Journal: Strahlentherapie Und Onkologie

Article Title: Irradiation alters extracellular vesicle microRNA load in the serum of patients with leukaemia

doi: 10.1007/s00066-024-02307-6

Figure Lengend Snippet: Differentially expressed miRNAs of EVs derived from serum of patients with leukaemia before irradiation vs. healthy donors. a Volcano plot; X axis: log2 transformed fold change; Y axis: −log10 transformed p -values. b Heatmap of upregulated and c heatmap of downregulated miRNAs in EVs from serum of patients with leukaemia. d Top 10 significant Kyoto Encyclopedia of Genes and Genomes database (KEGG) pathways affected by upregulated miRNAs of patients with leukaemia vs. healthy donors, ordered from top to bottom by p value, with the most significant pathway on the top. e Top 10 significant KEGG pathways affected by downregulated miRNAs of patients with leukaemia vs. healthy donors, ordered from top to bottom by p -value

Article Snippet: Workflow of miRNA sequencing data analysis (ArrayStar Inc.).

Techniques: Derivative Assay, Irradiation, Transformation Assay

Journal: Strahlentherapie Und Onkologie

Article Title: Irradiation alters extracellular vesicle microRNA load in the serum of patients with leukaemia

doi: 10.1007/s00066-024-02307-6

Figure Lengend Snippet: Significantly up- ( n = 23) and downregulated ( n = 16) miRNAs in EVs from serum of patients with leukaemia vs. healthy volunteers

Article Snippet: Workflow of miRNA sequencing data analysis (ArrayStar Inc.).

Techniques:

Journal: Strahlentherapie Und Onkologie

Article Title: Irradiation alters extracellular vesicle microRNA load in the serum of patients with leukaemia

doi: 10.1007/s00066-024-02307-6

Figure Lengend Snippet: Differentially expressed miRNAs of EVs derived from serum of patients with leukaemia after whole-body irradiation vs. before irradiation. a Volcano plot, b heatmap of upregulated and c heatmap of downregulated miRNAs in EVs from serum of patients with leukaemia after and before irradiation. d Top 10 KEGG pathways affected by upregulated miRNAs or e downregulated miRNAs from irradiated vs. non-irradiated patients with leukaemia

Article Snippet: Workflow of miRNA sequencing data analysis (ArrayStar Inc.).

Techniques: Derivative Assay, Irradiation

Journal: Strahlentherapie Und Onkologie

Article Title: Irradiation alters extracellular vesicle microRNA load in the serum of patients with leukaemia

doi: 10.1007/s00066-024-02307-6

Figure Lengend Snippet: Significantly up- ( n = 11) and downregulated ( n = 15) miRNAs in EVs from serum of patients with leukaemia after irradiation

Article Snippet: Workflow of miRNA sequencing data analysis (ArrayStar Inc.).

Techniques:

Journal: Strahlentherapie Und Onkologie

Article Title: Irradiation alters extracellular vesicle microRNA load in the serum of patients with leukaemia

doi: 10.1007/s00066-024-02307-6

Figure Lengend Snippet: Venn diagram analyses (program InteractiVenn, www.interactivenn.net ) of ( a ) upregulated or ( b ) downregulated miRNAs in AML, ALL and combined (AML+ALL = leukaemia) patients and of miRNAs ( c ) upregulated and ( d ) downregulated following TBI in AML, ALL and combined cohort patients (leukaemia)

Article Snippet: Workflow of miRNA sequencing data analysis (ArrayStar Inc.).

Techniques:

Journal: Genome Research

Article Title: Assessing DNA methylation detection for primary human tissue using Nanopore sequencing

doi: 10.1101/gr.279159.124

Figure Lengend Snippet: Overall comparison of DNA methylation calls between R9 and R10 data sets across the HG002 cell line, blood, and brain samples. ( A ) Methylation proportions of R9 (orange) and R10 (blue) data for the HG002 cell line when binned by bisulfite (green) methylation intervals. The portions of R9 and R10 methylation distributions that agree with the bisulfite methylation range are highlighted in green. ( B–D ) The violin plots underneath show methylation proportions of R9 and R10 data for the HG002 cell line, brain sample, and blood sample binned according to R9 intervals. Each violin plot has lines connecting the median interval points for better visualization of methylation trends. Distributions of CpG site methylation frequencies are depicted on the right side of each panel. ( E ) Stacked bar charts showing the breakdown of total CpG sites per technology per sample. These sites are further subset into CpG sites with 0% methylation frequency and 100% methylation frequency .

