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Ligation Sequencing Kit V14 features
This kit is recommended for users who:
- Want to achieve median raw read accuracy of Q20+ (99%) and above.
- Want to optimise their sequencing experiment for output.
- Require control over read length.
- Would like to utilise upstream processes such as size selection, whole genome amplification, or enrichment for long reads.
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Reduced representation methylation sequencing (RRMS)
Nanopore sequencing enables direct detection of methylated cytosines (e.g., at CpG sites), without the need for bisulphite conversion. CpG sites frequently occur in high density clusters called CpG islands (CGI) and most of vertebrate genes have their promoters embedded within CGIs.
Changes in methylation patterns within promoters is associated with changes in gene expression and disease states such as cancer: exploring methylation differences between tumour samples and normal samples can help to uncover mechanisms associated with tumour formation and development.
Adaptive sampling (AS) offers a fast, flexible and precise method to enrich for regions of interest (e.g. CGIs) by depleting off-target regions during sequencing itself with no requirement for upfront sample manipulation. Here we introduce Reduced Representation Methylation Sequencing (RRMS): combining Oxford Nanopore’s methylation detection with AS.
To read more about how the method works, and how it compares to other techniques for analysing methylation (e.g. EPIC arrays, bisulfite), please see our Introduction to Reduced Representation Methylation Sequencing.
Human sample sequencing
The RRMS protocol enables users to target 310 Mb of the human genome which are highly enriched for CpGs including all annotated CpG island, shores and shelves and >90% of promoter regions (100% of promoter with more than 4 CpGs). As well as other rich CpG regions in the genome.
To benchmark, we performed RRMS on five replicates of a metastatic melanoma cell line and its normal pair for a male individual (COLO829/COLO829_BL) and a triple negative breast cancer cell-line pair (HCC1395/HCC1935_BL). Each sample was run on a single MinION flow cell: RRMS resulted in high-confidence methylation calls (>10 overlapping reads) for 7.3–8.5 million CpGs per sample.
For comparison we also performed Reduced Representation Bisulfite Sequencing (RRBS), which typically yields 1.7–2.5 high-confidence calls per sample. More information on this comparison can be accessed in our RRMS performance document and poster.
Mouse sample sequencing
The RRMS protocol and a new .bed file have also been developed to target 308 Mb of the mouse genome, covering 100% of CpG island and promoter regions; as well as other rich CpG regions in the genome.
The performance of RRMS for mouse samples was characterised on replicates of a blastocyst-derived, embryonic stem cell line (ES-E14TG2a) and a leukemia cell-line (BALB/c AMuLV A.3R.1). A non-RRMS library was also run as a control. Each sample was run on a single MinION flow cell: RRMS resulted in high-confidence methylation calls (>10X reads per site) for 5.0–5.8 million CpGs per sample in the mouse genome, compared to ~400,000 CpGs in the control library.
Alternative vertebrate genomes could be sequenced using the RRMS protocol and a bespoke .bed file.
However, please note Oxford Nanopore Technologies has only validated this method using human and mouse samples. -
Introduction to the DNA extraction and Ligation Sequencing protocol for RRMS
This protocol describes how to carry out DNA extraction and reduced representation methylation sequencing (RRMS) using the Ligation Sequencing Kit V14 (SQK-LSK114) and the Adaptive Sampling feature in MinKNOW.
Steps in the sequencing workflow:
Prepare for your experiment
You will need to:
- Extract your DNA, fragment it using the Covaris g-TUBE, and check its length, quantity and purity. The quality checks performed during the protocol are essential in ensuring experimental success.
- Ensure you have your sequencing kit, the correct equipment and third-party reagents
- Download the software for acquiring and analysing your data
- Ensure that you have the correct .bed file for Adaptive Sampling
- Check your flow cell to ensure it has enough pores for a good sequencing run
Library preparationYou will need to:
- Repair the DNA, and prepare the DNA ends for adapter attachment
- Attach sequencing adapters supplied in the kit to the DNA ends
- Prime the flow cell, and load your DNA library into the flow cell
Sequencing and analysis
You will need to:
- Start a sequencing run using the MinKNOW software, which will collect raw data from the device and convert it into basecalled reads. While configuring the run, turn on the Adaptive Sampling setting and import a pre-prepared .bed file with your regions of interest, along with a FASTA reference file.
- Sequence the sample for a total of 96 hours, with two flow cell washes when the available pore count drops to around 40% of the starting pore count (typically after ~24 hours and the second time after ~48 hours).
- Use Dorado to call modified bases, for more information please refer to the Dorado github page.
- Use the commands recommended at the end of this protocol to aggregate the modified bases and perform CpG island annotation.