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Biotechnology Information transcriptome sequences
(A) Heatmap of DEG cluster analysis for DT and CK <t>transcriptomes.</t> The black dendrogram shows hierarchical clustering of samples and genes with similar expression patterns. The color scale represents normalized gene expression levels (yellow/orange = high expression, blue = low expression), demonstrating clear separation between CK and DT groups. (B) Volcano plot of differentially expressed genes. Orange dots indicate up-regulated genes (log 2 FC > 1, p < 0.05), blue dots indicate down-regulated genes (log 2 FC < −1, p < 0.05), and gray dots indicate non-significant genes.
Transcriptome Sequences, supplied by Biotechnology Information, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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1) Product Images from "A cellular view of drought adaptation in sugarcane: multi-omics integration reveals a quadruple module network linking water regulation, oxidative defense, cell wall remodeling, and cell cycle regulation"

Article Title: A cellular view of drought adaptation in sugarcane: multi-omics integration reveals a quadruple module network linking water regulation, oxidative defense, cell wall remodeling, and cell cycle regulation

Journal: PeerJ

doi: 10.7717/peerj.21396

(A) Heatmap of DEG cluster analysis for DT and CK transcriptomes. The black dendrogram shows hierarchical clustering of samples and genes with similar expression patterns. The color scale represents normalized gene expression levels (yellow/orange = high expression, blue = low expression), demonstrating clear separation between CK and DT groups. (B) Volcano plot of differentially expressed genes. Orange dots indicate up-regulated genes (log 2 FC > 1, p < 0.05), blue dots indicate down-regulated genes (log 2 FC < −1, p < 0.05), and gray dots indicate non-significant genes.
Figure Legend Snippet: (A) Heatmap of DEG cluster analysis for DT and CK transcriptomes. The black dendrogram shows hierarchical clustering of samples and genes with similar expression patterns. The color scale represents normalized gene expression levels (yellow/orange = high expression, blue = low expression), demonstrating clear separation between CK and DT groups. (B) Volcano plot of differentially expressed genes. Orange dots indicate up-regulated genes (log 2 FC > 1, p < 0.05), blue dots indicate down-regulated genes (log 2 FC < −1, p < 0.05), and gray dots indicate non-significant genes.

Techniques Used: Expressing, Gene Expression

(A) The abscissa indicates the selected DEGs related to drought stress in sugarcane, and the ordinate indicates the log 2 fold change of gene expression measured by RNA-seq (orange bars) and RT-qPCR (blue bars). The column height represents the differential expression multiple, and the smaller the height difference between orange and blue bars, the more consistent the expression trend between the two methods. (B) Correlation analysis of log 2 fold change values from RNA-seq ( x -axis) and RT-qPCR ( y -axis). Each blue square represents one DEG. The linear regression equation and R 2 value ( R 2 = 0.97581) indicate a high positive correlation between the two techniques, verifying the reliability of the transcriptome sequencing data.
Figure Legend Snippet: (A) The abscissa indicates the selected DEGs related to drought stress in sugarcane, and the ordinate indicates the log 2 fold change of gene expression measured by RNA-seq (orange bars) and RT-qPCR (blue bars). The column height represents the differential expression multiple, and the smaller the height difference between orange and blue bars, the more consistent the expression trend between the two methods. (B) Correlation analysis of log 2 fold change values from RNA-seq ( x -axis) and RT-qPCR ( y -axis). Each blue square represents one DEG. The linear regression equation and R 2 value ( R 2 = 0.97581) indicate a high positive correlation between the two techniques, verifying the reliability of the transcriptome sequencing data.

Techniques Used: Gene Expression, RNA Sequencing, Quantitative RT-PCR, Quantitative Proteomics, Expressing, Sequencing

Related Articles

Sequencing:

Article Title: Subacute ruminal acidosis induces hepatic injury in dairy goats via oxidative stress and ferritinophagy-ferroptosis axis.
Article Snippet: .. Transcriptomic data were deposited in the US National Center for Biotechnology Information (NCBI) sequence read archive under accession number PRJNA98397. ..

