Find research datasets worth reusing

opencc-by-4.0May 2023View details →

Differential abundance and gene set enrichment in plasma of cancer patients versus controls

<p>Version update: Human Protein Atlas v23 used for gene set enrichment analysis (instead of Human Protein Atlas v18)</p> <p><br>DESeq2 differential abundance output for genes with q < 0.05 and |log<sub>2</sub> fold change| > 1 in cancer vs control plasma samples:</p> <ul> <li><em>differentialabundance_pancancer.txt</em>: tables with differentially abundant genes (|log2(fold change)|>1 and adjusted p>0.05) per cancer-control comparison (cancertype) in a pan-cancer plasma sample cohort (25 locally advanced to metastatic cancer types - 7 or 8 patients per type - vs 8 cancer-free control donors)</li> <li><em>differentialabundance_threecancer.txt</em>: tables with differentially abundant genes (|log2(fold change)|>1 and adjusted p>0.05) per cancer-control comparison (cancertype) in the three-cancer plasma cohort (ovarian, prostate and uterine cancer - 11 or 12 patients per type - vs 20 cancer-free controls) <ul> <li>Gene_id: Ensembl gene id (GChr38 v91); baseMean: mean of normalized counts for all samples; log2FoldChange: log2 fold change for cancer vs control; lfcSE: standard error for cancer vs control; stat: Wald statistic for cancer vs control; pvalue: Wald test p-value for cancer vs control; padj: Benjamini-Hochberg corrected p-value; cancertype: respective cancer type abbreviation of cancer patient plasma samples that were compared to plasma samples of controls.</li> </ul> </li> </ul> <p>Gene set enrichment analyses based on fold change ranked gene lists (cancer versus control) - results obtained with fgea (v1.22.0):</p> <ul> <li><em>customgenesets.txt</em>: custom gene set lists based on RNA Atlas (&Human Protein Atlas), Tabula Sapiens, GTEX, TCGA data. <ul> <li>Reference: reference to create gene sets (including RNA Atlas, Human Protein Atlas, Tabula Sapiens, GTEX, and TCGA); set: set name; genes: gene list for set</li> </ul> </li> <li><em>GSEA_pancancer.txt</em> & <em>GSEA_threecancer.txt</em>: gene set enrichment results based on fold change ranked gene list (specific cancer type versus controls) in pan-cancer cohort and three-cancer cohort, respectively <ul> <li>Sets: gene set category (HALLMARK and KEGG: Hallmark and Canonical Pathways gene sets obtained from MSigDB (v2022.1); CUSTOM: custom tissue and cell type specific gene sets as defined in <em>customgenesets.txt)</em>; pathway: pathway/set name; pval: enrichment p-value; padj: Benjamini-Hochberg adjusted p-value; log2err: expected error for the standard deviation of the P-value logarithm; ES: enrichment score, same as in Broad GSEA implementation; NES: enrichment score normalized to mean enrichment of random samples of the same size; size: size of the pathway after removing genes without statistic values; leadingEdge: leading edge genes that drive the enrichment; Disease: respective cancer type abbreviation of cancer patient plasma samples that were compared to plasma samples of controls</li> </ul> </li> </ul>

opencc-by-4.0Jan 2019View details →

Summary statistics - Imputed gene associations identify replicable trans-acting genes enriched in transcription pathways and complex traits

<p>Summary statistics for all trans-acting/target gene pairs tested in our manuscript.</p> <p>Preprint available: <a href="https://doi.org/10.1101/471748">https://doi.org/10.1101/471748</a></p>

opencc-by-4.0Mar 2019View details →

Enrichment of gene sets altered in dram1 mutant zebrafish larvae

<p>Enrichment of gene sets altered in dram1 mutant zebrafish larvae.</p> <p>A. Gene Ontology categories significantly over and underrepresented in the significant genes differentially regulated between <em>dram1</em><sup>∆19n/∆19n </sup>PBS-injected mutants compared to <em>dram1</em><sup>+/+</sup> larvae.</p> <p>B. Gene sets from the MSigDB C2 database significantly positively correlated to the <em>dram1</em><sup>∆19n/∆19n </sup>mutants transcriptome compared to <em>dram1</em><sup>+/+</sup> larvae.</p> <p>C. Gene sets from the MSigDB C2 database significantly negatively correlated to the <em>dram1</em><sup>∆19n/∆19n </sup>mutants transcriptome compared to <em>dram1</em><sup>+/+</sup> larvae.</p>

