Post-transcriptional RNA modification occurs on all types of RNA and plays a vital role in regulating every aspect of RNA function. Thanks to the development of high-throughput sequencing technologies, transcriptome-wide profiling of RNA modifications has been made possible. With the accumulation of a large number of high-throughput datasets, bioinformatics approaches have become increasing critical for unraveling the epitranscriptome. We review here the recent progress in bioinformatics approaches for deciphering the epitranscriptomes, including epitranscriptome data analysis techniques, RNA modification databases, disease-association inference, general functional annotation, and studies on RNA modification site prediction. We also discuss the limitations of existing approaches and offer some future perspectives. More than 170 different types of RNA modifications have been identified, and the majority of them are methylation modification. Ribonucleoside modification contains many chemical components that are added to A, G, C or U. Some modifications come from non-enzymatic processes or oxidative damage. Others are RNA editing, which was originally described as a process of adding polyuridine residues to selected RNAs coding regions. Since then, editing has expanded to include the removal or addition of RNA bases, although further differences remain somewhat inconsistent. There are 111 modifications that can be found in tRNAs, 33 in rRNAs, 17 in mRNA, 11 in lncRNAs and other noncoding RNAs 6A demethylase 6A profiling approach MeRIP-seq (or m6A-seq) 6A), pseudouridine (ψ), N4-acetylcytidine (ac4C), N1-methyladenosine (m1A), N7-methylguanosine (m7G), 2′O-methylations , 5-methylcytidine (m5C), 5-hydroxymethylcytidine Cytidine (hm5c) and inosine (I). Please refer to two recent reviews in this perspective However, due to the lack of effective means to detect RNA methylation, the research on RNA methylation had long been stagnant. For a long time, researchers had considered RNA modification as a mechanism of fine-tuning gene expression regulation, and mostly limited to noncoding tRNA and rRNA. The importance of RNA modifications was not fully aware of until the discovery of human fat mass gene FTO as a RNA m6-methyladenosine (m6A) is the most abundant and the most well studied chemical modification on eukaryotic mRNA 6A sites have been discovered. These include YTH family proteins  6A sites have been identified in the transcriptome, suggesting that this modification may have a wide-ranging effect on gene expression regulation 6A sites 6A can also affect translation extension by influencing the anticodon pairing rate and fidelity of both mRNA and tRNA 6A modification is also involved in the gene regulation of histone modifications 6A site 6A sites in the 5′UTR region 6A modifications, including but not limited to, promoting the learning and memory capability of mice by m6A-mediated binding of the protein YTHDF1 to mRNA 6A sites on mRNAs 6A methylation of pri-miRNA 6A demethylase can disrupt the circadian clock and extend the circadian clock cycle, while overexpression of demethylase shortens the cycle 6A-mediated regulation of mRNA stability also plays important roles in stem cell differentiation 6A methylation are presented in a large number of mRNAs encoded by genes associated with human diseases, especially cancers 6A level in R-2HG sensitive leukemia cells which in turn decreases the stability of MYC/CEBPA transcripts, leading to suppression of MYC/CEBPA-related signaling pathways and thus inhibition of the proliferation of FTO-highly expressed cancer cells N5C) is a wild-spread post-transcriptional RNA modification that has been detected in rRNA, tRNA, mRNA, lncRNA, viral RNA and etc. 5C was primarily catalyzed by RNA methyltransferase DNMT2 and NSUN2 along with its homologs 5C marks on RNAs formed by NSUNs can contribute to promoting the overall protein synthetic level in cells in terms of protecting tRNA from shearing or promoting the formation of ribosomes 5C addition on the HIV-1 mRNA and perturb the translation of HIV-1 mRNA by inhibiting the recruitment of ribosomes and alternative splicing of viral RNA 5C modification, preferentially recognizing methylation C by its cold shock domain (CSD) 5C deposition on RNAs will vary their stability among different RNA species. It may prevent mRNA decay though the binding of YBX1 with mRNA stabilizer PABPC1A 5C modification, which was mediated by the ablation of NSUN2 5C modification with the regulation of the thermal adaptability in animal and plant cells by regulating the functions of tRNA and mRNA 5C exhibiting important physiological roles in drosophila 5-methylcytosine (mAdenosine-inosine (A-to-I) RNA editing is the main form of RNA editing in mammals 6A methylation, pseudouridine (ψ) is one of the most