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rnaseq data preprocessing pipeline (NextGen Sciences)

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NextGen Sciences rnaseq data preprocessing pipeline
Baseline information of patients and PDOX

Rnaseq Data Preprocessing Pipeline, supplied by NextGen Sciences, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/rna-seq+data+pre-processing/rnaseq+data+preprocessing+pipeline/pmc08336020-68-2-18
Average 90 stars, based on 1 article reviews

rnaseq data preprocessing pipeline - by Bioz Stars, 2026-10

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1) Product Images from "Metabolic and transcriptomic profiles of glioblastoma invasion revealed by comparisons between patients and corresponding orthotopic xenografts in mice"

Article Title: Metabolic and transcriptomic profiles of glioblastoma invasion revealed by comparisons between patients and corresponding orthotopic xenografts in mice

Journal: Acta Neuropathologica Communications

doi: 10.1186/s40478-021-01232-4

Figure Legend Snippet: Baseline information of patients and PDOX

Techniques Used:

other:

Article Title: Metabolic and transcriptomic profiles of glioblastoma invasion revealed by comparisons between patients and corresponding orthotopic xenografts in mice
Article Snippet: Preprocessing of RNA sequencing (RNAseq) data was performed following the standard pipeline and recommendations from bcbio-nextgen (version 1.0.4, http://bcbio-nextgen.readthedocs.org/en/latest/ ).

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(A) Principal component analysis (PCA) plot of the bulk RNA-seq data from isolated TCR-transduced CD8 T cells following 48 hours of co-culture with each UM-SCC47 cell line and treatment condition (n = 4 donors per condition). (B-C) Log 2 normalized counts for (B) TNFRSF9 and (C) KLRC1 . (D) Heatmap of activation-induced genes defined as genes upregulated in the wildtype E7 11-20 SCT or HLA-A2 OE + IL-12p70 condition relative to the empty vector control. Genes are sorted by the log 2 fold change of gene expression in the comparison of HLA-A2 OE + IL-12p70 over wildtype E7 11-20 SCT. (E) Log 2 fold change of the differentially expressed genes for HLA-A2 OE + IL-12p70 versus HLA-A2 OE ( y-axis ) and wildtype E7 11-20 SCT versus HLA-A2 OE ( x-axis ). Points are colored by their respective k-means cluster from the heatmap in (D) . The diagonal lines indicate the 0.75 delta log 2 fold change threshold between the two comparisons. Colored overlays and labels denote the genes selected for the IL-12-specific, shared, and avidity-specific gene sets.

Journal: bioRxiv

Article Title: High-avidity TCR signaling induces a distinct KLR-positive exhaustion state in human tumor-infiltrating CD8 T cells associated with immunotherapy response

doi: 10.64898/2026.05.29.728765

Figure Lengend Snippet: (A) Principal component analysis (PCA) plot of the bulk RNA-seq data from isolated TCR-transduced CD8 T cells following 48 hours of co-culture with each UM-SCC47 cell line and treatment condition (n = 4 donors per condition). (B-C) Log 2 normalized counts for (B) TNFRSF9 and (C) KLRC1 . (D) Heatmap of activation-induced genes defined as genes upregulated in the wildtype E7 11-20 SCT or HLA-A2 OE + IL-12p70 condition relative to the empty vector control. Genes are sorted by the log 2 fold change of gene expression in the comparison of HLA-A2 OE + IL-12p70 over wildtype E7 11-20 SCT. (E) Log 2 fold change of the differentially expressed genes for HLA-A2 OE + IL-12p70 versus HLA-A2 OE ( y-axis ) and wildtype E7 11-20 SCT versus HLA-A2 OE ( x-axis ). Points are colored by their respective k-means cluster from the heatmap in (D) . The diagonal lines indicate the 0.75 delta log 2 fold change threshold between the two comparisons. Colored overlays and labels denote the genes selected for the IL-12-specific, shared, and avidity-specific gene sets.

Article Snippet: Bulk RNA-seq data pre-processing was performed by Plasmidsaurus.

Techniques: RNA Sequencing, Isolation, Co-Culture Assay, Activation Assay, Plasmid Preparation, Control, Gene Expression, Comparison

(A) Module scores of the bulk RNA-seq gene sets. Datapoints are the median module score per patient among the cells in the cluster (n = 30-32 patients per cluster; Dunnett’s multiple comparisons test). (B) Mean Z-scored RNA expression per cluster of the genes in the bulk RNA-seq gene sets. Genes are sorted by the delta Z-score of the Tex-KLR cluster minus the mean of the Tex-Conv and Tex-CCR6 clusters. Only genes detected in > 5% of cells in at least one cluster are shown. (C) Avidity-specific gene set module score of the cells within representative expanded Tex clonotypes from each quartile (Q). The score distribution of all cells of the expanded Tex clones is overlaid. (D) Mean Z-scored RNA expression for the pseudo-bulked expanded Tex clonotypes of each quartile for selected general Tex genes and Tex-KLR marker genes. The sign and significance of the Spearman correlation between the avidity-specific gene set module score and each gene are indicated in the quartile 1 (Q1) column. (E) RNA expression for the pseudo-bulked expanded Tex clonotypes following variance stabilized transformation (VST). The large points represent the mean expression for the quartile, and smaller points represent individual clonotypes (n = 82 clonotypes for Q1, 81 each for the other quartiles; DESeq2 Wald test). (F) Expression of the top avidity-specific gene set ( Methods ) and the Tex-KLR signature in FACS-sorted CD8+ TIL subsets from human melanoma samples as quantified by UCell (n = 364 clonotypes from 5 patients for singlet vs. cancer cell doublet and n = 137 clonotypes from 3 patients for singlet vs. APC doublet). Statistical significance was tested using linear regression with patient, clonotype, and log10(clonotype cell count) as fixed-effect covariates.

Journal: bioRxiv

Article Title: High-avidity TCR signaling induces a distinct KLR-positive exhaustion state in human tumor-infiltrating CD8 T cells associated with immunotherapy response

doi: 10.64898/2026.05.29.728765

Figure Lengend Snippet: (A) Module scores of the bulk RNA-seq gene sets. Datapoints are the median module score per patient among the cells in the cluster (n = 30-32 patients per cluster; Dunnett’s multiple comparisons test). (B) Mean Z-scored RNA expression per cluster of the genes in the bulk RNA-seq gene sets. Genes are sorted by the delta Z-score of the Tex-KLR cluster minus the mean of the Tex-Conv and Tex-CCR6 clusters. Only genes detected in > 5% of cells in at least one cluster are shown. (C) Avidity-specific gene set module score of the cells within representative expanded Tex clonotypes from each quartile (Q). The score distribution of all cells of the expanded Tex clones is overlaid. (D) Mean Z-scored RNA expression for the pseudo-bulked expanded Tex clonotypes of each quartile for selected general Tex genes and Tex-KLR marker genes. The sign and significance of the Spearman correlation between the avidity-specific gene set module score and each gene are indicated in the quartile 1 (Q1) column. (E) RNA expression for the pseudo-bulked expanded Tex clonotypes following variance stabilized transformation (VST). The large points represent the mean expression for the quartile, and smaller points represent individual clonotypes (n = 82 clonotypes for Q1, 81 each for the other quartiles; DESeq2 Wald test). (F) Expression of the top avidity-specific gene set ( Methods ) and the Tex-KLR signature in FACS-sorted CD8+ TIL subsets from human melanoma samples as quantified by UCell (n = 364 clonotypes from 5 patients for singlet vs. cancer cell doublet and n = 137 clonotypes from 3 patients for singlet vs. APC doublet). Statistical significance was tested using linear regression with patient, clonotype, and log10(clonotype cell count) as fixed-effect covariates.

Article Snippet: Bulk RNA-seq data pre-processing was performed by Plasmidsaurus.

