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55 results for “long read assembly”
Metagenome-assembled genomes from Stordalen Mire, Sweden (2019) (MAGs from long-read, short-read, & hybrid assemblies)
<p>METHODS:</p> <p>Soil samples (6 total) were collected at the Stordalen Mire site in 2019 from two depths (1-5 & 20-24 cm below ground) across three habitats (Palsa, Bog, and Fen). DNA was extracted based on the protocol described by <a href="http://dx.doi.org/10.17504/protocols.io.yxmvm244bg3p/v1">Li et al. (2024)</a>. For short reads, libraries were prepared at the Joint Genome Institute (JGI) with the KAPA Hyperprep kit, and sequenced with Illumina NovaSeq 6000. For long reads, libraries were prepared with the SMRTbell Express Template Prep Kit 2.0 (PacBio), then sequenced using PacBio Sequel IIe at JGI. PacBio data was processed at JGI to form filtered CCS (Circular Consensus Sequencing) reads. </p> <p>Assemblies were generated with short-only, long-only, and hybrid read sources: <strong>Short-only</strong> was assembled with <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411777/">metaSPAdes</a> (v3.15.4) using <a href="https://zenodo.org/records/10806928">Aviary</a> (v0.5.3) with default parameters. <strong>Long-only</strong> was assembled with <a href="https://www.nature.com/articles/s41592-020-00971-x">metaFlye</a> (v2.9-b1768) using <a href="https://zenodo.org/records/10806928">Aviary</a> (v0.5.3) with default parameters. <strong>Hybrid</strong> assembly was performed using <a href="https://zenodo.org/records/10806928">Aviary</a> v0.5.3 with default parameters. This involved a step-down procedure with long-read assembly through <a href="https://www.nature.com/articles/s41592-020-00971-x">metaFlye</a> (v2.9-b1768), followed by short-read polishing by <a href="https://genome.cshlp.org/content/27/5/737">Racon</a> (v1.4.3), <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0112963">Pilon</a> (v1.24) and then Racon again. Next, reads that didn't map to high-quality metaFlye contigs were hybrid assembled with <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411777/">SPAdes (--meta option)</a> and binned out with <a href="https://peerj.com/articles/7359/">MetaBAT2</a> (v2.1.5). For each bin, the reads within the bin were hybrid assembled using <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005595">Unicycler</a> (v0.4.8). The high-coverage metaFlye contigs and Unicycler contigs were then combined to form the assembly fasta file. Genome recovery was performed using <a href="https://zenodo.org/records/10806928">Aviary</a> v0.5.3 with samples chosen for differential abundance binning by <a href="https://zenodo.org/records/10939393">Bin Chicken</a> (v0.4.2) using <a href="https://zenodo.org/records/7130825">SingleM metapackage S3.0.5</a>. This involved initial read mapping through <a href="https://zenodo.org/records/10531254">CoverM</a> (v0.6.1) using <a href="https://academic.oup.com/bioinformatics/article/34/18/3094/4994778">minimap2</a> (v2.18) and binning by <a href="https://peerj.com/articles/1165/">MetaBAT</a>, <a href="https://peerj.com/articles/7359/">MetaBAT2</a> (v2.1.5), <a href="https://www.nature.com/articles/s41587-020-00777-4">VAMB</a> (v3.0.2), <a href="http://doi.org/10.1038/s41467-022-29843-y">SemiBin</a> (v1.3.1), <a href="https://zenodo.org/records/10460259">Rosella</a> (v0.4.2), <a href="https://www.nature.com/articles/nmeth.3103">CONCOCT</a> (v1.1.0) and <a href="https://academic.oup.com/bioinformatics/article/32/4/605/1744462">MaxBin2</a> (v2.2.7). Genomes were analyzed using <a href="https://www.nature.com/articles/s41592-023-01940-w">CheckM2</a> (v1.0.2) and clustered at 95% ANI using <a href="https://zenodo.org/records/10526086">Galah</a> (v0.4.0).</p> <p> </p> <p>FILES:</p> <ul> <li><strong>EMERGE_MAGs_2019_long-short-hybrid.tar.gz</strong> - Archive containing the MAG files (.fna).</li> <li><strong>metadata_MAGs_2019_EMERGE.tsv</strong> - Table containing source sample names and accessions, GTDB classifications, CheckM2 quality information, NCBI GenomeBatch- and MIMAG(6.0)-formatted attributes, and other metadata for the MAGs.</li> </ul> <p> </p> <p>FUNDING:</p> <p>This research is a contribution of the EMERGE Biology Integration Institute (<a href="https://emerge-bii.github.io/">https://emerge-bii.github.io/</a>), funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.</p> <p>This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632. DE-SC0010580. and DE-SC0016440.</p> <p>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.</p> <p>Data from the Joint Genome Institute (JGI) was collected under BER Support Science Proposal 503530 (DOI: <a href="https://doi.org/10.46936/10.25585/60001148">10.46936/10.25585/60001148</a>), conducted by the U.S. Department of Energy Joint Genome Institute (<a href="https://ror.org/04xm1d337">https://ror.org/04xm1d337</a>), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.</p>
Draft genome assembly of a Japanese Oikopleura dioica male individual (O3), using Nanopore long reads.