abundant 5C) modification in DNAs was also observed in RNAs, exhibiting important physiological roles in drosophila 4-acetylcytidine (ac4C) is revealed as a mRNA modification catalyzed by the acetyltransferase NAT10 6,2′-O-dimethyladenosine (m6Am) in mRNA is another type of reversible methylation, and its modification status in the 5′ cap influences stability of cellular mRNAs 1-methyladenosine (m1A) methylation is a newly discovered reversible epigenetic modification that can be de-methylated by RNA repair enzyme ALKBH1 1A methylation enzymes and its recognition proteins. Unlike m6A, m1A has even lower abundance, predominantly distributed in the 5′UTR region of mRNA which may be involved in regulating translation initiation process Our knowledge of RNA modification is continuing to expand rapidly. In addition to the widely studied mThanks to the development of sequencing technologies, transcriptome-wide profiling of RNA modifications has been made possible by a number of different techniques such as MeRIP-seq and RNA BS-seq. With the accumulation of large amount of high-throughput datasets, bioinformatics approaches have been increasingly needed for unraveling the epitranscriptome as a cost effective avenue. We systematically reviewed in the following the emerging topics and recent progress in bioinformatics approaches for deciphering the epitranscriptomes, including epitranscriptome data analysis techniques, RNA modification databases, disease-association inference, general functional prediction, and RNA modification site prediction methods . The review is divided into the following sections. We firstly introduced the tools for epitranscriptome data analysis as well as some epitranscriptome profiling technologies. Then we summarized the algorithms for methylation sites prediction and the existing databases dedicated for RNA modifications. We in the next analyzed the disease marker and association prediction related to RNA modifications. In the end, we briefly discussed the limitations of existing epitranscriptiome bioinformatics approaches and offered some possible future perspectives see .Fig. 1Th2With the advances in next-generation sequencing approaches, many experimental methods have been designed to profile various types of RNA modified nucleotides. Meanwhile, a number of computational programs were developed for the analysis of the massive high-throughput sequencing data generated. We briefly reviewed here a few software tools developed for epitranscriptome profiling data generated from MeRIP-Seq, RNA BS-Seq, etc.2.16A-Seq) Methylated RNA Immunoprecipitation sequencing  or monoclonal (17-3-4-1) antibodies in randomly interrupted RNA fragment library to construct the IP sample, so the segments carrying the methylation mark are over-represented in the IP samples compared with the input control sample, which can be viewed as the standard RNA-Seq library. Mapping the reads of IP sample to the reference genome will form a peak of reads coverage near the modification sites. Through the statistical analysis of IP over input enrichment on the genomic sliding windows, we can infer the locations of m6A modification sites across the genome or exome. Several computational tools have been developed for site detection  from MeRIP-seq data (see ), and among them, MACS MeRIP-Seq immunoprecipitates the RNA fragments containing the modification with anti-mdata see , and amo2.1.2. Although classic approaches based on Fisher’s exact test  Differential methylation analysis focuses on the changes of modification status between two different biological conditions. Although changes in absolute abundance of methylation may be equally important, current formulation overwhelmingly focused on the relative abundance or methylation proportion, specifically, the ratio of methylated to total RNAs, as previously modeled by Table 22.1.3Epitranscriptome serve as an important layer of post-transcriptional regulation, and the count-based quantification in MeRIP-Seq data could potentially shed light on the mechanics of conditional specific regulation through RNA modification. Although the transcript specific regulatory mechanism of the epitranscriptome is still unclear, the clustering partition of the methylation sites on distance metric evaluated through the conditional specific methylation profiles may associate with the targeting of RNA modification regulators  See . Some re2.1.4The quality of MeRIP-seq data can be evaluated by the conventional 2nd generation sequencing quality control pipeline such as FASTQC. Recently, trumpet R package is developed for MeRIP-Seq specific quality examination 2.21A RNA modification has a methyl group on the Watson-Crick face of adenosine, which resulted in cDNA synthesis differing from that of an unmodified adenosine on the RNA template. For the RNA