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31 results for “Enrichment factor”
Pleiotropic Expression Quantitative Trait Loci Are Enriched in Enhancers and Transcription Factor Binding Sites and Impact More Genes
<p>This dataset comprises two files that accompany the article (link to be added upon publication).</p> <h2>1. gwas2eqtl_colocalization_full.tar.gz</h2> <p>This file contains the complete colocalization dataset generated using the code from the following GitHub repository: gwas2eqtl. This dataset is used as input for the pleiotropic eQTL analysis available at gwas2eqtl_pleiotropy, which produces the figures in the article.</p> <p><strong>File structure:</strong></p> <blockquote> <p>.<br>└── gwas417<br> └── coloc<br> ├── ebi-a-GCST000998<br> │ └── pval_5e-08<br> │ └── r2_0.1<br> │ └── kb_1000<br> │ └── window_1000000<br> │ ├── Alasoo_2018_ge_macrophage_IFNg+Salmonella.tsv<br> │ ├── Alasoo_2018_ge_macrophage_IFNg.tsv<br>...</p> </blockquote> <p>Each TSV file contains the following columns:</p> <blockquote> <p>chrom pos rsid ref alt eqtl_gene_id gwas_beta gwas_pval gwas_id eqtl_beta eqtl_pval eqtl_id PP.H4.abf SNP.PP.H4 nsnps PP.H3.abf PP.H2.abf PP.H1.abf PP.H0.abf coloc_variant_id coloc_region<br>1 109272258 rs4970834 C T ENSG00000168765 -0.12874.25001047052626e-09 ebi-a-GCST000998 -0.250697 0.0893351 Alasoo_2018_ge_macrophage_IFNg+Salmonella 0.108283426725895 0.0520205502224409 6 0.000552728832012655 2.09397277427793e-07 0.890881419183881 0.000282215860934432 1_109279544_G_A 1:108779544-109779543<br>1 109274968 rs12740374 G T ENSG00000168765 -0.103341 1.63998546891446e-09 ebi-a-GCST000998 -0.197397 0.172673Alasoo_2018_ge_macrophage_IFNg+Salmonella 0.108283426725895 0.0857585178966856 6 0.000552728832012655 2.09397277427793e-07 0.890881419183881 0.000282215860934432 1_109279544_G_A 1:108779544-109779543<br>1 109275216 rs660240 T C ENSG00000168765 0.1044492.78997299740827e-09 ebi-a-GCST000998 0.214165 0.139318 Alasoo_2018_ge_macrophage_IFNg+Salmonella 0.108283426725895 0.0557749486050279 6 0.000552728832012655 2.09397277427793e-07 0.890881419183881 0.000282215860934432 1_109279544_G_A 1:108779544-109779543<br>1 109275684 rs629301 G T ENSG00000168765 0.1054716.129993302249e-10 ebi-a-GCST000998 0.197397 0.172673 Alasoo_2018_ge_macrophage_IFNg+Salmonella 0.108283426725895 0.22229240584331 6 0.000552728832012655 2.09397277427793e-07 0.890881419183881 0.000282215860934432 1_109279544_G_A 1:108779544-109779543<br>1 109278889 rs602633 T G ENSG00000168765 0.1034352.15998134341707e-09 ebi-a-GCST000998 0.226329 0.102673 Alasoo_2018_ge_macrophage_IFNg+Salmonella 0.108283426725895 0.0782482718431504 6 0.000552728832012655 2.09397277427793e-07 0.890881419183881 0.000282215860934432 1_109279544_G_A 1:108779544-109779543<br>...</p> </blockquote> <p> </p> <p>The dataset provides colocalization statistics for GWAS-eQTL pairs, including posterior probabilities and variant annotations.</p> <h2>2. gwas2eqtl0.1.3.tsv.gz</h2> <p>This file is a filtered version of the colocalization dataset, refined based on cutoffs of PP.H4.abf ≥ 0.75 and SNP.PP.H4 ≥ 0. This subset is utilized in the gwas2eqtl web application for data visualization.</p> <p>Sample Columns:</p> <blockquote> <p>chrom pos19 pos38 cytoband rsid ref alt gwas_trait gwas_class gwas_beta eqtl_gene_symbol eqtl_beta eqtl_id eqtl_gene_id gwas_id gwas_pval eqtl_pval pp_h4_abf snp_pp_h4 tophits_variant_id nsnps<br>1 1163804 1228424 1p36.33 rs7515488 C T Inflammatory bowel disease Autoimmune dis. 0.0874308 ANKRD65 -0.175816 BrainSeq_ge_brain ENSG00000235098 ebi-a-GCST003043 2.85292266979231e-10 0.000841046 0.978425254116226 6.18454060493069e-12 1_1312114_T_C 3<br>1 1163804 1228424 1p36.33 rs7515488 C T Inflammatory bowel disease Autoimmune dis. 0.0874308 ANKRD65 -0.175816 BrainSeq_ge_brain ENSG00000235098 ieu-a-294 2.85292266979231e-10 0.000841046 0.974019788384412 7.52286530905187e-12 1_1312114_T_C 4<br>1 1163804 1228424 1p36.33 rs7515488 C T Inflammatory bowel disease Autoimmune dis. 0.0874308 ANKRD65 -0.293529 CommonMind_ge_DLPFC_naive ENSG00000235098 ebi-a-GCST003043 2.85292266979231e-10 2.30452e-06 0.953333690803618 6.9758581004380506e-15 1_1312114_T_C 6<br>1 1163804 1228424 1p36.33 rs7515488 C T Inflammatory bowel disease Autoimmune dis. 0.0874308 ANKRD65 -0.293529 CommonMind_ge_DLPFC_naive ENSG00000235098 ieu-a-294 2.85292266979231e-10 2.30452e-06 0.951092499048109 7.0876793540547e-15 1_1312114_T_C 7<br>1 1163804 1228424 1p36.33 rs7515488 C T Inflammatory bowel disease Autoimmune dis. 0.0874308 ANKRD65 -0.510549 FUSION_ge_adipose_naive ENSG00000235098 ebi-a-GCST003043 2.85292266979231e-10 1.2212e-06 0.974412793352836 1.70246427912903e-11 1_1312114_T_C 6</p> </blockquote> <p> </p> <p>This filtered dataset focuses on high-confidence colocalization events for functional exploration of genetic associations and regulatory mechanisms.