<p>This draft assembly was used to validate the chrY scaffolds of the OSKA2016 reference genome in the publication “A genome database for a Japanese population of the larvacean Oikopleura dioica”, Development Growth and Differentiation, Wang and coll., 2020 (in press). It is provided as supplemental data for the reproducibility of this work; please note that no further polishing has been done to correct sequencing errors.</p> <p>Genome sequence reads were produced on a MinION sequencer (Oxford Nanopore Technologies) using high-molecular weight DNA from a male individual of the Oikopleura dioica species of zooplankton. The individual was related to the laboratory strain established from a western Japanese population that was used to produce the OSKA2016 reference genome. The raw reads were basecalled with the Guppy software version 3.3.0 using its dna_r9.4.1_450bps algorithm, and deposited in the European Nucleotide Archive (Study ID: PRJEB38559). The draft assembly was made with the Flye software version 2.7 with the options --genome-size 65m and --min-overlap 3000.</p>
Metagenomics assemblies and high-quality MAGs for "Long-read metagenomics to retrieve high-quality metagenome-assembled genomes from canine feces"
<p>This dataset includes the different metagenomics assemblies analyzed and its summary (_info.txt file):</p> <p>- <a href="https://zenodo.org/api/files/3a502803-82f7-4b51-ac62-7c1dfcdcb680/100_assembly.fasta">100_assembly.fasta</a> is the Flye 2.7 metagenomics assembly merging HMW and non-HMW datasets</p> <p>- <a href="https://zenodo.org/api/files/3a502803-82f7-4b51-ac62-7c1dfcdcb680/75_assembly.fasta">75_assembly.fasta</a> is the Flye 2.7 metagenomics assembly including 75% of random data of the merged dataset.</p> <p>- <a href="https://zenodo.org/api/files/3a502803-82f7-4b51-ac62-7c1dfcdcb680/50_assembly.fasta">50_assembly.fasta</a> is the Flye 2.7 metagenomics assembly including 50% of random data of the merged dataset.</p> <p>- <a href="https://zenodo.org/api/files/3a502803-82f7-4b51-ac62-7c1dfcdcb680/HMW_assembly.fasta?versionId=749ff6fd-2642-4ad1-971a-7f3404baa595">HMW_assembly.fasta</a> is the Flye 2.7 metagenomics assembly for HMW dataset.</p> <p>Moreover, it also includes the eight frameshift-corrected high-quality MAGs analyzed in the manuscript. </p>
Tandem repeat catalog of the human genome generated from long-read assemblies
<p>Allele sequences of polymorphic loci (VCF) and README for all version 2 (2.0 + 2.1) files</p>
Companion data deposit of manuscript: Evaluating and improving the representation of bacterial contents in long-read metagenome assemblies
<p>This upload contains the metagenome assemblies and their binning results generated & described in the manuscript "Evaluating and improving the representation of bacterial contents in long-read metagenome assemblies" (preprint version: arxiv2210.00098, "Towards complete representation of bacterial contents in metagenomic samples"). </p> <p>Mapping of sample names in the file names and the descriptors used as in the manuscript can be found in table S1, which is available along with the manuscript and also included in the supplementary_tables_and_figures tar archive here.</p>
Simulation data for benchmarking de novo long read transcriptome assembly software