Article Title: Comparative Analyses of Gene and Protein Expressions and the Lipid Contents in Intramuscular and Subcutaneous Fat Tissues in Fattening Steers
Article Snippet: .. Transcriptomic sequence reads for all samples are available under the National Center for Biotechnology Information with accession project number PRJNA1308542 ( https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1308542 ) accessed on 20 August 2025. ..

Article Title: Systems pharmacology approaches decipher the anti-cancer efficacy of ethnopharmacological agents in hepatocellular carcinoma.
Article Snippet: .. Transcriptomic data has been uploaded to the National Center for Biotechnology Information (NCBI) Sequence Read Archive team for facilitating the deposition of transcriptomic data (Accession number: PRJNA1115821). .. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the iProX partner repository ( h t t p s : / / w w w . i p r o x . c n / p a g e / S S V 0 2 4 . h t m l ; u r l = 1 7 4 9 7 1 9 0 4 6 0 9 9 G m M C , p a s s w o r d: cxyp) Received: 3 June 2025; Accepted: 5 November 2025 Abbreviation Full term CHM Chinese herbal medicine TGF-β Transforming growth factor-beta signaling pathway HCC Hepatocellular carcinoma ESCC Esophageal squamous cell carcinoma DLBCL Diffuse large B-cell lymphoma MAPK Mitogen-activated protein kinase signaling pathway NF-κB Nuclear factor kappa-light-chain-enhancer of activated B cells signaling pathway TNF Tumor necrosis factor IL-17 Interleukin-17 cAMP Cyclic adenosine monophosphate cGMP-PKG Cyclic guanosine monophosphate-protein kinase G p53 Tumor protein p53 PI3K/AKT Phosphoinositide 3-kinase/protein kinase B AMPK AMP-activated protein kinase RIG-I-like Retinoic acid-inducible gene I-like NOD-like Nucleotide-binding oligomerization domain-like PD-L1 Programmed death-ligand 1 Toll-like Toll-like receptor JAK-STAT anus Kinase-Signal transducer and activator of transcription VEGF Vascular endothelial growth factor Wnt Wingless/integrated EGFR Epidermal Growth Factor Receptor AP-1/BTG Activator protein 1/B-cell translocation gene signaling pathway DMSO Dimethyl sulfoxide DMEM Dulbecco’s modified eagle medium FBS Fetal bovine serum MTT 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay BRPS Bicinchoninic acid reducing protein assay Annexin-VFITC/PI Annexin V-fluorescein Isothiocyanate/propidium iodide staining DEG Differentially expressed genes DEP Differentially expressed protein GO Gene ontology KEGG Kyoto encyclopedia of genes and genomes WGCNA Weighted gene coexpression network analysis SDC Sodium deoxycholate BCA Bicinchoninic acid TCEP Tris (2-carboxyethyl) phosphine CAA Chloroacetamide DIA Data-independent acquisition LSDs Least significant differences TME Tumor microenvironment PKAs Protein kinases CAFs EPACs Cancer-associated fibroblasts Exchange proteins activated NAFLD Nonalcoholic fatty liver disease EMT Epithelial-mesenchymal transition PCA Principal component analysis Table 3.

Article Title: Integrated analysis of DNA methylation and gene expression in dairy cows during early pregnancy.
Article Snippet: .. Data Availability Statement: The raw Reduced Representation Bisulfite Sequencing (RRBS) and transcriptomic sequencing data generated in this study have been deposited in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database under the accession numbers GSE274885 (RRBS data) and GSE274986 (transcriptomic data), which are publicly accessible at https://www.ncbi.nlm.nih.gov/geo/. ..