templates containing m1A modification, the products of transcription arrest were presented in the synthesized cDNA, i.e., incorporation of mismatched dNTPs at modification position and abortive cDNA fragments, and this erroneous information generated during cDNA synthesis was termed as reverse transcription signatures. The signals of RT arrest were traditionally detected by either capillary electrophoresis or polyacrylamide gelelectrophoresis (PAGE) 1A) modification The combination of Reverse Transcription (RT) and high-throughput sequencing has emerged as an effective approach for identification of RNA modification through the analysis of the misincorporation during complementary DNA (cDNA) synthesis or abortive RT-products 2.35C) is a type of chemical modification on the carbon 5 atom of cytosine, which can be detected by high-throughput sequencing of RNA treated by bisulfite (RNA Bisulfite sequencing) that converts all unmodified cytosine into uracil leaving modified cytosine (m5C) unaffected 5C epitranscriptome 5C) is that a transcriptome-wide view of m5C modification at single-based resolution can be provided. It is worth noting that, although both 5-methylcytosine and 5-hydroxymethylcytosine are resistant to deamination and thus can not be differentiated by bisulfite sequencing, the extremely low level of 5-hydroxymethylcytosine in human and mouse mRNAs 5C epitranscriptome. Several toolkits support the analysis of data generated from RNA Bisulfite sequencing  RNAmod , and the potential impact of m6A modification on diseases was revealed by incorporating the information of disease-associated SNPs derived from different databases, including GWAS catalog Furthermore, to explore the effect of genetic variants on RNA modifications, m6ASNP 36A RNA methylation is the most widely studied, and it is also the predictive target of the earliest as well as the most sophisticated predictive approaches. We summarized in 6A and other types of RNA modifications, respectively. These works together greatly improved our understanding of the distribution of multiple types of RNA modifications in different species  model can automatically extract the non-linear features. Several deep learning-based methods have been developed. These methods firstly extracted the positive modification sites from existing studies and database like Met-DB and RMBase, and then selected negative samples to build a standard data set to train and test the proposed approach.6A or m1A modifications; BERMP 6A; DeepMRMP 6A, m1A, pseudouridine and m5C; the DL models of DeepM6ASeq The most widely used deep learning models are convolutional neural network (CNN), which can effectively learn the motif related features from RNA sequence, and recurrent neural network (RNN), which can learn the non-linear sequential features from RNA sequence, including long short-term memory unit (LSTM) and gated recurrent unit (GRU). Gene2vec 6A RNA methylation prediction, it is worth noting that deep learning has also been applied to other modifications as well. For example, CNNPSP 1A sites. Please refer to 6A modifications.Although most of these approaches focused on m6A which embedded the MeRIP-Seq reads count with RNA sequence to predict condition-specific  m6A sites. There are diverse strategies to encode the input features. We summarize in the following 4 major kinds of encoding strategies, including:(1)one-hot embedding;(2)RNA/DNA nucleotides to vector embedding;(3)RNA sequence and MeRIP-Seq reads count embedding;(4)neighboring methylation state embedding.The input features of most DL-based methods are RNA or DNA sequence except Deep-m6A sites around the predicted m6A site to 501-dimensional binary vectors. RNA sequence and MeRIP-Seq reads count embedding is only adopted by Deep-m6A, which firstly encoded the RNA sequence using one-hot encoding and then embedded the normalized MeRIP-Seq IP sample reads count mapped to the corresponding nucleotide’s genome position to the binary vector of each nucleotide. The embedded MeRIP-Seq IP reads count can represent the m6A methylation level under specific condition like cancer, which provides the power to predict condition-specific m6A sites.One-hot encoding is widely used for sequence analysis 4Knowledge bases with the comprehensive collection and curation of various information related to transcriptome-wide RNA modifications are often critical for elucidating their biological functions as well as for developing bioinformatics tools. A number of works have been accomplished addressing various aspects of RNA modifications including basic properties, pathway, distribution, disease association, visualization and GO functions. We review in the following a few databases related to RNA modifications.4.1https://mods.rna.albany.edu/.RNAMDB 4.2https://iimcb.genesilico.pl/modomics/.The