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4,582 results for “Gene regulation”
Data from: Single-nucleus RNA-seq and ATAC-seq in outbred rats with divergent cocaine addiction behaviors reveal long-term changes in gene regulation and GABAergic inhibition in the amygdala
<p>This dataset accompanies our publication titled: "Single-nucleus RNA-seq and ATAC-seq in outbred rats with divergent cocaine addiction behaviors reveal long-term changes in gene regulation and GABAergic inhibition in the amygdala."</p> <p><strong>Files Included:</strong></p> <p><strong>1. geno.N26.vcf.gz</strong><br> - Description: Contains genotypes for 26 Heterogeneous Stock rats whose gene expression was predicted.</p> <p><strong>2. pred_expr.Brain.N26.tsv</strong><br> - Description: This tab-delimited table contains predicted relative gene expression in the brain for 26 Heterogeneous Stock rats. <br> - Details: Predictions were made for 8,997 genes from linear models based on cis-eQTLs from whole brain hemisphere tissue downloaded from the <a href="https://ratgtex.org/download/">RatGTEx Portal</a>. A gene is included in the table if it had at least one significant cis-eQTL, and if its predicted expression in these 26 animals had nonzero variance. The values in the table give the predicted log2(relative expression), where log2(2) = 1 is the baseline expression from the two haplotypes of the gene if it had only reference alleles at all its regulatory loci.<br> - Additional Info: Predictions were generated using <strong>gene_expr_pred.py</strong> available at https://github.com/PejLab/gene_expr_pred<br> An explanation of the prediction model is given in https://doi.org/10.1101/2022.01.28.478116</p> <p><strong>3. Behavioral data.xlsx</strong><br> - Description: Contains behavioral data for the Heterogeneous Stock (HS) rats.<br> - Organization: Each sheet in the file corresponds to data for a specific figure.</p> <p><strong>Additional Dataset Locations:</strong></p> <p>The primary datasets generated during this study can be found on the Gene Expression Omnibus under accession number <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE212417">GSE212417</a></p> <p><strong>Publicly Available Datasets Utilized:</strong></p> <p>- Rattus norvegicus Ensembl v98 reference genome and genome assembly: <a href="http://useast.ensembl.org/Rattus_norvegicus/Info/Index">Rnor_6.0 </a><br> - JASPAR2022 transcription factor binding profiles for vertebrates: <a href="https://jaspar.genereg.net/">JASPAR</a><br> - ENCODE Honeybadger 2 ChIP-seq: <a href="https://personal.broadinstitute.org/meuleman/reg2map/">Broad Institute</a><br> - Liu et al. 2019106 GWAS for tobacco and nicotine addiction summary statistics: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6358542/">PubMed</a><br> - RatGTEx Portal tissue-specific cis-eQTLs: <a href="https://ratgtex.org/download/">RatGTEx Portal </a><br> - 1000 Genomes European reference panel: <a href="https://alkesgroup.broadinstitute.org/LDSCORE/">Alkes Group</a><br> - KEGG pathways: <a href="https://www.kegg.jp/kegg/rest/keggapi.html">KEGG API</a></p>
The cacao gene atlas: A transcriptome developmental atlas reveals highly tissue-specific and dynamically-regulated gene networks in Theobroma cacao L
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Extensive, transient, and long-lasting gene regulation in a song-controlling brain area during testosterone-induced song development in adult female canaries
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Data from: The roles of growth regulation and appendage patterning genes in the morphogenesis of treehopper pronota
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Managing the tradeoff between reproduction and survival requires flexibility in behavior and gene regulation in three-spined stickleback
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The yellow gene regulates behavioral plasticity by repressing male courtship in Bicyclus anynana butterflies
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Transcription regulator ACTR contributes pathogenicity through mediating ACT toxin synthesis gene ACTS4 in Alternaria alternata
