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67 results for “hg19”
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>
PMD hypomethylation human (hg19) neural network scores
<p>Global loss of DNA methylation in mammalian genomes occurs cumulatively as a mitotic process during aging and cancer, primarily in Partially Methylated Domains (PMDs). It has been shown that local sequence context (100bp) has a strong effect on the rate of demethylation of individual CpG dinucleotides within PMDs. Here, we train a deep learning model to characterize this sequence dependence further, finding that methylation loss can be predicted from a CpG’s 150bp sequence context alone with an AUC of 0.95. We use re-methylation rates of newly synthesized DNA to show that CpGs with fast-loss sequence context are inefficiently re-methylated. Interestingly, we find that the 10% of CpGs predicted to have the “slowest” rate of loss lose almost no DNA methylation in healthy cell types. These same slow-loss CpGs lose a significant amount of DNA methylation in cancer, suggesting that they could be responsible for deregulation of genes and transposable elements that are associated with DNA hypomethylation in cancer.</p> <p>This directory contains the Nov. 18, 2020 version of the human (hg19) CpG hypomethylation Neural network scores in a single tab-delimited (bedgraph) file:<br> <strong>multitissue-nn-scores.allCGs.0based.hg19.bedgraph.gz</strong><br> with the following columns:<br> 1: chromosome (hg19)<br> 2: start coord (hg19, 0-based)<br> 3: end coord (hg19, 0-based)<br> 4: multi-tissue NN score (0-1). Close to 0 is classified as slow-loss CpG, close to 1 is classified as fast loss CpG5: Num CpGs in 150 bp window (including central CpG, so minimum is 1).</p> <p> </p> <p>The full version of the NN scores with additional details are in the file <strong>zhou-bian.allCGs.1based.hg19.tsv.gz</strong></p> <p>Each row is a CG which provides (1) chromosome, (2) the corresponding C coordinate on the forward (watson) strand of the reference genome in one-based coordinates, (3) Neural network score, (4) number of CpGs within the 150bp sequence centered on this CpG, including the center CpG, (5) CpG is within a CpG island (0, no; 1, yes), CpG is within ENCODE blacklist (0, no; 1, yes)</p> <p> Here the CpG islands are the union set of Irizarry (Irizarry et al. 2009, Nat Genet), Takai-Jones (Takai et al. 2002, PNAS), Gardner-Gardin CGIs (Gardner-Gardin et al. 1987, J Mol Biol.). The blacklist was downloaded from https://github.com/Boyle-Lab/Blacklist/tree/master/lists.</p> <p>Additional files are included here:<br> <strong>zhou_pmds.0based.hg19.bed.gz</strong>: Input PMD CpGs from the Zhou (multi-tissue) dataset<br> <strong>bian_pmds.crc01.0based.hg19.bed.gz</strong>: Input PMD CpGs from the Bian (intra-tumor) dataset<br> <strong>zhou_bian_train_test_data.tar.gz</strong>: All training and test CpGs, including labels and sequence windows.</p> <p> </p> <p> </p>
cao_esophageal_wgbs_hg19
<p>A HDF5-backed RangedSummarizedExperiment for WGBS Data (hg19 CpG sites) for 10 esophageal cancer tumours and 9 matching normal esophageal samples from the paper 'Cao, Wei, et al. "Multi-faceted epigenetic dysregulation of gene expression promotes esophageal squamous cell carcinoma." <em>Nature communications</em> 11.1 (2020): 3675.'. </p>
Prediction of nucleosome dyads for the K562 cell line in the hg19 genome assembly
<p><strong><a href="https://andre-rendeiro.com/2015/05/12/predicting_dyads_from_mnase">Predicting dyads from MNase-seq data</a></strong></p> <p>I needed the location of nucleosomal dyads in the K562 cell line (ENCODE tier 1 line). Surprisingly, although plenty of MNase-seq data for that cell line is available, no nucleosome and dyad prediction exists.</p> <p><strong>Running NuMap</strong></p> <p>I found the <a href="http://www-hsc.usc.edu/~valouev/NuMap/NuMap.html">NuMap</a> software by Anton Valouev to do exactly what I intended.</p> <p>Since it is in a somewhat obscure page and this seemed to be the only place where this software was, I have <a href="https://github.com/afrendeiro/NuMap">uploaded it into a Github repository</a> for the sake of preservation (<a href="https://github.com/orphancode/NuMap">https://github.com/orphancode/NuMap</a>).</p> <p>Predicting dyads from MNase-seq data with NuMap seemed trivial: I downloaded the <a href="http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeSydhNsome/">K562 MNase-seq data set</a> (11 replicates ~85Gb!!), combined all replicates and ran NuMap on the data(instructions on the Github README).