sequencing analysis workflows  (BioExpress)

Journal: bioRxiv

Article Title: MethylBench: A comprehensive benchmark of DNA methylation profiling methods across diverse sequencing platforms

doi: 10.64898/2026.04.28.721268

Figure Lengend Snippet: This figure summarizes key quality control and performance metrics across five DNA methylation profiling techniques (ONT, PacBio, RRBS, TWIST, WGEC) applied to blood, fibroblast and GIAB samples. (A): Barplots showing the mean CpG coverage per sample and method; dashed lines indicate multiples of a 10× coverage threshold & numbers represent the rounded mean CpG coverage. (B): Mean methylation levels per sample and method, shown both unfiltered and with a 10× coverage cutoff, revealing reduced variance across platforms at higher coverage. In the GIAB cohort, the deviation observed for one PacBio sample is likely attributable to its markedly lower overall sequencing coverage, reflecting technology-specific sensitivity of PacBio methylation calling to reduced molecule sampling rather than insufficient CpG-level filtering. (C): Log-scale line plot of the number of overlapping CpG sites retained at increasing coverage thresholds, indicating a rapid decline in shared CpGs with stricter filters. (D): Dot plot comparing the number of unique reads obtained per sample-method combination, with long-read methods showing fewer but larger alignments.

Article Snippet: Matched blood and fibroblast samples from five human individuals were analyzed using six DNA methylation profiling technologies: whole-genome enzymatic conversion (WGEC) using the NEBNext Enzymatic Methyl-seq Kit (New England Biolabs, Ipswich, USA), Twist Targeted Methylation Sequencing Workflow (Twist Bioscience, South San Francisco, USA), reduced representation bisulfite sequencing (RRBS), Illumina EPIC array and Oxford Nanopore Technologies (ONT).

Techniques: Control, DNA Methylation Assay, Methylation, Sequencing, Sampling

Journal: bioRxiv

Article Title: MethylBench: A comprehensive benchmark of DNA methylation profiling methods across diverse sequencing platforms

doi: 10.64898/2026.04.28.721268

Figure Lengend Snippet: Methylation density distributions across samples and methods for the samples Blood3, Fibro4 and GIAB2, with EPIC included. The coverage filter was set to 10x for all methods and then overlapped with the EPIC data. The pronounced intermediate methylation peak observed exclusively for ONT in blood likely reflects its single-molecule measurement principle combined with the high cellular heterogeneity of blood, resulting in genuine intermediate methylation states that are attenuated or discretized by aggregation-based array and bisulfite sequencing approaches.

Article Snippet: Matched blood and fibroblast samples from five human individuals were analyzed using six DNA methylation profiling technologies: whole-genome enzymatic conversion (WGEC) using the NEBNext Enzymatic Methyl-seq Kit (New England Biolabs, Ipswich, USA), Twist Targeted Methylation Sequencing Workflow (Twist Bioscience, South San Francisco, USA), reduced representation bisulfite sequencing (RRBS), Illumina EPIC array and Oxford Nanopore Technologies (ONT).

Techniques: Methylation, Methylation Sequencing

Journal: bioRxiv

Article Title: MethylBench: A comprehensive benchmark of DNA methylation profiling methods across diverse sequencing platforms

doi: 10.64898/2026.04.28.721268

Figure Lengend Snippet: (A): UpSet plot showing the overlap of significantly DMCs between blood and fibroblast samples, as identified using a wilcoxon test. Only EPIC and TWIST identify significant DMCs, demonstrating the reduced statistical power of ONT, RRBS and WGEC under non-parametric testing. (B): Cross-platform scatter plot comparing Δ β values between EPIC and TWIST. CpGs are colored by concordance: green indicates CpGs significant and directionally consistent in both platforms, red and blue mark EPIC- or TWIST-specific DMCs, respectively. The strong correlation (r = 0.96) indicates high cross-platform reproducibility under a variance-robust test. (C): Boxplots showing the mean coverage of CpGs included in each platform’s DMC set. Sequencing-based assays display broader coverage variability compared to EPIC, reflecting differences in sequencing depth and genomic sampling. (D): Variance of CpG methylation in blood and fibroblast samples across platforms. EPIC exhibits the lowest within-group variance due to array normalization, whereas sequencing-based platforms show higher dispersion linked to read depth and CpG representation.

Article Snippet: Matched blood and fibroblast samples from five human individuals were analyzed using six DNA methylation profiling technologies: whole-genome enzymatic conversion (WGEC) using the NEBNext Enzymatic Methyl-seq Kit (New England Biolabs, Ipswich, USA), Twist Targeted Methylation Sequencing Workflow (Twist Bioscience, South San Francisco, USA), reduced representation bisulfite sequencing (RRBS), Illumina EPIC array and Oxford Nanopore Technologies (ONT).

Techniques: Sequencing, Sampling, CpG Methylation Assay, Dispersion