Article Snippet: The HG002 EpiQC bisulfite sequencing MethylSeq data set used to benchmark the HG002 ONT bisulfite sequencing data set was generated as part of the SEQC2 Epigenomics Quality Control Study ( ).

Techniques: Comparison, DNA Methylation Assay, Methylation

Journal: Genome Research

Article Title: Assessing DNA methylation detection for primary human tissue using Nanopore sequencing

doi: 10.1101/gr.279159.124

Figure Lengend Snippet: Genome-wide and regional methylation calling trends in HG002 R9 and R10 data sets. ( A ) Methylation calls for all CpG motifs (strand combined) in HG002 R9 and R10. ( B ) Scatterplot of 2 kb whole-genome windows with a minimum of 10 CpGs for R9 and R10. The 0–100 diagonal line is overlaid in cyan for reference. ( C , D ) Average methylation over the 29,124 filtered human promoters with a minimum of 10 CpGs plotted as a histogram and a scatter plot. ( E,F ) A histogram and scatter plot of the CpG island BED regions with more than 10 CpGs in HG002. All plots are limited to CpG sites with >5× read coverage. The y -axis is log scaled.

Article Snippet: The HG002 EpiQC bisulfite sequencing MethylSeq data set used to benchmark the HG002 ONT bisulfite sequencing data set was generated as part of the SEQC2 Epigenomics Quality Control Study ( ).

Techniques: Genome Wide, Methylation

Journal: Genome Research

Article Title: Assessing DNA methylation detection for primary human tissue using Nanopore sequencing

doi: 10.1101/gr.279159.124

Figure Lengend Snippet: Comparison of HG002 immortalized cell line methylation calling between technologies. ( A ) Methylation proportion histograms for each technology. ( B ) Pairwise site-specific CpG methylation proportion comparison between technologies. ( C ) Site-specific CpG methylation proportion comparison between R10 and bisulfite sequencing. ( D ) RMSE values for pairwise comparisons between technology in Illumina 150 bp paired-end mappable regions as defined by BisMap. ( E ) RMSE for pairwise comparisons between technologies in Illumina 150 bp paired-end hard-to-map regions.

Article Snippet: The HG002 EpiQC bisulfite sequencing MethylSeq data set used to benchmark the HG002 ONT bisulfite sequencing data set was generated as part of the SEQC2 Epigenomics Quality Control Study ( ).

Techniques: Comparison, Methylation, CpG Methylation Assay, Methylation Sequencing

Journal: Genome Research

Article Title: Assessing DNA methylation detection for primary human tissue using Nanopore sequencing

doi: 10.1101/gr.279159.124

Figure Lengend Snippet: Haplotype-specific methylation differences and similarities between cell, blood, and brain samples. From top to bottom , each plot shows the genome coordinates, labeled gene models (if present), haplotype-aware read mappings with modified bases as black (methylated), or colored (unmethylated) circles, a smoothed methylation fraction plot, and a coverage plot. The highlighted region corresponds to a 75 bp deletion (Chr16:88,534,247–88,534,321) in haplotype 2 of the HG002 cell line that coincides with haplotype-specific methylation. Coordinates across the bottom refer to methylation bins used in the smoothed methylation plot. ( A ) Haplotype-specific methylation differences and similarities between the R9 and R10 sequenced HG002 cell line. ( B ) Haplotype-specific methylation differences and similarities between the R10 sequenced cell line, blood, and brain samples.

Article Snippet: The HG002 EpiQC bisulfite sequencing MethylSeq data set used to benchmark the HG002 ONT bisulfite sequencing data set was generated as part of the SEQC2 Epigenomics Quality Control Study ( ).

Techniques: Methylation, Labeling, Modification

Journal: Biotechnology for Biofuels and Bioproducts

Article Title: Comparative metagenomics reveals the metabolic flexibility of coastal prokaryotic microbiomes contributing to lignin degradation

doi: 10.1186/s13068-025-02605-w

Figure Lengend Snippet: The reconstructed MAGs from L6 ( A ) and L18 ( B ) consortia. Sankey diagrams showed recovered archaeal and bacterial MAG information at different taxonomic levels (relative abundance > 1%), based on the GTDB classification. Numbers indicate the relative abundance of MAGs recovered for that lineage. Relative abundance was determined by mapping each MAG against quality-filtered metagenome reads using CoverM. Significant differences between L6 and L18 consortia are marked with asterisks ( p < 0.05). Data are from three biological replicates

Article Snippet: The metagenome sequencing data were deposited in the NCBI SRA database under the accession number PRJNA1113784, as well as in the NODE ( https://www.biosino.org/node/ ) under project ID OEP005460 (Experimental ID OEX00028629).

Techniques: