Table 1.

Overview of the characteristics of each whole genome and targeted sequencing technology

	Whole genome			Targeted
	ONT	WGBS	EM-seq	EPIC
DNA Input	1–5 µg	1 – 500 ng	10–200 ng	250 ng–500 ng
Single-base Resolution	Yes	Yes	Yes	No
Approximate Run Time	80–84 h	20–24 h	20–24 h	30 min
Yield [Gb]	139	163	137	NA
Sequencing Coverage (x)	34	46	41	NA
Total Reads (M)	7.5	1132.5	986	NA
Number of QC-Passed Reads (M)	NA*	1041.7	976	NA
Percentage of Mapped Reads	90.8%	99.87%	99.99%	NA
Percentage of Mapped Duplicates	0	9.5%	7.0%	NA
Mean Read Length (bp)	16,922	150	150	NA
Longest Read (bp)	856,100	150	151	NA
Number of Called CpGs	56,715,299	53,912,145	54,178,937	865,596
Computational Run Time	Very high	High	High	Low
Complexity of Analytic Pipeline**	High	Medium	Medium	Low
Generated Data Size (GB)	~ 1200	~ 120	~ 70	~ 150mb
Turnaround Time (TAT)	7–12 days	6–10 days	6–10 days	3–4 days

For each parameter, the value represents the mean value of all samples

^* Unlike WGBS and EM-seq, ONT incorporates QC at raw read levels. Instead of traditional quality control (QC) filtering, guppy uses multiple long reads to correct sequencing errors rather than removing reads. As ONT sequencing produces long reads, aggressive QC filtering could disproportionately remove long reads, severely affecting genome coverage

^** ONT, WGBS and EM-seq require an analytic pipeline for sequencing data, including alignment, base calling, and QC criteria on sequencing depth. The high complexity of the ONT pipeline’s is due to the need for software (e.g., Dorado) to detect nucleotides from signal-level data, whereas WGBS and EM-seq directly output nucleotide sequences. The EPIC array requires QC criteria that remove CpGs that could be affected by poor hybridization, such as CpGs close to known SNPs, and converts intensity signals to methylation values