<p>Method of simulation of differentially expressed biological replicates</p> <p>We first obtained a subset of transcripts that are widely expressed in the GTEx v9 dataset (92 samples) using Gencode comprehensive annotation (v44). We kept transcripts with more than 5 reads in at least 15 samples after Salmon quantification (18145 genes, 40509 transcripts), and stored their mean count per million (CPM) values as the control group’s baseline expression. We then generated a perturbed set of CPM values where transcript expression was changed by: (1) randomly selecting 1000 genes and changing all transcripts belonging to that gene concordantly (500 genes 2 fold up and 500 genes 2 fold down), (2) selected another 1000 genes randomly, and then select 2 random transcripts from the gene and swap their expression, (3) selected another 1000 genes randomly, and then select 1 random transcript to change its expression (500 transcripts 2 fold up and 500 transcripts 2 fold down). The updated CPM were stored as the perturbed group baseline expression. We then generated a count matrix and CPM matrix for 3 control replicates and 3 perturbed replicates with gamma distribution, followed by a Poisson distribution <a href="https://www.zotero.org/google-docs/?cUP4ui">(Baldoni et al., 2024)</a>. Both long-read and short-read FASTQ files were simulated using SQANTI-SIM with default settings and ONT R9.4 cDNA error profile (v 0.2.1) <a href="https://www.zotero.org/google-docs/?Qyopst">(Mestre-Tomás et al., 2023)</a>. The long read data contained 6 million reads in total, and an average read length of 1085 bp, and short read data was 100 bp paired-end. We then subsampled the short-read data to match the total number of base pairs in the long read data (6.5 billion bases). The simulated data was non-stranded, and contains 2000 DE genes, 2000 genes with DTU, 5927 transcripts with DTU and 6933 DE transcripts.</p> <p> </p>
Genome annotations of Drosophila melanogaster and Drosophila simulans wild-type strains from long read sequencing assemblies
<p>Genome assemblies were performed for eight wild-type strains of Drosophila melanogaster and Drosophila simulans from Oxford Nanopore long read sequencing (please refer to Mohamed et al. Cells 2020 (doi:10.3390/cells9081776)). Assemblies were deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB50024 (<a href="https://www.ebi.ac.uk/ena/browser/view/PRJEBxxxx">https://www.ebi.ac.uk/ena/browser/view/</a>PRJEB50024).</p> <p>Transposable Element annotations: we used RepeatMasker 4.1.0 (<a href="http://repeatmasker.org/">http://repeatmasker.org/</a>) -species Drosophila, followed by OneCodeToFindThemAll (Bailly-Bechet et al. 2014) with default parameters.</p> <p>Gene annotations: We retrieved gtf files from FlyBase : <a>ftp.flybase.net/genomes/D</a><a>rosophila_melanogaster/dmel_r6,46_FB2022_03/gft/dmel-all-r6.46.gtf.gz</a> and <a>ftp.flybase.net/genomes/Drosophila_simulans/dsim_r2,02_FB2017_04/gtf/dsim-all-</a><a>r2,02.gtf.gz</a>. The corresponding fasta files were also downloaded from FlyBase: <a>ftp.flybase.net/genomes/Drosophila_melanogaster/dmel_r6,46_FB2022_03/</a><a>fasta</a><a>/dmel-all-</a><a>chromosome-</a><a>r6.46.</a><a>fasta</a><a>.gz</a> and <a>ftp.flybase.net/genomes/Drosophila_simulans/dsim_r2,02_FB2017_04/</a><a>fasta</a><a>/dsim-all-</a><a>chromosome-</a><a>r2,02.</a><a>fasta</a><a>.gz</a>. We used Liftoff (Shumate and Salzberg, 2020) to lift over gene annotations from the references to our genome assemblies. We used -flank 0.2 and only kept the “gene” and “exon” terms.</p>
Supplementary files for the manuscript "Assembly of Long Error-Prone Reads Using Repeat Graphs"