Techniques: RNA Sequencing, RNA Expression, Clone Assay, Marker, Transformation Assay, Expressing, Cell Characterization

(A) UMAP plot of the CD8 T cell atlas (Xue et al., 2024). (B) Feature plots showing module scores for each bulk RNA-seq gene set. Only the genes that were present in > 5% of the cells in at least one cluster were used to apply the module score. (C) Violin plots of the module score for each bulk RNA-seq gene set. Datapoints indicate the median module score for each sample in the cluster. (D) Avidity-specific gene set module score of the cells within representative expanded Tex clonotypes from each quartile. The distribution of all cells of expanded Tex clones is shown as an overlaid black line. (E) Smoothed histogram of the normalized avidity-specific gene set module score distributions for the cells in the expanded Tex clonotypes ( solid line ) with the inter-quartile range ( shaded ribbon ). (F) Heatmap of the mean Z-scored RNA expression for the pseudo-bulked expanded Tex clonotypes of each quartile for selected general Tex genes and Tex-KLR marker genes. The sign and significance of the Spearman correlation between the avidity-specific gene set module score with each gene are indicated in the quartile 1 (Q1) column. (G) Pseudo-bulked RNA expression of expanded Tex clonotypes following variance stabilized transformation (VST). The large points connected by the line represent the mean expression, and small points represent individual clonotypes (n = 80 clonotypes for Q1-Q3, 79 for Q4; DESeq2 Wald test).

Journal: bioRxiv

Article Title: High-avidity TCR signaling induces a distinct KLR-positive exhaustion state in human tumor-infiltrating CD8 T cells associated with immunotherapy response

doi: 10.64898/2026.05.29.728765

Figure Lengend Snippet: (A) UMAP plot of the CD8 T cell atlas (Xue et al., 2024). (B) Feature plots showing module scores for each bulk RNA-seq gene set. Only the genes that were present in > 5% of the cells in at least one cluster were used to apply the module score. (C) Violin plots of the module score for each bulk RNA-seq gene set. Datapoints indicate the median module score for each sample in the cluster. (D) Avidity-specific gene set module score of the cells within representative expanded Tex clonotypes from each quartile. The distribution of all cells of expanded Tex clones is shown as an overlaid black line. (E) Smoothed histogram of the normalized avidity-specific gene set module score distributions for the cells in the expanded Tex clonotypes ( solid line ) with the inter-quartile range ( shaded ribbon ). (F) Heatmap of the mean Z-scored RNA expression for the pseudo-bulked expanded Tex clonotypes of each quartile for selected general Tex genes and Tex-KLR marker genes. The sign and significance of the Spearman correlation between the avidity-specific gene set module score with each gene are indicated in the quartile 1 (Q1) column. (G) Pseudo-bulked RNA expression of expanded Tex clonotypes following variance stabilized transformation (VST). The large points connected by the line represent the mean expression, and small points represent individual clonotypes (n = 80 clonotypes for Q1-Q3, 79 for Q4; DESeq2 Wald test).

Article Snippet: Bulk RNA-seq data pre-processing was performed by Plasmidsaurus.

Techniques: RNA Sequencing, Clone Assay, RNA Expression, Marker, Transformation Assay, Expressing

(A) UMAP plot of an scRNA-seq dataset of murine CD8 T cells collected during the acute and chronic lymphocytic choriomeningitis virus (LCMV) infection models from the spleen, lung, and liver (Daniel et al., 2022). (B) Feature plots of the module score for each bulk RNA-seq gene set. Genes were converted to the equivalent mouse gene, and only the genes within the gene set that were present in > 5% of the cells in at least one cluster were used to apply the module score. (C-E) Module scores of the (C) avidity-specific, (D) IL-12-specific, and (E) shared gene sets in the CD8 T cell clusters from day 21 post-infection with the chronic LCMV strain. (F) Spearman correlation of Klr genes in the dataset with the avidity-specific gene set module score at day 21 post-infection with the chronic LCMV strain. Bars are colored by -log 10 (FDR). (G) Dot plot of selected activation-associated genes among the Tex clusters from day 21 post-infection with the chronic LCMV model.