MODOMICS database 4.36A transcriptome methylation. The MeT-DB database includes three parts, Core DB, TREW DB and Functional DB. The Core database contains context-specific m6A peaks and single-base sites. For each predicted m6A peak, its chromosomal location, including start/end position, strand information, p-value, fold enrichment and q-value were reported. Moreover, m6A peaks, single-based m6A sites, motif, peak distribution plot, gene expression profiles were available and can be downloaded from the web page. The TREW database annotates target sites of m6A readers, writers and erasers, then the target site was further annotated with transcript regions  and RNA type. In addition, some useful tools are provided in the MeT-DB web interface: the table view to facilitate researchers to explore and search the data in details, the genome browser to help the user visualize and compare m6A peaks and functions data, and the tool module that includes Guitar Plot and m6A-Driver for investigating the functions of m6A methyl transcriptome. It has undergone two versions http://compgenomics.utsa.edu/MeTDB/.MeT-DB (MethyTranscriptome DataBase) 4.4http://rna.sysu.edu.cn/rmbase/.RMBase 4.56A modification. Raw data resource of m6Avar can be mainly categorized into two parts, SNPs and m6A sites. In terms of SNPs, germline and somatic variants were obtained from dbSNP and TCGA database. A large number of disease-associated SNPs were obtained from GWAS, ClinVar etc. Furthermore, all of the SNPs were annotated by gene conservation scores and deleterious levels scoring from 0 to 5. In terms of m6A sites, they were acquired according to different confidence levels from high to low by using various strategies. The m6A sites with a high confidence level were derived from 7 miCLIP experiments and 2 PA-m6A-seq experiments. The m6A sites that have a medium confidence level were derived from 244 MeRIP-seq experiments and m6A sites that have a low confidence level were derived from a transcriptome-wide prediction based on Random Forest algorithm. Furthermore, the location of each m6A site was annotated by the transcript structure, including the CDS, 3′UTR, 5′UTR, start codon and stop codon. Combined with SNPs and m6A sites, m6AVar explored m6A-associated variants which were defined by evaluating whether it has the potential to alter the DRACH motif or other sequence features essential for m6A modification. Particularly, more than 2000 disease-related variants have been identified by linking the m6A-associated variants with GWAS and ClinVar data. m6AVar also provided an user-friendly web interface with multiple statistical diagrams and genome browser through which users can browse all of the m6A-associated variants and search data by various criteria. The m6AVar database is freely accessible at: http://m6avar.renlab.org/.m6AVar 4.66A modifications across 11 species. To offer insights into the cell line- or tissue-specificity of m6A modification, REPIC supports query of m6A modifications by cell lines and tissue types. Peak annotation and sample annotation are available. Peak annotation includes genomic position, fold enrichment and genomic feature. Sample annotation includes the data source, read mapping statistics, metagene profiles and results from motif analysis. To better display multiple dimensional m6A modification information across the entire genome, REPIC provides a genome browser to visualize m6A peaks, fold enrichment and gene expression. REPIC is accessible at https://epicmod.uchicago.edu/repic/index.php.REPIC 4.7http://RNAedit.com.RADAR 56A modifications is linked to a number of pathophysiological disorders, including obesity related traits 6A modification is involved in multiple forms of human cancer and plays a crucial role in different cancer contexts, such as in breast cancer 6A modification can be critical for understanding the disease pathogenesis. While wet lab experiments were often restricted by their costs in time and labor, computational approaches offered a viable avenue. We briefly summarized in the following some recent works related to in silico identification and prediction of disease association of RNA modifications including the relevant enzymes and the sites.Recent studies demonstrated that aberrant m6A regulators for prediction of survivals at pan-cancer level, which was based on 25 publically available datasets encompassing 13 cancer types. However, the construction of RNAMethyPro is based on silico analysis, which is controversial for obtaining the biological and clinical characteristics related to m6A, as well as for determining the specific functional modules of patients at high risk. Therefore, further mechanism and independent clinical verification are still appreciated to further validate that RNAMethyPro as a robust predictive signal in a