<p>Supplementary files for the manuscript "Assembly of Long Error-Prone Reads Using Repeat Graphs"</p> <p> </p> <p>Contents<br> ---------</p> <p>* `human_assemblues` - Flye assemblies of the human ONT sequencing data + QUAST benchmarking<br> of Flye, Canu and MaSuRCA assemblies. Scripts for assembly graph analysis are also included.</p> <p>* `nctc_assemblis` - Flye assemblies of the NCTC 21 bacterial dataset.</p> <p>* `yeast_assemblies` - working directories Flye, Canu, Falcon, Hinge and Miniasm assemblies of <br> yeast PB and ONT datasets + final assemblies + quast report. Some large files <br> (such as read alignments) were deleted.</p> <p>* `worm_assemblies` - working directories Flye, Canu, Falcon, Hinge and Miniasm assemblies of <br> the c. elegans dataset + final assemblies + quast report. Some large files <br> (such as read alignments) were deleted. `tandem_misassemblies` directory contain<br> the detailed analysis of nine tandem misassemblies. We recommend "gepard" dot-plotter for visualization.</p> <p>* `metagenome_assemblies` - Flye and Canu assemblies of a PacBio mock metagenome dataset.<br> In addition to metagenome assemblies, each bacteria was reassembled separately to<br> estimate the rate of divergence between the target genomes and the available references.</p> <p>* `simulated_data` - two assemblies of the simulated data illustrating Figure 1 (from Appendix I),<br> as well as simulated unbridged repeats benchmark.</p> <p><br> Software versions and parameters<br> --------------------------------</p> <p>* Flye - 2.3.5 (commit 20afeda)<br> * Canu - 1.7.1 (commit dfa60b8)<br> * Falcon - 0.3.0 (FALCON-Integrate commit 7498ef9)<br> * HINGE - 0.5.0 (commit 79fdf66)<br> * Miniasm - 0.2-r168-dirty (commit 40ec280) / Minimap2 2.8-r711-dirty (commit 8fc5f8d)<br> * Quast - 5.0.0 (commit de6973bb)</p> <p>Flye and Canu were run with the default parameters. The config files / scripts for<br> Falcon, HINGE and Miniasm could be found in the 'asm_config' archive folder.</p> <p>The HUMAN (but not the HUMAN+) assembly was generated with the earlier <br> Flye version 2.3.2 (released on Feb 20 2018) to provide a fair comparison <br> with the Canu and MaSuRCA assemblies (which were not updated since the release of Flye 2.3.2).<br> We note that the HUMAN assembly using the latest Flye version 2.3.5 has <br> NGA50 = 7.3 Mb and improves over the Flye 2.3.2 assembly (NGA50 = 6.3Mb). <br> HUMAN+ was assembled using the latest Flye and Canu versions (as of September 2018).</p> <p>The code for unbridged repeat resolution is currently available <br> in a separate 'flye-trestle' branch (commit 6100d32)</p>
Dataset for "Progessive improvement of the Australian blacklip abalone (Haliotis rubra) genome assembly with Nanopore long reads, hybrid meta assembly and haplotig purging
<p>This Zenodo archive contains the blacklip abalone genome assemblies and and their BUSCO completeness calculations. Genome annotation (gff3 format), CDS, protein sequences and Orthofinder2 output were also included.</p>
Polished Assemblies for "GoldPolish-Target: Targeted long-read genome assembly polishing"
<p>GoldPolish-Target is a targeted genome assembly polishing tool that uses long reads. We tested GoldPolish-Target on Oxford Nanopore Technologies datasets with a human cell line (NA24385) and Drosophila melanogaster (fruit fly). Here, we provide the data for the GoldRush baseline (unpolished) assembly and the GoldPolish-Target and medaka polished assemblies of these long-read datasets.</p>
