitate accurate sequence read mapping and unbiased variant discovery
<p><strong>Description of the datasets</strong></p> <p>Data are organized as folders and compressed with tar.gz.</p> <p>There are two compressed data folder: <strong>data </strong>which used for cattle genome graphs experiment and <strong>data_human</strong> which we used for human genome graphs experiment. </p> <p><strong>Cattle genome graphs experiments</strong></p> <p>First you need to unzip the file using command <em>tar -xvzf data.tar.gz</em>. After unzipping, the data folder is organized as follows:</p> <ul> <li>Utilities: contain bovine ARS-UCD 1.2 fasta reference with the accompanying index.</li> <li>Bin: contain the softwares used in the paper (vg, liftover, vcf2diploid)</li> <li>Part1: data for analysis in variant prioritization section, further subdivided into: <ul> <li>vcf_sim: variant files from four animal in each breed used to simulate reads</li> <li>reads_sim: simulated short reads used for read mapping</li> <li>vcf_freq: variants augmented to graphs filtered based on allele frequency</li> </ul> </li> <li>Part2: data used for analysis in the section of graph mapping with breeds-filtered variants, further subdivided into: <ul> <li>vcf_breed: variant files used to graphs construction.</li> </ul> </li> <li>Part3: data used for analysis in the section of consensus genome, further subdivided into: <ul> <li>read_sims: simulated reads as in the part1, but the coordinates are liftovered to the new consensus genomes.</li> <li>reference: contain the original reference and consensus references.</li> <li>vcf_consensus: contain major allele variants to construct consensus genomes.</li> </ul> </li> <li>Part4: data analysis in the section of whole genome graph construction and variant genotyping. <ul> <li>vcf_construct: variants from chromosome 1-29 from 82 Brown Swiss used to construct BSW whole genome graph.</li> <li>BSW_graph: whole genome Brown Swiss graph with the three accompanying indexes (xg,gcsa, and gbwt).</li> </ul> </li> </ul> <p><strong>Human genome graphs experiments</strong></p> <p>First you need to unzip the <em>data_human</em> file using command <em>tar -xvzf data</em><em>_hum.tar.gz</em>. After unzipping, the data folder is organized as follows:</p> <ul> <li>reference: the g1k_v37 reference used as a graph backbone</li> <li>vcf_sim: variant files from four individuals in each population used to simulate reads</li> <li>reads_sim: simulated short reads used for read mapping</li> <li>vcf_freq: variants augmented to graphs filtered based on allele frequency</li> </ul>
Reference Sequence Library Resources - Maine-eDNA
<p>The following files and resources are associated with the Maine-eDNA Reference Library Research Group - aiming to create reference sequence library resources for researchers part of Maine-eDNA or otherwise interested in leveraging eDNA tools for research in the Gulf of Maine.</p> <p>These include:</p> <ul> <li> <p>RoughWorkflow.zip</p> </li> <ul> <li> <p>contains a NCBI scraping script to build reference databases based on an input species list, a configuration file for the script, and genbankr version - most useful for shorter species lists (time-intensive)</p> </li> </ul> <li> <p>12S_REFDB.fasta</p> </li> <ul> <li> <p>A DADA2-compliant reference library for 12S sequences, built with the RoughWorkflow based on the GNRMaineSpecies_May2024 species list</p> </li> </ul> <li> <p>COI_REFDB.fasta</p> </li> <ul> <li> <p>A DADA2-compliant reference library for COI sequences, built with the RoughWorkflow based on the GNRMaineSpecies_May2024 species list</p> </li> </ul> <li> <p>GitHub Repo - referee - <a href="https://github.com/BigelowLab/referee">https://github.com/BigelowLab/referee</a></p> </li> <ul> <li> <p>Scripts for building reference databases for the Maine-eDNA project through downloading GenBank - this workflow is suggested especially for large species lists</p> </li> </ul> <li> <p>GitHub Repo - refdbtools - <a href="https://github.com/BigelowLab/refdbtools">https://github.com/BigelowLab/refdbtools</a> </p> </li> <ul> <li> <p>R language package to assist in making eDNA reference databases</p> </li> </ul> <li> <p>SpeciesListMeta_Shareable.xlsx</p> </li> <ul> <li> <p>Describes