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68 results for “Pangenomics”
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>
A pangenome-guided manually curated library of transposable elements for Zymoseptoria tritici
<p>A manually-curated TE consensus library generated using a panel of 19 reference genomes for <em>Zymoseptoria tritici</em><sup>1-3</sup> along with reference genome assemblies for the sister species <em>Z. ardabiliae</em>, <em>Z. brevis</em>, <em>Z. pseudotritici</em>, and <em>Z. passerinii<sup>4</sup></em>. </p> <p> </p> <p><strong>Methods</strong></p> <p>Putative TE consensus sequences were first obtained by annotating all 23 genome assemblies<sup>1–4</sup> with Earl Grey with default settings (v3.0; <a href="https://github.com/TobyBaril/EarlGrey">https://github.com/TobyBaril/EarlGrey</a>)<sup>5,6</sup>. Consensus sequences generated from each reference genome were clustered using CD-Hit-Est (v4.8.1)<sup>7,8</sup> to group sequences with 90% similarity across 80% of the longer sequence length (<em>-n 8 -d 0 -aL 0.8 -c 0.90 -G 0 -g 1 -b 500 -r 1</em>) to reduce redundancy whilst preventing the collapsing of chimeric sequences. Consensus sequences <100bp were removed, as these are unlikely to represent true TE sequences. Each consensus sequence was then subject to manual curation as described by Goubert et al. (2022)<sup>9</sup>. Briefly, genomic copies of each TE were obtained using a “BLAST, Extract, Extend” process to recover genomic copies from each of the 23 reference genome assemblies with 1,000 flanking bases at either end<sup>9,10</sup>. For families with >100 BLASTN hits, the 25 longest hits were selected, along with 75 random hits. Multiple alignments were generated for each putative TE family using MAFFT (v7.505) with the --auto flag<sup>11</sup>. Columns composed of >=80% gaps were removed with T-COFFEE (v13.45.0.4846264)<sup>12</sup>. Subsequently, all sequence alignments were manually curated to define TE boundaries and remove regions of low conservation and rare insertions. Following manual curation, new majority-rule consensus sequences were generated with EMBOSS (v6.6.0.0) cons<sup>13</sup>. TE-Aid (<a href="https://github.com/clemgoub/TE-Aid/">https://github.com/clemgoub/TE-Aid/</a>) was used to aid visual inspection and to identify diagnostic features for classification of extended consensus sequences. Following this, TIRs were recorded if present, and nhmmscan (HMMER v3.3.2)<sup>14</sup> was used to identify homology to known curated elements in Dfam (v3.7). Combining this information, each TE consensus sequence was manually classified using available information following the naming convention ‘>ZymTri_2023_family_[n]#[Classification]/[Family]’. Consensus sequences classified with low confidence have a ‘?’ added to the name, as well as the string ‘_LowConf’. To reduce redundancy in the final TE library, sequences were clustered to the family-level using the 80-80-80 rule implemented in CD-hit-est<sup>9,15 </sup>(<em>-d 0 -aS 0.8 -c 0.8 -G 0 -g 1 -b 500 -r 1</em>). The representative sequence for each cluster was manually selected to select the sequence with the highest classification confidence, also defined as the ‘most intact consensus’. Chimeric sequences erroneously clustered were manually separated to retain sequences for the chimeric TE and the individual elements that generated the chimer.</p> <p> </p> <p><strong>References</strong></p> <p>1. Badet, T., Oggenfuss, U., Abraham, L., McDonald, B. A. & Croll, D. A 19-isolate reference-quality global pangenome for the fungal wheat pathogen Zymoseptoria tritici. <em>BMC Biol.</em> <strong>18</strong>, 12 (2020).</p> <p>2. Goodwin, S. B. <em>et al.</em> Finished genome of the fungal wheat pathogen Mycosphaerella graminicola reveals dispensome structure, chromosome plasticity, and stealth pathogenesis. <em>PLoS Genet.</em> <strong>7</strong>, e1002070 (2011).</p> <p>3. Plissonneau, C., Hartmann, F. E. & Croll, D. Pangenome analyses of the wheat pathogen Zymoseptoria tritici reveal the structural basis of a highly plastic eukaryotic genome. <em>BMC Biol.