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305 results for “gene clusters”
CAbiNet: Joint clustering and visualization of cells and genes for single-cell transcriptomics
<p>We here provide the data sets to reproduce the results in our manuscript "CAbiNet: Joint clustering and visualization of cells and genes for single-cell transcriptomics". Our package "CAbiNet" can be downloaded from https://github.com/VingronLab/CAbiNet. The scripts to reproduce the results in our manuscript can be found from https://github.com/VingronLab/CAbiNet_paper.</p><p>You can find the description of folders in 'Data.zip' in the README.md file.</p>
The Minimum Information about a Biosynthetic Gene Cluster (MIBiG) data repository
<p>This dataset was originally published alongside the Minimum Information about a Biosynthetic Gene Cluster (MIBiG) data standard publication(s).</p> <p>It contains JSON files following the MIBiG data standard. Additional information on proteins/genes associated to biosynthetic gene clusters described by MIBiG can be found in the GenBank (gbk) and fasta files.</p> <p>This dataset was uploaded with permission from the corresponding author(s).</p> <p>For more information, see https://mibig.secondarymetabolites.org/.</p>
The North Pacific Eukaryotic Gene Catalog: clustered nucleotide metatranscripts and read counts
<p>This data continues with the development of the NPEGC Trinity <em>de novo</em> metatranscriptome assemblies from the protein data repository of <a href="../doi/10.5281/zenodo.10472589">The North Pacific Eukaryotic Gene Catalog</a>. The nucleotide sequences corresponding to the NPEGC cluster representatives are collected together in these repository files:<br><br><em>NPac.G1PA.bf100.id99.nt.fasta.gz</em><br><em>NPac.G2PA.bf100.id99.nt.fasta.gz</em><br><em>NPac.G3PA.bf100.id99.nt.fasta.gz</em><br><em>NPac.G3PA_diel.bf100.id99.nt.fasta.gz</em><br><em>NPac.D1PA.bf100.id99.nt.fasta.gz</em><br><br>A full description of this data is published in Scientific Data, available here: <a href="https://www.nature.com/articles/s41597-024-04005-5" target="_blank" rel="noopener">The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations</a>. Please cite this publication if your research uses this data:<br><br>Groussman, R. D., Coesel, S. N., Durham, B. P., Schatz, M. J., & Armbrust, E. V. (2024). The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations. <em>Scientific Data</em>, <em>11</em>(1), 1161.<br><br>These nucleotide sequences have been sourced from the Zenodo repository for raw assemblies: <a href="../records/7332796">The North Pacific Eukaryotic Gene Catalog: Raw assemblies from Gradients 1, 2 and 3</a></p> <p>Key processing steps are sampled below with links to the detailed code on the main github code repository: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog">https://github.com/armbrustlab/NPac_euk_gene_catalog</a></p> <p><br>Code used to build the kallisto indices and map the short reads against indices with kallisto are online in the code repository here: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/nt_data/NPEGC.nt_kallisto_counts.sh">NPEGC.nt_kallisto_counts.sh</a><br><br>There are two main steps:<br>1. Generate the kallisto index on the sets of clustered nucleotide metatranscripts<br>2. Map the short reads from environmental samples back to the assembly index</p> <p>As generated above, kallisto generates separate results files for each of the sample files. Even after compression, the total size of the tarballed kallisto output results directories are prohibitively large (>50GB). We use the code in this template R script to join together the 'est_count' estimated count values for the tens of millions of protein sequences in each project metatranscriptome, along with length.</p> <p>The code in this template script was used for each project: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/nt_data/aggregate_kallisto_counts.R">aggregate_kallisto_counts.R</a><br>The output count files for each project are Gzip-compressed and uploaded to the NPEGC nucleotide data repository here: </p> <p><em>G1PA.raw.est_counts.csv.gz</em><br><em>G2PA.raw.est_counts.csv.gz</em><br><em>G3PA.raw.est_counts.csv.gz</em><br><em>G3PA_diel.raw.est_counts.csv.gz</em><br><em>D1PA.raw.est_counts.csv.gz</em></p>