<p>Host-selective ACT toxin are critical for the pathogenesis of the citrus fungal pathogen <em>Alternaria alternata</em>. The biosynthesis of ACT toxin is mainly regulated by multiple ACT toxin genes located in the secondary metabolite gene cluster. However, the regulatory hierarchy of ACT toxin synthesis by these ACT genes have not been explored. In this study, we reported a transcription regulator <em>ACTR</em> contributes ACT toxin biosynthesis through mediating ACT toxin synthesis gene ACTS4 in <em>Alternaria alternata.</em> We generated <em>ACTR</em>-disrupted and -silenced mutants in the tangerine pathotype of <em>A. alternata.</em> Phenotype analysis showed that the <em>ACTR</em> mutants displayed a significant loss of ACT toxin production and a decreased virulence on citrus leaves whereas the vegetative growth and sporulation were not affected, indicating an essential role of <em>ACTR</em> in both ACT toxin biosynthesis and pathogenicity. To elucidate the transcription network of ACTR, we performed RNA-Seq experiments on wild-type and <em>ACTR</em> null mutant and identified genes that were differentially expressed between two genotypes. Transcriptome profiling and RT-qPCR analysis demonstrated that the ACT toxin biosynthetic gene <em>ACTS4</em> is down-regulated in<em> ACTR </em>mutant<em>.</em> We generated <em>ACTS4 </em>knock-down mutant and found that the pathogenicity of <em>ACTS4</em> mutant was severely impaired. Interestingly, both <em>ACTR</em> and <em>ACTS4</em> are not involved in the response to different abiotic stresses including oxidative stress, salt stress, cell-wall disrupting regents, and Cu<sup>2+</sup>, indicating the function of these two genes is highly specific. In conclusion, our results highlight the important regulatory role of <em>ACTR</em> in ACT toxin biosynthesis through mediating ACT toxin synthesis gene ACTS4 and underline the essential role of in the tangerine pathotype of <em>A. alternata</em>.</p>
Distal and proximal hypoxia response elements cooperate to regulate organ-specific erythropoietin gene expression
<p>While it is well-established that distal hypoxia response elements (HREs) regulate hypoxia-inducible factor (HIF) target genes such as erythropoietin (Epo), an interplay between multiple distal and proximal (promoter) HREs has not been described so far. Hepatic Epo expression is regulated by a HRE located downstream of the <i>EPO</i> gene, but this 3' HRE is dispensable for renal <i>EPO</i> gene expression. We previously identified a 5' HRE and could show that both HREs direct exogenous reporter gene expression. Here, we show that whereas in hepatic cells the 3' but not the 5' HRE is required, in neuronal cells both the 5' and 3' HREs contribute to endogenous Epo induction. Moreover, two novel putative HREs were identified in the <i>EPO</i> promoter. In hepatoma cells HIF interacted mainly with the distal 3' HRE, but in neuronal cells HIF most strongly bound the promoter, to a lesser extent the 3' HRE, and not at all the 5' HRE. Interestingly, mutation of either of the two distal HREs abrogated HIF binding to the 3' and promoter HREs. These results suggest that a canonical functional HRE can recruit multiple, not necessarily HIF, transcription factors to mediate HIF binding to different distant HREs in an organ-specific manner.</p>
Mass spectrometry data for: A small protein coded within the mitochondrial canonical gene nd4 regulates mitochondrial bioenergetics
<p><span><strong>Background</strong>:</span> <span>Mitochondria have a central role in cellular functions, aging and in certain diseases. They possess their own genome, a vestige of their bacterial ancestor. Over the course of evolution, most of the genes of the ancestor have been lost or transferred to the nucleus. In humans, the mtDNA is a very small circular molecule with a functional repertoire limited to only 37 genes. Its extremely compact nature with genes arranged one after the other and separated by short non-coding regions suggests that there is little room for evolutionary novelties. This is radically different from bacterial genomes, which are also circular but much larger, and in which we can find genes inside other genes. These sequences, different from the reference coding sequences, are called alternative open reading frames or altORFs, and they are involved in key biological functions. </span><span>However, whether altORFs exist in mitochondrial protein-coding genes or elsewhere in the human mitogenome has not been fully addressed.</span></p> <p><span><strong>Results</strong>:</span> <span>We found a downstream alternative ATG initiation codon in the +3 reading frame of the human mitochondrial <em>nd4</em> gene. This newly characterized altORF encodes a 99-amino acids long polypeptide, MTALTND4, which is conserved in primates. Our custom antibody, but not the pre-immune serum, was able to immunoprecipitate MTALTND4 from HeLa cell lysates, confirming the existence of an endogenous MTALTND4 peptide. The protein is localized in mitochondria and cytoplasm and is also found in the plasma, </span><span>and it impacts cell and mitochondrial physiology. </span></p> <p><span><strong>Conclusions</strong>:</span> <span>Many human-mitochondrial-translated ORFs might have so far gone unnoticed. By ignoring mtaltORFs, we have underestimated the coding potential of the mitogenome.</span> <span>Alternative mitochondrial peptides such as MTALTND4 may offer </span><span>a new framework for the investigation of mitochondrial functions and diseases.</span></p>
