Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
34
datasets available to search
ShareScore release 0.9.0
Dataset results
34 results for “refseq”
Coat protein (CP) and trimmed replication-associated protein (Rep) amino acid alignments, phylogenetic analyses, and associated metadata for ICTV-approved begomovirus RefSeq species exemplars
<p>DATA RETRIEVAL</p> <p>Annotated begomovirus coding sequences corresponding to each begomovirus species exemplar with a RefSeq accession number listed in the ICTV Virus Metadata Resource (VMR #18, 2021-10-19, <a href="https://ictv.global/vmr">https://ictv.global/vmr</a>) were downloaded from GenBank in protein FASTA file format. CP and Rep amino acid sequences were extracted and split into separate data sets for analysis. We confirmed the identity of misannotated ORF products by performing a BLAST search. For exemplar sequences missing ORF annotations (listed in metadata spreadsheet), ORFfinder (<a href="https://www.ncbi.nlm.nih.gov/orffinder/">https://www.ncbi.nlm.nih.gov/orffinder/</a>) was used to identify CP and Rep ORFs that were subsequently translated and added to each corresponding data set after BLAST confirmation.</p> <p>ALIGNMENTS</p> <p>Multiple sequence alignments were constructed using the MUSCLE method (Edgar, 2004) as implemented in MEGA 11 (Tamura et al., 2021) and manually corrected using AliView v1.26<strong> </strong>(Larsson, 2014). After an initial alignment inspection, exemplars with either severely truncated (i.e., length < 50% of the average length of the protein) or very divergent (i.e., causing us to doubt protein homology) CP or Rep sequences were excluded from the data set. Due to the difficulties in aligning the Rep sequences at the N- and C- terminal ends, the Rep alignment was trimmed to eliminate all residues prior to the iteron related domain (i.e., the known Rep functional region closest to the Rep start (Arguello-Astorga & Ruiz-Medrano, 2001)) in the N-terminus and after a conserved geminivirus motif found near the C-terminus, which corresponds to where other circular, Rep-encoding single-stranded DNA viruses possess an arginine finger motif (Kazlauskas et al., 2019; Krupovic et al., 2020). In total, our CP and Rep data sets contained amino acid sequences from 432 begomovirus species exemplars that met our inclusion criteria.</p> <p>PHYLOGENETIC ANALYSIS</p> <p>Maximum likelihood (ML) trees were inferred with IQ-Tree v2.0.7 (Minh et al., 2020) using the best fitting substitution model identified by the built-in ModelFinder feature (Kalyaanamoorthy et al., 2017). Tree inference was performed with 3000 ultrafast bootstrap (UFBoot) replicates, a perturbation strength of 0.2 and a stopping rule requiring an iteration interval of 500 iterations between unsuccessful improvements to the local optimum. The -bnni flag was enabled to reduce the risk of overestimating branch supports with UFBoot due to severe model violations. The provided phylogenies in NEXUS format are midpoint-rooted and branches are colored based on traditional begomovirus geographic groupings: exemplars sampled in the Americas in orange and exemplars sampled in the 'Africa, Asia, Europe and Oceania' (AAEO) region in blue. </p> <p>METADATA</p> <p>Metadata associated with each ICTV-approved species exemplar (n=445) – including country of isolation, geographic designation (i.e., AAEO/Americas), genome segmentation (i.e., monopartite/bipartite), presence/absence of V2/AV2 gene and length of genome/DNA-A segments – are included. Exemplars not incorporated into the other analyses are highlighted in red on the spreadsheet.</p> <p> </p>