</p> <p>From NuMap output there are <a href="https://www.dropbox.com/s/asmp7bi40lrvtjb/K562_dyads.bed?dl=0">dyad positions in bed format</a> and you can also produce several metrics to evaluate how good the prediction was.</p> <p><strong>Distograms & phasograms</strong></p> <p>Valouev describes two measurements of the frequencies of distances between MNase-seq reads. The frequency of distances between reads mapping to opposite strands can be used to build a “distogram”, which ilustrates the expected nucleosome length (147 bp) - this is consistent across most eukaryotic cells. The frequency of distances between reads mapping to the same strand gives a measurement of the distance between nucleosomes, as they’re separated by some linker DNA - (Valouev calls this plot a “phasogram”). This measurement, on the other hand tends to be species and cell-type specific.</p> <p><strong>K562 predictions:</strong></p> <p>The expected 147 bp nucleosome length in K562 cells.</p> <p>The average distance between dyads in K562 cells seems to be 185 bp.</p> <p><strong>References:</strong></p> <p>Valouev, A., Johnson, S. M., Boyd, S. D., Smith, C. L., Fire, A. Z., Sidow, A. (2011). Determinants of nucleosome organization in primary human cells. Nature, 474(7352), 516–520. <a href="http://doi.org/10.1038/nature10002">http://doi.org/10.1038/nature10002</a></p>
Gene expression and splicing counts from 49 tissues from GTEx v6p genome build hg19 - non-strand specific
<p><strong>Dataset description:</strong></p> <p>49 folders, each corresponding to one tissue from GTEx v6p and containing the following files:</p> <ol> <li> <p>geneCounts: gene-level counts </p> </li> <li> <p>k_j: split counts spanning from one exon to another.</p> </li> <li> <p>k_theta: non-split counts covering a splice site</p> </li> <li> <p>n_psi3: total split counts from a given acceptor site</p> </li> <li> <p>n_psi5: total split counts from a given donor site</p> </li> <li> <p>n_theta: total split and non-split counts for a given splice site</p> </li> <li> <p>Sample annotation describing each sample from the dataset</p> </li> <li> <p>Description file with global information from the dataset</p> </li> </ol> <p>The gene counts were originated using the GTF file from <a href="http://www.gencodegenes.org/human/release_29lift37.html">release 29 of GENCODE</a>, and the split and non-split counts contain only the annotated junctions from the same release. Statistics are reported only for GENCODE-annotated introns and splice sites, in compliance with the regulations of the GTEx consortium. For a description of the samples, methods, and protocols, see the GTEx publication specified below.</p> <p><strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, the <a href="https://github.com/gagneurlab/drop">Detection of RNA outliers Pipeline (DROP) </a> can be used to compute gene expression and splicing outliers.<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation:</strong> gencode29<br> <strong>Strand specific: </strong>FALSE<br> <strong>Paired end: </strong>TRUE<br> <strong>Protocol: </strong>poly(A) enrichment</p> <p><strong>Contact:</strong> Vicente A. Yepez, yepez at in.tum.de; Christian Mertes, mertes at in.tum.de; Julien Gagneur, gagneur at in.tum.de</p> <p><strong>Citation:</strong> Write the following in the "Data availability" section of the manuscript or similar replacing the three citations by the ones from the References section below:</p> <blockquote> <p><strong>The count matrices for the GTEx samples <cite GTEx publication, see below> were downloaded from Zenodo (doi: 10.5281/zenodo.5596755) and were generated through DROP <cite DROP, see below> using the release 29 of the GENCODE annotation <cite GENCODE, see below>. </strong></p> </blockquote> <p>Also, write the following in the Acknowledgements section:<br> </p> <blockquote> <p><strong>The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The raw data used for the analyses described in this manuscript were obtained from the GTEx Portal on June 12, 2017, under accession number dbGaP phs00424.v6.p1.</strong></p> </blockquote> <p><br> </p>
Mappability tracks for human assemblies (hg19 and GRCh38)
<p>They were created by using the GEM mapper aligner (Derrien et al., 2012) allowing up to two mismatches and considering sliding windows of 100-mer. They are exploited by the EXCAVATOR2 tool for reducing technical biases of Read Count measure in WES/TS experiments.</p>
Gene expression and splicing counts from the Yepez, Gusic et al study - fibroblast, hg19, strand-specific, low seq depth