Article Title: Systems pharmacology approaches decipher the anti-cancer efficacy of ethnopharmacological agents in hepatocellular carcinoma
Article Snippet: .. Transcriptomic data has been uploaded to the National Center for Biotechnology Information (NCBI) Sequence Read Archive team for facilitating the deposition of transcriptomic data (Accession number: PRJNA1115821). .. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the iProX partner repository ( https://www.iprox.cn/page/SSV024.html;url=1749719046099GmMC,password : cxyp)

Article Title: Integrated analysis of DNA methylation and gene expression in dairy cows during early pregnancy
Article Snippet: This study was supported by the National Natural Science Foundation of China [32360901, 32060133], Xinjiang Corps Guiding Science and Technology Plan Project [2023ZD041, 2023ZD042], and the Natural Science Foundation of Xinjiang [2023LQ03003, 2022D01B197]. .. The raw Reduced Representation Bisulfite Sequencing (RRBS) and transcriptomic sequencing data generated in this study have been deposited in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database under the accession numbers GSE274885 (RRBS data) and GSE274986 (transcriptomic data), which are publicly accessible at https://www.ncbi.nlm.nih.gov/geo/. ..

Article Title: Ezetimibe protects against alpha-amanitin-induced hepatotoxicity by targeting the NTCP receptor: Mechanistic insights from in vitro and in vivo models.
Article Snippet: Background: α-Amanitin, the primary lethal toxin of Amanita phalloides, induces irreversible hepatotoxicity by selectively inhibiting RNA polymerase II, leading to transcriptional arrest.. Despite advancements in managing mushroom poisoning, a targeted antidote remains unavailable.. The sodium taurocholate co-transporting polypeptide (NTCP), a hepatic bile acid transporter, facilitates α-amanitin entry into hepatocytes.

Article Title: The interplay between intestinal lipid metabolism and immune response following dietary transition in mandarin fish (Siniperca chuatsi)
Article Snippet: .. The raw transcriptomic sequencing data have been deposited in the National Center for Biotechnology Information (NCBI) database under the accession number PRJNA1291933. .. Cytoscape software (version 3.10.0; Cytoscape Consortium, San Diego, CA, USA) was used to explore protein-protein interactions of differentially expressed genes through the STRING database (version 12.0; EMBL, Heidelberg, Germany) and visualize the interaction networks.

Methylation Sequencing:

Article Title: Integrated analysis of DNA methylation and gene expression in dairy cows during early pregnancy.
Article Snippet: .. Data Availability Statement: The raw Reduced Representation Bisulfite Sequencing (RRBS) and transcriptomic sequencing data generated in this study have been deposited in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database under the accession numbers GSE274885 (RRBS data) and GSE274986 (transcriptomic data), which are publicly accessible at https://www.ncbi.nlm.nih.gov/geo/. ..

Article Title: Integrated analysis of DNA methylation and gene expression in dairy cows during early pregnancy
Article Snippet: This study was supported by the National Natural Science Foundation of China [32360901, 32060133], Xinjiang Corps Guiding Science and Technology Plan Project [2023ZD041, 2023ZD042], and the Natural Science Foundation of Xinjiang [2023LQ03003, 2022D01B197]. .. The raw Reduced Representation Bisulfite Sequencing (RRBS) and transcriptomic sequencing data generated in this study have been deposited in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database under the accession numbers GSE274885 (RRBS data) and GSE274986 (transcriptomic data), which are publicly accessible at https://www.ncbi.nlm.nih.gov/geo/. ..

Generated:

Article Title: Integrated analysis of DNA methylation and gene expression in dairy cows during early pregnancy.
Article Snippet: .. Data Availability Statement: The raw Reduced Representation Bisulfite Sequencing (RRBS) and transcriptomic sequencing data generated in this study have been deposited in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database under the accession numbers GSE274885 (RRBS data) and GSE274986 (transcriptomic data), which are publicly accessible at https://www.ncbi.nlm.nih.gov/geo/. ..

