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.

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.

RRMS can be deployed on MinION Mk1B/Mk1D, GridION and PromethION P2S, P24 and P48 platforms.

When running on MinION/GridION, we recommend running a single sample per flow cell using our this protocol.

Alternatively, it is possible to multiplex up to 4 samples on a single PromethION flow cell, using our Ligation sequencing gDNA V14 - reduced representation methylation multiplex sequencing (RRMS) (SQK-NBD114.24) protocol.

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 islands, shores, shelves and >90% of promoter regions (100% of promoter with more than 4 CpGs). As well as other rich CpG regions in the genome. The total number of CpG sites in the .bed file is 7.18 million.

For benchmarking purposes, 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 Bisulphite 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.

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 preparation

The table below is an overview of the steps required in the library preparation, including timings and optional stopping points.

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.

DNA质控

选择符合质量和浓度要求的起始DNA至关重要的。使用过少或过多的DNA，或者质量较差的DNA（如，高度碎片化、含有RNA或化学污染物的DNA）都会影响文库制备。

有关如何对DNA样品进行质控，请参考起始DNA/RNA质控实验指南 。

化学污染物

从原始样本中提取DNA的方法不同，可能会导致经纯化的DNA中所残留的化学污染物不同。这会影响文库的制备效率和测序质量。请在牛津纳米孔社区的 Contaminants（污染物）页面 了解更多信息。

对于新用户，我们建议购买供Oxford Nanopore Technologies®连接测序的 NEBNext® 配套模块v2（目录号E7672S或E7672L） 。该配套模块内包含所有与连接测序试剂盒配套使用的NEB试剂。

之前版本的NEBNext® 配套模块（货号E7180S或E7180L）虽然兼容，但我们更推荐使用v2版本。得益于FFPEv2 DNA修复缓冲液和耐盐T4 DNA连接酶，v2版配套模块在dA尾添加和连接步骤上的效率更高。此外，使用v2版配套模块还能显著降低每个样本的制备成本。

请注意：在扩增子测序的相关实验中，无需使用NEBNext FFPE修复混合液。单独购买所需试剂将更为经济实惠。

Oxford Nanopore Technologies推荐您使用本实验指南中提及的所有第三方试剂，并已对其加以验证。我们尚未对其它替代试剂进行测试。

我们建议您按制造商说明准备待用的第三方试剂.

我们强烈建议您在开始测序实验前，对测序芯片的活性纳米孔数进行质检。质检需在您收到MinION /GridION /PremethION测序芯片12周之内进行，或者在您收到Flongle测序芯片四周内进行。Oxford Nanopore Technologies会对活性孔数量少于以下标准的芯片进行替换** ：

** 请注意：自收到之日起，芯片须一直贮存于Oxford Nanopore Technologies推荐的条件下。且质检结果须在质检后的两天内递交给我们。请您按照 测序芯片质检文档中的说明进行芯片质检。

请注意： 我们正在将部分试剂的包装形式由单次管装改为瓶装。

单次管装试剂：

部分试剂改为瓶装：

声明： 本产品包含由贝克曼库尔特公司（Beckman Coulter, Inc）生产的 AMPure XP 试剂，并可与试剂盒一起于-20°C 下储存（试剂稳定性将不受损害）。

请注意： DNA参照（DCS）是一段可比对到Lambda基因组的3'端、长度为3.6 kb 的标准扩增子。

Extract DNA from your sample(s) using one of our recommended extraction protocols.

For the benchmarking of this method, the Oxford Nanopore team extracted DNA from ~5 million cells using the protocol: Human cell line DNA – QIAGEN Puregene Cell Kit. The steps for this method are outlined below.
Note: this method is also suitable for mouse cell line DNA.

We also offer multiple mammalian sample extraction protocols, which you can use for other sample types.

To prepare fragmented gDNA for the library prep protocol, mechanical fragmentation is performed using a g-TUBE (Covaris) to shear DNA to a fragment length of approximately 6kb.

We recommend washing and reloading the flow cell after ~24 hours of sequencing. For this method, the flow cell is washed after ~24 hours of sequencing to restore pores to ensure efficient data acquisition. After an additional 24 hours of sequencing, the flow cell is washed and reloaded a second time. For this reason, enough library was generated for 3 flow cell loads in the adapter ligation step of the protocol.

• This washing procedure aims to remove most of the initial library and unblock the pores to prepare the flow cell for the loading of a subsequent library.
• Data acquisition in MinKNOW should be paused during the wash procedure and library loading.
• After the flow cell has been washed, the next library can be loaded.

You can navigate to the Pore Activity or the Pore Scan Results plot to see pore availability.

有关纳米孔数据分析的完整概述，包括碱基识别和次级分析，请参阅 数据分析 文档。

The sequencing device control and data acquisition are carried out by the MinKNOW software. Please ensure MinKNOW is installed on your computer. Further instructions for setting up your sequencing run can be found in the MinKNOW protocol.

