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opennotspecifiedJan 2021View details →

Fig. 4 in Comparative transcriptome analysis reveals the regulatory effects of acetylcholine on salt tolerance of Nicotiana benthamiana

Fig. 4. The Kyoto Encyclopedia of Genes and Genomes pathway enrichment scatter map (p <0.05). The X-axis (Rich Factor) represents the percentage of DEGs belonging to the corresponding pathway.

opennotspecifiedSep 2020View details →

Fig. 2 in Alkaloid chemophenetics and transcriptomics of the Nicotiana genus

Fig. 2. Euclidean-based hierarchical clustering of Nicotiana genus species. Prior to clustering mean root alkaloid biosynthesis gene expression as well as leaves and roots percentage alkaloid composition were subjected to log2 transformation. There are 3 black boxes enclosing species clustered together: first from top, 6 species from the Suaveolentes section; second, 9 species from the Suaveolentes section; and third, 4 species from the Rusticae section and 1 from the Undulatae section. Gene IDs and their acronyms (present in Table S2) together with alkaloid names are placed on the figure top. Clustering revealed transparent species segregation which corresponded to their sectional classification. Clusters where ≥50% of section species grouped together are enclosed by fade yellow dashed line, specifically: Tomentosae section (4/5), Paniculatae section (2/4), Suaveolentes section (17/21), Noctiflorae section (3/3), Rusticae section (4/5) and Repandae section (2/3).

opennotspecifiedSep 2020View details →

Fig. 1 in Alkaloid chemophenetics and transcriptomics of the Nicotiana genus

Fig. 1. Current state-of-the-art picture of alkaloid biosynthesis in Nicotiana genus. Gene acronyms are provided in red next to the enzymatic steps they correspond to. Each gene acronym is explained in boxes with full gene names. Compound full names are provided in black, with additional 2D structure representation of alkaloids: anabasine, nicotine, nornicotine, anatabine, cotinine, myosmine as well as nicotinic acid. Additional 2D structures are also presented for alkaloids intermediates: Δ1-piperdiene, N-methyl-Δ1-pyrrolinium cation, 3,6-dihydronicotinic acid, and 2,5-dihydropyridine. In faded green, field steps of polyamine biosynthesis are presented, whereas the faded red field encloses steps limited to plastid and root tissue. Question marks are located next to enzymatic steps that are currently only hypothesized and have not yet been confirmed by independent studies. The figure is based on a review by Dewey and Xie (2013) and subsequent studies, that characterized the functions of further genes, namely LDC (Bunsupa et al., 2014) and NUP1 (Kato et al., 2015), in the alkaloid biosynthetic pathway.

opennotspecifiedSep 2020View details →

Fig. 3 in Alkaloid chemophenetics and transcriptomics of the Nicotiana genus

Fig. 3. PCC matrix between gene expressions mean RPKM values and mean alkaloid accumulation metabolite data. Genes are presented with their Gene ID and Identifiers (full description of genes with names in Fig. 1 & Table S2). Six gene-specific comparisons were performed to calculate Pearson correlation coefficient, as presented in the table: leaf nornicotine to nicotine content ratio vs. leaf gene expression; total nicotine content (leaf and root) vs. root gene expression; total anatabine content (leaf and root) vs. root gene expression; root anatabine to nicotine and nornicotine content ratio vs. root gene expression; total anabasine content (leaf and root) vs. root gene expression; total alkaloid content leaf to root ratio vs. root gene expression. PCC values range from −1 to 1, with red white conditioning in negative to positive correlation. PCC values in bold and marked with thick borders represent correlations of significant importance.

Fig. 5 in Transcriptome profiling of two Dactylis glomerata L. cultivars with different tolerance in response to submergence stress

Fig. 5. Gene Ontology (GO) classification of genes that only expressed differently in tolerant material 'Dianbei' and expressed differently between two materials all the time. (a) Gene Ontology (GO) classification of 1395 genes only expressed differently in tolerant material 'Dianbei'; (b) GO classification of 18 genes that were found to expressed differently between two materials all the time.

Fig. 1 in Transcriptome profiling of two Dactylis glomerata L. cultivars with different tolerance in response to submergence stress

Fig. 1. Hierarchical clustering analysis of changes in gene expression in two D. glomerata cultivars under submergence tolerance.