Article Title: Integrated analysis of DNA methylation and gene expression in dairy cows during early pregnancy
Article Snippet: This study was supported by the National Natural Science Foundation of China [32360901, 32060133], Xinjiang Corps Guiding Science and Technology Plan Project [2023ZD041, 2023ZD042], and the Natural Science Foundation of Xinjiang [2023LQ03003, 2022D01B197]. .. The raw Reduced Representation Bisulfite Sequencing (RRBS) and transcriptomic sequencing data generated in this study have been deposited in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database under the accession numbers GSE274885 (RRBS data) and GSE274986 (transcriptomic data), which are publicly accessible at https://www.ncbi.nlm.nih.gov/geo/. ..

Gene Expression:

Article Title: Integrated analysis of DNA methylation and gene expression in dairy cows during early pregnancy.
Article Snippet: .. Data Availability Statement: The raw Reduced Representation Bisulfite Sequencing (RRBS) and transcriptomic sequencing data generated in this study have been deposited in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database under the accession numbers GSE274885 (RRBS data) and GSE274986 (transcriptomic data), which are publicly accessible at https://www.ncbi.nlm.nih.gov/geo/. ..

Article Title: Integrated analysis of DNA methylation and gene expression in dairy cows during early pregnancy
Article Snippet: This study was supported by the National Natural Science Foundation of China [32360901, 32060133], Xinjiang Corps Guiding Science and Technology Plan Project [2023ZD041, 2023ZD042], and the Natural Science Foundation of Xinjiang [2023LQ03003, 2022D01B197]. .. The raw Reduced Representation Bisulfite Sequencing (RRBS) and transcriptomic sequencing data generated in this study have been deposited in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database under the accession numbers GSE274885 (RRBS data) and GSE274986 (transcriptomic data), which are publicly accessible at https://www.ncbi.nlm.nih.gov/geo/. ..

Control:

Article Title: Ezetimibe protects against alpha-amanitin-induced hepatotoxicity by targeting the NTCP receptor: Mechanistic insights from in vitro and in vivo models.
Article Snippet: Background: α-Amanitin, the primary lethal toxin of Amanita phalloides, induces irreversible hepatotoxicity by selectively inhibiting RNA polymerase II, leading to transcriptional arrest.. Despite advancements in managing mushroom poisoning, a targeted antidote remains unavailable.. The sodium taurocholate co-transporting polypeptide (NTCP), a hepatic bile acid transporter, facilitates α-amanitin entry into hepatocytes.



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Image Search Results


Transcriptomic and TME characteristics associated with ECMSig in TCGA-GBM cohort (A) Volcano plot showing DEGs between ECMSig-high and ECMSig-low groups. Red dots: upregulated in high-risk; blue dots: upregulated in low-risk. Benjamini-Hochberg adjusted. (B) Gene set enrichment analysis (GSEA) plots showing enrichment of hallmark pathways. Pathways enriched in ECMSig-high and ECMSig-low groups are shown with their running enrichment scores (ESs) and ranked gene lists. Benjamini-Hochberg adjusted. (C) Heatmap showing the activity scores of selected oncogenic and tumor-related signaling pathways (rows) across TCGA-GBM samples (columns), annotated by ECMSig group and ECMSig score. Red indicates high activity, blue indicates low activity. ∗ p < 0.05. Wilcoxon signed-rank test. (D) Heatmap depicting the estimated infiltration levels of various immune and stromal cell types (rows) in TCGA-GBM samples (columns), stratified by ECMSig group and score. Red indicates high infiltration, blue indicates low infiltration. Cells significantly highly infiltrated in ECMSig-high are labeled in red, and those high in ECMSig-low group are in blue. ∗q < 0.05, ∗∗q < 0.01, ∗∗∗q < 0.001. Wilcoxon signed-rank test. Benjamini-Hochberg adjusted. (E and F) Scatterplots showing the spearman correlation between ECMSig score and (E) Macrophage_XCELL infiltration score and (F) immune_score_XCELL. The blue line represents the linear regression fit with 95% confidence interval bands. Spearman correlation test.