</em> <strong>16</strong>, 5 (2018).</p> <p>4. Feurtey, A. <em>et al.</em> Genome compartmentalization predates species divergence in the plant pathogen genus Zymoseptoria. <em>BMC Genomics</em> <strong>21</strong>, 588 (2020).</p> <p>5. Baril, T., Imrie, R. M. & Hayward, A. Earl Grey: a fully automated user-friendly transposable element annotation and analysis pipeline. (2022) doi:10.21203/rs.3.rs-1812599/v1.</p> <p>6. Baril, T., Galbraith, J. & Hayward, A. <em>Earl Grey</em>. (Zenodo, 2023). doi:10.5281/ZENODO.8116025.</p> <p>7. Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. <em>Bioinformatics</em> <strong>22</strong>, 1658–1659 (2006).</p> <p>8. Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. <em>Bioinformatics</em> <strong>28</strong>, 3150–3152 (2012).</p> <p>9. Goubert, C. <em>et al.</em> A beginner’s guide to manual curation of transposable elements. <em>Mob. DNA</em> <strong>13</strong>, 7 (2022).</p> <p>10. Camacho, C. <em>et al.</em> BLAST+: Architecture and applications. <em>BMC Bioinformatics</em> <strong>10</strong>, 1–9 (2009).</p> <p>11. Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. <em>Mol. Biol. Evol.</em> <strong>30</strong>, 772–780 (2013).</p> <p>12. Notredame, C., Higgins, D. G. & Heringa, J. T-coffee: a novel method for fast and accurate multiple sequence alignment. <em>J. Mol. Biol.</em> <strong>302</strong>, 205–217 (2000).</p> <p>13. Rice, P., Longden, L. & Bleasby, A. EMBOSS: The European Molecular Biology Open Software Suite. <em>Trends Genet.</em> <strong>16</strong>, 276–277 (2000).</p> <p>14. Wheeler, T. J. & Eddy, S. R. nhmmer: DNA homology search with profile HMMs. <em>Bioinformatics</em> <strong>29</strong>, 2487–2489 (2013).</p> <p>15. Wicker, T. <em>et al.</em> A unified classification system for eukaryotic transposable elements. <em>Nat. Rev. Genet.</em> <strong>8</strong>, 973–982 (2007).</p>
Supplementary Data - Impacts of selective digestive decontamination on the pangenome composition of ESBL-E. coli
<p>Supplementary Data of:</p> <p><span>Impacts of selective digestive decontamination on the pangenome composition of ESBL<em>-E. coli</em></span></p>
Bread wheat genomes graph pangenome
<p>A Giraffe and a GFA-formatted minigraph assembly of sixteen bread wheat cultivar genome assemblies.</p> <p>15-wheat10+.bed.gz is the relinearised graph from gfatools gfa2bed</p> <p>15-wheat10+.gfa.gz is the graph in GFA format as built by minigraph</p> <p>index.min, index.dist, index.giraffe.gbz are the same graph formatted for Giraffe alignments with vg giraffe v1.34.0 or later.</p> <pre><code class="language-bash">vg autoindex -w giraffe -g 15-wheat10+.gfa -t 16 -T ./ -V 1 --target-mem 850G</code></pre> <p>It is possible to convert the gfa graph to vg format:</p> <pre><code class="language-bash">vg convert -v -g 15-wheat10+.gfa > 15-wheat10+.vg</code></pre> <p>To use the index in alignments using vg v1.34.0 or later:</p> <pre><code class="language-bash">vg giraffe -Z index.giraffe.gbz -m index.min -d index.dist -f your_reads.fq > mapped.gam</code></pre> <p> </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>
The ScRAP yeast reference pangenome graphs
<p>The yeast reference pangenome graphs deposited here are constructed by <a href="https://www.evomicslab.org/">Evomics Lab</a> using the high-quality genome assemblies of globally-sampled <em>Saccharomyces cerevisiae </em>strains described in both the original <em>S. cerevisiae</em> Reference Assembly Panel (ScRAP; See <a title="O&rsquo;Donnell et al. Nature Genetics, 2023" href="https://www.nature.com/articles/s41588-023-01459-y" target="_blank" rel="noopener">O’Donnell et al. Nature Genetics, 2023</a>) and its future extensions. </p> <p>The detailed construction specification for each graphs are described below:</p> <p>1) ScRAP_v20230121_163asm.minigraph.gfa</p> <p>This graph was built upon 163 yeast genome assemblies from 142 strains. Briefly, we took the <em>S. cerevisiae</em> reference genome (denoted as 'SGDref') retrieved from the Saccharomyces genome database (SGD) as well as 162 assemblies from our recently released <em>S. cerevisiae</em> Reference