Journal: bioRxiv

Article Title: High-avidity TCR signaling induces a distinct KLR-positive exhaustion state in human tumor-infiltrating CD8 T cells associated with immunotherapy response

doi: 10.64898/2026.05.29.728765

Figure Lengend Snippet: (A) UMAP plot of an scRNA-seq dataset of murine CD8 T cells collected during the acute and chronic lymphocytic choriomeningitis virus (LCMV) infection models from the spleen, lung, and liver (Daniel et al., 2022). (B) Feature plots of the module score for each bulk RNA-seq gene set. Genes were converted to the equivalent mouse gene, and only the genes within the gene set that were present in > 5% of the cells in at least one cluster were used to apply the module score. (C-E) Module scores of the (C) avidity-specific, (D) IL-12-specific, and (E) shared gene sets in the CD8 T cell clusters from day 21 post-infection with the chronic LCMV strain. (F) Spearman correlation of Klr genes in the dataset with the avidity-specific gene set module score at day 21 post-infection with the chronic LCMV strain. Bars are colored by -log 10 (FDR). (G) Dot plot of selected activation-associated genes among the Tex clusters from day 21 post-infection with the chronic LCMV model.

Article Snippet: Bulk RNA-seq data pre-processing was performed by Plasmidsaurus.

Techniques: Virus, Infection, RNA Sequencing, Activation Assay

Association of BEND2 fusions with poor clinical outcomes (A) Diagram of sample composition for the pooled BEND2 cohort of pNET. (B) Kaplan-Meier curve of disease-specific survival rate regarding BEND2 rearrangement status. (C) Forest plot showing the hazard ratio (95% CI) in the univariate Cox regression and multivariate regression after adjusting for major clinicopathological features and the corresponding p values. The total number for the cohort, the number of cases per variable category, and the number of events (disease-specific deaths) for each level were also indicated.

Journal: Cell Reports Medicine

Article Title: Molecular taxonomy of pancreatic neuroendocrine tumors reveals BEND2 -fusions-driven transcriptional plasticity and therapeutic vulnerabilities

doi: 10.1016/j.xcrm.2026.102642

Figure Lengend Snippet: Association of BEND2 fusions with poor clinical outcomes (A) Diagram of sample composition for the pooled BEND2 cohort of pNET. (B) Kaplan-Meier curve of disease-specific survival rate regarding BEND2 rearrangement status. (C) Forest plot showing the hazard ratio (95% CI) in the univariate Cox regression and multivariate regression after adjusting for major clinicopathological features and the corresponding p values. The total number for the cohort, the number of cases per variable category, and the number of events (disease-specific deaths) for each level were also indicated.

Article Snippet: Pre-processed bulk RNA-seq data from pNET clinical samples and cell lines (this study) , Mendeley Data , https://data.mendeley.com/datasets/r9m66rjtxy/1.

Techniques:

Inter-tumor heterogeneity at single-nuclei level (A) UMAP representation of 43,619 nuclei isolated from nine pNET samples with tumor purity annotated at the top left corner. (B) UMAP showing a separation between tumor cells and non-tumor cells. Tumor cells were grouped according to the five-subtype bulk classification, referred as pseudo-bulk sn-clusters. (C) Dot plot of canonical marker genes across all identified cell populations. Dot size indicates the proportion of cells expressing a gene, and color intensity reflects mean expression levels. (D) Heatmap of the Hallmark signaling pathways specific for each of the five pseudo-bulk sn-clusters based on GSVA enrichment scores. (E) Regulon specificity plot showing the top six regulons identified for each of the five pseudo-bulk sn-clusters. The x axis of the plot represents the genes within the regulon, while the y axis represents the specificity score. (F) Validation of bulk subtype-specific regulon on the bulk pNET cohort using GSVA based on a set of target genes within a regulon.

Journal: Cell Reports Medicine

Article Title: Molecular taxonomy of pancreatic neuroendocrine tumors reveals BEND2 -fusions-driven transcriptional plasticity and therapeutic vulnerabilities

doi: 10.1016/j.xcrm.2026.102642

Figure Lengend Snippet: Inter-tumor heterogeneity at single-nuclei level (A) UMAP representation of 43,619 nuclei isolated from nine pNET samples with tumor purity annotated at the top left corner. (B) UMAP showing a separation between tumor cells and non-tumor cells. Tumor cells were grouped according to the five-subtype bulk classification, referred as pseudo-bulk sn-clusters. (C) Dot plot of canonical marker genes across all identified cell populations. Dot size indicates the proportion of cells expressing a gene, and color intensity reflects mean expression levels. (D) Heatmap of the Hallmark signaling pathways specific for each of the five pseudo-bulk sn-clusters based on GSVA enrichment scores. (E) Regulon specificity plot showing the top six regulons identified for each of the five pseudo-bulk sn-clusters. The x axis of the plot represents the genes within the regulon, while the y axis represents the specificity score. (F) Validation of bulk subtype-specific regulon on the bulk pNET cohort using GSVA based on a set of target genes within a regulon.

