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, 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.

Cómo realizar un control de calidad del ADN de la muestra inicial

Es importante que la muestra de ADN cumpla con los requisitos de cantidad y calidad. Usar demasiado ADN, poco o de mala calidad (p. ej., que esté muy fragmentado, que contenga ARN o contaminantes químicos), puede afectar a la preparación de la biblioteca.

Para realizar un control de calidad en la muestra de ADN, consulte el protocolo Input DNA/ RNA QC

Contaminantes químicos

Dependiendo de cómo se extraiga el ADN de la muestra cruda, ciertos contaminantes químicos pueden permanecer en el ADN purificado, lo cual afecta la eficacia de la preparación de la biblioteca y la calidad de la secuenciación. Encontrará más información sobre contaminantes en la página Contaminants de la comunidad Nanopore.

Recomendamos el módulo de acompañamiento NEBNext® Companion Module v2 for Oxford Nanopore Technologies® Ligation Sequencing (catalogue number E7672S or E7672L), que contiene todos los reactivos NEB necesarios para usar con el kit Ligation Sequencing Kit.

La versión previa, NEBNext® Companion Module de Oxford Nanopore Technologies® Ligation Sequencing (NEB, E7180S or E7180L) es compatible, pero el módulo recomendado v2 ofrece una ligación y adición de dA más efectiva, resultado del reactivo FFPEv2 DNA Repair Buffer y la ligasa Salt-T4 DNA Ligase, recpectivamente.
Con el módulo v2 también se consigue un notable ahorro de costes por preparación de muestra.

Nótese que, en los protocolos con amplicones no es necesario utilizar la mezcla de reparación de ADN, NEBNext FFPE DNA Repair Mix y es más rentable comprar los reactivos necesarios por separado.

Oxford Nanopore Technologies ha probado y recomienda el uso de todos los reactivos de otros fabricantes citados en este protocolo. No se han evaluado otras alternativas.

Recomendamos preparar estos reactivos siguiendo las instrucciones del fabricante.

Antes de empezar el experimento de secuenciación, recomendamos verificar el número de poros disponibles, presentes en la celda de flujo. La comprobación deberá realizarse en las primeras 12 semanas desde su adquisición, si se trata de celdas de flujo MinION, GridION o PromethION, y en las primeras cuatro semanas tras la compra de celdas de flujo Flongle. Oxford Nanopore Technologies sustituirá cualquier celda de flujo con un número de poros inferior al indicado en la tabla siguiente, siempre y cuando el resultado se notifique dentro de los dos días siguientes a la comprobación y se hayan seguido las instrucciones de almacenamiento. Para verificar la celda de flujo, siga las instrucciones del documento Flow Cell Check.

Nota: Hemos cambiando el formato de nuestros kits; hemos sustituido algunos de los viales de un solo uso por botellas de mayor contenido.

Formato de tubos monouso

Formato en botella

Nota: este producto contiene un reactivo, AMPure XP, fabricado por Beckman Coulter Inc., que puede conservarse con el kit a -20 °C sin perjudicar su estabilidad.

Nota: la muestra de control de ADN (DCS) es un amplicón estándar de 3,6 kb, que mapea el extremo 3' del genoma Lambda.

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.

Para obtener una descripción completa del análisis de datos de nanoporos, que incluya distintas posibilidades para el análisis de identificación y postidentificicación de bases, consultar el documento Data Analysis.

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

También disponemos de una página de preguntas frecuentes, FAQ, en la sección Support de la comunidad Nanopore.

Si ha probado las soluciones propuestas y continúa teniendo problemas, póngase en contacto con el departamento de asistencia técnica, bien por correo electrónico (support@nanoporetech.com) o a través del Live Chat de la comunidad Nanopore.

También disponemos de una página de preguntas frecuentes, FAQ, en la sección Support de la comunidad Nanopore.

Si ha probado las soluciones propuestas y continúa teniendo problemas, póngase en contacto con el departamento de asistencia técnica, bien por correo electrónico (support@nanoporetech.com) o a través del Live Chat de la comunidad Nanopore.