Assembly Panel (ScRAP; See <a title="O&rsquo;Donnell et al. Nature Genetics, 2023" href="https://www.nature.com/articles/s41588-023-01459-y" target="_blank" rel="noopener">O’Donnell et al. Nature Genetics, 2023</a>) to construct reference pangenome graph by using <a href="https://github.com/lh3/minigraph">minigraph</a> with the command ‘minigraph -cxggs -l 5000’. With the SGDref as the reference genome, we incrementally added those 162 assemblies of other <em>S. cerevisiae</em> strains into the graph according to their phylogenetic distances to SGDref. We further mapped each input assembly to the graph by minigraph with the command ‘minigraph -cxasm’ to calculate the corresponding mapping depth. </p> <p> </p>
Graph construction method impacts variation representation and analyses in a bovine super-pangenome
<p>Pangenomes for minigraph, pggb, and cactus containing assemblies from</p> <ul> <li>Hereford (cattle reference genome)</li> <li>Highland</li> <li>Brown Swiss</li> <li>Angus</li> <li>Simmental</li> <li>Original Braunvieh</li> <li>Piedmontese</li> <li>Nellore</li> <li>Brahman</li> <li>Yak</li> <li>Bison</li> <li>Gaur</li> </ul> <p> </p> <p>Also contains the genomic region classifications for the ARS-UCD1.2 reference genome for</p> <ul> <li>Satellites</li> <li>Tandem repeats</li> <li>Low mappability</li> <li>Repetitive regions</li> <li>Normal (everything else)</li> </ul>
Minigraph pangenome graphs for HPRC samples
<p>Minigraph pangenome graphs and derived results for HPRC samples. Glenn Hickey generated the graphs. Heng Li produced the per-assembly variant calls and annotated the alleles. See 00README.txt for file description.</p>
Bovine pangenome assemblies
<p>Assemblies used in analysis presented in <a href="https://doi.org/10.1101/2021.11.02.466900">https://doi.org/10.1101/2021.11.02.466900</a>, containing the ARS-UCD1.2 reference genome and 32 assemblies compressed using <a href="https://github.com/refresh-bio/agc">agc</a>.</p> <p>Breed/species included are:</p> <p>N - Nellore</p> <p>B - Brown Swiss</p> <p>G - Gaur</p> <p>P - Piedmontese</p> <p>H - Original Braunvieh (paternal)</p> <p>O - Original Braunvieh (maternal if also an H assembly, otherwise primary assembly)</p> <p>With the following breed/species and assembler combinations.</p> <p>{H,O,N,B,G,P}_hifiasm {H,O,N,B,G,P}_shasta {O,N,B,G,P}_flye {O,N,B,G,P}_raven {O,N,B,G,P}_hicanu {O,N,B,G,P}_peregrine</p>
Supplemental Material for Z. S. Cooper Dissertation Chapter 4 - Marinobacter Pangenomics
<p>This dataset contains main and supplemental data tables for a chapter of the doctoral dissertation by Zachary S. Cooper at the University of Washington School of Oceanography. The chapter is titled "Chapter 4: Evolutionary divergence of <em>Marinobacter </em>strains in cryopeg brines as revealed by pangenomics". Briefly, this chapter contains pangenomic analyses of novel genomes of the bacterial genus <em>Marinobacter</em> that are compared with representative species of the genus to discern evolutionary history and functional capabilities of this species in the extreme cryopeg environment. The main data tables include lists of genomes used in the analyses and their characteristics as described in the dissertation chapter. The supplemental data tables include gene cluster annotations with COGs (S1), functional frequencies across the pangenome (S2), functional enrichment analysis (S3), KEGG annotations (S4), KEGG pathway completion values (S5), average nucleotide identity values (S6), alignment coverage values (S7), and horizontal gene transfer analysis summaries (S8). The tables were produced from the combination of outputs of the bioinformatic programs Anvi'o, KEGG KofamKOALA, KEGG Decoder, PyANI, and HGTector2. Details on the methods and interpretations of this data are included in the dissertation.</p>
Fig. 4 in Algal genomics perspective: the pangenome concept beyond traditional molecular phylogeny and taxonomy
Fig. 4. The pangenome concept based on a comparison of gene inventory. Colored squares indicate commonly shared or newly acquired genes between species or populations.