<p><strong>File description:</strong></p> <ol> <li> <p>geneCounts: gene-level counts </p> </li> <li> <p>k_j: split counts spanning from one exon to another.</p> </li> <li> <p>k_theta: non-split counts covering a splice site</p> </li> <li> <p>n_psi3: total split counts from a given acceptor site</p> </li> <li> <p>n_psi5: total split counts from a given donor site</p> </li> <li> <p>n_theta: total split and non-split counts for a given splice site</p> </li> <li> <p>Sample annotation describing each sample from the dataset</p> </li> <li> <p>Description file with global information from the dataset</p> </li> </ol> <p>The gene counts were originated using the GTF file from release 34 of GENCODE <a href="https://www.gencodegenes.org/human/release_34">https://www.gencodegenes.org/human/release_34</a>, and the split and non-split counts contain only the annotated junctions from the same release.</p> <p><strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, DROP can be used to compute expression and splicing outliers (<a href="https://github.com/gagneurlab/drop">https://github.com/gagneurlab/drop</a>).</p> <p><strong>Number of samples:</strong> 127<br> <strong>Tissue:</strong> Fibroblast<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation:</strong> gencode34</p> <p><strong>Median mapped reads:</strong> 71 million<br> <strong>Disease</strong> (ICD-10: N): E88: 84, NONE: 12, F89: 6, G31: 3, R27: 3, E72: 3, G40: 2, R16: 2, K72: 2, P94: 2, E77: 1, E75: 1, G71: 1, G93: 1, Q78: 1, G82: 1, R29: 1, Q02: 1<br> <strong>Strand specific:</strong> True<br> <strong>Paired end:</strong> True</p> <p><strong>Dataset contact:</strong> Vicente Yepez, yepez at in.tum.de; Christian Mertes, mertes at in.tum.de; Julien Gagneur, gagneur at in.tum.de; Holger Prokisch, prokisch at helmholtz-muenchen.de</p> <p><strong>Citation:</strong> Cite both the resource using Zenodo's citation and the publication under References</p> <p> </p>
Gene expression and splicing counts from the Yepez, Gusic et al study - fibroblast, hg19, strand-specific, high seq depth
<p><strong>File description:</strong></p> <ol> <li> <p>geneCounts: gene-level counts </p> </li> <li> <p>k_j: split counts spanning from one exon to another.</p> </li> <li> <p>k_theta: non-split counts covering a splice site</p> </li> <li> <p>n_psi3: total split counts from a given acceptor site</p> </li> <li> <p>n_psi5: total split counts from a given donor site</p> </li> <li> <p>n_theta: total split and non-split counts for a given splice site</p> </li> <li> <p>Sample annotation describing each sample from the dataset</p> </li> <li> <p>Description file with global information from the dataset</p> </li> </ol> <p> </p> <p>The gene counts were originated using the GTF file from release 34 of GENCODE <a href="https://www.gencodegenes.org/human/release_34">https://www.gencodegenes.org/human/release_34</a>, and the split and non-split counts contain only the annotated junctions from the same release.</p> <p><strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, DROP can be used to compute expression and splicing outliers (<a href="https://github.com/gagneurlab/drop">https://github.com/gagneurlab/drop</a>).</p> <p><strong>Number of samples:</strong> 135<br> <strong>Tissue:</strong> Fibroblast<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation:</strong> gencode34</p> <p><strong>Median mapped reads:</strong> 116 million<br> <strong>Disease</strong> (ICD-10: N): E88: 112, G31: 8, NONE: 5, K72: 2, G71: 2, E72: 1, G93: 1, I42: 1, F82: 1, E75: 1, F89: 1<br> <strong>Strand specific:</strong> True<br> <strong>Paired end:</strong> True<br> <strong>Dataset contact:</strong> Vicente Yepez, yepez at in.tum.de; Christian Mertes, mertes at in.tum.de; Julien Gagneur, gagneur at in.tum.de; Holger Prokisch, prokisch at helmholtz-muenchen.de</p> <p><strong>Citation:</strong> Cite both the resource using Zenodo's citation and the publication under References</p> <p> </p>
target_rhabdoid_wgbs_hg19 and transcript counts
<p>A HDF5-backed RangedSummarizedExperiment for WGBS Data (hg19 CpG sites) for 69 rhabdoid tumours and RNA-seq transcript counts for 65 of these samples from the paper 'Chun, Hye-Jung E., et al. "Genome-wide profiles of extra-cranial malignant rhabdoid tumors reveal heterogeneity and dysregulated developmental pathways." <i>Cancer cell</i> 29.3 (2016): 394-406.'. </p>
Matrix N, TEs vs promoters, 100kb-wide windows, hg19
<p>Regulatory susceptibility matrix N, with rows as hg19 protein coding genes and columns as TE subfamilies. A TE is counted if it is found within 100kb of a gene promoter, strictly out of any promoter and any exon belonging to that gene.</p>
Revised transcript annotations for GRCh37 (hg19) reference genome and Ensembl v90.
