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Ensure you have installed the latest version of Dorado. To perform basecalling and methylation calling using Remora, open a terminal and enter the following commands:
dorado basecaller hac, 5mCG_5hmCG \
--reference {reference_fasta} {input_pod5_folder} | samtools view -e '[qs] >= {qscore_filter}' \
--output {out_pass_bam} \
--unoutput {out_fail_bam}
- 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.
- 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.
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Index the merged BAM file:
samtools index -@ 8 {input_pass_bam}
This will create a single sorted and indexed BAM file.This BAM file can be used to check the on-target coverage achieved during the Reduced Representation Methylation Sequencing (RRMS) run:
mosdepth -x -t 8 -n -b {target_bed} {out_prefix} {input_pass_bam}
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To create strand-aggregated methylation frequencies for all genomic positions (CpGs), run:
modkit pileup -cpg -combine-strands \ --threads 10 {out_bedmethyl} \ --ref {reference_fasta} \ {input_pass_bam}
This will create a BEDMETHYL file that summarises counts of modified and
unmodified bases for all modifications detected, in this case 5mC and 5hmC.- Modkit can be obtained from the following github repository: modKit repository.
- Detailed documentation on formats and features for modkit can be found here: modkit online documentation.
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IGV visualisation:
The generated BAM file contains canonical bases as well as per-read
modifications and can be loaded into IGV.
To visualise the per-read modification calls in IGV, load the BAM file and set "colour reads as" to "modifications (all)”.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}
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Differentially methylated regions detection:
Detection of differentially methylated regions across different samples can be performed using modkit dmr.
For more information check the modkit differential methylation documentation.
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Benchmarking results
For information about benchmarking the performance of RRMS for human samples, please see our RRMS performance document