Journal: iScience

Article Title: Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis

doi: 10.1016/j.isci.2026.115982

Figure Lengend Snippet: Transcriptomic and TME characteristics associated with ECMSig in TCGA-GBM cohort (A) Volcano plot showing DEGs between ECMSig-high and ECMSig-low groups. Red dots: upregulated in high-risk; blue dots: upregulated in low-risk. Benjamini-Hochberg adjusted. (B) Gene set enrichment analysis (GSEA) plots showing enrichment of hallmark pathways. Pathways enriched in ECMSig-high and ECMSig-low groups are shown with their running enrichment scores (ESs) and ranked gene lists. Benjamini-Hochberg adjusted. (C) Heatmap showing the activity scores of selected oncogenic and tumor-related signaling pathways (rows) across TCGA-GBM samples (columns), annotated by ECMSig group and ECMSig score. Red indicates high activity, blue indicates low activity. ∗ p < 0.05. Wilcoxon signed-rank test. (D) Heatmap depicting the estimated infiltration levels of various immune and stromal cell types (rows) in TCGA-GBM samples (columns), stratified by ECMSig group and score. Red indicates high infiltration, blue indicates low infiltration. Cells significantly highly infiltrated in ECMSig-high are labeled in red, and those high in ECMSig-low group are in blue. ∗q < 0.05, ∗∗q < 0.01, ∗∗∗q < 0.001. Wilcoxon signed-rank test. Benjamini-Hochberg adjusted. (E and F) Scatterplots showing the spearman correlation between ECMSig score and (E) Macrophage_XCELL infiltration score and (F) immune_score_XCELL. The blue line represents the linear regression fit with 95% confidence interval bands. Spearman correlation test.

Article Snippet: The single-cell transcriptomic sequencing dataset utilizing technology from the 10X Genomics platform was available under the accession number GEO: GSE182109 at the Gene Expression Omnibus (GEO) repository.

Techniques: Activity Assay, Protein-Protein interactions, Labeling

Single-cell RNA sequencing analysis revealing ECMSig expression across cell types and identification of prognostically relevant cell states in GBM (A) UMAP visualization of major cell types identified in GBM scRNA-seq data. (B) Dot plot showing the scaled average expression (color intensity) and percentage of cells expressing (dot size) canonical marker genes for each major cell type. (C) Dot plot showing the scaled average expression and percentage of cells expressing the seven ECMSig genes across major cell types. (D) UMAP plots showing the expression levels of individual ECMSig genes and overall ECMSig score across all cells. (E–G) UMAP plots illustrating Scissor-identified prognostically unfavorable (Scissor_Pos, red dashed circle) and favorable (Scissor_Neg, blue dashed circle; Scissor_Others, gray) cell subpopulations within (E) tumor cells, (F) myeloid cells, and (G) endothelial cells. (H–K) Violin plots comparing ECMSig scores among tumor cells grouped by Scissor status (H) and tumor type (I), and myeloid cells (J) or endothelial cells (K) grouped by Scissor status. ∗∗∗∗ p < 0.0001. Wilcoxon signed-rank test. (L) Dot plot showing differentially expressed marker genes between myeloid Scissor_Pos and other myeloid cells. Dot size indicates the fraction of cells in the group expressing the gene; color indicates average expression level.

Journal: iScience

Article Title: Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis

doi: 10.1016/j.isci.2026.115982

Figure Lengend Snippet: Single-cell RNA sequencing analysis revealing ECMSig expression across cell types and identification of prognostically relevant cell states in GBM (A) UMAP visualization of major cell types identified in GBM scRNA-seq data. (B) Dot plot showing the scaled average expression (color intensity) and percentage of cells expressing (dot size) canonical marker genes for each major cell type. (C) Dot plot showing the scaled average expression and percentage of cells expressing the seven ECMSig genes across major cell types. (D) UMAP plots showing the expression levels of individual ECMSig genes and overall ECMSig score across all cells. (E–G) UMAP plots illustrating Scissor-identified prognostically unfavorable (Scissor_Pos, red dashed circle) and favorable (Scissor_Neg, blue dashed circle; Scissor_Others, gray) cell subpopulations within (E) tumor cells, (F) myeloid cells, and (G) endothelial cells. (H–K) Violin plots comparing ECMSig scores among tumor cells grouped by Scissor status (H) and tumor type (I), and myeloid cells (J) or endothelial cells (K) grouped by Scissor status. ∗∗∗∗ p < 0.0001. Wilcoxon signed-rank test. (L) Dot plot showing differentially expressed marker genes between myeloid Scissor_Pos and other myeloid cells. Dot size indicates the fraction of cells in the group expressing the gene; color indicates average expression level.