Baseline assemblies for "ntLink: a toolkit for de novo genome assembly scaffolding and mapping using long reads" protocol
<p>ntLink is a flexible <em>de novo</em> genome scaffolding toolkit which can be run in various modes depending on the desired user output, with multiple new functionalities recently introduced. Here, we provide the baseline assembly datasets used in the ntLink protocol paper "ntLink: a toolkit for <em>de novo </em>genome assembly scaffolding and mapping using long reads". The provided assemblies are ABySS (short-read) and Flye (long-read) assemblies of <em>Caenorhabditis elegans </em>genome sequencing data. The ABySS (v2.1.4) assembly utilized paired-end short reads (accession DRR008444), and was run with the following parameters: k=64 l=40 s=1000 q=15 B=10G j=8 kc=3 H=4 S=1000-10000 N=9.The <em>C. elegans</em> Flye (v2.5) assembly was run using Oxford Nanopore long reads (accession SRR10028109) and the following parameters: --nano-raw SRR10028109.fastq -g100m -t48.</p>
De novo assembly of a long-read Amblyomma americanum genome
<p>Genome assemblies of Amblyomma americanum generated from PacBio HiFi sequencing of 50 individual female ticks. This repository contains the unphased diploid assembly generated by the Flye assembler (Arcadia_Amblyomma_americanum_asm001.fasta). In addition, there are two associated fasta files containing sequences generated by submitting the unphased diploid assembly to separation by the Purge_Dups pipeline (purged pseudo-haploid assembly and haplotig assembly).</p> <p>Flye assembler: https://github.com/fenderglass/Flye</p> <p>Purge_Dups pipeline: https://github.com/dfguan/purge_dups</p> <p>NCBI Bioproject: PRJNA932813</p>
De novo assembly of a long-read Amblyomma americanum genome (NCBI/Genbank deposited genome)
<p>Genome assembly of Amblyomma americanum generated from PacBio HiFi sequencing of 50 individual female ticks. This repository contains the phased pseudo-haploid tick genome generated after assembly using Flye, phasing using Purge_Dups, and clean-up using custom python scripts generated in-house. </p> <p>NCBI Bioproject: PRJNA932813</p>
Assemblies for "Linear time complexity de novo long read genome assembly with GoldRush"
<p>GoldRush is a <em>de novo</em> genome assembly algorithm with linear time complexity in the number of input long sequencing reads. We tested GoldRush on Oxford Nanopore Technologies datasets with different base error profiles describing the genomes of three human cell lines (NA24385, HG01243 and HG02055), Oryza sativa (rice), and Solanum lycopersicum (tomato). Here, we provide the assemblies for the GoldRush, Flye, Redbean and Shasta assemblies of these long read datasets.</p>
Long-read, chromosome-scale assembly of Vitis rotundifolia cv. Carlos and its unique resistance to Xylella fastidiosa subsp. fastidiosa.
<p>We assembled and annotated a new, long-read genome assembly for ‘Carlos’, a cultivar of muscadine that exhibits tolerance, to build upon the existing genetic resources available for muscadine. We are awaiting release of the genome through NCBI, so we have made the assembly and annotations available here.</p>
Data from: Improved genome assembly of the whiteleg shrimp Penaeus (Litopenaeus) vannamei using long- and short-read sequences from public databases
Open the record for dataset details and reuse information.