Uncovering a miltiradiene biosynthetic gene cluster in the Lamiaceae reveals a dynamic evolutionary trajectory
<p><span>The spatial organization of genes within plant genomes can drive evolution of specialized metabolic pathways. In this study we investigated the origin and subsequent evolution of a diterpenoid biosynthetic gene cluster (BGC) present throughout the Lamiaceae (mint) family. Terpenoids are important specialized metabolites in plants with </span><span>diverse</span><span> adaptive functions that enable environmental interactions, such as chemical defense. Based on core genes found in the BGCs of all species examined across the Lamiaceae, we predict a simplified version of this cluster evolved in an early Lamiaceae ancestor. The current composition of the extant BGCs highlights the dynamic nature of its evolution. We elucidate the terpene backbones made by the </span><span>Callicarpa americana</span><span> BGC enzymes, including miltiradiene and the novel terpene (+)-kaurene, and show oxidization activities of BGC cytochrome P450s. Our work reveals the fluid nature of BGC assembly and the importance of genome structure in contributing to the origin of novel metabolites.</span></p>
Integrated Omics-Based Discovery of Novel Genes, Secondary Metabolites Clusters, and Small Molecules in Penicillium spp. with Disparate Fungal Isolates
<p><em><span>Penicillium expansum</span></em><span> is a ubiquitous postharvest pathogen of pome fruit that causes blue mold decay of apple fruit while another member of the genus, <em>P. chrysogenum</em><span>,</span><em> </em>is a well-studied saprophyte used for antibiotic and small molecule production. While these two fungi have been investigated individually, the recent discovery of <em>P. chrysogenum </em>hindering <em>P. expansum</em> apple fruit infection has not been well studied. To shed light on this interaction between the two species, we conducted a comparative transcriptomic, metabolomic, and genomic study. Global transcriptional and metabolomic outputs were disparate between the species, nearly identical for the <em>P. chrysogenum </em>isolates, and different between <em>P. expansum </em>isolates. Further, the two <em>P. chrysogenum</em> genomes revealed secondary metabolite gene clusters that differed from <em>P. expansum</em>. This included the absence of an intact patulin gene cluster in <em>P. chrysogenum</em>, which corroborates the metabolomic data regarding the species’ inability to produce patulin. Additionally, <em>P. expansum </em>virulence gene homologues were identified in <em>P. chrysogenum </em>and were similarly transcriptionally regulated <em>in vitro</em>. Molecules with potential antimicrobial activity, and phytohormones like indole-3-acetic acid (IAA), were detected for the first time in <em>P. expansum</em> while pharmacological compounds like the well-studied antibiotic penicillin G were identified in <em>P. chrysogenum</em> culture supernatants. Our findings provide new omics-based resources that enable the study of small molecule production of interest, the potential of <em>Penicillium</em>-derived antimicrobials for postharvest decay control, and <em>P.</em> <em>expansum’s</em> metabolites roles in host-pathogen interactions. </span></p>
Streptomyces Biosynthetic Gene Clusters
<p>A collection of genbank files 11975 biosynthetic gene clusters from generated using antiSMASH v6.0.1</p>
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>
Uncovering a miltiradiene biosynthetic gene cluster in the Lamiaceae reveals a dynamic evolutionary trajectory
Open the record for dataset details and reuse information.