</p>
Data from: Challenging trophic position assessments in complex ecosystems: calculation method, choice of baseline, trophic enrichment factors, season and feeding guild do matter. A case study from Marquesas Islands coral reefs
<p>Assessments of ecosystem functioning are a fundamental ecological challenge and an essential foundation for ecosystem-based management. An understanding of species trophic positions (TP) is essential to characterize food web architecture. However, despite the intuitive nature of the concept, empirically estimating TP is a challenging task due to the complexity of trophic interaction networks. Various alternative methods are proposed to assess TPs, including different approaches to account for the different sources of organic matter at the base of the food web (the "baseline"). However, it is often not clear which methodological approach and which baseline choices are the most reliable. Using an ecosystem-wide assessment of a tropical reef (Marquesas Islands, French Polynesia, with available data for 70 coral reef invertebrate and fish species), we tested whether different commonly used TP estimation methods yield similar results and, if not, whether it is possible to identify the most reliable method. We found significant differences in TP estimates of up to 1.7 TP for the same species, depending on the method and the baseline used. When using bulk stable isotope data, the choice of the baseline significantly impacted TP values. Indeed, while δ<sup>15</sup>N values of macroalgae led to consistent TP estimates, those using phytoplankton generated unrealistically low TP estimates. The use of a conventional enrichment factor (i.e. 3.4 ‰) or a "variable" enrichment factor (i.e. according to feeding guilds) also produced clear discrepancies between TP estimates. Regarding the use of different calculation methods, TPs obtained with δ<sup>15</sup>N values of source amino acids (compound specifics isotope analysis) were close to those assessed with macroalgae, but evidenced the opposite seasonal pattern, with significantly lower TPs in winter than in summer for the majority of assessed species, with particularly pronounced differences for lower TP species. We use the observed differences to discuss possible drivers of the diverging TP estimates and the potential ecological implications.</p>
Data from: Challenging trophic position assessments in complex ecosystems: calculation method, choice of baseline, trophic enrichment factors, season and feeding guild do matter. A case study from Marquesas Islands coral reefs
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Song et al._Peak areas of aromatic acids and cations in seawater and SSA filter samples & Enrichment factors for aromatic acids and cations in submicron SSA
<p><a name="OLE_LINK2"></a><span>Aromatic acids are ubiquitous in seawater (SW) and can be transported to the atmosphere via sea spray aerosol (SSA). Despite their importance in affecting the global radiative balance, the contribution of marine aromatic acids and their</span><span><span> </span></span><span><span>transport mechanisms through SSA remain unclear.