variety of human cancers.RNAMethyPro 6A regulators were analyzed across more than 10,000 subjects representing 33 cancer types, and revealed significant correlation between activities of cancer hallmark-related pathways and expression levels of m6A regulators. Besides, the authors revealed that m6A reader IGF2BP3 maybe a potential oncogene for Clear cell renal cell carcinoma (ccRCC), even though they cannot reliably predict the prognosis of ccRCC patients based on the risk score according to the mRNA expression of m6A regulatory genes.In a study led by Li et al. 6AVar database 6A site and various diseases via disease-associated genetic mutations that may also lead to changes of RNA methylation status. To our knowledge, this is the first large-scale prediction study that linked individual RNA methylation sites to various diseases. In addition, it is a comprehensive database, which contains m6A related variables that may affect the m6A modification, which will help to interpret variables through the m6A function.The m6A sites were identified in cancer and non-cancer cells, which may demonstrate putative associations to cancer pathology. But due to the limitation of m6A sequence dataset, CVm6A, as well as most other databases, cannot fully determine the distribution of m6A on lncRNAs and other non-polyA RNAs.In the CVm6A database 6A sites to various diseases via a multi-layered heterogeneous network consisting of m6Asites, genes and diseases. The genes and sites were linked by association of expression levels and methylation levels, while genes and diseases are associated according to existing gene-disease association database.Based on a random walk with restart approach, DRUM 6A sites from a RNA co-methylation network derived epitranscriptome profiling data using hub-based or module-based methods. This is the first study for large-scale prediction of GO functions for individual m6A sites. However, the two methods used in m6Acomet achieved only marginal improvement compared with random guesses. Furthermore, there are more data sources, which can be integrated with RNA comethylation network to obtain more accurate functional labeling.By taking advantage of the guilt-by-association principle, m6Acomet 6A through integrating gene expressions, binding targets and binding motifs of large number of RNA binding proteins (RBPs) with a co-methylation network constructed using large-scale m6A methylomes across diverse cell states 6A epitranscriptome.Very recently, An et al developed a computational approach to systematically identify cell-specific trans regulators of m6With an increasing number of studies revealing the essence and importance of RNA modifications in general gene expression regulation and disease pathogenesis, RNA epigenetics Nevertheless, despite the rapid progress in epitranscriptome bioinformatics, there are still a number of limitations or open questions.5C First, technological bias and limitations may not have received sufficient attention during development of bioinformatics tools. For example, most of the existing RNA bisulfite data interpretation tools failed to consider the abundant RNA secondary structures that may generate a large number of false positive errors Secondly, although existing studies suggested that RNA modification can affect the structure of RNA 6A modification site prediction and disease association) have not been extended to other relatively less studied RNA modifications (such as m1A and Nm), even though the extension should be fairly straightforward from a computational perspective. Such basic bioinformatics infrastructure is essential and should be established for all types of RNA modifications that can be profiled transcriptome-wide at base-resolution.Thirdly, some bioinformatics pipelines have not been extended to keep up with the emergence of novel modifications arising from the development of new technologies. For example, the site prediction and disease association frameworks developed for well-studied modifications (such as the WHISTLE 76A-seq analysis approaches; Yujiao Tang, Xiangyu Wu, João Pedro de Magalhães and Daniel J. Rigden reviewed disease association and functional prediction; Bowen Song reviewed other bioinformatics tools for epitranscriptome data analysis; Jiani Ma, Hui Liu and Lin Zhang reviewed existing bioinformatics databases. All authors read, critically revised and approved the final manuscript.Jia Meng, Hui Liu and Xiujuan Lei initialized and coordinated the project. Yi Song, Rong Rong and Zhiliang Lu reviewed the biological background of RNA modifications; Lian Liu, Song-Yao Zhang, Kunqi Chen, Shao-Wu Zhang and Xiujuan Lei summarized sites prediction approaches; Lian Liu, Zhen Wei and Shao-Wu Zhang reviewed mThis work has been supported by National Natural Science Foundation of China ; China Postdoctoral