Fig. 4 in Phylogenomics of the tropical plant family Ochnaceae using targeted enrichment of nuclear genes and 250+ taxa

Fig. 4. Distribution of specimen ages and the number of loci recovered in the phylogenomic study of Ochnaceae. A, Histogram of the collection years of all Ochnaceae specimens; B & C, Relationship between the year of collection of the specimens and the number of loci recovered for tissue obtained from herbarium material (excluding specimens with silica-dried leaf material), analysed for Ochneae and all the remaining Ochnaceae separately, either using the consensus-alignment (B) or the sample-specific (C) reference-based assembly approach. Pearson correlation coefficients and confidence intervals are given for each group.

Fig. 2 in Phylogenomics of the tropical plant family Ochnaceae using targeted enrichment of nuclear genes and 250+ taxa

Fig. 2. RAxML trees based on the concatenated nuclear loci of Ochnaceae. A, Early-diverging branches of Ochnaceae and relationships within Quiinoideae based on the FAM dataset; B, Phylogenetic relationships of Sauvagesieae, Luxemburgieae and Testuleeae based on the SLT dataset. Numbers on the branches are bootstrap values>50%. Numbers in parentheses after species names correspond to the specimen IDs (only for species with multiple accessions). The indicated classification of subfamilies and tribes follows Schneider & al. (2014).

Fig. 1 in Phylogenomics of the tropical plant family Ochnaceae using targeted enrichment of nuclear genes and 250+ taxa

Fig. 1. Overview of the phylogenetic relationships of the major clades of Ochnaceae based on the FAM dataset together with images of representative species. The classification follows Schneider & al. (2014). Ochninae is by far the most species-rich clade comprising about two-thirds of the family's species and six genera (Brack. = Brackenridgea; Cmp. = Campylospermum, clades A and B; I. = Idertia; Ochna; Ouratea; Rh. = Rhabdophyllum). Letters around the tree refer to the photos (mostly flowers except where indicated) and the relative position of the displayed taxa on the tree. A, Medusagyne oppositifolia (Medusagynoideae); B, Froesia venezuelensis (Quiinoideae); C, Luxemburgia schwackeana (Luxemburgieae); D, Rhytidanthera sulcata; E, Cespedesia spathulata; F, Poecilandra retusa; G, Godoya antioquiensis; H, Wallacea insignis; I, Sauvagesia semicylindrifolia; J, Sauvagesia erecta (Sauvagesieae); K, Infructescence of Lophira lanceolata with accrescent sepals (Lophirinae); L, Flower of Elvasia kollmannii (Elvasiinae); M, Perissocarpa umbellifera; N, Fruiting Rhabdophyllum arnoldianum; O, Brackenridgea nitida; P, Campylospermum glaberrimum; Q, Ochna serrulata; R, Fruit of Ochna integerrima with drupelets sitting on enlarged receptacle; S, Fruit of Ouratea sp. with enlarged red receptable; T, Ouratea sp. — Photos: A, K & N from www.africanplants.senckenberg.de (Dressler & al., 2014–); B by Julio Schneider; C by William Milliken/ Royal Botanic Gardens, Kew; D by Sandra Reinales; E by Reinaldo Aguilar; F, H & M by Francisco Farroñay; G by John Clark; I, J, S & T by Domingos Cardoso; L by Claudio Nicoletti de Fraga; O by John Elliott; P by Warran McCleland; Q by Marja Broersma; R by Pierre Grard.

publicMay 2024View details →

dryad40/100

R code for differential gene expression and enrichment analyses

Open the record for dataset details and reuse information.