the sources from which the GNRMaineSpecies_May2024.csv and GNRMaineTaxonomiedSpecies_May2024.csv species lists were compiled - sources not associated with a link were found as separate files and are hosted elsewhere. Species list sources are courtesy of public datasets, Maine-eDNA researchers, and collaborators</p> </li> </ul> <li> <p>GNRMaineSpecies_May2024.csv</p> </li> <ul> <li> <p>A Maine (and surrounding area) species list ran through taxize’s gnr_resolve to resolve species names and fill out taxonomy (full results)</p> </li> </ul> <li> <p>GNRMaineTaxonomiedSpecies_May2024.csv</p> </li> <ul> <li> <p>A Maine (and surrounding area) species list ran through taxize’s gnr_resolve to resolve species names and fill out taxonomy (only species results that could be resolved with taxonomy)</p> </li> </ul> </ul> <p> </p> <p>Contact Beth Y. Davis - bethy.davis4@gmail.com for questions</p> <p> </p> <p>###</p> <p>Changelog:</p> <p>May 16, 2023 (Version 1.0) - Initial upload</p> <p>July 11, 2023 (Version 1.1) - Did additional cleaning to the MaineSpeciesList_Clean file and uploaded the new version - July2023_SpeciesList</p> <p>May 20, 2024 (Version v3) - Additional cleaning to correct deduplication errors and ran taxize's gnr_resolve to resolve names and fill out taxonomy. The version update adds two files, GNRMaineSpecies_May2024.csv containing the full result of gnr_resolve, and GNRMaineTaxonomiedSpecies_May2024.csv contains only those species from the original list that could be resolved with taxonomy. The SpeciesListMeta_Shareable.csv has not been updated but is still an accurate tracker of the sources from which the species names were gained from.</p> <p>November 27, 2024 (Version 4.0) - Updated Zenodo description and added the RoughWorkflow R files, 12S and COI files</p>
rbcL reference sequences of marine and freshwater diatoms
<p>This database has been modified from the R-syst::Diatom rbcL database, version 9, accessed on 3.18.2021. (see Rimet et al.2016 for details)</p> <p>Sequences of mostly <em>Pseudo-nitzschia</em> from the Gulf of Trieste and some others missing from the database were added.</p> <p>The names of the sequences in the .fasta file are formatted in a way that they can be used directly in dada2 for taxonomic classification. </p> <p>This database represents a comprehensive marine diatom sequence archive. </p>
UNITE+INSD 2024 Fungal ITS, ITS1, and ITS2 Reference Sequences
<p>These datasets were created from the 2024 release (Abarenkov et al. 2024) of the UNITE+INSD database as reference sequences for and by dnabarcoder (Vu et al. 2022). The ITS1 and ITS2 sequences were extracted using ITSx (Bengtsson-Palme et al. 2013).</p>
UNITE+INSD 2024 Fungal ITS, ITS1, and ITS2 Reference Sequences and classification
<p>These datasets were created from the 2024 release (Abarenkov et al. 2024) of the UNITE+INSD database as reference sequences for and by dnabarcoder (Vu et al. 2022). The ITS1 and ITS2 sequences were extracted using ITSx (Bengtsson-Palme et al. 2013).</p>
AntiRef: reference clusters of human antibody sequences
<p><strong>Motivation:</strong> Biases in the human antibody repertoire result in publicly available antibody sequence datasets containing many duplicate or highly similar sequences. These redundant sequences are a barrier to rapid similarity searches and reduce the efficiency with which these datasets can be used to train statistical or machine learning models of human antibodies. Identity-based clustering provides a solution, however, the extremely large size of available antibody repertoire datasets make such clustering operations computationally intensive and potentially out of reach for many scientists and researchers who would benefit from such data.</p> <p><strong>Results:</strong> AntiRef (Antibody Reference Clusters), which is modeled after UniRef, provides clustered datasets of filtered human antibody sequences. Starting from a dataset of ~335M unique, full-length, productive human antibody sequences from the Observed Antibody Space repository, several AntiRef cluster sets were generated. Due to the modular nature of recombined antibody genes, the clustering thresholds used by UniRef (100, 90 and 50 percent identity) to cluster general protein sequences are suboptimal for antibody clustering. AntiRef provides reference antibody sequence datasets clustered at a range of relevant identity thresholds: 100, 99, 98, 96, 94, 92 and 90 percent. AntiRef90, which uses the lowest clustering threshold of any AntiRef dataset, is roughly one-third the size of the filtered input dataset and less than half the size of the non-redundant AntiRef100.