Article Snippet: Pre-processed bulk RNA-seq data from pNET clinical samples and cell lines (this study) , Mendeley Data , https://data.mendeley.com/datasets/r9m66rjtxy/1.

Techniques: Isolation, Marker, Expressing, Protein-Protein interactions, Biomarker Discovery

Cell-cell communication network and in silico drug sensitivity prediction in pNET subtypes (A) Cell-cell communication network visualized in Cytoscape, depicting the number and strength of interactions between tumor and microenvironmental cell populations. Nodes represent cell types, and edges represent intercellular interactions, with edge width and color reflecting interaction strength and frequency. (B) Bubble plot of selected ligand-receptor pairs between tumor subtypes and non-tumor microenvironmental cell populations. (C) Correlation plots showing associations between gene expression and deconvoluted cell-type proportions in bulk pNET samples. (D) Kaplan-Meier survival curves showing different DSS rates between pNET patients with high and low expression of genes of interest, including NOTCH3 (left), CD74 (middle), and the geometric mean value of these two genes (right). Patients were categorized into different groups according to the optimal thresholding. (E) Mechanisms of action of the 10 compounds predicted by Connectivity Map (CMap) analysis to preferentially target the BEND2 fusion. (F) Predicted sensitivities to 12 HDAC inhibitors across pNET subtypes based on GDSC database analyses. Statistical comparisons were performed using the Kruskal-Wallis test. p < 0.1; ∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001.

Journal: Cell Reports Medicine

Article Title: Molecular taxonomy of pancreatic neuroendocrine tumors reveals BEND2 -fusions-driven transcriptional plasticity and therapeutic vulnerabilities

doi: 10.1016/j.xcrm.2026.102642

Figure Lengend Snippet: Cell-cell communication network and in silico drug sensitivity prediction in pNET subtypes (A) Cell-cell communication network visualized in Cytoscape, depicting the number and strength of interactions between tumor and microenvironmental cell populations. Nodes represent cell types, and edges represent intercellular interactions, with edge width and color reflecting interaction strength and frequency. (B) Bubble plot of selected ligand-receptor pairs between tumor subtypes and non-tumor microenvironmental cell populations. (C) Correlation plots showing associations between gene expression and deconvoluted cell-type proportions in bulk pNET samples. (D) Kaplan-Meier survival curves showing different DSS rates between pNET patients with high and low expression of genes of interest, including NOTCH3 (left), CD74 (middle), and the geometric mean value of these two genes (right). Patients were categorized into different groups according to the optimal thresholding. (E) Mechanisms of action of the 10 compounds predicted by Connectivity Map (CMap) analysis to preferentially target the BEND2 fusion. (F) Predicted sensitivities to 12 HDAC inhibitors across pNET subtypes based on GDSC database analyses. Statistical comparisons were performed using the Kruskal-Wallis test. p < 0.1; ∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001.

Article Snippet: Pre-processed bulk RNA-seq data from pNET clinical samples and cell lines (this study) , Mendeley Data , https://data.mendeley.com/datasets/r9m66rjtxy/1.