2.8M SNPs Chinese Spring RefSeq v2.1 dataset
<p>VCF file of 2,799,166 single nucleotide polymorphism (SNP) markers positioned onto the Chinese Spring reference assembly RefSeq v2.1 developed by the International Wheat Genome Sequence Consortium (IWGSC; Zhu et al., 2021). These SNPs were lifted from the 1,000 wheat exome project, originally positioned onto RefSeq v1.0 (He et al., 2019). The SNP projection from RefSeq v1.0 onto RefSeq v2.1 was accomplished using LiftOff (Shumate and Salzberg, 2021).</p> <p>References</p> <p>He F, Pasam R, Shi F, Kant S, Keeble-Gagnere G, Kay P, Forrest K, Fritz A, Hucl P, Wiebe K, et al: <strong>Exome sequencing highlights the role of wild-relative introgression in shaping the adaptive landscape of the wheat genome.</strong> <em>Nature Genetics </em>2019, <strong>51:</strong>896-904.</p> <p>Shumate A, Salzberg SL: <strong>Liftoff: accurate mapping of gene annotations.</strong> <em>Bioinformatics </em>2021, <strong>37:</strong>1639-1643.</p> <p>Zhu T, Wang L, Rimbert H, Rodriguez JC, Deal KR, De Oliveira R, Choulet F, Keeble-Gagnère G, Tibbits J, Rogers J, et al: <strong>Optical maps refine the bread wheat Triticum aestivum cv. Chinese Spring genome assembly.</strong> <em>The Plant Journal </em>2021, <strong>107:</strong>303-314..</p>
dudesdb_201709 - Fungi and Virus - RefSeq - Complete Genomes
<p>bowtie2 index and dudes database for the set of Fungal and Viral complete genomes from NCBI RefSeq, dating from 2017-09. The dudes database was made based on accession version numbers (DUDesDB.py option -m "av").</p>
dudesdb_201709 - Archaea and Bacteria - RefSeq - Complete Genomes
<p>bowtie2 index and dudes database for the set of Archaeal and Bacterial complete genomes from NCBI RefSeq, dating from 2017-09. The dudes database was made based on accession version numbers (DUDesDB.py option -m "av").</p>
dudesdb_201503 - Archaea and Bacteria - RefSeq - Complete Genomes
<p>bowtie2 index and dudes database (.ddb for version 0.06 and .npz for version 0.07) for the set of Archaeal and Bacterial complete genomes from NCBI RefSeq, dating from 2015-03. The dudes database was made based on accession version numbers (DUDesDB.py option -m "av").</p>
Linked collectors and determiners for: Fungal 18S Ribosomal RNA (SSU) RefSeq Targeted Loci Project.
Natural history specimen data linked to collectors and determiners held within, "Fungal 18S Ribosomal RNA (SSU) RefSeq Targeted Loci Project". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/3bfbda20-1a91-4cf9-992f-bc1e9e5b7dad">https://bionomia.net/dataset/3bfbda20-1a91-4cf9-992f-bc1e9e5b7dad</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/3bfbda20-1a91-4cf9-992f-bc1e9e5b7dad">https://gbif.org/dataset/3bfbda20-1a91-4cf9-992f-bc1e9e5b7dad</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Bacterial 16S Ribosomal RNA RefSeq Targeted Loci Project.
Natural history specimen data linked to collectors and determiners held within, "Bacterial 16S Ribosomal RNA RefSeq Targeted Loci Project". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/0b0dc293-3b26-49db-b9f1-817b31ebf603">https://bionomia.net/dataset/0b0dc293-3b26-49db-b9f1-817b31ebf603</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/0b0dc293-3b26-49db-b9f1-817b31ebf603">https://gbif.org/dataset/0b0dc293-3b26-49db-b9f1-817b31ebf603</a>. Formatted as a Frictionless Data package.