USEARCH Clustering Data for the Influenza Internal Genes
<p>These are the aligned sequence clusters and centroid datasets for the USEARCH clustering of the influenza A internal genes. </p>
Figures for: Biosynthetic gene clusters carried by plasmids may enhance adaptations to a changing ocean
<p>Figures created for the short communication: Biosynthetic gene clusters carried by plasmids may enhance adaptations to a changing ocean.</p>
Differentially expressed genes in berries and rachis of berry shrivel grape clusters used to prepare figures for a review
<p>Grapevine berry shrivel is a ripening disorder leading to significant economic losses in the worldwide wine and table grape industries. Sugar accumulation stops early after ripening onset accompanied with cell death in berreis and subtending pecicels and rachis finally resulting in berry shrinkage. To date, the triggers of BS remain unknown. The dataset supports figures prepared for an review which aims to summarize and critically discuss the current knowledge. Data are expressed as differentially expressed genes obtained from grape berries samples collected at six developmental stages (pre- until post-veraison) analysed with RNASeq and two pooled samples (pre- and BS symptomatic) from the rachis analyzed with a microarray study. Extracted information focus on primary metabolic processes including sugar transport and metabolism, organic acid metabolism, stress signaling and cell as well as cell wall organisation. Data are mean values of three biological represent and presented as log2 fold changes including statistical information. A meta-data sheet provides the most relevant information and references. </p>
Data for: Tomato root specialized metabolites evolved through gene duplication and regulatory divergence within a biosynthetic gene cluster
<p>Tremendous plant metabolic diversity arises from phylogenetically-restricted specialized metabolic pathways. Specialized metabolites are synthesized in dedicated cells or tissues, with pathway genes sometimes colocalizing in biosynthetic gene clusters (BGCs). However, the mechanisms by which spatial expression patterns arise and the role of BGCs in pathway evolution remain underappreciated. In this study, we investigated the mechanisms driving acylsugar evolution in the Solanaceae. Previously thought to be restricted to glandular trichomes, acyl sugars were recently discovered in cultivated tomato roots. We demonstrated that acyl sugars in cultivated tomato roots and trichomes have different sugar cores, identified root-enriched paralogs of trichome acyl sugar pathway genes, and characterized a key paralog required for root acyl sugar biosynthesis, <em>SlASAT1-LIKE</em> (<em>SlASAT1-L</em>), which is nested within a previously-reported trichome acyl sugar BGC. Finally, we provided evidence that <em>ASAT1-L</em> arose through duplication of its paralog, <em>ASAT1</em>, and was trichome-expressed before acquiring root-specific expression in the <em>Solanum</em> genus. Our results illuminate the genomic context and molecular mechanisms underpinning metabolic diversity in plants.</p>
Datasets for "Micromonosporaceae Biosynthetic Gene Cluster Diversity Highlights the Need for Broad Spectrum Investigation"
<p>In this data collection is:<br><strong>Data S1</strong>: A folder with all the fasta files, representing the 42 strains (41 <em>Micromonosporaceae</em>, 1 <em>Streptomycetaceae</em>).<br><strong>Data S2</strong>: A folder with all the .gbk files for the BGC regions predicted by antiSMASH v5.1.1. These files were used as inputs for BiG-SCAPE and BiG-SLiCE.<br><strong>Data S3</strong>: A folder with all the .gbk files for the BGC regions predicted by antiSMASH v6.1.0.<br><strong>Data S4</strong>: A folder containing all the Quast outputs for the 42 strains.<br><strong>Data S5</strong>: A folder containing all the BUSCO outputs for the 42 strains. Example scripts are provided for scraping relevant information from the individual BUSCO outputs.<br><strong>Data S6</strong>: A folder containing GTDB (Genome Taxonomy Database) classification results, and species-level grouping results using FastANI (95% cutoff).<br><strong>Data S7</strong>: A folder containing an Interactive Tree of Life (iTOL)-compatible bar chart annotation using antiSMASH v5.1.1 BGC region information.<br><strong>Data S8</strong>: A folder containing a word document that describes the parameters used with Ubuntu WSL (Windows Subsystem for Linux) on the command line for programs antiSMASH v6.1.2, BiG-SCAPE v1.1.2, and BiG-SLiCE v1.1.1. Also included are parameters for MDSC in python. An example script is also provided for batch queries of BGCs against BiG-SLiCE v1.1.1’s pre-processed dataset of ~1.2 million BGCs.<br><strong>Data S9</strong>: A folder containing the BiG-SCAPE visualization of the 38 <em>Micromonosporaceae</em> (post-QC filtering, excluding WMMA1363, WMMB482, WMMB486, and WMMC500) in Cytoscape.<br><strong>Data S10</strong>: A folder containing:<br>The pre-processed dataset of 1.2 million BGCs from BiG-SLiCE.<br>All report folders generated by BiG-SLiCE for the 779 <em>Micromonosporaceae </em>BGCs queried against the 1.2 million BGCs.<br>The results data.db and associated folders for the pre-processed dataset of 1.2 million BGCs.<br><strong>Data S11</strong>: A folder containing the scripts necessary to regenerate the figures and perform independent analyses, and the relevant data used for the analyses.<br><strong>Data S12: </strong>A folder containing the results of the nucleotide blast of WMMA1947.region12's siderophore contig against WMMD1120.region14's siderophore contig.</p> <p><strong>Supplementary Information: </strong>Supplementary Table S1 and Supplementary Figures S1-S181.</p>
Variable allelic expression of imprinted genes at the Peg13, Trappc9, Ago2 cluster in single neural cells
<p>Fig 1 5' RACE - Sequence tracks and analysis of alternative Trappc9 transcriptional start sites</p> <p>Fig 2 and suppl fig 3- Brain and Kidney tissue or Neural stem cells isolated from the Hippocampus region of newborn mice generated from a C57BL/6 (female) and a Cast/EiJ (male) cross (and its reciprocal cross) was used to determine allelic bias expression. A SNP located within an exon of Peg13, Trappc9, Ago2, Chrac1 and Kcnk9 was identified and amplified via pyrosequencing PCR with the percentage of SNP identification used to determine allele expression percentages. Additionally, Pyrorun sequences of reverse transcribed RNA from Trappc9 expression in different tissues. A hybrid cross between C57BL/6 and JF1 mouse was used to generate hybrid pups that were used to determine allele specificity of Trappc9 expression in Kidney and brain tissues.</p> <p>Figs 3, 4 & 5- Single neural stem cells were isolated from the Hippocampus of newborn mice generated from a hybrid cross. Some of these cells were differentiated In vitro and either the NSC or differentiated neurons were lysed and underwent a reverse transcription. The newly formed cDNA was used as a template to amplify expressed Peg13, Trappc9 or Ago2 transcripts which was then sent for Sanger sequencing. A SNP located within the exon was used to determine whether the transcript from that cell was generated from the maternal or paternal allele.</p> <p>Fig 6- Brain-specific regulatory elements were cloned into a pGL [Luc] vector containing a Trappc9 promoter. These newly generated plasmids were then transfected into either primary neuron or fibroblast cultures alongside a Renilla vector for normalization using Lipofectamine as a transfection reagent. After 48 hours the cells were lysed and analyzed using a Glomax illuminator to determine their impact on Luciferase expression compared to that of the pGL vector containing just the Trappc9 promoter. Additionally, Plasmid vectors that were used for transfection of primary neurons to determine the impact of brain-specific regulatory elements on transcription. Plasmids can be visualized using the free software pdraw.32 downloaded from http://acaclone.com/download/install.htm</p> <p>Suppl fig 2- Single Neural stem cells and in vitro differentiated neurons isolated from Mouse Hippocampus tissue underwent reverse transcription and a qPCR reaction intended to amplify cDNA of genes associated with specific neural cell types as a method of detecting cell fate and whether this had an impact on allele-specific expression and differential methylation.</p> <p>Suppl fig 4 & 5- DNA isolated from Neural stem cells was bisulfite converted for downstream identification of methyl group presence. CpG islands located at or near the promoters of the Peg13, Trappc9, Ago2, Chrac1 and Kcnk9 genes were amplified and cloned into a TOPO vector. The cloned segments were Sanger sequenced and compared to the original non-bisulfite converted sequence using the Quantification for methylation analysis (QUMA) tool to determine which CG dinucleotides were methylated and which weren't. Pyroruns determining methylation frequency at the CpG islands of these genes can be found in a separate upload on Zenodo.</p>
Genomic localization bias of secondary metabolite gene clusters and association with histone modifications in Aspergillus
<p>Table S4 (Distribution of Orthologous groups) associated with the publication 'Genomic localization bias of secondary metabolite gene clusters and association with histone modifications in Aspergillus' is deposited at Zenodo.</p>
Microscopy data: interaction of the gene ripply1 with RNA polymerase II clusters during early zebrafish embryo development
