This protocol describes an end-to-end process to prepare and sequence gDNA from human blood samples, and analyse the data using the wf-human-variation workflow in EPI2ME.

The identification of structural variants (SVs) and single nucleotide variants (SNVs) play a pivotal role in our understanding of genetic diversity, disease mechanisms, and evolutionary biology. This protocol aims to produce libraries with a read N50 ~30 kb and generate ~30x coverage of the genome, to provide robust calling of small and large variants as well as provide methylation and phasing information.

Briefly, genomic DNA is extracted from 1 ml of whole blood using the QIAGEN Puregene Blood Kit. DNA is size selected using the Short Fragment Eliminator Kit (EXP-SFE001) and then sheared with Megaruptor® 3 (Diagenode).
Note: Users who do not have access to the Megaruptor and wish to omit this step are likely to observe a drop in coverage.

The extracted DNA input is then prepared using our Ligation Sequencing Kit V14 (SQK-LSK114) and sequenced on a PromethION device. Detailed instructions for setting up the sequencing run on MinKNOW, washing and reloading the flow cell and downstream analysis are also included for a complete end-to-end protocol.

Your sequencing data is basecalled by MinKNOW, and the BAM output data is aligned and analysed using the wf-human-variation workflow which uses Sniffles2, Clair3 and modkit software to call structural variants (SVs), single nucleotide polymorphisms (SNPs) and for reporting DNA methylation.

Steps in the workflow

Prepare for your experiment

You will need to:

• Extract your input sample (human blood)
• Ensure you have your sequencing kit, the correct equipment, and third-party reagents
• Download the software for acquiring and analysing your data
• Check your flow cell to ensure it has enough pores for a good sequencing run

Sample preparation

Using the outlined extraction method, extract the gDNA from your human blood sample, perform size selection using the Short Fragment Eliminator Kit (EXP-SFE001), and fragment your gDNA using the Megaruptor.

Check the length, quantity and purity of your extracted material. The quality checks performed during the protocol are essential in ensuring experimental success.

Library preparation and sequencing

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

Library preparation	Process	Time	Stop option
DNA repair and end-prep	Repair and prepare the DNA ends for adapter attachment	35 minutes	4°C overnight
Adapter ligation and clean-up	Attach the sequencing adapters to the DNA ends	20 minutes	4°C short-term storage or for repeated use, such as re-loading your flow cell -80°C for single-use, long-term storage. We strongly recommend sequencing your library as soon as it is adapted.
Priming and loading the flow cell	Prime the flow cell and load the prepared library for sequencing	5 minutes
Washing and reloading the flow cell (x2)	Pause your sequencing run. Wash your flow cell with nuclease to remove the previous library load and unblock pores. Prime the flow cell and reload the prepared library to continue sequencing	60 minutes (x2)

Sequencing and analysis

You will need to:

• Start a sequencing run using the MinKNOW software which will collect raw data from the device and basecall reads.
• Start the EPI2ME software and select the wf-human-variation bioinformatics workflow to analyse your data.
• (Optional) Alternatively, external tools can be used to further analyse and explore your data.

Input requirements per sample for the extraction method:

• ≥ 1 ml of human blood in EDTA K2 vacuum tube

Input requirements per sample for the library preparation:

• 3 µg of SFE size selected and Megaruptor fragmented gDNA

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。

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。

Flow cell	保証する最小有効ポア数（以下の数未満のフローセルが交換対象となります）
Flongle Flow Cell	50
MinION/GridION Flow Cell	800
PromethION Flow Cell	5000

（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。

Overlay of fragment length profiles of a) gDNA extracted using the Puregene Blood Kit, b) gDNA extracted and size selected using SFE buffer. c) gDNA extracted, size selected and fragmented using Megaruptor 3 (MR).

For this method, the flow cell is washed after ~22 hours of sequencing to restore pores to ensure efficient data acquisition. After an additional 22 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.

Below you can find example data for pore states observed on a flow cell before and after wash steps are performed. Additionally, you can observe an example for the cummulative sequencing data output, including the wash and reload steps. The red asterisks indicate the flow cell wash and reloads.

Figure 1. Channel state over a 72 hour run. The flow cell washes are incorporated into the method to restore blocked pores, to allow continuous data acquisition. Red asterisks denote when a flush was performed.

Figure 2. Cumulative sequencing data output, over a 72 hour run. Red asterisks denote when a flush was performed.

Figure 3. Read length profile for a 30 kb N50 library. The approximately gaussian shape is characteristic of gDNA that has undergone SFE size selection and Megaruptor shearing. The short distribution of reads at ~5kb is due to premature termination of reads at the mux scan.

The sequencing device control, data acquisition and real-time basecalling are carried out by the MinKNOW software.

We recommend basecalling with the high accuracy (HAC) basecaller in real-time with BAM selected as output type using the P2 Solo or P24/P48 device.

You must generate a BAM file from your sequening run, as this is required for input into the wf-human-variation workflow.