Mash Sketch of RefSeq Bacterial Representative Genomes v217
<p>This was created to get a new mash reference that was current. The script to create this uses ncbi datasets and mash (https://github.com/UPHL-BioNGS/Grandeur/blob/main/bin/new_mash_ref.sh)<br> <br> This was created on April 27, 2023, and is RefSeq v217<br> <br> ```bash</p> <p>#/bin/bash<br> out=mash_db<br> <br> mkdir $out</p> <p>cd $out</p> <p>datasets summary genome taxon bacteria --reference --as-json-lines | \</p> <p>dataformat tsv genome --fields accession,assminfo-refseq-category,organism-name --elide-header | \</p> <p>grep representative | \</p> <p>tee representative_genomes.txt | \</p> <p>cut -f 1 > genome_ids.txt</p> <p>echo "$(date): Downloading genomes for ids"</p> <p>datasets download genome accession --inputfile genome_ids.txt --filename rep-genomes.zip</p> <p>echo "$(date): Decompressing zip file"</p> <p>unzip rep-genomes.zip</p> <p>echo "$(date): Creating file for mash"</p> <p>cat ncbi_dataset/data/*/*.fna | sed 's/ /_/g' | sed 's/,//g' > rep-genomes.fasta</p> <p>echo "$(date): Skeching rep-genomes.fasta"</p> <p>mash sketch -i -p 20 rep-genomes.fasta -o rep-genomes</p> <p>############################################################</p> <p>echo "$(date): File preparation is complete"</p> <p>ls -alh rep-genomes.fasta</p> <p>ls -alh rep-genomes.msh<br> <br> ```</p>
Refseq tailed phages
<p>5172 tailed phage genomes downloaded from Genbank on 10 May 2023 </p>
RefSeq bacterial protein (amino acid) sequences
<p><strong>Bacteria_Protein.fas.gz</strong></p><p>151,835,459 protein (amino acid) sequences extracted from 44,831 randomly selected bacterial genomes from NCBI's RefSeq (release 220). Sequences are named by their accession number, followed by "|" and their PGAP predicted function ("protein" tag). For example, the first sequence is named:</p><blockquote><p>WP_125174066.1|iron ABC transporter permease</p></blockquote><p>The process of creating the file involved the following steps.<br><strong>Step 1.</strong> Download 318,613 faa and fna files associated with a bacterial assembly in RefSeq. The following query was used:<br><i>esearch -db assembly -query '"Bacteria"[Organism] AND "latest refseq"[properties] AND "refseq has annotation"[properties]' | esummary | xtract -pattern DocumentSummary -element FtpPath_RefSeq</i><br><strong>Step 2.</strong> Verify all protein coding sequences match the expected protein sequence lengths within three codons, otherwise skip the assembly.<br><strong>Step 3.</strong> Remove all redundant protein coding or protein sequences in a genome. Only exact duplicates were removed, but they were removed from both nucleotides and proteins. Hence, a duplicated amino acid sequence would be discarded along with its coding sequence even if the coding sequence was unique. This was done to keep the two sets of sequences consistent.<br><strong>Step 4.</strong> Name sequences by their accession and PGAP predicted function, separated by a "|" character. The PGAP predicted function is generally uniform, although there are subtle difference between some taxon specific predictions. The predicted function is reasonably dependable but certainly not perfect.<br><strong>Step 5.</strong> Discard any sequences without a predicted function ("hypothetical protein"). These were discarded under the assumption that the protein's function would be required for downstream uses of the sequences.<br><strong>Step 6.</strong> Append protein and protein coding (nucleotide) sequences from randomly ordered assemblies to separate gzipped FASTA formatted files until the Zenodo file size limit was met for either file. Hence, there are many exact duplicate sequences in the set, but none for the sequences from each genome.</p><p>The final sets of sequences are intended to provide large sets of matched protein coding (nucleotide) and protein (amino acid) sequences with consistent labels. The FASTA descriptions in both files are identical. Note, the protein coding sequences do not exactly translate into the protein sequences because of slight differences in length (typically inclusion/exclusion of the first or last codon), as well as use of different translation tables depending on the organism.</p><p>See <i>Related works</i> for the companion file of protein coding (nucleotide) sequences (DOI: 10.5281/zenodo.10031801).</p>