Science Foundation [2018 M640949]; Fundamental Research Funds for the Central Universities ; XJTLU Key Program Special Fund [KSF-T-01].Lian Liu: Writing - original draft, Methodology, Investigation. Bowen Song: Writing - original draft, Methodology, Investigation. Jiani Ma: Writing - original draft, Methodology, Investigation. Yi Song: Writing - original draft, Methodology, Investigation. Song-Yao Zhang: Writing - original draft, Methodology, Investigation. Yujiao Tang: Writing - original draft, Methodology, Investigation. Xiangyu Wu: Writing - original draft, Methodology, Investigation. Zhen Wei: Writing - review & editing. Kunqi Chen: Writing - review & editing. Jionglong Su: Writing - review & editing, Resources, Supervision. Rong Rong: Writing - review & editing, Resources, Supervision. Zhiliang Lu: Writing - review & editing, Resources, Supervision. João Pedro de Magalhães: Writing - review & editing, Resources, Supervision. Daniel J. Rigden: Writing - review & editing, Resources, Supervision. Lin Zhang: Writing - review & editing, Resources, Supervision. Shao-Wu Zhang: Writing - review & editing, Resources, Supervision. Yufei Huang: Writing - review & editing, Resources, Supervision. Xiujuan Lei: Writing - review & editing, Resources, Supervision, Funding acquisition, Project administration. Hui Liu: Writing - review & editing, Resources, Supervision, Funding acquisition, Project administration. Jia Meng: Writing - review & editing, Resources, Supervision, Funding acquisition, Project administration.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) was first detected in late December 2019 and has spread worldwide. Coronaviruses are enveloped, positive sense, single-stranded RNA viruses and employ a complicated pattern of virus genome length RNA replication as well as transcription of genome length and leader containing subgenomic RNAs. Although not fully understood, both replication and transcription are thought to take place in so-called double-membrane vesicles in the cytoplasm of infected cells. Here we show detection of SARS-CoV-2 subgenomic RNAs in diagnostic samples up to 17 days after initial detection of infection and provide evidence for their nuclease resistance and protection by cellular membranes suggesting that detection of subgenomic RNAs in such samples may not be a suitable indicator of active coronavirus replication/infection. During SARS-CoV-2 replication subgenomic RNAs (sgRNA) are transcribed and subsequently translated into viral proteins. Here, Alexandersen et al. provide evidence that sgRNA is not necessarily an indicator for active viral replication, but can be detected up to 17 days after symptom onset in clinical samples. This virus could rather easily transmit from person to person and rapidly spread worldwide4. SARS-CoV-2 belongs to the Order Nidovirales, Family Coronaviridae, Subfamily Orthocoronavirinae, Genus Betacoronavirus, Subgenus Sarbecovirus, Species Severe acute respiratory syndrome-related coronavirus and individuum SARS-CoV-2 with the addition of the strain/sequence, e.g., SARS-CoV-2 Wuhan-Hu-1 as the reference strain5.Human coronavirus disease 2019 (COVID-19) emerged in late December 20196. After having entered the host cell, replication of coronaviruses initially involves generation of a complementary negative sense genome length RNA for amplification of plus strand virus genome RNA, as well as transcription of a series of plus strand subgenomic RNAs all with a common leader joined to gene sequences in the 3′-end of the virus genome. Virus replication and transcription both involve cytoplasmic membrane structures forming virus replication/transcription organelles. These structures include virus proteins derived from proteolytic processing of the polyprotein encoded in the 5′ two thirds of the virus genome (termed Open Reading Frame (Orf) 1a and 1b) with a minus 1 ribosomal frameshift between Orf1a and 1b, and translated from the full length plus sense virus genome RNA. A set of subgenomic RNAs are also generated, most likely from a complex mechanism involving paused negative sense RNA synthesis leading to a nested set of negative sense RNAs from the 3′end of the virus genome joined to a common 5′-leader sequence of approximately 70 nucleotides8. The pausing of the virus replication/transcription complex occurs at so-called transcription-regulatory sequences (TRS) located immediately adjacent to open reading frames for these virus genes10. These nested negative sense RNAs in turn serve as templates for transcription of plus strands able to serve as a nested set of virus mRNAs for translation of specific proteins from the 3′-third of the virus genome7. These subgenomic mRNAs of SARS-CoV-2, as illustrated in Kim et al.9, are thought to encode the following virus proteins: structural proteins spike (S), envelope (E), membrane (M) and nucleocapsid protein (N) and several accessory proteins for SARS-CoV-2 thought to include 3a, 6, 7a, 7b, 8, and 109. Furthermore, it appears that the expression of the N protein is required for efficient coronavirus subgenomic mRNA transcription7.Similar to other coronaviruses, SARS-CoV-2 is an enveloped, positive sense, single stranded RNA virus with a genome of nearly 30,000 nucleotides11. While it is still unclear whether this RNA synthesis takes place inside or on the outside of these vesicles, it is thought that the membranes somehow “protect” the synthesized RNA, including double stranded RNA, from host cell recognition and response, and also from experimental exposure to RNase12. In addition, it has been shown that coronavirus cytosolic RNA is protected from so-called “nonsense-mediated decay” (NMD) by the virus N protein and thus are more stable in that environment compared to what would have been expected for nonspliced RNA13.The subcellular site/s of coronavirus RNA replication and transcription in the cytoplasm of infected cells is not fully defined, but thought to involve so-called “double-membrane vesicles” (DMV) in or on, which the virus replication complex synthesize the needed double and single stranded full length genomic and subgenomic RNAs14. However, it is important to stress, that unless specific steps to remove cellular membranes are used for sample preparation and virion purification, such subgenomic coronavirus RNAs are tightly associated with membrane structures, and less purified coronavirus preparations are well known to include subgenomic RNAs that, similar to virion RNA, are nuclease resistant15.While it was originally thought that coronavirus virions contained subgenomic RNAs in addition to the virus plus strand genomic length RNA, it has now been shown that these subgenomic RNAs do not contain a packaging signal and are not found in highly purified, cellular membrane free, coronavirus virions9. That study indicated that while the predicted spike , Orf3a, envelope , membrane , Orf6, Orf7a, and nucleocapsid protein  subgenomic RNAs were found at high levels in cell culture, only low levels of the Orf7b subgenomic RNA was detected and the Orf10 subgenomic RNA  was detected at extremely low level 9. This far, little has been published in regards to the presence of SARS-CoV-2 subgenomic RNAs in samples from infected people. A single study by Wölfel et al.16, looked specifically for the presence of the E gene subgenomic RNA by a PCR and took the presence of subgenomic RNA as an indication of active virus infection/transcription. That study could detect E gene subgenomic RNA at a level of only 0.4% of the virus genome RNA in sputum samples from days 4–9 of infection, but only up to day 5 in throat swab samples16. That study assumed a correlation between the presence of the subgenomic E mRNA and active virus replication/transcription and thus active infection, however, this assumption may not be accurate considering what has been mentioned above about the membrane associated nature of coronavirus RNA and their stability/protection from the host cell response and from RNases.One study has been published looking at the abundance of subgenomic RNAs for SARS-CoV-2 cultured in Vero cells17 using the same E gene PCR mentioned above16. That very recent study detected subgenomic E RNA in swab samples from hospitalized patients up to 22 days after onset of clinical symptoms17. Thus, it is becoming clear that the presence, and thus detection, of SARS-CoV-2 subgenomic RNAs in diagnostic samples is rather prolonged and consequently not a good marker/indication of active virus replication/transcription or active/recent infection. Despite that, a number of high-profile studies21 have continued to use presence or reduction of subgenomic RNA level as evidence of or protection from active infection, and consequently, we believe it is important to understand that these subgenomic RNAs may be present for a significant time after active infection.In this work we describe the detection of SARS-CoV-2 subgenomic RNAs in routine diagnostic oropharyngeal/nasopharyngeal swabs up to 17 and 11 days after first detection by next generation sequencing (NGS) and PCR, respectively. Our finding of extended detection of subgenomic RNA in diagnostic samples has subsequently been supported by another study (available as preprint)PRJNA636225) while no reads were found in the negative control sample , but filtering so only reads containing part of the leader sequence were included and then looked at where these reads had been mapped. A total of between 8 and 256,123 reads containing the leader sequence were found in our positive samples while none was detected in the negative sample GC-28/67  from RNAseq on a sputum sample from an infected