Sequencing accuracy and systematic errors of nanopore direct RNA sequencing

Liu-Wei, Wang; Toorn, Wiep van der; Bohn, Patrick; Hölzer, Martin; Smyth, Redmond P.; Kleist, Max von

doi:10.1101/2023.03.29.534691

Cited by 7 publications

(5 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These results contradicted the generally accepted view that IVT RNA should be more accurately base called than wild-type RNA in DRS due to the lack of modifications. Nonetheless, similar results were reported in the context of SARS-CoV-2 ( 43 ). A parallel SINV IVT RNA sample was synthesized and sequenced, and the reads reproducibly showed slightly lower accuracy.…”

Section: Resultssupporting

confidence: 88%

See 1 more Smart Citation

Utilization of nanopore direct RNA sequencing to analyze viral RNA modifications

Tan,

Guo,

Wang

et al. 2024

mSystems

View full text Add to dashboard Cite

Modifications on viral RNAs (vRNAs), either genomic RNAs or RNA transcripts, have complex effects on the viral life cycle and cellular responses to viral infection. The advent of Oxford Nanopore Technologies Direct RNA Sequencing provides a new strategy for studying RNA modifications. To this end, multiple computational tools have been developed, but a systemic evaluation of their performance in mapping vRNA modifications is lacking. Here, 10 computational tools were tested using the Sindbis virus (SINV) RNAs isolated from infected mammalian (BHK-21) or mosquito (C6/36) cells, with in vitro -transcribed RNAs serving as modification-free control. Three single-mode approaches were shown to be inapplicable in the viral context, and three out of seven comparative methods required cutoff adjustments to reduce false-positive predictions. Utilizing optimized cutoffs, an integrated analysis of comparative tools suggested that the intersected predictions of Tombo_com and xPore were significantly enriched compared with the background. Consequently, a pipeline integrating Tombo_com and xPore was proposed for vRNA modification detection; the performance of which was supported by N 6 -methyladenosine prediction in severe acute respiratory syndrome coronavirus 2 RNAs using publicly available data. When applied to SINV RNAs, this pipeline revealed more intensive modifications in subgenomic RNAs than in genomic RNAs. Modified uridines were frequently identified, exhibiting substantive overlapping between vRNAs generated in different cell lines. On the other hand, the interpretation of other modifications remained unclear, underlining the limitations of the current computational tools despite their notable potential. IMPORTANCE Computational approaches utilizing Oxford Nanopore Technologies Direct RNA Sequencing data were almost exclusively designed to map eukaryotic epitranscriptomes. Therefore, extra caution must be exercised when using these tools to detect vRNA modifications, as in most cases, vRNA modification profiles should be regarded as unknown epitranscriptomes without prior knowledge. Here, we comprehensively evaluated the performance of 10 computational tools in detecting vRNA modification sites. All tested single-mode methods failed to differentiate native and in vitro- transcribed samples. Using optimized cutoff values, seven tested comparative tools generated very different predictions. An integrated analysis showed significant enrichment of Tombo_com and xPore predictions against the background. A pipeline for vRNA modification detection was proposed accordingly and applied to Sindbis virus RNAs. In conclusion, our study underscores the need for the careful application of computational tools to analyze viral epitranscriptomics. It also offers insights into alphaviral RNA modifications, although further validation is required.

show abstract

Section: Resultssupporting

confidence: 88%

“…The corresponding FAST5 data were uploaded to the Figshare repository together with other data sets. Additionally, a lower accuracy of IVT reads was also reported in the context of SARS-CoV-2 ( 43 ). In our data, IVT reads showed lower accuracies across A, C, U, and G without evident biases ( Fig.…”

Section: Discussionmentioning

confidence: 90%

Utilization of nanopore direct RNA sequencing to analyze viral RNA modifications

Tan,

Guo,

Wang

et al. 2024

mSystems

View full text Add to dashboard Cite

show abstract

“…Notably, a critical component of this process is the basecaller, which is trained on a specific dataset and responsible for converting the raw current intensity signal that is output by the sequencing machine into a nucleotide sequence. The currently used basecalling model, which we refer to as 'default' (provided with guppy 6.0.6 and upwards), shows overall per-read accuracy of 90% using ONT's proprietary basecaller Guppy (42). Previous efforts have been made to develop novel, more accurate basecalling softwares, such as RODAN for RNA and Chiron and DeepNano for DNA (70)(71)(72), among others.…”