RefSeq bacterial protein coding (nucleotide) sequences
<p><strong>Bacteria_Nucleotide.fas.gz</strong></p><p>151,835,459 protein coding (nucleotide) sequences extracted from 44,831 randomly selected bacterial genomes from NCBI's RefSeq (release 220). Sequences are named by their accession number, followed by "|" and their PGAP predicted function ("protein" tag). For example, the first sequence is named:</p><blockquote><p>WP_125174066.1|iron ABC transporter permease</p></blockquote><p>The process of creating the file involved the following steps.<br><strong>Step 1.</strong> Download 318,613 faa and fna files associated with a bacterial assembly in RefSeq. The following query was used:<br><i>esearch -db assembly -query '"Bacteria"[Organism] AND "latest refseq"[properties] AND "refseq has annotation"[properties]' | esummary | xtract -pattern DocumentSummary -element FtpPath_RefSeq</i><br><strong>Step 2.</strong> Verify all protein coding sequences match the expected protein sequence lengths within three codons, otherwise skip the assembly.<br><strong>Step 3.</strong> Remove all redundant protein coding or protein sequences in a genome. Only exact duplicates were removed, but they were removed from both nucleotides and proteins. Hence, a duplicated amino acid sequence would be discarded along with its coding sequence even if the coding sequence was unique. This was done to keep the two sets of sequences consistent.<br><strong>Step 4.</strong> Name sequences by their accession and PGAP predicted function, separated by a "|" character. The PGAP predicted function is generally uniform, although there are subtle difference between some taxon specific predictions. The predicted function is reasonably dependable but certainly not perfect.<br><strong>Step 5.</strong> Discard any sequences without a predicted function ("hypothetical protein"). These were discarded under the assumption that the protein's function would be required for downstream uses of the sequences.<br><strong>Step 6.</strong> Append protein and protein coding (nucleotide) sequences from randomly ordered assemblies to separate gzipped FASTA formatted files until the Zenodo file size limit was met for either file. Hence, there are many exact duplicate sequences in the set, but none for the sequences from each genome.</p><p>The final sets of sequences are intended to provide large sets of matched protein coding (nucleotide) and protein (amino acid) sequences with consistent labels. The FASTA descriptions in both files are identical. Note, the protein coding sequences do not exactly translate into the protein sequences because of slight differences in length (typically inclusion/exclusion of the first or last codon), as well as use of different translation tables depending on the organism.</p><p>See <i>Related works</i> for the companion file of protein (amino acid) sequences (DOI: 10.5281/zenodo.10030000).</p>
Refseq datasets for training frame classification
<p>The data is based on andomly selected viral and bacterial genomes and the human193(GRCh38.p13) reference genome which were downloaded from GenBank. From each original nucleic acid sequences we created mutliple patches of length 300 in all possible reading frames using a sliding window on the initial sequence and its reversed complement. For the train and val file, the resulting patches are translated to amino acid sequences of length 100 where the DNA_test file contains the nucleic acid sequences patches of length 300. The data is stored in the FASTA format according to the following convention:<br> <br> >{ID}_subsequence{patch index}_frame{frame index}|{class marker}|{frame index}<br> sequence<br> <br> with<br> <br> ID - denotes the ReSeq accession of the original sequence in the Refseq dataset.