A library of reporters of the global regulators of gene expression of Escherichia coli
<p>The topology of the transcription factor network (TFN) of <em>E. coli</em> is far from uniform, with 22 global regulator (GR) proteins controlling one-third of all genes. So far, their production rates cannot be tracked by comparable fluorescent proteins. We developed a library of fluorescent reporters for 16 GRs. Each consists of a single-copy plasmid coding for GFP fused to the full-length copy of the native promoter. We tracked their activity in exponential and stationary growth, and under weak and strong stresses. We show that the reporters have high sensitivity and specificity to all stresses tested and detect single-cell variability in transcription rates. Given the influence of GRs on the TFN, we expect that the new library will contribute to dissecting global transcriptional stress-response programs of <em>E. coli</em>. Moreover, it can be invaluable in bio-industrial applications that tune those programs to, instead of cell growth, favor productivity while reducing energy consumption.</p>
Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast
<p> </p> <p><strong>Abstract</strong></p> <p><strong>Recent progress in high throughput single cell RNA-seq (scRNA-seq) has activated the development of data-driven inferring methods of gene regulatory networks. Most network estimations assume that perturbations produce downstream effects. However, the effects of gene perturbations are sometimes compensated by a gene with redundant functionality (functional compensation). In order to avoid functional compensation, previous studies constructed double gene deletions, but its vast nature of gene combinations was not suitable for comprehensive network estimation. We hypothesized that functional compensation may emerge as a noise change without mean change (noise-only change) due to varying physical properties and strong compensation effects. Here, we show compensated interactions, which are not detected by mean change, are captured by noise-only change quantified from scRNA-seq. We investigated whether noise-only change genes caused by a single deletion of STP1 and STP2, which have strong functional compensation, are enriched in redundantly regulated genes. As a result, noise-only change genes are enriched in their redundantly regulated genes. Furthermore, novel downstream genes detected from noise change are enriched in “transport”, which is related to known downstream genes. Herein, we suggest the noise difference comparison has the potential to be applied as a new strategy for network estimation that capture even compensated interaction.</strong></p> <p><em><strong>SourceCode</strong></em></p> <p><strong><em>Main.R</em></strong><br> Please execute code along with the guide in this script</p> <p><em><strong>CreateBASiCS.R</strong></em><br> Create BASiCS chain objects, which include mean and noise data, from the raw count matrix. This step takes ~48 hours. Use of HPC is recommended</p> <p><em><strong>DifferenceTestAll.R</strong></em><br> Execute comparison with regard to the mean and noise of expression level quantified from all cells. Test enrichment to the redundantly regulated genes.</p> <p><em><strong>DifferenceTestCluster.R</strong></em><br> Execute comparison with regard to the mean and noise of expression level quantified from cells belonging to the same cluster. Test enrichment to the redundantly regulated genes.</p> <p><em><strong>STP12_detail_analysis.Rmd</strong></em><br> Output results mentioned in the discussion. Code is placed within the chunk.</p> <p><em><strong>CreateSupFig2.R</strong></em><br> Generate supplement figure 2</p> <p><em><strong>CreateFigure4AndS6.R</strong></em><br> Generate figure 4 and supplement figure 6</p> <p><strong>YeastGeneNameList.csv</strong><br> Data that convert ORF id to the common name<br> This data includes 3 columns, Name, ORF, and SGD.<br> - Name: Common name (e.g., TSC3)<br> - ORF: ORF id with Yeastract format (e.g., YBR058C.A)<br> - SGD: ORF id with SGD format (e.g., YBR058C-A)</p> <p><br> <strong>GeneticInteraction(ORF).csv</strong><br> Genetic interaction data downloaded from Yeastract. This data includes two-column, V1 and V2.<br> The interaction is described as two matrices such as V1 output to V2.</p> <p><strong>NoiseChangeGeneList</strong><br> This folder contains data required for generating tables 1 and S2.</p>
