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25,372 results for “Transcriptomics”
Figure 3 in Identification of candidate genes involved with dicamba resistance in waterhemp (Amoronthus tuberculotus) via transcriptomics analyses
Figure 3. Biological process GO-term enrichment analysis. Circle size represents the significance of overrepresented enrichment, and color gradient represents the significance of conditional enrichment. The x axis represents the number of genes annotated with each GO-term in the y axis.
Figure 2 in Identification of candidate genes involved with dicamba resistance in waterhemp (Amoronthus tuberculotus) via transcriptomics analyses
Figure 2. Volcano plot of all genes with key differentially expressed genes highlighted. Major genes with potential involvement in dicamba resistance are labeled according to their homologous UniprotKB ID. Genes in red and blue were significantly up- and downregulated, respectively, in dicamba-resistant relative to sensitive plants. The y axis refers to −log10 false discovery rate (FDR), and the x axis refers to the log2 expression fold change (FC).
Figure 8 in Identification of candidate genes involved with dicamba resistance in waterhemp (Amoronthus tuberculotus) via transcriptomics analyses
Figure 8. Proposed dicamba resistance mechanisms in the CHR population. Currently, knowledge about the synthetic auxin effect on plants indicates an overproduction of abscisic acid (ABA), leading to a large production of reactive oxygen species (ROS) and plant death (Christoffoleti et al. 2015; Gaines 2020). The proposed resistance mechanism is that enhanced response to oxidative stress via peroxidases and glutathione S-transferases alleviates dicamba toxicity. Other putative resistance mechanisms, such as glycosylation of dicamba and ABA, are also proposed with transport via ATP-binding cassette (ABC) transporters for further degradation. Overproduction of salicylic acid is also proposed as a potential tool for alleviating oxidative stress. Created with BioRender.com.
Figure 5 in Identification of candidate genes involved with dicamba resistance in waterhemp (Amoronthus tuberculotus) via transcriptomics analyses
Figure 5. Weighted co-expression network analysis results. (A) Gene expression dendrogram for module assignment where a total of 33 modules were identified. (B) Traitmodule correlation plot with values outside parentheses representing Pearson correlation and values inside parentheses representing the significance correlation P-values. Correlation values range from −1 to 1, with red values indicating a positive association and blue values indicating a negative association with dicamba resistance. ME refers to modules followed by their color code.
Figure 1 in Identification of candidate genes involved with dicamba resistance in waterhemp (Amoronthus tuberculotus) via transcriptomics analyses
Figure 1. Plant selection and phenotype classification for RNA-seq. Photos show the differences in phenotypes of some of the individuals selected for sequencing: (A) resistant plants and (B) sensitive plants. Selection was done based on visual damage estimation, biomass, and plant area measured via image analysis (Bobadilla et al. 2022). Photos were taken 14 d after treatment with dicamba at 560 g ai ha−1. The graph shows the relationship between biomass and plant area across resistant and sensitive individuals.
Figure A1 in Transcriptomic analysis of Bursaphelenchus xylophilus treated by a potential phytonematicide, punicalagin
Figure A1: Principal components analysis of variation (A) and correlation coefficient analysis (B) among sequenced transcriptomes to show correlation among samples (control, CK1-3 and the treated samples, P1-3).
Figure A3 in Transcriptomic analysis of Bursaphelenchus xylophilus treated by a potential phytonematicide, punicalagin
Figure A3: Observation of the normal PWNs (A) and punicalagin-treated PWNs twisting abnormally (B) under microscope.
Figure 5 in Transcriptomic analysis of Bursaphelenchus xylophilus treated by a potential phytonematicide, punicalagin
Figure 5: Expression of six differentially expressed genes by (A) RNA-Seq, (B) qRT-PCR and (C) their correlation.
Figure A2 in Transcriptomic analysis of Bursaphelenchus xylophilus treated by a potential phytonematicide, punicalagin
Figure A2: Annotated KEGG pathway of Endocytosis (A), Peroxisome (B) and MAPK signaling pathways (C) about differentially expressed genes. Genes in blue frames with red borders were up-regulated, genes in blue frames with yellow borders were down-regulated and genes in blue frames with sky-blue borders were simultaneously up-regulated and down-regulated.
Figure 4 in Transcriptomic analysis of Bursaphelenchus xylophilus treated by a potential phytonematicide, punicalagin
Figure 4: Annotation of differentially expressed genes (DEGs). (A) Column diagram of DEGs using GO annotation. The bottom X-axis indicates the number of genes annotated on different GO terms. The X-axis indicates the ratios of genes annotated on different GO terms to all terms used for the GO annotation. (B) Scatter diagram of DEGs with GO enrichment. (C) KOG functional classification of DEGs.