<br> sequence - either nucleic acid sequence patch of length 300 (DNA_test) or amino acid sequence of length 100 (train, val)<br> patch_index - denotes the starting triplet of the given patch within the original sequence or reverse complemented sequence (i.e. 3*patch_index is the starting index of frame 0 in the original sequence)<br> class marker - indicates the taxonomic domain<br> 0 - virus<br> 1 - bacteria<br> 2 - human / mammal<br> frame index - indicates the reading frame<br> 0 - on-frame<br> 1 - shifted by one<br> 2 - shifted by two<br> 3 - reverse complemented<br> 4 - shifted by one and reverse complemented<br> 5 - shifted by two and reverse complemented<br> <br> <br> The data is split into test, training and validation set which contain the following number of patches per frame:<br> <br> <br> - train: 1.700.944<br> - test: 212.618<br> - val: 212.618<br> </p>
Mash Sketch of RefSeq Bacterial Reference Genomes
<p>The mash reference that can be downloaded from <a href="https://mash.readthedocs.io/en/latest/data.html">the mash documentaion</a> is for RefSeq version 70.</p> <p>I do not inherently have a problem with RefSeq version 70, but RefSeq is well past version 200 now. </p> <p>RefSeq updates four times year, and I needed an easy way to create and distribute a mash sketch file of the representative bacterial/prokaryotic genomes.<br><br>This is intended to be a place to hold the mash sketches from <a href="https://github.com/erinyoung/update_mash_dist">https://github.com/erinyoung/update_mash_dist</a>.<br><br>The mash sketch file from erinyoung/update_mash_dist requires git lfs to be installed when cloning the repository, which is cumbersome for some users.<br><br>The update requency is intended to mirror that of RefSeq (i.e. 4 time a year), but... is likely to be less frequent than that.<br><br>Don't hesitate to <a href="https://github.com/erinyoung/update_mash_dist/issues">submit an issue</a> if this needs to get updated.<br><br>I do have some prior zenodo repositories (https://zenodo.org/records/10519852 , https://zenodo.org/records/7887021 , and https://zenodo.org/records/7348463 ) which hold the same mash sketch reference, but the refseq version is in the title. I'd rather have one repository that gets updated rather than create new repositories each time.<br><br>This is how the mash reference file was created:<br><br></p> <pre><code># Step 1. Download Datasets and Dataformat </code></pre> <pre><code>wget https://ftp.ncbi.nlm.nih.gov/pub/datasets/command-line/v2/linux-amd64/datasets wget https://ftp.ncbi.nlm.nih.gov/pub/datasets/command-line/v2/linux-amd64/dataformat chmod +x datasets dataformat</code></pre> <pre><code> # Step 2. Download Mash <br></code></pre> <pre><code> wget https://github.com/marbl/Mash/releases/download/v2.3/mash-Linux64-v2.3.tar tar -xvf mash-Linux64-v2.3.tar </code></pre> <pre><code><br> # Step 3. Get a list of all the genomes # Note: this also changes how some of the names are represented datasets summary genome taxon bacteria --reference --as-json-lines | \ dataformat tsv genome --fields accession,organism-name --elide-header | \ sed 's/\[//g' | \ sed 's/\]//g' | \ sed 's/["'\'']//g' | \ sed 's/endosymbiont of /endosymbiont_of_/g' > \ ids.txt # Step 4. Download the reference files and sketch them # Note: Since this is done in Github Actions (GA), I need to keep everything below 30G. # The best way to do this is to download the process each reference file individually, and then combine it to the whole. # This obviously does not need to be followed if not under those same limitations. while read line do id=$(echo $line | awk '{print $1}') ge=$(echo $line | awk '{print $2}') if [ ! -n "$ge" ] ; then ge="unknown" ; fi sp=$(echo $line | awk '{print $3}') if [ ! -n "$sp" ] ; then sp="unknown" ; fi datasets download genome accession $id unzip