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rna  (TaKaRa)


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    Structured Review

    TaKaRa rna
    Rna, supplied by TaKaRa, used in various techniques. Bioz Stars score: 94/100, based on 203 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/reference+rna/bio_rxiv__64898__2026__03__05__709814-50-8-16?v=TaKaRa
    Average 94 stars, based on 203 article reviews
    rna - by Bioz Stars, 2026-07
    94/100 stars

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    Image Search Results


    (a) Flowchart of BioGAIP analyzing RNA-seq data. The expected endpoint of this task is the generation of a list of differentially expressed genes (DEGs). During the successive process, human intervention can be introduced to prompt BioGAIP for specific downstream analyses. (b) Flowchart of BioGAIP analyzing ATTSS events. The endpoint of this task is to generate a list of differential ATTSS events. The LLM model employed in the analysis processes for (a) is qwen3-max and for (b) is grok-4-fast-reasoning. Titles in green boxes indicate normally executed steps. Titles in brown boxes indicates steps where BioGAIP encountered an error. Titles in blue boxes indicate troubleshooting steps performed by BioGAIP, with blue text showing the specific error cause(s) (when present). Titles in dark green boxes indicate steps that involved manual prompt input by the user. Arrows denote the direction of the workflow, troubleshooting flows are shown with dashed lines.

    Journal: bioRxiv

    Article Title: BioGAIP: A Scalable, User-Friendly and Robust LLM-Powered Multi-Agent System for Automated Bioinformatics Tasks

    doi: 10.64898/2026.05.16.720484

    Figure Lengend Snippet: (a) Flowchart of BioGAIP analyzing RNA-seq data. The expected endpoint of this task is the generation of a list of differentially expressed genes (DEGs). During the successive process, human intervention can be introduced to prompt BioGAIP for specific downstream analyses. (b) Flowchart of BioGAIP analyzing ATTSS events. The endpoint of this task is to generate a list of differential ATTSS events. The LLM model employed in the analysis processes for (a) is qwen3-max and for (b) is grok-4-fast-reasoning. Titles in green boxes indicate normally executed steps. Titles in brown boxes indicates steps where BioGAIP encountered an error. Titles in blue boxes indicate troubleshooting steps performed by BioGAIP, with blue text showing the specific error cause(s) (when present). Titles in dark green boxes indicate steps that involved manual prompt input by the user. Arrows denote the direction of the workflow, troubleshooting flows are shown with dashed lines.

    Article Snippet: The single-cell RNA-seq reference package (refdata-gex-GRCh38-2024-A.tar.gz) from the Cell Ranger website ( https://www.10xgenomics.com/support/software/cell-ranger/downloads ).

    Techniques: RNA Sequencing

    (a) Volcano plot of DEGs in bulk RNA-seq data of met-associated primary SCLC and never-met primary SCLC. (b) UMAP of cell annotation and FOXA2 expression level in each component based scRNA-seq. (c) Correlation plot of the top 100 highly expressed genes in FOXA2 + vs. FOXA2 - cells from Kawasaki et al. gene set, relative to FOXA2 expression defined by Kawasaki et al . (d,e) Density plot of ASCL1 ChIP-seq gene loci at the FOXA2 and PROX1 gene loci in two SCLC cell lines (H1836 and SHP-77). (f) Density plot at the FOXA2 locus of ATAC-seq data derived from ASCL1 + FOXA2 + PDX vs ASCL1 + FOXA2 - PDX tumors.

    Journal: bioRxiv

    Article Title: BioGAIP: A Scalable, User-Friendly and Robust LLM-Powered Multi-Agent System for Automated Bioinformatics Tasks

    doi: 10.64898/2026.05.16.720484

    Figure Lengend Snippet: (a) Volcano plot of DEGs in bulk RNA-seq data of met-associated primary SCLC and never-met primary SCLC. (b) UMAP of cell annotation and FOXA2 expression level in each component based scRNA-seq. (c) Correlation plot of the top 100 highly expressed genes in FOXA2 + vs. FOXA2 - cells from Kawasaki et al. gene set, relative to FOXA2 expression defined by Kawasaki et al . (d,e) Density plot of ASCL1 ChIP-seq gene loci at the FOXA2 and PROX1 gene loci in two SCLC cell lines (H1836 and SHP-77). (f) Density plot at the FOXA2 locus of ATAC-seq data derived from ASCL1 + FOXA2 + PDX vs ASCL1 + FOXA2 - PDX tumors.

    Article Snippet: The single-cell RNA-seq reference package (refdata-gex-GRCh38-2024-A.tar.gz) from the Cell Ranger website ( https://www.10xgenomics.com/support/software/cell-ranger/downloads ).

    Techniques: RNA Sequencing, Expressing, ChIP-sequencing, Derivative Assay

    (a) Volcano plot of differential ATTSS events. (b) RNA-seq density plots showing the high distal TSS usage of RAB35 in met-associated primary tumors and never-met primary tumors. (c) The Venn diagram between different ATTSS events and differential expression genes. (d) The effect of different ATTSS events on gene coding region. (e) The comparison of distal TSS usage of RAB35 between never-met primary and met-associated primary tumor. (f) The comparison of gene expression of RAB35 between never-met primary and met-associated primary tumor. Statistical significance of distal TSS usage was assessed using the DATTS , statistical significance of gene expression was assessed using the DESeq2 . The analysis of (c) was completed by human experts based on the output of BioGAIP.