Mimulus cardinalis plasticity analyses and R scripts for: Spatial variation in high temperature-regulated gene expression predicts evolution of plasticity with climate change in the scarlet monkeyflower
<p>A major way that organisms can adapt to changing environmental conditions is by evolving increased or decreased phenotypic plasticity. In the face of current global warming, more attention is being paid to the role of plasticity in maintaining fitness as abiotic conditions change over time. However, given that temporal data can be challenging to acquire, a major question is whether evolution in plasticity across space can predict adaptive plasticity across time. In growth chambers simulating two thermal regimes, we generated transcriptome data for western North American scarlet monkeyflowers (<i>Mimulus cardinalis</i>) collected from different latitudes and years (2010 and 2017) to test hypotheses about how plasticity in gene expression is responding to increases in temperature, and if this pattern is consistent across time and space. Supporting the genetic compensation hypothesis, individuals whose progenitors were collected from the warmer-origin northern 2017 descendant cohort showed lower thermal plasticity in gene expression than their cooler-origin northern 2010 ancestors. This was largely due to a change in response at the warmer (40ºC) rather than cooler (20ºC) treatment. A similar pattern of reduced plasticity, largely due to a change in response at 40ºC, was also found for the cooler-origin northern versus the warmer-origin southern population from 2017. Our results demonstrate that reduced phenotypic plasticity can evolve with warming and that spatial and temporal changes in plasticity predict one another.</p>
Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation
<p>During activation, T cells undergo extensive changes in gene expression which shape the properties of cells to exert their effector function. Therefore, understanding the genetic regulation of gene expression during T cell activation provides essential insights into how genetic variants influence the response to infections and immune diseases. We generated a single-cell map of expression quantitative trait loci (eQTL) across a T cell activation time-course. We profiled 655,349 CD4+ naive and memory T cells, capturing transcriptional states of unstimulated cells and three time points of cell activation in 119 healthy individuals. We identified 38 cell clusters, including stable clusters such as central and effector memory T cells and transient clusters that were only present at individual time points of activation, such as interferon-responding cells. We mapped eQTLs using a T cell activation trajectory and identified 6,407 eQTL genes, of which a third (2,265 genes) were dynamically regulated during T cell activation. We integrated this information with GWAS variants for immune-mediated diseases and observed 127 colocalizations, with significant enrichment in dynamic eQTLs. Immune disease loci colocalized with genes that are involved in the regulation of T cell activation, and genes with similar functions tended to be perturbed in the same direction by disease risk alleles. Our results emphasize the importance of mapping context-specific gene expression regulation, provide insights into the mechanisms of genetic susceptibility of immune diseases, and help prioritize new therapeutic targets.</p> <p>This dataset comprises of summary stats for eQTLs identified in the study (parquet files) and the ones which passed significance threshold (tensor_out.tar.gz archive). Files are described by cell subset (CD4 Naive, CD 4 Memory, TEMRA, TCM, etc.), time since activation (16h, 4h, 5days) as described in the publication (preprint https://doi.org/10.1101/2021.12.06.470953)</p>
Dataset of results for a copper switch for inducing CRISPR/Cas9-based transcriptional activation tightly regulates gene expression in Nicotiana benthamiana.