Techniques: In Silico, Gene Expression, Expressing

BEND2 fusions induce transcriptional reprogramming and morphological plasticity in pNET tumor cells (A) Phase-contrast microscopy images of BON1 cells at 48 h post-induction showing morphological changes following overexpression of BEND2 -only, CHD7 - BEND2 , EWSR1 - BEND2 , or mCherry control. (B) Bar plot showing the number of differentially expressed genes at 12 and 48 h across all BON1 cell line models, including EWSR1 (E), BEND2 (B), CHD7 - BEND2 (CB), and EWSR1 - BEND2 (EB), compared to the mCherry (M) control. (C) Heatmap illustrating transcriptomic clustering of BON1 cell lines at 48 h (D) Venn diagram showing overlapping significantly upregulated transcription factors (TFs) in CHD7 - BEND2 and EWSR1 - BEND2 models compared to controls at 12 h (E) Bar plot of ASCL1 expression across BON1 cell line models at 12 and 48 h. Data are represented as mean ± SEM. (F) Heatmap showing transcriptional activation of neurodevelopmental, mesenchymal, and immune-related TFs in fusion-expressing BON1 cells at 48 h, accompanied by ASCL1 downregulation, activation of GAST-high subtype-specific regulons, and upregulation of immune checkpoint genes PDCD1 and CD274 . (G) Heatmap illustrating temporal transcriptomic shifts in BON1 fusion models using a 50-gene classifier (25 neuroendocrine and 25 non-neuroendocrine genes) derived from human SCLC lines. (H) GSEA results showing transcriptional reprogramming at 48 h in BEND2 fusion lines compared to 12 h (I) Bar plot showing GATA6 expression uniquely and robustly upregulated in BEND2 fusion lines at both 12 and 48 h. Data are represented as mean ± SEM. (J) Bar plot showing specific upregulation of POMC in BON1 cells expressing EWSR1 - BEND2 at 48 h. Data are represented as mean ± SEM. (K) Bar plot showing markedly higher POMC expression in the EWSR1 - BEND2 -positive tumor compared to the CHD7 - BEND2 -positive tumor in the clinical cohort.

Journal: Cell Reports Medicine

Article Title: Molecular taxonomy of pancreatic neuroendocrine tumors reveals BEND2 -fusions-driven transcriptional plasticity and therapeutic vulnerabilities

doi: 10.1016/j.xcrm.2026.102642

Figure Lengend Snippet: BEND2 fusions induce transcriptional reprogramming and morphological plasticity in pNET tumor cells (A) Phase-contrast microscopy images of BON1 cells at 48 h post-induction showing morphological changes following overexpression of BEND2 -only, CHD7 - BEND2 , EWSR1 - BEND2 , or mCherry control. (B) Bar plot showing the number of differentially expressed genes at 12 and 48 h across all BON1 cell line models, including EWSR1 (E), BEND2 (B), CHD7 - BEND2 (CB), and EWSR1 - BEND2 (EB), compared to the mCherry (M) control. (C) Heatmap illustrating transcriptomic clustering of BON1 cell lines at 48 h (D) Venn diagram showing overlapping significantly upregulated transcription factors (TFs) in CHD7 - BEND2 and EWSR1 - BEND2 models compared to controls at 12 h (E) Bar plot of ASCL1 expression across BON1 cell line models at 12 and 48 h. Data are represented as mean ± SEM. (F) Heatmap showing transcriptional activation of neurodevelopmental, mesenchymal, and immune-related TFs in fusion-expressing BON1 cells at 48 h, accompanied by ASCL1 downregulation, activation of GAST-high subtype-specific regulons, and upregulation of immune checkpoint genes PDCD1 and CD274 . (G) Heatmap illustrating temporal transcriptomic shifts in BON1 fusion models using a 50-gene classifier (25 neuroendocrine and 25 non-neuroendocrine genes) derived from human SCLC lines. (H) GSEA results showing transcriptional reprogramming at 48 h in BEND2 fusion lines compared to 12 h (I) Bar plot showing GATA6 expression uniquely and robustly upregulated in BEND2 fusion lines at both 12 and 48 h. Data are represented as mean ± SEM. (J) Bar plot showing specific upregulation of POMC in BON1 cells expressing EWSR1 - BEND2 at 48 h. Data are represented as mean ± SEM. (K) Bar plot showing markedly higher POMC expression in the EWSR1 - BEND2 -positive tumor compared to the CHD7 - BEND2 -positive tumor in the clinical cohort.

Article Snippet: Pre-processed bulk RNA-seq data from pNET clinical samples and cell lines (this study) , Mendeley Data , https://data.mendeley.com/datasets/r9m66rjtxy/1.

Techniques: Microscopy, Over Expression, Control, Expressing, Activation Assay, Derivative Assay

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rnaseq data preprocessing pipeline (NextGen Sciences)

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1) Product Images from "Metabolic and transcriptomic profiles of glioblastoma invasion revealed by comparisons between patients and corresponding orthotopic xenografts in mice"

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