opencc-zeroDec 2016View details →

Data from: Targeted enrichment of large gene families for phylogenetic inference: phylogeny and molecular evolution of photosynthesis genes in the Portullugo clade (Caryophyllales)

Hybrid enrichment is an increasingly popular approach for obtaining hundreds of loci for phylogenetic analysis across many taxa quickly and cheaply. The genes targeted for sequencing are typically single-copy loci, which facilitate a more straightforward sequence assembly and homology assignment process. However, this approach limits the inclusion of most genes of functional interest, which often belong to multi-gene families. Here we demonstrate the feasibility of including large gene families in hybrid enrichment protocols for phylogeny reconstruction and subsequent analyses of molecular evolution, using a new set of bait sequences designed for the "portullugo" (Caryophyllales), a moderately sized lineage of flowering plants (∼2200 species) that includes the cacti and harbors many evolutionary transitions to C4 and CAM photosynthesis. Including multi-gene families allowed us to simultaneously infer a robust phylogeny and construct a dense sampling of sequences for a major enzyme of C4 and CAM photosynthesis, which revealed the accumulation of adaptive amino acid substitutions associated with C4 and CAM origins in particular paralogs. Our final set of matrices for phylogenetic analyses included 75–218 loci across 74 taxa, with ∼50% matrix completeness across datasets. Phylogenetic resolution was greatly improved across the tree, at both shallow and deep levels. Concatenation and coalescent-based approaches both resolve the sister lineage of the cacti with strong support: Anacampserotaceae + Portulacaceae, two lineages of mostly diminutive succulent herbs of warm, arid regions. In spite of this congruence, BUCKy concordance analyses demonstrated strong and conflicting signals across gene trees. Our results add to the growing number of examples illustrating the complexity of phylogenetic signals in genomic-scale data.

opencc-zeroFeb 2024View details →

Dataset: T-DNA characterization of genetically modified 3-R-gene late blight resistant potato events with a novel procedure utilizing the Samplix Xdrop® Enrichment Technology

<p>Before commercialization of genetically modified crops, the events carrying the novel DNA must be thoroughly evaluated for agronomic, nutritional, and molecular characteristics. Over the years, Polymerase Chain Reaction-based methods, Southern blot, and short-read sequencing techniques have been utilized for collecting molecular characterization data. Multiple genomic applications are necessary to determine the insert location, flanking sequence analysis, characterization of the inserted DNA, and determination of any interruption of native genes. These techniques are time-consuming and labor-intensive, making it difficult to characterize multiple events. Current advances in sequencing technologies are enabling whole genomic sequencing of modified crops to obtain full molecular characterization. However, in polyploids, such as the tetraploid potato, it is a challenge to obtain whole genomic sequencing coverage that meets regulatory approval of the genetic modification. Here we describe an alternative to labor-intensive applications with a novel procedure using Samplix Xdrop® enrichment technology and next-generation Nanopore sequencing technology to more efficiently characterize the T-DNA insertions of four genetically modified potato events developed by the Feed the Future Global Biotech Potato Partnership: DIA_MSU_UB015, DIA_MSU_UB255, GRA_MSU_UG234 and GRA_MSU_UG265 (derived from regionally important varieties Diamant and Granola). Using the Xdrop® /Nanopore technique, we obtained a very high sequence read coverage within the T-DNA and junction regions. In three of the four events, we were able to use the data to confirm single T-DNA insertions, identify insert locations, identify flanking sequences, and characterize the inserted T-DNA. We further used the characterization data to identify native gene interruption and confirm the stability of the T-DNA across clonal cycles. These results demonstrate the functionality of using the Xdrop® /Nanopore technique for T-DNA characterization. This research will contribute to meeting regulatory safety and regulatory approval requirements for commercialization with small shareholder farmers in target countries within our partnership.</p>

opencc-by-4.0Nov 2024View details →

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>

openapache2.0Nov 2024View details →

SaintGSE: Transformer-based efficient and explainable gene set enrichment analysis