<p>Custom transcript annotations generated using the reviseAnnotations package. </p> <p>Reference genome: GRCh37<br> Ensembl version: 90</p> <p>See the GitHub page of reviseAnnotations for more details:<br> https://github.com/kauralasoo/reviseAnnotations</p>
Helperfiles for using hmmix for calling archaic introgression into present day humans (both hg19 and hg38)
<p>These files are:</p> <p>1) Strict callability masks from 1000 genomes project in hg19 and hg38 coordinates</p> <p>2) Outgroup files:</p> <p>hg38_Outgroup_1000g_HGDP.txt: Frequencies of derived alleles in 490 present individuals with Sub-Saharan related ancestry: 426 from 1000genomes project and 64 from HGDP (total=490) Only first two columns are used by hmmix. The remaining columns what the reference base (hg38 refgenome), ancestral base, derived bases and the frequency of the derived bases in HGDP and 1000genomes</p> <p>hg19_Outgroup_1000g.txt: Frequencies of derived alleles in 292 present individuals with Sub-Saharan related ancestry from 1000genomes</p> <p>3) The mutation rate files are based on the outgroup files. They report the mutation rate in 1 Mb window scaled by the genomewide mutation rate </p> <p>4) The reference genome for hg19 and hg38</p> <p>5) The ancestral allele calls for hg19 and hg38</p>
30-mer mappable regions in the human hg19 genome
<p>Knowing where reads can uniquely map in the genome is useful for nascent RNA assays, both in statistical calculations and to make predictions.</p> <p>The dataset was created using the bowtie 1 aligner. The genome was windows at 30 basepair genomic intervals and mapped back to the genome. If the read maps to more than one place, the read is thrown away. Therefore the regions captured in the dataset are regions that any read at least 30 basepairs long will map to uniquely. The shell script originally used to create this dataset has been lost.</p>
tcga_450k_array_hg19
<p>A HDF5-backed RangedSummarizedExperiment for Infinium HumanMethylation450 BeadChip array methylation data for all TCGA samples</p>
Sei framework resources (hg19 and hg38 FASTA files)
<p>This is the `resources` directory that should be downloaded in order to run the Sei framework code. It contains the hg19 and hg38 genome FASTA files downloaded from UCSC, as well as the index files generated by `pyfaidx` Python package for fast indexing and querying of genome coordinate sequences. </p>
SpliceAI_rocksdb_hg19_chr6
<p>SpliceAI RocksDB for chromosome 6 of hg19 as used in the AbSplice publication: <a href="https://www.nature.com/articles/s41588-023-01373-3">https://www.nature.com/articles/s41588-023-01373-3</a></p> <p>Precomputed SpliceAI scores for all SNVs and indels up to 4 nucleotides are stored in this database. To use this database for fast computation of SpliceAI predictions see: <a href="https://github.com/gagneurlab/spliceai_rocksdb">https://github.com/gagneurlab/spliceai_rocksdb</a></p> <p>This is also implemented in the AbSplice package: <a href="https://github.com/gagneurlab/absplice">https://github.com/gagneurlab/absplice</a></p> <p>This dataset includes SpliceAI scores. The scores are free for academic and not-for-profit use; other use requires a commercial license from Illumina, Inc., see the GitHub repository of SpliceAI: <a href="https://github.com/Illumina/SpliceAI/tree/master">https://github.com/Illumina/SpliceAI/tree/master</a></p>
SpliceAI_rocksdb_hg19_chr5