Fig. 3 in Transcriptome profiling of two Dactylis glomerata L. cultivars with different tolerance in response to submergence stress

Fig. 3. Gene Ontology (GO) classification of assembled unigenes in two D. glomerata cultivars, submergence-tolerant 'Dianbei' and submergence-sensitive 'Anba'.

Fig. 4 in Transcriptome profiling of two Dactylis glomerata L. cultivars with different tolerance in response to submergence stress

Fig. 4. Scatterplot of enriched KEGG pathways for differentially expressed genes between two D. glomerata cultivars.

Fig. 7 in Transcriptome sequencing of the apricot (Prunus armeniaca L.) and identification of differentially expressed genes involved in drought stress

Fig. 7. Effects of control and drought stress on leaf microstructure of apricot. a, c, e, represent the leaf stomata, vertical section, and cuticle in the control group, respectively. b, d, f, represent the leaf stomata, vertical section, and cuticle in the drought stress group, respectively.

Fig. 4 in Transcriptome sequencing of the apricot (Prunus armeniaca L.) and identification of differentially expressed genes involved in drought stress

Fig. 4. KEGG enrichment of annotated DEGs in Treat versus Control. The Y-axis shows the KEGG pathway and the X-axis shows the Rich factor. This q value goes from purple to red, which means from 1 to 0. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3 in Transcriptome sequencing of the apricot (Prunus armeniaca L.) and identification of differentially expressed genes involved in drought stress

Fig. 3. GO classifications of DEGs for Treat versus Control. The Y-axis represents the number of DEGs in a category. The BP, CC and MF represent biological process, cellular component and molecular function respectively.

Fig. 5. SSR motifs distribution. The X in Transcriptome sequencing of the apricot (Prunus armeniaca L.) and identification of differentially expressed genes involved in drought stress

Fig. 5. SSR motifs distribution. The X-axis is SSR type, the Y-axis value is the coordinate, the specific number of repetitions should correspond to the legend according to the color, and the Z-axis is the number of SSR. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 1 in Transcriptome sequencing of the apricot (Prunus armeniaca L.) and identification of differentially expressed genes involved in drought stress

Fig. 1. Gene Function Classification of the assembled unigenes. Unigenes with BLAST hits were classified into three major categories and 56 sub-categories in GO. The Y-axis shows the number of genes in each sub-category.

opencc-by-4.0Aug 2023View details →

Non-targeted metabolomics and transcriptomics reveal mechanisms of metabolic differences among roots, stems, and leaves of Cudrania tricuspidata

<p>We detected a total of 1254 metabolites from the three tissues of Cudrania roots, stems, and leaves, and all metabolites were annotated and classified into eight categories by the KEGG database: steroids, lipids, antibiotics, vitamins and cofactors, nucleic acids, peptides, carbohydrates, and organic acids. Flavonoid-rich roots and stems of Cudrania were significantly different from the transcripts of leaves. GO and KEGG enrichment analyses revealed that the differential genes were mainly enriched in Photosynthesis - antenna proteins, Zeatin biosynthesis, Flavone and flavonol biosynthesis, Monoterpenoid biosynthesis pathway. The expression of flavonoid and flavonol biosynthesis-related genes was significantly up-regulated in roots and stems. From the perspective of the differences in metabolites among roots, stems and leaves of Cudrania, it can provide a basis for revealing the material basis of the differences in medicinal properties and efficacy of different parts.</p>

opencc-by-4.0Oct 2023View details →

Transcriptome-derived SNP markers for population assignment of sandfish, Holothuria (Metriatyla) scabra

<p>The genotype data used for the assignment analyses provided as a GenePop file (.gen)</p>

Identification of Diabetic Nephropathy Biomarkers Through Transcriptomics

ClinicalTrials.gov study NCT05378282. IPD Sharing: NO. Countries: 1. Publications: 29.

Immune Signature of Chronic Hand Eczema Unveiled by Spatial Transcriptomics and Single-Cell Proteomics

ClinicalTrials.gov study NCT06884163. IPD Sharing: NO. Countries: 1. Publications: 6.

Genomic and Transcriptomic Predictors of Sequential SG Sensitivity After T-DXd in ER+/HER2-Low Metastatic Breast Cancer

ClinicalTrials.gov study NCT06665178. IPD Sharing: NO. Countries: 1. Publications: 3.

Real-world Clinical Effectiveness of Whole Genome and Transcriptome Analysis to Guide Advanced Cancer Care

ClinicalTrials.gov study NCT04141397. IPD Sharing: NO. Countries: 0. Publications: 1.