<p>CRISPR-based programmable transcriptional activators (PTAs) are used in plants for rewiring gene networks. Better tuning of their activity in a time and dose-dependent manner should allow precise control of gene expression. Here, we report the optimization of a Copper Inducible system called CI-switch for conditional gene activation in Nicotiana benthamiana. In the presence of copper, the copper-responsive factor CUP2 undergoes a conformational change and binds a DNA motif named copper-binding site (CBS). In this study, we tested several activation domains fused to CUP2 and found that the non-viral Gal4 domain results in strong activation of a reporter gene equipped with a minimal promoter, offering advantages over previous designs. To connect copper regulation with downstream programmable elements, several copper-dependent configurations of the strong dCasEV2.1 PTA were assayed, aiming at maximizing activation range, while minimizing undesired background expression. The best configuration involved a dual copper regulation of the two protein components of the PTA, namely dCas9:EDLL and MS2:VPR, and a constitutive RNA pol III-driven expression of the third component, a guide RNA with anchoring sites for the MS2 RNA-binding domain. With these optimizations, the CI/dCasEV2.1 system resulted in copper-dependent activation rates of 2,600-fold and 245-fold for the endogenous N. benthamiana DFR and PAL2 genes, respectively, with negligible expression in the absence of the trigger. The tight regulation of copper over CI/dCasEV2.1 makes this system ideal for the conditional production of plant-derived metabolites and recombinant proteins in the field.</p>
Stop-codon recoding in bacteriophages may regulate translation of lytic genes
<p><strong>This has some basic datasets for bacteriophages that use alternative genetic codes, and their close standard code relatives. </strong></p> <p>I have included the following:</p> <p>- Genomes for all alternatively coded phages and their relatives</p> <p>- Predicted proteins all alternatively coded phages and their relatives</p> <p>- A sheet with some basic information about these phages</p> <p>- Terminase treefile (Figure 2A) </p> <p>- Genomes for crAss-like phages used in the alternative code bias analysis (Figure 4)</p> <p>- Genomes for Agate phages used in the alternative code bias analysis (Figure 4) as well as the ANI analysis (Figure 3A)</p> <p>- Untrimmed lysogenic contigs for prophages (Like those shown in Figure 5)</p> <p> </p>
Transcriptome Analysis of Retinoic Acid-Inducible Gene I Overexpression Reveals the Potential Genes for Autopha-gy-related Negative Regulation
<p>Supplementary table 1: Primer pairs used for quantitative RT-PCR analysis. Supplementary file 2: All DEGs are listed in the excel file.</p>
Serum response factor utilizes distinct promoter- and enhancer-based mechanisms to regulate cytoskeletal gene expression in macrophages.