Refer to the links below containing the detailed instructions for setting up the device and sequencing run:

• PromethION 24 and 48: "Starting a sequencing run with PromethION 24 and 48"

• PromethION 2 Solo: "Starting a sequencing run on PromethION 2 Solo"

Below are the recommended sequencing parameters for MinKNOW.

We recommend setting the run time to 100 hours to accommodate for the two flow cell washes, using the modified bases option for basecalling and ensuring a BAM output is selected. All other sequencing parameters can be kept to their default settings. Below are our current recommendations:

Positions

Flow cell position: [user defined]

Experiment name: [user defined]

Flow cell type: FLO-PRO114M

Sample ID: [user defined]

Kit

Kit selection: Ligation Sequencing Kit (SQK-LSK114)

Run configuration

Sequencing and analysis

Basecalling: On [default] Modified bases: On with '5mC & 5hmC CG contexts' selected Model: High-accuracy basecalling (HAC) [default]

Barcoding: Disabled [default]

Alignment: Off [default]
We do not currently recommend live alignment during sequencing, as it can slow down system processing.

Adaptive sampling: Off [default]

Advanced options Active channel selection: On [default] Time between pore scans: 1.5 [default] Reserve pores: On [default]

Data targets

Run limit: 100 hours [default]

Output

Output format .POD5: On [default] .FASTQ: On [default] .BAM: On

Filtering: On [default] Qscore: 9 [default] Minimum read length: 200 bp [default]

For the analysis of human blood DNA sequence data, we recommend the wf-human-variation workflow. This end-to-end software pipeline is implemented using the Nextflow workflow language and implements methods for the calling of single nucleotide polymorphisms (SNPs), structural variants (SVs), and for reporting DNA methylation information.

The wf-human-variation workflow is best run from the BAM file produced by MinKNOW when the modified base model for basecalling is selected. If sequence read mapping to the reference genome is not performed by MinKNOW, we recommend to perform the basecalling using the wf-basecalling workflow. Ensure you save the outputs in BAM format by providing the --output_bam option.

The tools below are used in the analysis workflow and can be run in isolation or together:

1. Sniffles2 calls SVs and file output include an HTML report of QC metrics and VCF format list of variants and their quality scores.

2. Clair3 calls SNPs and file output includes an HTML report of QC metrics and VCF format list of variants and their quality scores.

3. modkit extracts methylation information from the provided BAM file which is summarised in a BEDmethyl format file.

The wf-human-variation workflow is preconfigured using appropriate parameters and requires tuning only for the choice of reference genome and Clair3 model. Please see the project’s documentation for further details.

The results from the wf-human-variation workflow can be further explored by viewing in a track-based genome browser such as IGV can be assessed for known pathogenicity through tertiary analysis software.

The wf-human-variation workflow is intended to be run using the Nextflow software.

We recommend performing downstream analysis using EPI2ME which facilitates bioinformatic analyses by allowing users to run Nextflow workflows in a Graphical User Interfaces (GUI). EPI2ME maintains a collection of bioinformatic workflows which are curated and actively maintained by experts in long-read sequence analysis. The collection of all our available EPI2ME workflows can be found here.

For new users, the quick start guide can be found here outlining how to use this interface.

The primary workflow outputs include:

• gzipped VCF file containing the SNPs in the dataset from --snp
• gzipped VCF file containing the SVs in the dataset from --sv
• gzipped bedMethyl file aggregating modified base counts from --mod
• HTML report detailing the primary findings of the workflow for QC metrics, and SNP and SV calling
• If an unaligned BAM file was provided, the workflow will ouput a CRAM file containing the alignments used to make the downstream variant calls.

The secondary workflow outputs:

• mosdepth ouputs include:
  • {sample_name}.mosdepth.global.dist.txt: a cumulative distribution indicating the proportion of total bases for each and all reference sequences
    
  • {sample_name}.regions.bed.gz: the mean coverage for each region in the provided BED file
    
  • {sample_name}.thresholds.bed.gz: the number of bases in each region that are covered at or above each threshold value (1, 10, 20, 30X)

• bamstats ouputs include:
  • {sample_name}.readstats.tsv.gz: a gzipped TSV summarising per-alignment statistics produced by bamstats
  • {sample_name}.ftagstat.tsv: a text file with summary alignment statistics for each reference sequence

It is possible to phase SNPs, SVs and modified bases by providing the --phased option.

To improve the accuracy of SV calling, specify a suitable tandem repeat BED for your reference with --tr_bed.

Aggregation of methylation calls with --mod requires data to be basecalled with a model that includes base modifications, providing the MM and ML BAM tags. To do so on MinKNOW, ensure 'Modified bases' option is selected during basecalling set up, with the '5mC' model selected.

Ensure to retain the input reference when basecalling or alignment is performed as CRAM files cannot be read without the corresponding input reference.

For a full list of available basecalling models, refer to the Dorado documentation.

Due to the extended sequencing time, and the multiple flow cell washes and library reloads, we do not recommend re-using the flow cells used in this method.

Re-using these flow cells for subsequent sequencing experiments may result in insufficient data generation for analysis.

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。