<h1>SaintGSE: Transformer-based efficient and explainable gene set enrichment analysis</h1> <div> </div> <div>SaintGSE is an artificial intelligence model designed to predict human gene-pathway relationships using large-scale differentially expressed gene (DEG) datasets. By leveraging an autoencoder and the SAINT transformer model, SaintGSE overcomes challenges in gene expression analysis, such as data scarcity, model compatibility, and interpretability. This project fine-tuned codes from the SAINT project (https://github.com/somepago/saint), licensed under the Apache License 2.0. </div> <div> </div> <div> </div> <h2>Key Features</h2> <div> </div> <div>  * AI-Driven Pathway Prediction: Uses autoencoders and the SAINT model to analyze gene expression data and predict related signaling pathways.</div> <div> </div> <div>  * Osteoarthritis Study: Applied to osteoarthritis (OA) to identify key pathways and potential therapeutic targets.</div> <div> </div> <div>  * Explainability: Utilizes Shapley additive explanations (SHAP) to interpret model predictions and identify influential genes.</div> <div> </div> <div> </div> <h2>Installation</h2> <div> </div> <div>Before installation, we recommend to build a conda environment from the attached yml file and activate it.</div> <div>Our code has been tested with python=3.8 on linux.</div> <div> </div> <div>```</div> <div>$ cd /path/to/SaintGSE</div> <div>$ conda env create -f saintgse_env.yml</div> <div>$ conda activate saintgse_env</div> <div>```</div> <div> </div> <div>After downloading all the files, please extract the contents of all compressed directories by running the following command in your terminal:</div> <div> </div> <div>```</div> <div>$ find . -name "*.tar.gz" -exec tar -xzvf {} \;</div> <div>$ rm *.tar.gz</div> <div>```</div> <div> </div> <div>Once the file structure is formed as follows, the preparation for using SaintGSE is complete.</div> <div> </div> <div>```</div> <div>.</div> <div>├── datasets</div> <div>│   ├── AE_100cycle_model.pth</div> <div>│   ├── AE_enrichment.tsv</div> <div>│   ├── AEshap</div> <div>│   │   ├── shap_values_latent_dim_1.csv</div> <div>│   │   ├── shap_values_latent_dim_2.csv</div> <div>│   │   ├── shap_values_latent_dim_3.csv</div> <div>│   │   ├── ...</div> <div>│   │   ├── ...</div> <div>│   │   └── shap_values_latent_dim_256.csv</div> <div>│   ├── bestmodels</div> <div>│   │   └── binary</div> <div>│   │       ├── Chronic_Myeloid_Leukemia</div> <div>│   │       │   └── testrun</div> <div>│   │       │       └── saint_gse_model.pth</div> <div>│   │       │── ...</div> <div>│   │       └── Selencompound_Biosynthesis</div> <div>│   │           └── testrun</div> <div>│   │               └── saint_gse_model.pth</div> <div>│   ├── gene_list.pkl</div> <div>│   ├── MGI_Gene_Model_Coord.tsv</div> <div>│   └── pathway_list_in_DEG.txt</div> <div>```</div> <div> </div> <div> <p>The code in this dataset is also accessible via GitHub. You can find the GitHub repository at the following link:</p> <p><a href="https://github.com/MSjeon27/SaintGSE" target="_new" rel="noopener">https://github.com/MSjeon27/SaintGSE</a></p> </div> <div> </div> <h2>DEG dataset preparation</h2> <div>Prior to SaintGSE analysis, prepare DEG data to be used as input in .tsv format as follows. In the column, the official gene symbol of DEGs is located, and the row adds the log2 fold change value in each DEG group. An example is as follows.</div> <div> </div> <div>```</div> <div>LAP3 CD99 HS3ST1 MAD1L1 LASP1 SNX11</div> <div>'mock-6' vs 'LPS-6' -1.3 0 2.4 0 0.7 0</div> <div>'mock-6' vs 'EBOV-6' -1.3 0 2.3 0 0.6 0</div> <div>```</div> <div> </div> <h2>Usage</h2> <div> </div> <h3>Step 0. Preprocessing the input DEG (from pyDESeq2 result)</h3> <div> </div> <div>Currently, SaintGSE has the function of converting mouse genes into human genes. The preprocessing code serves to change the human or mouse DEG data into the format used for SaintGSE.</div> <div> </div> <div>* human DEGs</div> <div>```</div> <div>$ preprocessing.py --query_fc /path/to/your/DEGs.tsv --out Preprocessed_fc.tsv</div> <div>```</div> <div> </div> <div>* mouse DEGs</div> <div>```</div> <div>$ preprocessing.py --query_fc /path/to/your/DEGs.tsv --org mouse --out Preprocessed_fc.tsv</div> <div>```</div> <div> </div> <div> </div> <h3>Step 1. Training SaintGSE for a target pathway</h3> <div> </div> <div>SaintGSE can be used to analyze new gene expression datasets for pathway prediction:</div> <div> </div> <div>```</div> <div>$ SaintGSE.py --pathway 'Proteins Involved in Osteoarthritis' --pretrain</div> <div>```</div> <div> </div> <div> </div> <h3>Step 2. Prediction through SaintGSE</h3> <div> </div> <div>```</div> <div>$ SaintGSE.py --predict Preprocessed_fc.tsv --pathway 'Proteins Involved in Osteoarthritis'</div> <div>```</div> <div> </div> <div>The results of the predictions are as follows.</div> <div> </div> <div>```</div> <div>tensor([[1.]], device='cuda:0')</div> <div>```</div> <div> </div> <div>This indicates that your DEG data is related to the target signaling path.</div> <div> </div> <div> </div> <div> </div> <h3>Step 3. Interpretation the result of SaintGSE (Get Relative SHAP contribution for each DEGs)</h3> <div>```</div> <div>$ Interpret.py -d Preprocessed_fc.tsv -p 'Proteins Involved in Osteoarthritis'</div> <div>```</div> <div> </div> <div>The result of interpretation produces the following files for each sample.</div> <div> </div> <div>```</div> <div><Sample_name>_<target_pathway>_significant_gene_shap.csv</div> <div>```</div> <div> </div> <div>This represents the relative SHAP contribution for each gene in the DEG data. In the subsequent analysis, it is recommended to focus on the genes with the relative SHAP contributions in the top 35% to 50% as we suggested in the paper, depending on the number of DEGs.</div> <div> </div> <div> </div> <h2>How to Cite</h2> <div> </div> <div>If you use this model or repository in your research, please cite it as follows:</div> <div> </div> <div>```</div> <div>Jeon, MS & Nam, JH et al., "SaintGSE: Transformer-based efficient and explainable gene set enrichment analysis," 2024. GitHub repository. Available at: https://github.com/MSjeon27/SaintGSE</div> <div>```</div> <div> </div> <div>For more information or any questions regarding citation, feel free to contact us (msjeon27@cau.ac.kr).</div> <p> </p>