</p> <p><strong>Datasets:</strong> AntiRef comprises a series of datasets, each representing one of several clustering thresholds. AntiRef datasets were generated by a nested clustering procedure similar to UniRef which, proceeding in order of decreasing stringency, clusters the representative sequences from the preceding round of clustering. AntiRef datasets can be found at the following links:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.7474657">AntiRef100</a>: representative sequences resulting from clustering all filtered AntiRef input sequences at 100% identity.</li> <li><a href="https://doi.org/10.5281/zenodo.7475961">AntiRef99</a>: representative sequences resulting from clustering AntiRef100 at 99% identity.</li> <li><a href="https://doi.org/10.5281/zenodo.7476040">AntiRef98</a>: representative sequences resulting from clustering AntiRef99 at 98% identity.</li> <li><a href="https://doi.org/10.5281/zenodo.7487182">AntiRef96</a>: representative sequences resulting from clustering AntiRef98 at 96% identity.</li> <li><a href="https://doi.org/10.5281/zenodo.7487199">AntiRef94</a>: representative sequences resulting from clustering AntiRef96 at 94% identity.</li> <li><a href="https://doi.org/10.5281/zenodo.7487264">AntiRef92</a>: representative sequences resulting from clustering AntiRef94 at 92% identity.</li> <li><a href="https://doi.org/10.5281/zenodo.7487298">AntiRef90</a>: representative sequences resulting from clustering AntiRef92 at 90% identity.</li> </ul> <p><strong>Files:</strong> The following files are included in the primary AntiRef data repository:</p> <ul> <li><em>antiref_cluster-manifest.csv.gz:</em> A compressed CSV file containing the cluster assignments for every sequence in the AntiRef input dataset. For each AntiRef round, cluster names correspond to the sequence ID of the representative sequence (as determined by MMSeqs2). The nested clustering process conserves cluster names between iterations, meaning the clustering lineage of any sequence can easily be traced across all AntiRef datasets.</li> <li><em>download_heavy.txt</em>: A plain text file (generated by the <a href="http://opig.stats.ox.ac.uk/webapps/oas/">Observed Antibody Space</a>) containing the commands necessary to download all antibody heavy chain sequences used to create AntiRef.</li> <li><em>download_light.txt:</em> A plain text file (generated by the <a href="http://opig.stats.ox.ac.uk/webapps/oas/">Observed Antibody Space</a>) containing the commands necessary to download all antibody light chain sequences used to create AntiRef.</li> </ul> <p><strong>Code:</strong> All code used to generate AntiRef (data download, filtering, and clustering) is available under the MIT license on <a href="http://www.github.com/briney/antiref">GitHub</a>.</p>
mTAGs: taxonomic profiling using degenerate consensus reference sequences of ribosomal RNA gene
<p>mTAGs is a tool for the taxonomic profiling of metagenomes. It detects sequencing reads belonging to the small subunit of the ribosomal RNA (SSU-rRNA) gene and annotates them through the alignment to full-length degenerate consensus SSU-rRNA reference sequences. The tool is capable of processing single-end and pair-end metagenomic reads, takes advantage of the information contained in any region of the SSU-rRNA gene and provides relative abundance profiles at multiple taxonomic ranks (Domain, Phylum, Class, Order, Family, Genus and OTUs defined at a 97% sequence identity cutoff).</p>
PalaeoChip Arctic v1.0 reference and bait sequences.
<p>Organelle reference sequences of Holarctic fauna and flora from GenBank NCBI, as well as the sequences of the individual baits designed to target those referneces in the PalaeoChip Arctic v1.0 bait-set.</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.