<p>SpliceAI RocksDB for chromosome 5 of hg19 as used in the AbSplice publication: <a href="https://www.nature.com/articles/s41588-023-01373-3">https://www.nature.com/articles/s41588-023-01373-3</a></p> <p>Precomputed SpliceAI scores for all SNVs and indels up to 4 nucleotides are stored in this database. To use this database for fast computation of SpliceAI predictions see: <a href="https://github.com/gagneurlab/spliceai_rocksdb">https://github.com/gagneurlab/spliceai_rocksdb</a></p> <p>This is also implemented in the AbSplice package: <a href="https://github.com/gagneurlab/absplice">https://github.com/gagneurlab/absplice</a></p> <p>This dataset includes SpliceAI scores. The scores are free for academic and not-for-profit use; other use requires a commercial license from Illumina, Inc., see the GitHub repository of SpliceAI: <a href="https://github.com/Illumina/SpliceAI/tree/master">https://github.com/Illumina/SpliceAI/tree/master</a></p>
SpliceAI_rocksdb_hg19_chr4
<p>SpliceAI RocksDB for chromosome 4 of hg19 as used in the AbSplice publication: <a href="https://www.nature.com/articles/s41588-023-01373-3">https://www.nature.com/articles/s41588-023-01373-3</a></p> <p>Precomputed SpliceAI scores for all SNVs and indels up to 4 nucleotides are stored in this database. To use this database for fast computation of SpliceAI predictions see: <a href="https://github.com/gagneurlab/spliceai_rocksdb">https://github.com/gagneurlab/spliceai_rocksdb</a></p> <p>This is also implemented in the AbSplice package: <a href="https://github.com/gagneurlab/absplice">https://github.com/gagneurlab/absplice</a></p> <p>This dataset includes SpliceAI scores. The scores are free for academic and not-for-profit use; other use requires a commercial license from Illumina, Inc., see the GitHub repository of SpliceAI: <a href="https://github.com/Illumina/SpliceAI/tree/master">https://github.com/Illumina/SpliceAI/tree/master</a></p>
SpliceAI_rocksdb_hg19_chr2
<p>SpliceAI RocksDB for chromosome 2 of hg19 as used in the AbSplice publication: <a href="https://www.nature.com/articles/s41588-023-01373-3">https://www.nature.com/articles/s41588-023-01373-3</a></p> <p>Precomputed SpliceAI scores for all SNVs and indels up to 4 nucleotides are stored in this database. To use this database for fast computation of SpliceAI predictions see: <a href="https://github.com/gagneurlab/spliceai_rocksdb">https://github.com/gagneurlab/spliceai_rocksdb</a></p> <p>This is also implemented in the AbSplice package: <a href="https://github.com/gagneurlab/absplice">https://github.com/gagneurlab/absplice</a></p> <p>This dataset includes SpliceAI scores. The scores are free for academic and not-for-profit use; other use requires a commercial license from Illumina, Inc., see the GitHub repository of SpliceAI: <a href="https://github.com/Illumina/SpliceAI/tree/master">https://github.com/Illumina/SpliceAI/tree/master</a></p>
SpliceAI_rocksdb_hg19_chr1
<p>SpliceAI RocksDB for chromosome 1 of hg19 as used in the AbSplice publication: <a href="https://www.nature.com/articles/s41588-023-01373-3">https://www.nature.com/articles/s41588-023-01373-3</a></p> <p>Precomputed SpliceAI scores for all SNVs and indels up to 4 nucleotides are stored in this database. To use this database for fast computation of SpliceAI predictions see: <a href="https://github.com/gagneurlab/spliceai_rocksdb">https://github.com/gagneurlab/spliceai_rocksdb</a></p> <p>This is also implemented in the AbSplice package: <a href="https://github.com/gagneurlab/absplice">https://github.com/gagneurlab/absplice</a></p> <p>This dataset includes SpliceAI scores. The scores are free for academic and not-for-profit use; other use requires a commercial license from Illumina, Inc., see the GitHub repository of SpliceAI: <a href="https://github.com/Illumina/SpliceAI/tree/master">https://github.com/Illumina/SpliceAI/tree/master</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
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.