Section: Discussionmentioning

confidence: 99%

“…Basecalling errors in nanopore sequencing data can arise due to various factors, such as homopolymeric regions, RNA secondary structures or helicase mis-steps (42)(43)(44). However, a major factor affecting the basecalling 'error' patterns is the RNA basecalling model of choice, and more specifically, the data that was used to train this model.…”

Section: Introductionmentioning

confidence: 99%

Enhanced detection of RNA modifications and mappability with high-accuracy nanopore RNA basecalling models

Diensthuber,

Pryszcz,

Llovera

et al. 2023

Preprint

View full text Add to dashboard Cite

In recent years, nanopore direct RNA sequencing (DRS) has established itself as a valuable tool for studying the epitranscriptome, due to its ability to detect multiple modifications within the same full-length native RNA molecules. While RNA modifications can be identified in the form of systematic basecalling ‘errors’ in DRS datasets,N6-methyladenosine (m6A) modifications produce relatively low ‘errors’ compared to other RNA modifications, limiting the applicability of this approach to m6A sites that are modified at high stoichiometries. Here, we demonstrate that the use of alternative RNA basecalling models, trained with fully unmodified sequences, increases the ‘error’ signal of m6A, leading to enhanced detection and improved sensitivity even at low stoichiometries. Moreover, we find that high-accuracy alternative RNA basecalling models can show up to 97% median basecalling accuracy, outperforming currently available RNA basecalling models, which show 91% median basecalling accuracy. Notably, the use of high-accuracy basecalling models is accompanied by a significant increase in the number of mapped reads –especially in shorter RNA fractions– and increased basecalling error signatures at pseudouridine (Ψ) and N1-methylpseudouridine (m1Ψ) modified sites. Overall, our work demonstrates that alternative RNA basecalling models can be used to improve the detection of RNA modifications, read mappability and basecalling accuracy in nanopore DRS datasets.

show abstract

“…[13,[15][16][17][18][19][20] In any analysis of these data, the pipeline must consider the high background error rate in the data (~10 %). [21] Nanopore direct RNA sequencing applied to cellular RNAs has predominantly focused on Ψ and 2'-O-methyl modifications in rRNA, as well as m 6 A in the DRACH (D = A, G, or U; R = A or G; H = A, C, or U) consensus motif in mRNA. [17,19,22] Recently, our laboratory has demonstrated the potential of this method to achieve semiquantitative sequencing of 31 of the 36 modifications of 15 different chemical types in E. coli rRNA.…”

Section: Introductionmentioning

confidence: 99%

Nanopore Direct RNA Sequencing for Modified Uridine Nucleotides Yields Signals Dependent on the Physical Properties of the Modified Base

Fleming,

Dingman,

et al. 2024

Israel Journal of Chemistry

View full text Add to dashboard Cite

Sequencing for RNA modifications with the nanopore direct RNA sequencing platform provides ionic current levels, helicase dwell times, and base call data that differentiate the modifications from the canonical form. Herein, model RNAs were synthesized with site‐specific uridine (U) base modifications that enable the study of increasing an alkyl group size, halogen identity, or a change in base acidity to impact the nanopore data. The analysis concluded that increases in alkyl size trend with greater current blockage but a similar change in base‐call error was not found. The addition of a halogen series to C5 of U revealed that the current levels recorded a trend with the water‐octanol partition coefficient of the base, as well as the base call error. Studies with U modifications that are deprotonated (i. e., anionic) under the sequencing conditions gave broad current levels that influenced the base call error. Some modifications led to helicase dwell time changes. These insights provide design parameters for modification‐specific chemical reagents that can shift nanopore signatures to minimize false positive reads, a known issue with this sequencing approach.

show abstract

Sequencing accuracy and systematic errors of nanopore direct RNA sequencing

Cited by 7 publications

References 49 publications

Utilization of nanopore direct RNA sequencing to analyze viral RNA modifications

Utilization of nanopore direct RNA sequencing to analyze viral RNA modifications

Enhanced detection of RNA modifications and mappability with high-accuracy nanopore RNA basecalling models

Nanopore Direct RNA Sequencing for Modified Uridine Nucleotides Yields Signals Dependent on the Physical Properties of the Modified Base

Contact Info

Product

Resources

About