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BMC Genomics
BMC Genomics
BMC Genomics
1471-2164
BioMed Central London

39227767
10748
10.1186/s12864-024-10748-7
Correction
Correction: Comparing methylation levels assayed in GCrich regions with current and emerging methods
Guanzon Dominic 13
Ross Jason P. 1
Ma Chenkai 1
Berry Oliver 2
Liew Yi Jin yijin.liew@csiro.au

12
1 https://ror.org/03jh4jw93 grid.492989.7 CSIRO Health & Biosecurity, Westmead, NSW Australia
2 grid.1016.6 0000 0001 2173 2719 Environomics Future Science Platform, CSIRO, Crawley, WA Australia
3 https://ror.org/00rqy9422 grid.1003.2 0000 0000 9320 7537 Translational Extracellular Vesicles in Obstetrics and Gynae-Oncology Group, Faculty of Medicine, University of Queensland Centre for Clinical Research, The University of Queensland, Queensland, Australia
3 9 2024
3 9 2024
2024
25 829© Crown 2024
2024
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pmcCorrection: BMC Genomics25, 741 (2024). 10.1186/s12864-024-10605-7

Following publication of the original article several errors were reported in Table 1.

In the “EPIC” column of the row “Turnaround time (from DNA extracts)” the value was given as ‘2 days’ but should be ‘3 days’.

In the “ONT” column for the row “Relative costs (per sample)” the values were given as:

$$ (for ONT Cas9)

$ (for whole genome)

The correct values are:

$$ (for ONT Cas9)

\$\$\$\$\$ (for whole genome)

The updated Table 1 with the corrected values in bold is given in this Correction article and the original article has been updated.

Table 1 Picking the right tool for the job

Criteria	EM-seq	WGBS	EPIC	ONT	
Flexibility in DNA conversion	NEB-only	Any bisulphite conversion kit	N/A	
Flexibility in library construction	NEB-only	More options	Illumina-only	ONT-only	
Flexibility in sequencing	Illumina-only	Depends on library type	Illumina-only	ONT-only	
Experimental complexity	Well-established protocols which can be performed by trained scientists.	Protocols actively being developed and slightly more complex.	
Data analysis complexity	Robust and mature packages/pipelines available	Pipelines are still in flux	
Turnaround time (from DNA extracts)	2–4 days	3 days	1–2 days (data is streamed)	
Relative costs (per sample)	$$	$$$	$	$$ (for ONT Cas9)

\$\$\$\$\$ (for whole genome)

	
Strengths	Cheaper than WGBS. Coverage more evenly distributed across genome. Data quality better from GC-rich loci than WGBS.	Easier to compare against publicly available data (most are WGBS/RRBS). Bisulphite conversion (without library building) cheaper than enzymatic conversion, better suited for translation into amplicon-based assays.	Very cost effective for getting a subset of methylated and biologically relevant positions across more samples. Ideal for model organisms.	Almost unbiased coverage regardless of context. Quickest turnaround time. Least affected by GC-context biases.	
Weaknesses	Increased laboratory time than WGBS. Comparisons against existing data should consider readout divergences at GC-rich loci.	Coverage and methylation readouts biases very pronounced at GC-rich loci.	Not very practical for non-model organisms. Custom panels possible but less cost effective and less reliable.	Higher inputs required.

Methylation data from whole genome possible, but more costly. Methylation calls are not binary, unlike bulk of existing data. Higher complexity in sequencing and in analysis.

	
Practical considerations involved in all four methods, as well as their relative strengths and weaknesses

The online version of the original article can be found at 10.1186/s12864-024-10605-7.

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