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Reduced Representation Methylation Sequencing (RRMS)
Rocio Esteban
July 27, 2022
2 min
Reduced Representation Methylation Sequencing (RRMS)
Contents
01
Background
02
Adapative sampling for CpG containing regions
03
Benchmarks
04
Data Availability
05
Summary

We are excited to release the new protocol for Reduced Representation Methylation Sequencing (RRMS), login required, from the Nanopore Applications Team.

Background

CpG dinucleotides frequently occur in high-density clusters called CpG islands (CGI) and >60% of human genes have their promoters embedded within CGIs. Determining the methylation status of cytosines within CpGs is of substantial biological interest: alterations 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 elucidate mechanisms associated with tumour formation and development. Nanopore sequencing enables direct detection of methylated cytosines (e.g. at CpG sites), without the need for bisulfite conversion.

Adapative sampling for CpG containing regions

Oxford Nanopore’s 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 the sequencing run itself with no requirement for upfront sample manipulation. Here we introduce Reduced Representation Methylation Sequencing (RRMS), which combines Oxford Nanopore’s methylation API, Remora, with AS, to target 310 Mb of the human genome including regions which are highly enriched for CpGs including ~28,000 CpG islands, ~50,600 shores and ~42,700 shelves as well as ~21,600 promoter regions.

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 (login required).

Benchmarks

To benchmark the performance of RRMS, 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) and ran each sample on a separate MinION flow cell (one sample per flow cell). RRMS resulted in high-confidence methylation calls for 7.3 - 8.5 million CpGs per sample (covering ~90+% of promoters, CGIs, shores and shelves, and covering 28% of all CpGs in the human genome). For comparison we also performed Reduced Representation Bisulfite Sequencing (RRBS), which typically yields 1.7 - 2.5 high-confidence calls per sample (approximately 10% of the total number of CpGs in the human genome). Methylation frequencies called by RRMS and RRBS are highly similar for those CpGs which are covered by both technologies (R > 0.967). Furthermore, with RRMS we were able to detect ~62 Mb of differentially methylated regions (DMRs) between tumour and normal pairs, of which a high proportion overlapped with cancer census genes: this demonstrates the value of RRMS for providing key information for tumour characterisation as well as methylation status. In comparison RRBS yielded ~20 Mb of DMRs. For more information about benchmarking the performance of RRMS, please see our RRMS performance document (login required).

Data Availability

Data, including BAM files with per-read modification calls as well as the raw data (FAST5s) for all samples is available in our ONT Open Datasets S3 bucket:

s3://ont-open-data/rrms_2022.07/

We also have available the matched reduced representation bisulfite sequencing data for the four samples, processed with Bismarck to produce methylation frequencies:

s3://ont-open-data/rrms_2022.07/bisulfite/

Summary

Combined with its ease of use and ability to scale to a high number of samples, RRMS is well suited to investigating methylation differences in large cohorts, as well as providing deeper insights into the mechanisms behind diseases such as cancer and monitoring tumour progression. For more information, please see our poster.


Tags

#datasets#human cell-line#R9.4.1#basecalling#5mC#methylation

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