</span></span><span> Herein, the distribution of particle size and number concentration of SSA produced in SW containing nine different aromatic acids (i.e., benzoic acids, benzenedicarboxylic acids, hydroxybenzoic acids, vanillic acid, and syringic acid) was studied using a custom-made SSA simulation chamber; moreover, the enrichment of aromatic acids in SSA and</span><span> </span><span>their emission flux to the atmosphere were analyzed. Transmission electron microscopy (TEM) images clearly revealed that aromatic acids can be transferred to the nascent SSA. Interestingly, the morphology associated with benzenedicarboxylic acids-coated particles showed that aromatic acids can promote the growth of other surfaces of sea salt, thus making the sea salt core spherical. Aromatic acids showed a significant enrichment behavior at the air-sea interface, which clearly indicated that SSA represent a source of aromatic acids in the atmosphere. Vanillic acid had the largest global emission flux through SSA (962 tons yr<sup>-1</sup>), even though its concentration in SW<span> </span>was lower. The calculated results indicated that the global annual flux of aromatic acids was not only affected by the concentration in SW, but also by their enrichment factor (EF). These data are critical for further quantifying the contribution of organic acids to the atmosphere via SSA, which may provide an estimate of the potential influence of the atmospheric feedbacks to the ocean carbon cycle.</span></p>
The Effects of Beta-glucan Enriched Oatcake Consumption on Metabolic Disease Risk Factors
ClinicalTrials.gov study NCT02615444. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Contextual Factors-Enriched Standard Care on Mechanical Neck Pain
ClinicalTrials.gov study NCT06361472. IPD Sharing: NO. Countries: 1. Publications: 30.
Effects of Aleurone-enriched Products on Cardiovascular Risk Factors
ClinicalTrials.gov study NCT02150356. IPD Sharing: Not stated. Countries: 1. Publications: 2.
The Effect of Yogurt Enriched With Flaxseed on Cardiovascular Risk Factors in Type 2 Diabetic Patients
ClinicalTrials.gov study NCT02436369. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Data from: Amino acid-specific δ15N trophic enrichment factor in fish fed with formulated diets varying in protein quantity and quality
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Transcription factor networks disproportionately enrich for heritability of blood cell phenotypes [10x ATAC + GEX Multiome]
GEO Series GSE274113. Homo sapiens. 42 samples. Type: Other.
Evidence that conserved essential genes are enriched for pro-longevity factors
GEO Series GSE203259. Saccharomyces cerevisiae. 10 samples. Type: Expression profiling by high throughput sequencing.
Transcription factor networks disproportionately enrich for heritability of blood cell phenotypes [PRO-Seq]
GEO Series GSE274110. Homo sapiens. 4 samples. Type: Other.
Isolation and Characterization of a Novel Mammary Adenocarcinoma, MCa-P1362, with Hormone Receptor Expression, Human Epidermal Growth Factor Receptor 2 Positivity, and Enrichment in Cancer and Mesench
GEO Series GSE252042. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Transcription factors enriched in tumor-associated enhancers in VHL-deficient ccRCC
GEO Series GSE86092. Homo sapiens. 8 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Transcription factor enrichment analysis (TFEA): Quantifying the activity of hundreds of TFs from a single experiment
GEO Series GSE142419. Homo sapiens. 4 samples. Type: Other.
Dietary supplementation with a novel fish oil enriched in very-long-chain-polyunsaturated-fatty-acids improves cardiometabolic risk factors and retinal function in mice
GEO Series GSE221559. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Exploring the Factors regulating RNA Enrichment in EVs
GEO Series GSE279376. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Lineage transcription factors ASCL1, NKX2-1, and PROX1 are enriched at super enhancers and co-regulate subtype-specific genes in small cell lung cancer [RNA-seq]
GEO Series GSE151000. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing.
PRDM16 Functions as A Compact Myocardium-Enriched Transcription Factor Required to Maintain Compact Myocardial Cardiomyocyte Identity in Left Ventricle (Spatial Transcriptomics)
GEO Series GSE179392. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Single-nucleus sequencing reveals enriched expression of genetic risk factors in Extratelencephalic Neurons sensitive to degeneration in ALS
GEO Series GSE226753. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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