Raw Nanopore data for "Nanopore Long-Read Guided Complete Genome Assembly of Hydrogenophaga intermedia, and Genomic Insights into 4-Aminobenzenesulfonate, p-Aminobenzoic Acid and Hydrogen Metabolism in the Genus Hydrogenophaga"
<p>This is the raw Nanopore dataset (fast5) for Hydrogenophaga intermedia PBC. The gDNA was prepared using the now obsolete SQK-NSK007 kit and sequenced on a MINION R9 Flowcell. </p>
Dataset for "Nanopore-led long-read genome assembly of the Australian yabby, Cherax destructor"
<p>Intermediate_Assemblies.tar.gz: Intermediate genome assemblies e.g. raw wtdbg assembly (CD.raw.fa), polished wtdbg assembly (CD.cns.fa), 1st pilon polished assembly (CDF2_pilon1.fasta), 2nd pilon polished assembly (CDF2_pilon2.fasta) and RNA-scaffolded assembly (CDF2_pilon2_prna.fasta). Folders with run_"assembly name" are BUSCO output for each of the assembly.</p> <p>BRAKER2.tar.gz: BRAKER2 genome annotation output containing the initial set of predicted protein-coding genes as well as training intermediate files.</p> <p>BUSCOv3.tar.gz: BUSCO assessment of publicly available Decapod crustacean genome assemblies</p> <p>Cdes.filtered.codingseq: Filtered set of protein-coding sequences</p> <p>Cdes.filtered.faa: Translation of the filtered protein-coding sequences</p> <p>CDF2.NCBI.fasta.masked.gz: Repeat-masked (softmasked) Cherax destructor genome</p> <p>Cqua_transcriptome.tar.gz: rnaSPAdes output (combined fasta) of all Cherax quadricarinatus transcriptomes and its reduced dataset generated by EvidentialGene. </p> <p>Quast.tar.gz: Quast output of all Decapod crustacean genome assemblies assessed in this study</p> <p>Repeat_Annotation.tar.gz: Repeat annotation (.gff3) based on Cherax destructor-specific de novo repeat library and its summary (.tbl)</p> <p>RepeatLibrary.tar.gz: Cherax destructor-specific de novo repeat library generated by RepeatModeler</p> <p>Wtdbg2_assembly.log: Wtdbg2.5 log file showing exact command used, kmer distribution, memory usage and assembly duration.</p> <p>CAZY_Annotation.tar.gz: dbCAN2 Identification of CAZy in the selected crustacean proteomes as well as list of cellulase-associated GH groups (glycoside hydrolase). </p> <p>Orthofinder.tar.gz: Orthofinder2 output and proteomes of each crustacean used to infer orthologous clustering.</p> <p>GH9_Analysis.tar.gz: Selected GH9-associated protein sequences, amino acid alignment and IQTree output. </p> <p>Cdes_mito.gbf: GenBank file of the annotated complete mitogenome</p> <p>Cdes.filtered.codingseq: Cherax destructor protein-coding genes with homology to other crustacean proteomes based on Orthofinder2 orthologous grouping. </p> <p>Cdes.filtered.faa: Cherax destructor proteins with homology to other crustacean proteomes based on Orthofinder2 orthologous grouping. </p> <p>Cdes.ortholog.list: List of predicted Cherax destructor proteins with homology to other crustacean proteomes based on Orthofinder2 orthologous grouping. </p>
De novo genome assembly of rice varieties using Nanopore long reads
<p>Genome sequences for Sugimura et al. (2024) of the rice (O. sativa) varieties 'Hitomebore' and 'Arroz da Terra.'</p> <p>Yusaku Sugimura, Kaori Oikawa, Yu Sugihara, Hiroe Utsushi, Eiko Kanzaki, Kazue Ito, Yumiko Ogasawara, Tomoaki Fujioka, Hiroki Takagi, Motoki Shimizu, Hiroyuki Shimono, Ryohei Terauchi, Akira Abe. Impact of rice GENERAL REGULATORY FACTOR14h (GF14h) on low-temperature seed germination and its application to breeding. PLoS Genet 20(8): e1011369. https://doi.org/10.1371/journal.pgen.1011369</p> <p>bioRxiv doi: https://doi.org/10.1101/2024.02.16.580620</p>
Long read genome assembly of Automeris io (Lepidoptera: Saturniidae) an emerging model for the evolution of deimatic displays
<p>Automeris moths are a morphologically diverse group with 145 described species that have a geographic range that spans from the New World temperate zone to the Neotropics. Many Automeris have hindwing eyespots that are thought to deter or disrupt the attack of potential predators, allowing the moth time to escape. Some species in the genus have vestigial eyespots or lack them completely, suggesting that this trait may provide a selective benefit. The Io moth (Automeris io), known for its striking eyespots, is the most widely studied species within the genus and is an emerging model system to study the evolution of deimatism, a predatory defense that combines visual stimuli and movement. Here we present a high-quality, PacBio HiFi genome assembly for Io moth to aid existing research on the molecular development of eyespots. Genomic research is needed to address questions involving antipredatory defenses and eyespot pattern development. BUSCO analysis for this genome shows a completeness of 98.4%, and N50 of 15.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.