Fig. 1 in Algal genomics perspective: the pangenome concept beyond traditional molecular phylogeny and taxonomy
Fig. 1. Phase-contrast microscopy images of diverse algal taxa. A. Rhodella maculata CCMP736 (Rhodophyta). B. Dixoniella grisea CCMP1916 (Rhodophyta). C. Emiliania huxleyi (Haptophyta). D. Diacronema lutheri LIMS-PS-0073 (Haptophyta). E. Proteomonas sulcata (Cryptophyta). F. Rhinomonas nottbecki (Cryptophyta). G. Coolia monotis (Alveolata). H. Sungminbooa australiensis (Pelagophyceae; Stramenopiles). I. Halamphora pseudohyalina (Bacillariophyceae; Stramenopiles). J. Navicula avium (Bacillariophyceae; Stramenopiles). K. Thalassiosira gravida (= T. rotula; Bacillariophyceae; Stramenopiles). L. Ditylum sol (Bacillariophyceae; Stramenopiles). Multifocus light microscopy images were merged, and white balances were properly adjusted by Adobe Photoshop and Illustrator (scale bars: A-F, and H-J = 15 μm; G, and K = 40 μm; L = 100 μm).
Fig. 3 in Algal genomics perspective: the pangenome concept beyond traditional molecular phylogeny and taxonomy
Fig. 3. Major photosynthetic algal lineages in the eukaryote Tree of Life (eToL). The eToL is reconstructed based on previous studies (Burki et al., 2019; Keeling and Burki, 2019; Strassert et al., 2019; Bhattacharya and Price, 2020; Sibbald and Archibald, 2020).
Fig. 2. The red algal phylogenomic approaches. A. Concatenated multigene phylogeny using 170 in Algal genomics perspective: the pangenome concept beyond traditional molecular phylogeny and taxonomy
Fig. 2. The red algal phylogenomic approaches. A. Concatenated multigene phylogeny using 170 plastid genes (Muñoz-Gómez et al., 2017). B. Concatenated multigene phylogeny using 4,777 nuclear genes (Lee et al., 2019). C. Intertwining phylogenetic network tree of red algal plastid and nuclear multigene phylogenies.
