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.

インプットDNAのQC方法

インプットDNAの量と品質の要件を満たすことが重要です。DNAの使用量が少なすぎたり多すぎたり、あるいは品質の低いDNA（例としてDNAが非常に断片化されていたり、RNAや化学汚染物質が含まれている場合など）を使用すると、ライブラリーの調製に影響を及ぼす可能性があります。

DNAサンプルの品質管理の方法については、Input DNA/RNA QC protocolのプロトコルをご覧ください。

コンタミネーション

DNAの抽出する方法によっては、精製DNAに特定の化学汚染物質が残留する可能性があり、ライブラリ調製の効率やシークエンシングの品質に影響を及ぼす可能性があります。コンタミネーションについての詳細は、コミュニティーの Contaminants page をご覧ください。

NEBNext® Companion Module v2 for Oxford Nanopore Technologies® Ligation Sequencing (catalogue number E7672S or E7672L), を購入されることをお勧めします。この商品にはLigation Sequencing Kitで使用するために必要なすべてのNEB試薬が含まれています。

旧バージョンのNEBNext® Companion Module for Oxford Nanopore Technologies® Ligation Sequencing (NEB, E7180S or E7180L)と互換性がありますが、新しい「モジュールｖ2」では、FFPEv2 DNA Repair BufferとSalt-T4 DNA Ligaseにより、より効率的なdA-tailingとligationが可能になりました。v2モジュールを使用すると、サンプルあたりのコストを削減できます。

（注： アンプリコンのプロトコールについては、NEBNext FFPE DNA Repair Mixをお買い求めいただくことも可能ですが、試薬を別途でお買い求めいただく方がコスト的に有利です。

このプロトコールで使用されているすべてのサードパーティー試薬は、当社が検証し、使用を推奨しているものです。Oxford Nanopore Technologiesでは、それ以外の試薬を用いたテストは行っていません。

すべてのサードパーティ製試薬については、製造元の指示に従って使用の準備をすることをお勧めします。

シークエンシング実験を開始する前に、フローセルのポアの数を確認することを強くお勧めします。このフローセルの確認は、MinION/GridION/PromethIONの場合は代理店への到着から１２週間以内に行ってください。またはFlongle Flow Cellの場合は代理店への到着から4週間以内に行う必要があります。Oxford Nanopore Technologiesは、フローセルチェックの実施から2日以内に結果が報告され、推奨される保管方法に従っていた場合に、以下の表に記載されているナノポアの有効数に満たさない場合には、フローセルを交換します。 フローセルのチェックを行うには、Flow Cell Check documentの指示に従ってください。

（注： 現在のキットの容器を変更しています。今までは実験毎に使い捨てのシングルユーズチューブを使用していましたが、バッファー単位のボトル容器に変更しています。

シングルユースチューブの場合:

ボトル容器の場合:

（注： 本製品には、Beckman Coulter、Inc.製のAMPure XP試薬が含まれており、試薬の安定性を損なうことなくキットと共に-20 ° Cで保存することができます。

（注： DNA Control Sample（DCS）は3.6kbのアンプリコンで、Lambda genomeの3'末端をマッピングしたプレップコントロールです。

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.

For a full overview of nanopore data analysis, which includes options for basecalling and post-basecalling analysis, please refer to the Data Analysis document.

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

Nanopore Community Support セクションにFAQをご用意しています。

ご提案された解決策を試しても問題が解決しない場合は、テクニカルサポートに電子メール (support@nanoporetech.com)または LiveChat in the Nanopore Communityでご連絡ください。

Nanopore Community Support セクションにFAQをご用意しています。

ご提案された解決策を試しても問題が解決しない場合は、テクニカルサポートに電子メール (support@nanoporetech.com)または LiveChat in the Nanopore Communityでご連絡ください。