ncbi_dataset.zip cp ncbi_dataset/data/*/*_genomic.fna ${ge}_${sp}_${id}.fasta if [ ! -f RefSeqSketches_${version}.msh ] then mash sketch ${ge}_${sp}_${id}.fasta -o RefSeqSketches_${version} else mash sketch ${ge}_${sp}_${id}.fasta -o ${ge}_${sp}_${id} mv RefSeqSketches_${version}.msh tmp.msh mash paste RefSeqSketches_${version} tmp.msh ${ge}_${sp}_${id}.msh rm tmp.msh ${ge}_${sp}_${id}.msh fi rm ${ge}_${sp}_${id}.fasta rm -rf ncbi_dataset/ rm ncbi_dataset.zip rm README.md rm md5sum.txt done < ids.txt</code></pre> <pre><br><br>To use</pre> <pre><code># download file wget <insert url for file> mask sketch sample.fasta RefSeqSketches_<version>.msh > mash_results.txt # These results are unsorted, so many find it useful to sort them. sort -gk3 mash_results.txt > sorted_mash_results.txt</code></pre> <p> <br>The should look like the following:</p> <pre><code>2024CK-00429-UT-M03999-240412_contigs.fa Streptococcus_pyogenes_GCF_900475035.1.fasta 0.0116661 0 643/1000 2024CK-00429-UT-M03999-240412_contigs.fa Streptococcus_dysgalactiae_GCF_016128095.1.fasta 0.0782587 0 107/1000 2024CK-00429-UT-M03999-240412_contigs.fa Streptococcus_canis_GCF_900636575.1.fasta 0.132399 2.34894e-153 32/1000 2024CK-00429-UT-M03999-240412_contigs.fa Streptococcus_agalactiae_GCF_001552035.1.fasta 0.164662 1.32611e-72 16/1000 2024CK-00429-UT-M03999-240412_contigs.fa Streptococcus_castoreus_GCF_000425025.1.fasta 0.174408 2.34302e-58 13/1000 2024CK-00429-UT-M03999-240412_contigs.fa Streptococcus_didelphis_GCF_000380005.1.fasta 0.182269 8.30736e-49 11/1000 2024CK-00429-UT-M03999-240412_contigs.fa Streptococcus_uberis_GCF_900475595.1.fasta 0.186761 5.62934e-44 10/1000 2024CK-00429-UT-M03999-240412_contigs.fa Streptococcus_iniae_GCF_000831485.1.fasta 0.191731 3.33152e-39 9/1000 2024CK-00429-UT-M03999-240412_contigs.fa Streptococcus_ictaluri_GCF_000188015.2.fasta 0.197292 1.75608e-34 8/1000 2024CK-00429-UT-M03999-240412_contigs.fa Streptococcus_phocae_GCF_001302265.1.fasta 0.203604 2.46548e-30 7/1000</code></pre> <pre> </pre>
Refseq database for DeGeCI
<p>Postgresql database dump files for DeGeCI for Refseq releases 89 and 204.</p> <p>Each dump file creates two tables:</p> <p>- kmer_plus-16-<refseqreleasenumber></p> <p>- kmer_minus-16-<refseqreleasenumber></p> <p>These represent the de-Brujin graph with k=16 for the respective RefSeq release.</p> <p>For installation instructions follow the README file provided with the software, available at https://doi.org/10.5281/zenodo.7010515</p> <p> </p>
GTDB and RefSeq-RDP databases parsed for species assignment
<p>GTDB and RefSeq-RDP databases parsed for the <em>addSpecies()</em> dada2 function. Both databases were parsed with `<a href="https://github.com/antonioggsousa/GTDB-RefSeq-RDP-assign-spp-dada2/blob/master/script/parsing_DB_dada2_spp_assign.py">parsing_DB_dada2_spp_assign.py</a>` python script using as input both databases downloaded at: <a href="https://zenodo.org/record/2541239#.XM2UgCOZPOQ">https://zenodo.org/record/2541239#.XM2UgCOZPOQ</a>. The python script and a detailed description explaining the code and databases versions can be found at: <a href="https://github.com/antonioggsousa/GTDB-RefSeq-RDP-assign-spp-dada2">https://github.com/antonioggsousa/GTDB-RefSeq-RDP-assign-spp-dada2</a></p> <p> </p>
Distributions of alternative start codons for all microbial Refseq genomes
<p>Distributions of alternative start codons for all microbial Refseq genomes</p>
Distribution of alternative start codons for each microbial Refseq genome
<p>Distributions of alternative start codons for each microbial Refseq genome. </p>
Viral RefSeq databases for Centrifuge, Kraken2 and DIAMOND