Arab Pangenome Reference
<p>Pangenomes represent a significant shift from relying on a single reference sequence to a robust set of assemblies, Arab populations remain significantly underrepresented; hence, we present the first Arab Pangenome Reference (APR) utilizing 53 individuals of diverse Arab ethnicities. We assembled nuclear and mitochondrial pangenomes using 35.27X high-fidelity long reads, 54.22X ultralong reads and 65.46X Hi-C reads yielded contiguous haplotype-phased <em>de novo</em> assemblies of exceptional quality, with an average N50 of 124.28 Mb. We discovered 111.96 million base pairs of novel euchromatic sequences absent from existing human pangenomes, the T2T-CHM13, GRCh38 reference human genomes, and other public datasets. We identified 8.94 million population-specific small variants and 235,195 structural variants within the Arab pangenome. We detected 883 gene duplications including 15.06% associated with recessive diseases and 1,436 bp of novel mitochondrial pangenome sequence. Our study provides a valuable resource for future genomic medicine initiatives in Arab population and other global populations.</p>
Brassica napus, rapa, oleracea pangenome assemblies and annotations
<p>All pangenome assemblies and annotations.</p> <p> </p> <p>Rapa_Oleracea_Napus_v1.4_Pangenomes.zip - the annotations in gff3 and fasta, once in the standard EVM naming format, once in the Brassica Consortium standard.</p> <p>Brassica_napus_rapa_oleracea_pangenome_assemblies.zip - the assemblies in fasta. The main references + unassigned pangenome contigs.</p>
Data and code for 'Pseudogenes act as a neutral reference for detecting selection in prokaryotic pangenomes'
<p>This repository contains the code and files for reproducing the analyses and results reported in 'Pseudogenes act as a neutral reference for detecting selection in prokaryotic pangenomes' by Gavin M. Douglas and B. Jesse Shapiro (<a href="https://doi.org/10.1038/s41559-023-02268-6">https://doi.org/10.1038/s41559-023-02268-6</a>).</p> <p>File organization and descriptions:</p> <ul> <li><strong>code/</strong> - Contains GitHub repository releases of code used in manuscript (the other folders contain datafiles only). This code is provided here as well as on GitHub to ensure long-term access. <ul> <li><strong>handy_pop_gen-1.1.0/</strong> - release v1.1.0 of the convenience repository (used for specific data processing and analysis steps referred to in the manuscript).</li> <li><strong>pangenome_pseudogene_null-1.1.0/ </strong>- Main code repository for manuscript.</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>broad_pangenome_analysis/</strong> <ul> <li><strong>element_info/element_counts.tsv.gz</strong> - Counts of (filtered) pseudogenes and intact genes called per genome accession.</li> <li><strong>element_info/gene_sizes.tsv.gz</strong> - Gene sizes in base-pairs.</li> <li><strong>element_info/pseudogene_sizes.tsv.gz</strong> - Filtered pseudogene sizes in base-pairs.</li> <li><strong>element_info/element_percent_coverage/*tsv.gz</strong> - Tables containing the percent genome coverage of genes and pseudogenes, by accession and averaged over accessions per species separately.</li> <li><strong>example_Mycoplasmopsis_bovis_panaroo_output.csv.gz</strong> - Panaroo output table for <em>Mycoplasmopsis bovis</em>, which was used for an example. Corresponds to the <em>gene_presence_absence.csv</em> file in the raw Panaroo output.</li> <li><strong>focal_and_non.focal_full_to_short.tsv.gz</strong> - Mapfile of full to short (and unique) species ids used in analysis. Primarily to include species ids in cluster names without making them unnecessarily long.</li> <li><strong>genome_info/accessions.tsv.gz</strong> - Genome accessions used for broad pangenome analysis (note that not all genome accessions could be downloaded [and were ignored], which is indicated in the "could_download" column).</li> <li><strong>genome_info/genome_sizes.tsv.gz</strong> - Sizes of all genomes used for the broad pangenome analysis.</li> <li><strong>metrics_additional_subsamples.tsv.gz</strong> - Contains columns also found in the <em>pangenome_and_related_metrics.tsv.gz</em> file below, but based on genome subsamplings of 3 and 20, rather than 9.</li> <li><strong>model_output/pangenome_linear_models.rds</strong> - R Data Serialization files containing the output of R linear model objects (generated by lm and provided as an R list object). There are separate elements in the list for the mean number of genes, genomic fluidity, percentage singletons (si), and si/sp.</li> <li><strong>model_output/linear_model_coef.tsv.gz</strong> - Coefficient summary table for all linear models.