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Benchmarking nanopore methylation analysis by comparison to publicly available bisulphite datasets


Date: 19th May 2022

Genomic DNA can be sequenced on nanopore devices without the need for fragmentation, amplification or strand synthesis, so long-range methylation information is retained in the data

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Fig. 1 Benchmarking Remora methylation-calling a) pipeline b) evenness c) depth d) runtime e) called CpGs f) reproducibility g) phasing h) haplotype DMR annotation i) imprinted gene example

Benchmarking of nanopore methylation analysis reveals lower bias, higher mapping rates, greater reproducibility and faster analysis than seen with bisulphite data

'Epigenetics' refers to heritable alterations of DNA that do not change the nucleotide sequence. 5-methyl cytosine (5mC) most frequently occurs in mammalian cells in CpG dinucleotides and can alter gene expression by suppressing transcription. During Nanopore sequencing of native DNA, the signature of modified bases is present in the raw signal. To benchmark Nanopore methylation calling using Remora models (V1), we generated two replicates of human genome HG002 obtaining 20x per sample (ONT_1,ONT_2). We also included a HG002 40x sample (ONT_3). We analysed all samples using the pipeline shown (Fig. 1a) and compared calls to publicly available bisulfite datasets. Nanopore read depth is more uniform than the bisulfite data (Fig. 1b), maps to the genome fully (Fig. 1c) and requires far less analysis time than bisulfite data (Fig. 1d). We obtain a higher percentage of successfully called CpGs (>90%) at moderate overall read depth (Fig. 1e). Nanopore 5mC calls correlate well with bisulfite (0.94) and are highly reproducible (0.95) (Fig. 1f). Ultra-Long HG002 data was also generated producing a N50 of 80 kb and a mapped coverage of 35x of the human genome. We were able to phase methylation across 90% of the called CpGs and >80% of the CpGs in the human genome (Fig. 1g). We were also able to identify features that are differentially methylated between haplotypes (Fig. 1h). Modbamtools was used for exploring a DMR overlapping two imprinted genes PEG3 (Paternal-expressed gene) and ZIM2 (Fig. 1i).

Fig. 2 Differential methylation in a skin cancer sample compared to its matched normal a)-c) performance d) long region of DMR e) DMR in promoters, gene bodies and repeats f) PTEN locus

Nanopore sequencing of unamplified genomic DNA from cancer and matched normal samples enables screening for differentially methylated regions across the whole genome

A common downstream analysis of methylation calls is the detection of differentially methylated regions (DMRs) between multiple samples/conditions, such as between cancer samples and their matched normals. Compared to targeted approaches like reduced-representation bisulfite sequencing (RRBS), nanopore sequencing allows us to investigate methylation changes on a genome-wide level. To illustrate this, we sequenced a skin cancer cell line (COLO829) and a matched normal sample (COLO829BL) to 100X, called methylation in both datasets and detected differentially methylated regions (Fig. 2a). As no WGS bisulfite dataset was available, we compared our results to RRBS data. The nanopore dataset covered 28.8 million CpGs (representing 99% of all CpGs), whereas RRBS covered ~500,000 CpGs at this depth. Virtually all CpGs covered by RRBS were covered by ONT (Fig. 2b). Using the nanopore dataset we identified differentially methylated regions between the tumour and normal across the whole genome. Fig. 2c) shows all detected DMRs stratified by their length and average methylation frequency difference between tumour and normal. The longest contiguous DMRs are regions of hypomethylation in the tumour sample. Fig. 2d) shows an example of such a region on chromosome 15. This locus is imprinted in healthy individuals, i.e. one of the alleles is epigenetically silenced. However, in the tumour, a complete lack of methylation can be observed over more than 1.5 Mb. Promoters, gene bodies, ALU and LINE-2 elements show higher methylation in the tumour while LINE-1 elements show hypomethylation (Fig. 2e). Finally, to illustrate smaller-scale methylation changes we chose to look at the known tumour suppressor PTEN, which shares a bidirectional promoter with its upstream neighbour KLLN. Here we found a 1.4 kb hypermethylated region overlapping with its promoter and 12 kb deletion covering exons 6-7. Nanopore cDNA reads confirm the missing exons and lower PTEN expression in the tumour.

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