Article Snippet: The single-cell transcriptomic sequencing dataset utilizing technology from the 10X Genomics platform was available under the accession number GEO: GSE182109 at the Gene Expression Omnibus (GEO) repository.

Techniques: Single Cell, RNA Sequencing, Expressing, Marker

Spatial transcriptomic analysis revealing co-localization of ECMSig, hypoxia, Scissor-Positive cells, and pericytes in GBM (A) Spatial feature plots for four GBM samples. Each row represents a sample. Columns show spatial heatmaps of: ECMSig score, hypoxia signature score, tumor Scissor_Pos signature score, myeloid Scissor_Pos signature score, endothelial Scissor Pos signature score, and pericyte marker signature score. Color scale indicates scaled expression or score (low to high). Each dot represents a spatial barcoded spot.

Journal: iScience

Article Title: Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis

doi: 10.1016/j.isci.2026.115982

Figure Lengend Snippet: Spatial transcriptomic analysis revealing co-localization of ECMSig, hypoxia, Scissor-Positive cells, and pericytes in GBM (A) Spatial feature plots for four GBM samples. Each row represents a sample. Columns show spatial heatmaps of: ECMSig score, hypoxia signature score, tumor Scissor_Pos signature score, myeloid Scissor_Pos signature score, endothelial Scissor Pos signature score, and pericyte marker signature score. Color scale indicates scaled expression or score (low to high). Each dot represents a spatial barcoded spot.

Article Snippet: The single-cell transcriptomic sequencing dataset utilizing technology from the 10X Genomics platform was available under the accession number GEO: GSE182109 at the Gene Expression Omnibus (GEO) repository.

Techniques: Marker, Expressing

(A) Heatmap of DEG cluster analysis for DT and CK transcriptomes. The black dendrogram shows hierarchical clustering of samples and genes with similar expression patterns. The color scale represents normalized gene expression levels (yellow/orange = high expression, blue = low expression), demonstrating clear separation between CK and DT groups. (B) Volcano plot of differentially expressed genes. Orange dots indicate up-regulated genes (log 2 FC > 1, p < 0.05), blue dots indicate down-regulated genes (log 2 FC < −1, p < 0.05), and gray dots indicate non-significant genes.

Journal: PeerJ

Article Title: A cellular view of drought adaptation in sugarcane: multi-omics integration reveals a quadruple module network linking water regulation, oxidative defense, cell wall remodeling, and cell cycle regulation

doi: 10.7717/peerj.21396

Figure Lengend Snippet: (A) Heatmap of DEG cluster analysis for DT and CK transcriptomes. The black dendrogram shows hierarchical clustering of samples and genes with similar expression patterns. The color scale represents normalized gene expression levels (yellow/orange = high expression, blue = low expression), demonstrating clear separation between CK and DT groups. (B) Volcano plot of differentially expressed genes. Orange dots indicate up-regulated genes (log 2 FC > 1, p < 0.05), blue dots indicate down-regulated genes (log 2 FC < −1, p < 0.05), and gray dots indicate non-significant genes.

Article Snippet: The transcriptome sequences and annotation information of Saccharum spp. suspension cells under drought stress determined in this study have been uploaded to the National Center for Biotechnology Information ( PRJNA1403770 ) for access by relevant researchers.