<p>Microscopy image data containing fluorescently labeled gene loci, recruited RNA polymerase II, and elongating RNA polymerase II.</p> <p>This data set is for the gene <em>ripply1</em> and is obtained from fixed zebrafish embryos, collected at the developmental stages oblong, sphere, dome, 30% epiboly, and 50% epiboly (indicated in the file names). Data were recorded using an instant-SIM microscope (iSIM, VisiTech UK) with a 100X TIRF oil immersion objective (Nikon, NA 1.49, CFI SR HP Apo TIRF 100XAC Oil). Two independent experiments were performed (IF1, IF2 in the file name), for each experiment, two samples were prepared per stage and experiment (001, 002 in the file name).</p> <p>The image data are in the ND2 format (Nikon proprietary) and can be imported using the BioFormats importer (Open Microscopy Environment).</p>
Microscopy data: interaction of the gene klf2b with RNA polymerase II clusters during early zebrafish embryo development
<p>Microscopy image data containing fluorescently labeled gene loci, recruited RNA polymerase II, and elongating RNA polymerase II.</p> <p>This data set is for the gene <em>klf2b</em> and is obtained from fixed zebrafish embryos, collected at the developmental stages oblong, sphere, dome, 30% epiboly, and 50% epiboly (indicated in the file names). Data were recorded using an instant-SIM microscope (iSIM, VisiTech UK) with a 100X TIRF oil immersion objective (Nikon, NA 1.49, CFI SR HP Apo TIRF 100XAC Oil). Two independent experiments were performed (IF1, IF2 in the file name), for each experiment, one or two samples were prepared per stage and experiment (001, 002 in the file name).</p> <p>The image data are in the ND2 format (Nikon proprietary) and can be imported using the BioFormats importer (Open Microscopy Environment).</p>
Microscopy data: interaction of the gene foxd5 with RNA polymerase II clusters during early zebrafish embryo development
<p>Microscopy image data containing fluorescently labeled gene loci, recruited RNA polymerase II, and elongating RNA polymerase II.</p> <p>This data set is for the gene <em>foxd5</em> and is obtained from fixed zebrafish embryos, collected at the developmental stages oblong, sphere, dome, 30% epiboly, and 50% epiboly (indicated in the file names). Data were recorded using an instant-SIM microscope (iSIM, VisiTech UK) with a 100X TIRF oil immersion objective (Nikon, NA 1.49, CFI SR HP Apo TIRF 100XAC Oil). Two independent experiments were performed (IF1, IF2 in the file name), for each experiment, two or three samples were prepared per stage and experiment (001, 002 in the file name).</p> <p>The image data are in the ND2 format (Nikon proprietary) and can be imported using the BioFormats importer (Open Microscopy Environment).</p>
Microscopy data: interaction of the gene gadd45ga with RNA polymerase II clusters during early zebrafish embryo development
<p>Microscopy image data containing fluorescently labeled gene loci, recruited RNA polymerase II, and elongating RNA polymerase II.</p> <p>This data set is for the gene <em>gadd45ga</em> and is obtained from fixed zebrafish embryos, collected at the developmental stages oblong, sphere, dome, 30% epiboly, and 50% epiboly (indicated in the file names). Data were recorded using an instant-SIM microscope (iSIM, VisiTech UK) with a 100X TIRF oil immersion objective (Nikon, NA 1.49, CFI SR HP Apo TIRF 100XAC Oil). Two independent experiments were performed (IF1, IF2 in the file name), for each experiment, two samples were prepared per stage and experiment (001, 002 in the file name).</p> <p>The image data are in the ND2 format (Nikon proprietary) and can be imported using the BioFormats importer (Open Microscopy Environment).</p>
Microscopy data: interaction of the gene drll2 with RNA polymerase II clusters during early zebrafish embryo development
<p>Microscopy image data containing fluorescently labeled gene loci, recruited RNA polymerase II, and elongating RNA polymerase II.</p> <p>This data set is for the gene <em>drll2</em> and is obtained from fixed zebrafish embryos, collected at the developmental stages oblong, sphere, dome, 30% epiboly, and 50% epiboly (indicated in the file names). Data were recorded using an instant-SIM microscope (iSIM, VisiTech UK) with a 100X TIRF oil immersion objective (Nikon, NA 1.49, CFI SR HP Apo TIRF 100XAC Oil). Two independent experiments were performed (IF1, IF2 in the file name), for each experiment, two samples were prepared per stage and experiment (001, 002 in the file name).</p> <p>The image data are in the ND2 format (Nikon proprietary) and can be imported using the BioFormats importer (Open Microscopy Environment).</p>
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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)
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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.