Figure 3 in Transcriptomic analysis of Bursaphelenchus xylophilus treated by a potential phytonematicide, punicalagin
Figure 3: Functional annotation of unigenes. (A) GO functional annotation statistics on level 2. (B) KOG annotation statistics. (C) KEGG pathway annotation statistics. A. Cellular processes. B. Environmental information processing. C. Genetic information processing. D. metabolism. E. Organismal systems.
Figure 1 in A Draft Transcriptome Announcement of Anguina tritici
Figure 1: Blob Tool Analysis of Final assembly. (A) Mapped Read GC content (B) Assembly mapping to different phyla.
Figure 3. Designing a in Omics in Weed Science: A Perspective from Genomics, Transcriptomics, and Metabolomics Approaches
Figure 3. Designing a metabolomics study. (A) The various approaches for performing a metabolomics experimental study. GC-MS, gas chromatography–mass spectrometry; HILIC-LC-MS/MS, hydrophilic interaction chromatography for liquid chromatography–tandem mass spectrometry; LC-MS/MS, liquid chromatography–tandem mass spectrometry. (B) The general metabolomics workflow. It involves formulating a biological question, setting up an experimental design to test the hypothesis, sample treatment and harvest, metabolite extraction, clean-up, chromatographic separation, identification, statistical validation, and functional interpretation.
Figure 1 in Omics in Weed Science: A Perspective from Genomics, Transcriptomics, and Metabolomics Approaches
Figure 1. Classical systems biology concept and omics organization. The central dogma of molecular biology covers the progressive functionalization of the genotype to the phenotype. The omics techniques track and capture various molecular entities across the biological system.
Single-cell proteo-transcriptomic profiling reveals altered characteristics of stem and progenitor cells in patients receiving cytoreductive hydroxyurea in early-phase chronic myeloid leukemia
<p>This repository contains CITE-seq data generated from CML stem and progenitor cells before and after hydroxyurea treatment using the BD Rhapsody Single-Cell Analysis System. </p> <p><strong><br>File descriptions:</strong></p> <p>1. RSEC-adjusted UMI count files generated using the BD Rhapsody Targeted Analysis Pipeline (v. 1.10.1):</p> <ul> <li>CartridgeS1_RSEC_MolsPerCell.csv</li> </ul> <p>2. RSEC-adjusted UMI counts for cells remaining after cell quality filtering using SeqGeq software (genes expressed vs library size):</p> <ul> <li>CartridgeS1_RSEC_MolsPerCell_postQC.csv</li> </ul> <p>3. Sample tag (sample of origin) calls for each putative cell, outputted by the BD Rhapsody Targeted Analysis Pipeline (v. 1.10.1). </p> <ul> <li>CartridgeS1_Sample_Tag_Calls.csv</li> </ul> <p> </p>
Mice transcriptome
Open the record for dataset details and reuse information.
Fig. 6 in Comparative transcriptome analysis of three Bactrocera dorsalis (Diptera: Tephritidae) organs to identify functional genes in the male accessory glands and ejaculatory duct
Fig. 6. Four examples of the tissue expression profiling of unknown distinct unigenes (>500 bp) expressed highly in male accessory glands and ejaculatory duct tissue of Bactrocera dorsalis. Relative expression levels were determined as described in Fig. 5.
Fig. 4 in Comparative transcriptome analysis of three Bactrocera dorsalis (Diptera: Tephritidae) organs to identify functional genes in the male accessory glands and ejaculatory duct
Fig. 4. Kyoto encyclopedia of gene and genomes (KEGG) analysis of unigenes expressed highly in male accessory glands and ejaculatory duct tissue of Bactrocera dorsalis. Each category contains more than 1 unigene sequences.
Fig. 2 in Comparative transcriptome analysis of three Bactrocera dorsalis (Diptera: Tephritidae) organs to identify functional genes in the male accessory glands and ejaculatory duct
Fig. 2. Clusters of orthologous groups (COG) functional classification of unigenes expressed highly and specifically in male accessory glands and ejaculatory duct tissue of Bactrocera dorsalis.
Fig. 1 in Comparative transcriptome analysis of three Bactrocera dorsalis (Diptera: Tephritidae) organs to identify functional genes in the male accessory glands and ejaculatory duct
Fig. 1. Statistics of sequences expressed specifically in each analyzed tissue of Bactrocera dorsalis.
ScienceDex guides
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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.