Techniques: Expressing, Gene Expression

(A) The abscissa indicates the selected DEGs related to drought stress in sugarcane, and the ordinate indicates the log 2 fold change of gene expression measured by RNA-seq (orange bars) and RT-qPCR (blue bars). The column height represents the differential expression multiple, and the smaller the height difference between orange and blue bars, the more consistent the expression trend between the two methods. (B) Correlation analysis of log 2 fold change values from RNA-seq ( x -axis) and RT-qPCR ( y -axis). Each blue square represents one DEG. The linear regression equation and R 2 value ( R 2 = 0.97581) indicate a high positive correlation between the two techniques, verifying the reliability of the transcriptome sequencing data.

Journal: PeerJ

Article Title: A cellular view of drought adaptation in sugarcane: multi-omics integration reveals a quadruple module network linking water regulation, oxidative defense, cell wall remodeling, and cell cycle regulation

doi: 10.7717/peerj.21396

Figure Lengend Snippet: (A) The abscissa indicates the selected DEGs related to drought stress in sugarcane, and the ordinate indicates the log 2 fold change of gene expression measured by RNA-seq (orange bars) and RT-qPCR (blue bars). The column height represents the differential expression multiple, and the smaller the height difference between orange and blue bars, the more consistent the expression trend between the two methods. (B) Correlation analysis of log 2 fold change values from RNA-seq ( x -axis) and RT-qPCR ( y -axis). Each blue square represents one DEG. The linear regression equation and R 2 value ( R 2 = 0.97581) indicate a high positive correlation between the two techniques, verifying the reliability of the transcriptome sequencing data.

Article Snippet: The transcriptome sequences and annotation information of Saccharum spp. suspension cells under drought stress determined in this study have been uploaded to the National Center for Biotechnology Information ( PRJNA1403770 ) for access by relevant researchers.

Techniques: Gene Expression, RNA Sequencing, Quantitative RT-PCR, Quantitative Proteomics, Expressing, Sequencing

Transcriptomic profiling of the pretreatment biopsy sample. RNA-seq was used to obtain gene expression profiles of tumor samples. A global embedding of cancer types was calculated by transforming these gene expression profiles using PCA, followed by a t-SNE transformation to obtain a 2-dimensional approximate “map” of transcriptional similarity. The new patient sample is projected onto a pre-calculated PCA embedding space based on historical samples analyzed at Caris. Its proximity to other samples on the map reflects shared transcriptional programs with these samples. Only the immediate neighborhoods of samples should be considered for further interpretation since t-SNE embeddings do not preserve similarity correlations over long distances. Each point on the t-SNE map represents a different sample. The target symbol represents the present patient’s pretreatment biopsy sample. PCA, principal component analysis; t-SNE, t-stochastic neighbor embedding.

Journal: Therapeutic Advances in Medical Oncology

Article Title: Lineage infidelity in FH-deficient RCC with secondary somatic alterations: a case report and implications for diagnosis and treatment

doi: 10.1177/17588359261456676

Figure Lengend Snippet: Transcriptomic profiling of the pretreatment biopsy sample. RNA-seq was used to obtain gene expression profiles of tumor samples. A global embedding of cancer types was calculated by transforming these gene expression profiles using PCA, followed by a t-SNE transformation to obtain a 2-dimensional approximate “map” of transcriptional similarity. The new patient sample is projected onto a pre-calculated PCA embedding space based on historical samples analyzed at Caris. Its proximity to other samples on the map reflects shared transcriptional programs with these samples. Only the immediate neighborhoods of samples should be considered for further interpretation since t-SNE embeddings do not preserve similarity correlations over long distances. Each point on the t-SNE map represents a different sample. The target symbol represents the present patient’s pretreatment biopsy sample. PCA, principal component analysis; t-SNE, t-stochastic neighbor embedding.

Article Snippet: To confirm the diagnosis, molecular testing (next-generation sequencing (NGS); whole exome and whole transcriptome sequencing, Caris Life Sciences (Irving, Texas, United States)) revealed an S419P mutation in the FH gene, along with pathogenic mutations in KMT2A , NF2 , and TP53 , discussed in greater detail below.

Techniques: RNA Sequencing, Gene Expression, Transformation Assay