</li> <li><strong>pangenome_and_related_metrics.tsv.gz</strong> - Metrics used for broad pangenome analysis across 670 prokaryotic species. Note that this table was filtered down to 668 species after excluding those with < 9 genomes.</li> <li><strong>pangenome_and_related_metrics_filt.tsv.gz</strong> - Filtered table, as described above.</li> <li><strong>taxonomy.tsv.gz</strong> - Taxonomy for all species used for this analysis, taken from GTDB. Row names are species names.</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>indepth_10_species_analysis/</strong> <ul> <li><strong>cluster_breakdown_tables/</strong> - Folder containing tables providing breakdown of how clusters are distributed by element type, pangenome partition, and species. Provided for easy plotting.</li> <li><strong>cluster_COG_annot.tsv.gz</strong> - Mapping of cluster IDs to COG annotations.</li> <li><strong>cluster_filt_lengths_and_additional.tsv.gz</strong> - Metadata on clusters, most pertinently the length of the representative sequence in the cluster (which was used to filter out some clusters, below the cut-off which pseudogenes could not be called).</li> <li><strong>cluster_member_breakdown.tsv.gz </strong>- Table providing information on each element (called pseudogenes and intact genes) and provides information such as what cluster they are part of, what species and genome accession they are found in, etc.</li> <li><strong>cluster_types.rds</strong> - R Data Serialization file containing R list providing breakdown of all clusters into categories (intact/pseudogene/mixed, where mixed means containing both pseudogene and intact elements).</li> <li><strong>COG_enrichment_results/ultra.cloud-COG-gene-enrichments.tsv.gz</strong> - Output file with enrichment test summaries for COG IDs in significant COG categories, which was run for the ultra-cloud pangenome partition model only.</li> <li><strong>element_glmm_input.tsv.gz </strong>- Table containing all information used for fitting generalized linear mixed models.</li> <li><strong>focal_species.txt</strong> - Names of species used for the in-depth analysis.</li> <li><strong>genome_info/ </strong>- Folder containing the genome accessions (and the corresponding genome sizes) for all ten analyzed species.</li> <li><strong>glmm_output/</strong> - Folder containing R Data Serialization files containing output R objects after fitting generalized linear mixed models (only ultra-rare files are present, due to file size constraints).</li> <li><strong>per_genome_element.type_percent_coverages.rds</strong> - R Data Serialization file containing R list providing the percent coverage by intact genes vs pseudogenes per accession (nested by species)</li> </ul> </li> </ul>
Multi-method gene clusters at species-level resolution for 125 prokaryotic pangenomes
<p>This dataset contains 9 sets of species-level gene clusters and high-resolution species trees for 125 representative bacterial and archaeal species, encompassing a total of 6,851 nearly complete genomes. Each set represents a different approach to homology-, orthology-, and synteny-based gene clustering as implemented by 6 popular tools for comparative genomics and pangenome analysis (Roary, panX, OrthoFinder, MMseqs2/PanACoTa, CD-HIT, and eggNOG-mapper).</p> <p>For <em>Escherichia coli</em>, <em>Cutibacterium acnes</em>, <em>Bacteroides uniformis</em>, and <em>Staphylococcus epidermidis</em>, we provide additional sets that combine high-quality genomes with different proportions of medium- and low-quality metagenome-assembled genomes (MAGs).</p> <p>This dataset is a helpful resource for benchmarking gene clustering tools and pangenome analysis workflows, as well as for testing their robustness with respect to the presence of incomplete or contaminated genomic assemblies.</p> <p><strong>Reference:</strong> Manzano-Morales S, Liu Y, González-Bodí S, Huerta-Cepas J, Iranzo J. 2022. Comparison of gene clustering criteria reveals intrinsic uncertainty in pangenome analyses. <em>bioRxiv</em> doi: <a href="https://doi.org/10.1101/2022.09.25.509376">10.1101/2022.09.25.509376</a></p>
Data from: Multispecies pangenomes reveal a pervasive influence of population size on structural variation
Open the record for dataset details and reuse information.
PanGEM Toolbox - Prochlorococcus pangenome
<p>Collection of all currently (1/29/2020) available genomes of Prochlorococcus, for use with the PanGEM Toolbox (https://github.com/jrcasey/PanGEM). Genomes were downloaded from NCBI and from IMG. </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.