<p>Owing to technological advances in ancient DNA, it is now possible to sequence viruses from the past to track down their origin and evolution. However, ancient DNA data is considerably more degraded and contaminated than modern data making the identification of ancient viral genomes particularly challenging. Several methods to characterise the modern microbiome (and, within this, the virome) have been developed; in particular, tools that assign sequenced reads to specific taxa in order to characterise the organisms present in a sample of interest. While these existing tools are routinely used in modern data, their performance when applied to ancient microbiome data to screen for ancient viruses remains unknown. </p> <p>In this work, we conducted an extensive simulation study using public viral sequences to establish which tool is the most suitable to screen ancient samples for human DNA viruses. We compared the performance of four widely used classifiers, namely Centrifuge, Kraken2, DIAMOND and MetaPhlAn2, in correctly assigning sequencing reads to the corresponding viruses. To do so, we simulated reads by adding noise typical of ancient DNA to a set of publicly available human DNA viral sequences and to the human genome. We fragmented the DNA into different lengths, added sequencing error and C to T and G to A deamination substitutions at the read termini. Then we measured the resulting sensitivity and precision for all classifiers. </p> <p>Across most simulations, more than 228 out of the 233 simulated viruses are recovered by Centrifuge, Kraken2 and DIAMOND, in contrast to MetaPhlAn2 which recovers only around one third. Overall, Centrifuge and Kraken2 have the best performance with the highest values of sensitivity and precision. We found that deamination damage has little impact on the performance of the classifiers, less than the sequencing error and the length of the reads. Since Centrifuge can handle short reads (in contrast to DIAMOND and Kraken2 with default settings) and since it achieves the highest sensitivity and precision at the species level across all the simulations performed, it is our recommended tool. Regardless of the tool used, our simulations indicate that, for ancient human studies, users should use strict filters to remove all reads of potential human origin. Finally, we recommend to verify which species are present in the database used, as it might happen that default databases lack sequences for viruses of interest.</p>
CAMI2 Challenge - Human Microbiome Project Toy Database - sample 19 - regenerated using recent RefSeq representative genomes
Open the record for dataset details and reuse information.
Refseq Test Subsets for Frame Classification with and without Errors
<p>These test files extend the<a href="https://zenodo.org/record/4306248"> 'Refseq datasets for training frame classification</a>' dataset. It provided the original test file and three variations containing erroneous sequences to simulate realistic data.</p> <p>The data is based on randomly selected viral and bacterial genomes and the human193(GRCh38.p13) reference genome which was downloaded from GenBank. From each original nucleic acid sequence, we created multiple patches of length 300 in all possible reading frames using a sliding window on the initial sequence and its reversed complement. The data is stored in the FASTA format according to the following convention:<br> <br> >{ID}_subsequence{patch index}_frame{frame index}|{class marker}|{frame index}<br> sequence<br> <br> with<br> <br> ID - denotes the ReSeq accession of the original sequence in the Refseq dataset.<br> sequence - nucleic acid sequence patch of length 300 or 250<br> patch_index - denotes the starting triplet of the given patch within the original sequence or reverse complemented sequence (i.e. 3*patch_index is the starting index of frame 0 in the original sequence)<br> class marker - indicates the taxonomic domain<br> 0 - virus<br> 1 - bacteria<br> 2 - human / mammal<br> frame index - indicates the reading frame<br> 0 - on-frame<br> 1 - shifted by one<br> 2 - shifted by two<br> 3 - reverse complemented<br> 4 - shifted by one and reverse complemented<br> 5 - shifted by two and reverse complemented</p> <p>Each file contains 212.618 patches per frame.</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.