    Journal: bioRxiv

    Article Title: BioGAIP: A Scalable, User-Friendly and Robust LLM-Powered Multi-Agent System for Automated Bioinformatics Tasks

    doi: 10.64898/2026.05.16.720484

    Figure Lengend Snippet: (a) Volcano plot of differential ATTSS events. (b) RNA-seq density plots showing the high distal TSS usage of RAB35 in met-associated primary tumors and never-met primary tumors. (c) The Venn diagram between different ATTSS events and differential expression genes. (d) The effect of different ATTSS events on gene coding region. (e) The comparison of distal TSS usage of RAB35 between never-met primary and met-associated primary tumor. (f) The comparison of gene expression of RAB35 between never-met primary and met-associated primary tumor. Statistical significance of distal TSS usage was assessed using the DATTS , statistical significance of gene expression was assessed using the DESeq2 . The analysis of (c) was completed by human experts based on the output of BioGAIP.

    Article Snippet: The single-cell RNA-seq reference package (refdata-gex-GRCh38-2024-A.tar.gz) from the Cell Ranger website ( https://www.10xgenomics.com/support/software/cell-ranger/downloads ).

    Techniques: RNA Sequencing, Quantitative Proteomics, Comparison, Gene Expression

    (a) Heatmap of differential ATTSS events based on DTUI values. (b) RNA-seq density plots show the high distal TSS usage of AP4E1 in two met-associated primary tumors and two never-met primary tumors. (c) The comparison of distal TSS usage of AP4E1 between never-met primary and met-associated primary tumor. (d) The comparison of gene expression of AP4E1 between never-met primary and met-associated primary tumor. (e-f) The comparison of gene expressions of RAB35 (e) and AP4E1 (f) between normal and sample tumor in GES60052. Statistical significance of distal TSS usage was assessed using the DATTS , statistical significance of gene expression was assessed using the DESeq2 .

    Journal: bioRxiv

    Article Title: BioGAIP: A Scalable, User-Friendly and Robust LLM-Powered Multi-Agent System for Automated Bioinformatics Tasks

    doi: 10.64898/2026.05.16.720484

    Figure Lengend Snippet: (a) Heatmap of differential ATTSS events based on DTUI values. (b) RNA-seq density plots show the high distal TSS usage of AP4E1 in two met-associated primary tumors and two never-met primary tumors. (c) The comparison of distal TSS usage of AP4E1 between never-met primary and met-associated primary tumor. (d) The comparison of gene expression of AP4E1 between never-met primary and met-associated primary tumor. (e-f) The comparison of gene expressions of RAB35 (e) and AP4E1 (f) between normal and sample tumor in GES60052. Statistical significance of distal TSS usage was assessed using the DATTS , statistical significance of gene expression was assessed using the DESeq2 .

    Article Snippet: The single-cell RNA-seq reference package (refdata-gex-GRCh38-2024-A.tar.gz) from the Cell Ranger website ( https://www.10xgenomics.com/support/software/cell-ranger/downloads ).

    Techniques: RNA Sequencing, Comparison, Gene Expression

    Inhibition of PABPC1 expression reduces EBOV replication. HeLa cells were mock-transfected (reagent only) or transfected with two distinct PABPC1 siRNAs or nontargeting AllStars negative control siRNAs. At 48 h post-transfection, cells were infected with EBOV at an MOI of 0.1. A.) At 16 hpi, samples were inactivated in 10% neutral-buffered formalin and RNAFISH was performed using probes detecting (+) sense NP and VP35 RNA (magenta). Nuclei were visualized by staining with Hoechst (blue). Scale bar = 250 μM. B.) Quantification of NP and VP35 RNA staining in panel A was performed in ImageJ by calculating the area occupied by mRNA signal and normalizing it to the area occupied by Hoechst (nuclei) signal. C.) In a parallel set of samples, total RNA was isolated by lysing the cells with Trizol reagent at 16 hours post infection. NP RNA levels were quantified by one-step RT-qPCR using a (+) sense NP probe to detect EBOV RNA and are normalized to the level of β-Actin RNA. D) Depletion of PABPC1 in siRNA-treated cells. A parallel set of samples were subjected to SDS-PAGE followed by western blotting with PABPC1 and actin antibodies. Molecular weight markers in kDa are shown at left.

    Journal: bioRxiv

    Article Title: A Yeast Two-Hybrid Protein Domain Screening Approach for Ebola Virus-Human Protein Interactions Identifies PABPC1 as a Host Factor Required for Replication

    doi: 10.64898/2026.03.05.709814

    Figure Lengend Snippet: Inhibition of PABPC1 expression reduces EBOV replication. HeLa cells were mock-transfected (reagent only) or transfected with two distinct PABPC1 siRNAs or nontargeting AllStars negative control siRNAs. At 48 h post-transfection, cells were infected with EBOV at an MOI of 0.1. A.) At 16 hpi, samples were inactivated in 10% neutral-buffered formalin and RNAFISH was performed using probes detecting (+) sense NP and VP35 RNA (magenta). Nuclei were visualized by staining with Hoechst (blue). Scale bar = 250 μM. B.) Quantification of NP and VP35 RNA staining in panel A was performed in ImageJ by calculating the area occupied by mRNA signal and normalizing it to the area occupied by Hoechst (nuclei) signal. C.) In a parallel set of samples, total RNA was isolated by lysing the cells with Trizol reagent at 16 hours post infection. NP RNA levels were quantified by one-step RT-qPCR using a (+) sense NP probe to detect EBOV RNA and are normalized to the level of β-Actin RNA. D) Depletion of PABPC1 in siRNA-treated cells. A parallel set of samples were subjected to SDS-PAGE followed by western blotting with PABPC1 and actin antibodies. Molecular weight markers in kDa are shown at left.

    Article Snippet: Human Reference Total RNA, a mixture of total RNA from five human tissues, was purchased from TaKaRa Clontech (#636690).

    Techniques: Inhibition, Expressing, Transfection, Negative Control, Infection, Staining, Isolation, Quantitative RT-PCR, SDS Page, Western Blot, Molecular Weight