opencc-by-4.0May 2024View details →

Gene Enrichment Map Data from gProfiler Analysis - Selected MPK Interactions of Arabidopsis thaliana

<p>Gene enrichment analysis results for the selected predicted MPK interactions are included in the supplementary materials.</p>

opencc-by-4.0Apr 2019View details →

Pathways enriched in downstream target genes of miRNAs associated with NT-proBNP and OPN

<p>The full set of 30 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that were significantly enriched (FDR-adjusted p<0.01) in the predicted mRNA targets of either the OPN- or NT-proBNP-associated miRNAs; 21 of these pathways were significantly enriched in both sets of targets.</p> <p>KEGG: Kyoto Encyclopedia of Genes and Genomes; miRNAs: microRNAs; NT-proBNP: N-terminal pro <a href="http://www.mayomedicallaboratories.com/test-catalog/Clinical+and+Interpretive/83873">B-type natriuretic peptide</a>; OPN: osteopontin</p>

Fig. 3 in Phylogenomics of the tropical plant family Ochnaceae using targeted enrichment of nuclear genes and 250+ taxa

Fig. 3. Continues. For caption, see next part.

publicSep 2017View details →

Data from: Targeted enrichment of large gene families for phylogenetic inference: phylogeny and molecular evolution of photosynthesis genes in the Portullugo clade (Caryophyllales)

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