Table 1 RNA modification detection tools for direct RNA sequencing data sequenced with ONT

From: The RMaP challenge of predicting RNA modifications by nanopore sequencing

RNA detection method

Tested RNA modifications

Method approach

Ref.

nanoRMS/nanoRMS2

ψ, Nm, m6A

Direct & signal comparison

41

EpiNano

m6A, ψ, m2G, m7G, m3U

Signal comparison & error-profile

43

m6anet

m6A

Direct

52

Magnipore

any

Signal comparison

44

xPore

m6A

Signal comparison

45

Yanocomp

m6A

Signal comparison

47

Nanocompore

m6A, Ino, ψ, m5C, m62A, m1G

Signal comparison

46

ELIGOS

m6A, m1A, m5C, hm5C, f5C, m7G, Ino, ψ, 5moU

Signal comparison & error-profile

39

JACUSA2

m6A

Signal comparison

50

Tombo

any

Signal comparison & direct

ONT

Nanom6A

m6A

Direct

53

DENA

m6A

Direct

54

mAFiA

m6A

Direct

55

Penguin

ψ

Direct

56

MINES

m6A

Direct

58

Nano-ID

e5U, Br5U, I5U, S4U, S6G

Direct

59

DRUMMER

m6A

Error-profile

48

DiffErr

Error-profile

49

CHEUI

m6A, m5C

Direct

57

NanoPsu

ψ

Direct

60

NanoSPA

ψ, m6A

Direct

61

TandemMod

m1A, m6A, m5C, m7G, hm5C

Direct

62

IL-AD

m1A, m6A, m5C, 5mC, 5hmC, ψ

Direct

63

nanoDoc

ψ, m7G, m5C, Cm, Gm, m6A, m1A, m2G, m5U …

Direct

51

ModiDeC

m6A, ψ, Ino, Gm, m1A

Direct

64

  1. Direct approaches take only one sample as input to predict modifications. Comparative approaches, as well as error-profile analysis, take two samples as input, typically a modified sample compared to an unmodified control.