<p>Cells of the monocyte/macrophage lineage play essential roles in tissue homeostasis and immune responses, but mechanisms underlying the coordinated expression of cytoskeletal genes required for specialized functions of these cells, such as directed migration and phagocytosis, remain unknown. Here, using genetic and genomic approaches, we provide evidence that serum response factor (SRF) regulates both general and cell type-restricted components of the cytoskeletal gene expression program in macrophages. Genome-wide location analysis of SRF in macrophages demonstrates enrichment of SRF binding at ubiquitously expressed target gene promoters, as expected, but also reveals that the majority of SRF binding sites associated with cell type-restricted target genes are at distal inter- and intragenic locations. Most of these distal SRF binding sites are established by the prior binding of the macrophage- and the B cell-specific transcription factor PU.1 and exhibit histone modifications characteristic of enhancers. Consistent with this, representative cytoskeletal target genes associated with these elements require both SRF and PU.1 for full expression. These findings suggest that SRF uses two distinct molecular strategies to regulate programs of cytoskeletal gene expression: a promoter-based strategy for ubiquitously expressed target genes and an enhancer-based strategy at target genes that exhibit cell type-restricted patterns of expression.</p>
Crown morphology in Norway spruce (Picea abies [Karst.] L.) as adaptation to mountainous environments is associated with single nucleotide polymorphisms (SNPs) in genes regulating seasonal growth rhythm
Trees growing at high altitude or latitude have to be adapted, amongst others, to the lower temperatures, a shorter vegetation period, heavier snow load and frost desiccation. Association between molecular genetic markers and climatic variables may provide evidence for the genetic control of climatic adaptation. With increasing genomic resources, several genes with importance to climatic adaptation are identified over a wide range of tree species. Commonly, circadian clock genes are linked to the adaptation to lower temperatures and especially to a shortened vegetation period, as they are regulating metabolic and phenological processes in the day-night shift and seasonal change. Potentially adaptive "candidate" genes associated with latitudinal and elevational gradients were identified in several Picea spp. Before molecular markers became available to study climatic adaptation, phenotypic traits measured in natural populations and/or common garden studies were used to search for their association with climate variables. In Norway spruce, the crown architecture is the most noticeable trait associated with altitude and the related environment. The mountainous narrow-crowned morphotype is characterised by superior resistance to snow breakage in regions with heavy snow fall. In total, the crown shape was assessed in 765 individual trees from mountainous regions in the Thuringian Forest, the Ore Mountains (Saxony) and Harz Mountains (Lower-Saxony/Saxony-Anhalt), and they were genotyped at 44 single nucleotide polymorphisms (SNPs) in 24 adaptive trait related candidate genes. Six SNPs in three genes, APETALA 2-like 3 (AP2L3), GIGANTEA (GI), and mitochondrial transcription termination factor (mTERF) were associated with variation in crown shape. GI has previously been identified in angiosperms and gymnosperms to be associated with temperature and growth cessation. Our results showed that crown morphology in Norway spruce is associated with genetic markers which are putatively involved in the complex process of genetic adaptation to climatic conditions at high altitudes.
Specifying cellular context of transcription factor regulons for exploring context-specific gene regulation programs
