DNA methylation-calling tools for Oxford Nanopore sequencing: a survey and human epigenome-wide evaluation

Liu, Yang; Rosikiewicz, Wojciech; Pan, Ziwei; Jillette, Nathaniel; Wang, Ping; Taghbalout, Aziz; Foox, Jonathan; Mason, Christopher; Carroll, Martin; Cheng, Albert; Li, Sheng

doi:10.1186/s13059-021-02510-z

Table 1 Current DNA methylation calling tools for Nanopore sequencing

From: DNA methylation-calling tools for Oxford Nanopore sequencing: a survey and human epigenome-wide evaluation

Tools	DNA modification				Input required	Support multi-read FAST5 file?	Flow cells compatibility	Model trained on	Algorithm	Reported performance
Tools	4mC	5mC	5hmC	6 mA	Input required	Support multi-read FAST5 file?	Flow cells compatibility	Model trained on	Algorithm	Reported performance
Nanopolish [9]		✓			Basecalled FAST5	✓	R7.3, R9, R9.4, R9.4.1, R9.5, R10	E. coli	Hidden Markov model (HMM)	Accuracy = 0.94 (5mC, Homo sapiens)
Tombo/ Nanoraw [20]	✓	✓		✓	Raw FAST5		R9.4, R9.4.1, R9.5	no model	Mann-Whitney and Fisher’s exact test	Accuracy = 0.839, AUC = 0.78
SignalAlign [39]		✓	✓	✓	Basecalled FAST5		R7.3, R9.4^a	Synthetic nucleotides	Hidden Markov model with a hierarchical Dirichlet process (HMM-HDP)	Accuracy = 0.76 (for 5hmC, 5mC)
SignalAlign [39]		✓	✓	✓	Basecalled FAST5		R7.3, R9.4^a	E. coli		Accuracy = 0.96 (for 5mC), Precision = 0.92
Guppy [32]		✓		✓	Raw FAST5	✓	R7.3, R9, R9.4, R9.4.1, R9.5, R10, R10.3	Homo sapiens and E. coli	Recurrent neural network	N/A
NanoMod [31]		✓			Basecalled FAST5, requires control sequence		R7.3, R9	no model	Kolmogorov-Smirnov test	Precision = 0.9
mCaller [33]				✓	Basecalled FAST5		R9, R9.4, R9.5	E. coli	Neural network	Accuracy = 0.954, AUC = 0.99
DeepSignal [35]		✓		✓	Basecalled FAST5 processed by Tombo re-squiggle module		R9, R9.4, R9.4.1	E. coli	Bidirectional RNN with LSTM+Inception structure	Accuracy = 0.92 (5mC, Homo sapiens), 0.90(m6A), Precision = 0.97
DeepMod [34]		✓		✓	FAST5 with raw signals and base calls		R9, R9.4, R9.4.1	E. coli	Bidirectional recurrent neural network (RNN) with long short-term memory (LSTM)	Precision = 0.99, AUC > 0.97
Megalodon [36]		✓		✓	Raw FAST5^b	✓	R9.4.1, R10.3	Homo sapiens and E. coli^c	Recurrent neural network^d	N/A^e
methBERT [23]		✓		✓	Raw FAST5		R9	Homo sapiens and E. coli	Bidirectional encoder representations from transformers (BERT)	Precision = 0.9147 (5mC, Homo sapiens)^f
METEORE [38]		✓		✓	Methylation calling per-read results^g		R9.4.1	E. coli	Random forest (RF), multiple linear regression (REG)	Root mean square error (RMSE) = 0.0687 (5mC, E.coli)^h
DeepMP [37]		✓		✓	Basecalled FAST5 processed by Tombo re-squiggle module		R9, R9.4	Homo sapiens, E. coli, pUC19	Convolutional neural network (RNN)	F-score = 0.9324 (5mC, Homo sapiens)ⁱ

^aSignalAlign’s compability on R9.4 is only validated on 5mC and 6 mA, not 5hmC
^bMegalodon must obtain the intermediate output from the basecall neural network, and Guppy is the recommended backend to obtain this output from FAST5
^cThe model is trained in biological contexts only on Homo sapiens and E. coli. Users have to specify the model from the modified base models included in basecaller Guppy or research models in ONT Rerio repository
^dMegalodon’s functionality centers on the anchoring of the high-information neural network basecalling output to a reference sequence
^eThe performance for Megalodon is not available since it is still under active development, no available published paper yet
^fOnly 5mC precision on Homo sapiens at per-site level is listed here, more performance parameter (AUC, Recall) of 5mC at per-site level and per-read level, and 5mC/6 mA performance on E.coli are available in the original paper
^gMETEORE combine the outputs from two or more methylation calling tools
^hRMSE is for METEORE RF model combining Megalodon and DeepSignal at per-site level on selected sites of E.coli
ⁱOnly 5mC overall accuracy on Homo sapiens at per-read level is listed here. More performance parameters on Homo sapiens, and 5mC performance on E.coli, 5 mA performance on pUC19 plasmid are available in the original paper

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