• Select the Ligation Sequencing Kit (SQK-LSK114) in kit selection.

• Turn basecalling OFF.
  Note: Basecalling will be carried out post-sequencing in the downstream analysis section of the protocol.

• Turn Adaptive Sampling ON, and select Enrich.
  Input the human reference file for alignment and the .bed file for enumerating regions (check online catalogue for the human RRMS .bed file).

• Set the run duration for a minimum of 96 hours.

• Set up your desired output parameters.
  To ensure the downstream analysis functions correctly, we recommend keeping the default options of the output file format (.POD5).

• Click “Start” begin the sequencing run.

Dorado stand-alone can be used for basecalling using the dorado basecaller. Open a terminal and enter the following commands:

dorado basecaller hac,5mCG_5hmCG \
--secondary “no” -Y \
--reference {reference_fasta} {input_pod5_folder} \
| samtools view -e '[qs] >= {qscore_filter}' \
--output {out_pass_bam} \
--unoutput {out_fail_bam}

Notes:

• We recommend using the high accuracy model (hac) for RRMS sequencing runs. However, if using the super accurate model (sup), ensure you are utilizing the correct model in the above command.

• Alignment can be performed while basecalling by providing a reference FASTA file, the recommended human reference file can be downloaded here.

• Secondary alignments are discarded by using “--secondary no” and -Y option is enabled, to allow soft-clipping supplementary alignments.

• We recommend setting the qscore filter to 10.

• Please note, GPU compute is needed to perform basecalling with dorado, more information on how to run dorado can be found in the github repository.

RRMS target bed file can be downloaded from the AS catalogue available here.

Mosdepth is used to check coverage on target regions for the barcodes of interest:

mosdepth -x -t 8 -n -b {target_bed} {out_prefix} {input_pass_bam}

Human variation pipeline is used to aggregate modifications per genomic positions using modkit.

The workflow is available in the following repository: wf-human-variation github.

The documentation can be found in the following space: wf-human-variation EPI2ME page

Modification calling:

For most RRMS runs we recommend running the following command:

nextflow run https://github.com/epi2me-labs/wf-human-variation \
-profile singularity \  
--mod \
--bam <bam> \
--bed RRMS_human_hg38.bed \
--ref GCA_000001405.15_GRCh38_no_alt_analysis_set.fasta \
--sample_name <sample> --out_dir <output_dir>

(Optional) For haplotype-specific methylation:

If haplotype-specific methylation is required, you can provide options “--snp –phased“ to aggregate modifications identified on each of the haplotypes (i.e. one bedmethyl file for each of the haplotypes will be generated):

nextflow run https://github.com/epi2me-labs/wf-human-variation \
-profile singularity \  
--mod --snp --phased \
--bam <bam> \
--bed RRMS_human_hg38.bed \
--ref GCA_000001405.15_GRCh38_no_alt_analysis_set.fasta \
--sample_name <sample> --out_dir <output_dir>

Note: For this specific analysis, a sample coverage of >30X is recommended.

For detection of differentially methylated regions across different samples “modkit dmr” can be used.

For more information check the modkit documentation available here.

The BAM file(s) generated by dorado contains canonical bases as well as per-read modifications stored in MM and ML BAM tags. To visualise the per-read modification calls, IGV can be used to load the BAM file and set "colour reads as" to “base modification 2-color (all)”.

If phasing was performed using wf-human-variation pipeline, the haplotagged BAM file can be uploaded in IGV and alignments can be grouped by haplotype using the IGV option “group by” and selecting “phase”.

Per-position methylation frequencies can also be visualised in IGV by using BIGWIG format. For this, modkit is used to generate BEDGRAPH files using the following command:

modkit pileup --cpg --combine-strands --bedgraph \ 
--threads 10 --prefix {out_prefix} \  
--ref {reference_fasta} \ 
{out_folder} {input_pass_bam}

Please note, a different bedgraph file will be created for each of the modifications present, in this case 5mC and 5hmC.

Next, bedGraphToBigWig is used to generate bigwig files which can be uploaded together with your BAM file in IGV:

bedtools sort -i {out_folder}/{prefix}_m_CG0_combined.bedgraph | cut -f 1-4 > {out_folder}/{prefix}_m_CG0_combined_sort.bedgraph

bedGraphToBigWig {out_folder}/{prefix}_m_CG0_combined_sort.bedgraph {reference_chrSize} {out_mod_bed_agg_filt_bigwig}

For information about benchmarking the performance of RRMS for human samples, please see our RRMS performance document

我们还在 Nanopore 社区的“Support”板块 提供了常见问题解答（FAQ）。

如果以下方案仍无法解决您的问题，请通过电邮(support@nanoporetech.com)）或微信公众号在线支持（NanoporeSupport）联系我们。

我们还在 Nanopore 社区的“Support”板块 提供了常见问题解答（FAQ）。

如果以下方案仍无法解决您的问题，请通过电邮(support@nanoporetech.com)）或微信公众号在线支持（NanoporeSupport）联系我们。