<p>This repository contains the raw and processed files used in Minaeva et al. 2024.</p> <p>In this version, we have revised the regulon construction pipeline and expanded the dataset to cover 40 common cell lines.</p> <p>The code used to generate these files is available at <a href="https://github.com/LappalainenLab/chip_seq_regulons" target="_new" rel="noreferrer">GitHub - LappalainenLab/chip_seq_regulons</a>.</p> <p>The descriptions of the files contained within each subdirectory are as follows:</p> <h3>1-dataset_stats</h3> <ul> <li><code>per_gene_stats_{approach}_{cell_line}.tsv</code>: Number of TFs regulating a gene according to the respective approach (S2Mb, M2Kb, or S2Kb) in a given cell line.</li> <li><code>per_tf_stats_{approach}_{cell_line}.tsv</code>: Number of target genes regulated by a TF according to the respective approach (S2Mb, M2Kb, or S2Kb) in a given cell line.</li> </ul> <h3>1-network_enrichment</h3> <ul> <li><code>enrich_scores_remap_all_tfs_K562.tsv</code>: Results of fitting logistic regression for testing the enrichment of the K562 regulon in other biological networks (PPI, coexpression, experimental trans-networks).</li> </ul> <h3>2-plot_decoupler_comparison_benchmark_across_cells</h3> <ul> <li><code>{cell_line}_comparison_benchmark.tsv</code>: Results of benchmarking S2Mb, M2Kb, CollecTri, Dorothea, ChIP-Atlas, RegNet, and TRRUST regulons using the decoupler package and the KnockTF database. Cell lines considered are K562, HepG2, and MCF7 (see Methods for benchmarking pipeline details).</li> </ul> <h3>2-plot_decoupler_filter_benchmark_across_methods</h3> <ul> <li><code>{cell_line}_filtering_benchmark.tsv</code>: Results of benchmarking S2Mb, M2Kb, and S2Kb regulons with different filters applied using the decoupler package and the KnockTF database. Cell lines considered are K562, HepG2, and MCF7 (see Methods for benchmarking pipeline details).</li> </ul> <h3>3-tf_activity</h3> <ul> <li><code>aml_k562_activity_{regulon}_sc.tsv</code>: Results of TF activity analysis based on a respective regulon between healthy hematopoietic stem cells (HSCs) and abnormal AML progenitor cells following the decoupler pipeline. Regulons considered are K562-specific ChIP-Atlas and M2Kb regulons, and generalized CollecTri regulon.</li> <li><code>aml_activity_estimates_hsc_sc.tsv</code>: Summary of the TF activity analysis for statistically significantly dysregulated TFs between healthy HSCs and abnormal AML progenitor cells across regulons.</li> <li><code>aml_dhsc_ahsc_activity_{regulon}_sc.tsv</code>: Results of TF activity analysis based on a respective regulon between leukemic activated and dormant HSCs following the decoupler pipeline. Regulons considered are K562-specific ChIP-Atlas and M2Kb regulons, and generalized CollecTri regulon.</li> <li><code>aml_activity_estimates_dhsc_ahsc_sc.tsv</code>: Summary of the TF activity analysis for statistically significantly dysregulated TFs between leukemic activated and dormant HSCs across regulons.</li> <li><code>bc_bas_activity_{regulon}.tsv</code>: Results of TF activity analysis based on a respective regulon between healthy epithelial breast cells and malignant epithelial cells from basal breast cancer following the decoupler pipeline. Regulons considered are MCF7-specific ChIP-Atlas and M2Kb regulons, and generalized CollecTri regulon.</li> <li><code>bc_activity_estimates_bas.tsv</code>: Summary of the TF activity analysis for statistically significantly dysregulated TFs between healthy epithelial breast cells and malignant epithelial cells from basal breast cancer across regulons.</li> <li><code>bc_lum_activity_{regulon}.tsv</code>: Results of TF activity analysis based on a respective regulon between healthy epithelial breast cells and malignant epithelial cells from luminal type A breast cancer following the decoupler pipeline. Regulons considered are MCF7-specific ChIP-Atlas and M2Kb regulons, and generalized CollecTri regulon.</li> <li><code>bc_activity_estimates_lum.tsv</code>: Summary of the TF activity analysis for statistically significantly dysregulated TFs between healthy epithelial breast cells and malignant epithelial cells from luminal type A breast cancer across regulons.</li> <li><code>hep_activity_{regulon}.tsv</code>: Results of TF activity analysis based on a respective regulon between neoplastic and healthy liver cells following the decoupler pipeline. Regulons considered are HepG2-specific ChIP-Atlas and M2Kb regulons, and generalized CollecTri regulon.</li> <li><code>hep_activity_estimates.tsv</code>: Summary of the TF activity analysis for statistically significantly dysregulated TFs between neoplastic and healthy liver cells across regulons.</li> </ul> <h3>3-tf_disease_enrichment</h3> <ul> <li><code>aml_{database}_enrich_{regulon}_hsc_sc.tsv</code>: Results of enrichment analysis of dysregulated TFs identified based on a respective regulon between healthy HSCs and abnormal AML progenitor cells following the decoupler pipeline. Databases considered are COSMIC, DisGeNet, OMIM, and KEGG. Regulons considered are K562-specific ChIP-Atlas and M2Kb regulons, and generalized CollecTri regulon.</li> <li><code>aml_{database}_enrich_{regulon}_dhsc_ahsc_sc.tsv</code>: Results of enrichment analysis of dysregulated TFs identified based on a respective regulon between leukemic activated and dormant HSCs following the decoupler pipeline. Databases considered are COSMIC, DisGeNet, OMIM, and KEGG. Regulons considered are K562-specific ChIP-Atlas and M2Kb regulons, and generalized CollecTri regulon.</li> <li><code>bc_{database}_enrich_{regulon}_bas.tsv</code>: Results of enrichment analysis of dysregulated TFs identified based on a respective regulon between healthy epithelial breast cells and malignant epithelial cells from basal breast cancer following the decoupler pipeline. Databases considered are COSMIC, DisGeNet, and OMIM. Regulons considered are MCF7-specific ChIP-Atlas and M2Kb regulons, and generalized CollecTri regulon.</li> <li><code>bc_{database}_enrich_{regulon}_lum.tsv</code>: Results of enrichment analysis of dysregulated TFs identified based on a respective regulon between healthy epithelial breast cells and malignant epithelial cells from luminal type A breast cancer following the decoupler pipeline. Databases considered are COSMIC, DisGeNet, and OMIM. Regulons considered are MCF7-specific ChIP-Atlas and M2Kb regulons, and generalized CollecTri regulon.</li> <li><code>hep_{database}_enrich_{regulon}.tsv</code>: Results of enrichment analysis of dysregulated TFs identified based on a respective regulon between neoplastic and healthy liver cells following the decoupler pipeline. Databases considered are COSMIC, DisGeNet, OMIM, and KEGG. Regulons considered are HepG2-specific ChIP-Atlas and M2Kb regulons, and generalized CollecTri regulon.</li> </ul> <h3>regulons</h3> <ul> <li><code>{cell_line}_regulon.tsv</code>: S2Mb, M2Kb, and S2Kb regulons generated in this study with all acquired annotations (see Methods for details).</li> </ul> <p>External regulons used for comparison. Cell lines considered are K562, HepG2, MCF7, and GM12878:</p> <ul> <li><code>ChIP-Atlas_target_genes_{cell_line}.tsv</code>: Customized ChIP-Atlas regulons (see Methods for details).</li> <li><code>Revised_Supplemental_Table_S3_Normal.csv</code>: Dorothea regulon collected from supplementary materials of Garcia-Alonso et al. (2019).</li> </ul> <h3>s3-network_enrichment</h3> <ul> <li><code>enrich_scores_remap_all_tfs_{cell_line}.tsv</code>: Results of fitting logistic regression for testing the enrichment of cell-line-specific regulons in PPI networks (see Methods and corresponding GitHub repository for details). Cell lines considered are K562, HepG2, MCF7, and GM12878.</li> </ul> <p> </p>
Data from: Solanum lycopersicum CLASS-II KNOX genes regulate fruit anatomy via gibberellin-dependent and independent pathways
<p>The pericarp is the predominant tissue determining the structural characteristics of most fruits. However, the molecular and genetic mechanisms controlling pericarp development remain only partially understood. Previous studies have identified that CLASS-II KNOX genes regulate fruit size, shape, and maturation in Arabidopsis thaliana and Solanum lycopersicum. Here we characterized the roles of the Solanum lycopersicum CLASS-II KNOX (TKN-II) genes in pericarp development via a detailed histological, anatomical, and karyotype analysis of the TKN-II knockdown (35S:amiR-TKN-II) fruits. We identify that 35S:amiR-TKN-II pericarps contain more cells around their equatorial perimeter and fewer cell layers than the control. In addition, the cell sizes but not the ploidy levels of these pericarps were dramatically reduced.</p> <p>Further, we demonstrate that fruit shape and pericarp layer number phenotypes of the 35S:amiR-TKN-II fruits can be overridden by the procera mutant, known to induce a constitutive response to the plant hormone gibberellin. However, neither the procera mutation nor exogenous gibberellin application can fully rescue the reduced pericarp width and cell size phenotype of 35S:amiR-TKN-II pericarps. Our findings establish that TKN-II genes regulate tomato fruit anatomy, acting via gibberellin to control fruit shape but utilizing a gibberellin-independent pathway to control the size of pericarp cells.</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)
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.