human (Supplementary Table SRR11810731 and SRR11810737) produced the highest number of subgenomic reads, followed by an RNA-Seq experiment performed in cell culture using a Nanopore platform (SRR11267570). The selected RNA-Seq experiments performed on clinical samples typically generated very low levels of reads mapping to the virus genome and consequently to the leader sequence. The Artic network primers22 also detected subgenomic reads in virus culture experiments (ERR4157962 and ERR4157960).To look at this in more detail, we downloaded a selection of SRA’s generated from different sample types, different sequencing platforms and employing different library strategies. Reads belonging to subgenomic RNA could be identified in all samples except sample (SRR11267570 and SRR11810737 had any reads mapped to the subgenomic Orf7b (0.2–0.3% of reads having the leader sequence).The subgenomic RNAs with the highest number of reads mapped in the SRA’s were the N and Orf7a followed by the Orf3a and M gene. The subgenomic S gene and Orf6 were typically low and no reads were mapped to the subgenomic Orf10 in any sample. Only sample The number of reads mapped to either the first 21,500 nucleotides (nt) of the reference virus genome, to the subgenomic region from nucleotide 21,500 onward, to subgenomic RNA containing the leader sequence, to the included cellular control mRNA amplicons and reads not mapped to any of these are summarized in Fig. We then looked further at the range of reads mapped to SARS-CoV-2 amplicons in the samples, as the abundance of different size amplicons could possibly be influenced by sample quality or virus load, in particular in poor quality or low virus load samples. The results are shown in the last 3 columns of Table .Due to the variability in individual amplicon abundances observed, we then compared the abundance of reads mapped to SARS-CoV-2 genomic amplicons to the abundance of reads mapped specifically to subgenomic RNAs. This comparison was done in a number of different ways including a comparison of the total reads for the first 21,500 nucleotides of the virus genome with the total number of subgenomic reads; the most abundant amplicon in the first 21,500 nt of the virus genome with the most abundant subgenomic RNA amplicon reads; the most abundant full virus genome amplicon with the most abundant subgenomic RNA amplicon reads; and the average full virus amplicons reads with the average subgenomic RNA amplicons reads as presented in Fig. The results for PCR testing of samples for detection of specific genomic and subgenomic targets are shown in Table The differences in Ct values between the 7a subgenomic and the genomic targets for the samples with a positive PCR is shown in Table For strand specific PCR, we focused on the seven samples that were most likely to have a sufficient virus RNA load to allow potential detection of the strand-specificity of the detected SARS-CoV-2 RNA. While all seven samples were positive for positive sense SARS-CoV-2 RNA, sample GC-23/60 sampled 11 days after first detection (sample GC-14/33/37) was only borderline positive, and only for positive sense subgenomic RNA close to the level of detection of these assays (sensitivity approximately 10-fold lower than the non-strand specific PCRs). Positive sense genomic RNA was only detected at a relatively low level in the PCR on sample GC-13/35  or only positive sense of the subgenomic 7a RNA is small, it may possibly indicate that more of the subgenomic RNA as compared to the genomic RNA is of negative sense. Although the number of samples is very low, this is supported by the fact that the difference in Cts obtained between the positive sense and negative sense genomic target is 8.6–8.8 Cts (150-fold more positive sense RNA), while it is only five Cts (20-fold more positive sense RNA) for the subgenomic RNA in the two samples for which detectable levels were present ((Samples GC-25/65 and GC-55/68), Supplementary Table To analyse if samples potentially contained SARS-CoV-2 double stranded RNA, we attempted treatment of samples with the single-stranded RNase If before strand-specific PCRs. The selected samples included samples GC-26/66, GC-11/34/38, GC-14/33/37, GC-25/65, and GC-55/68 and the two strongest fractions from the membrane association/fractionation resistance protocol described below. However, after treatment with RNase If, which is described to have a preference for degradation of single-stranded RNA over double-stranded RNA, these samples were completely negative for both positive and negative strand SARS-CoV-2 RNA by PCR. This may be due to a number of factors, including double stranded RNA being below detection limits or possibly that virus plus and negative strands may not have properly annealed before the RNase treatment. Alternatively, the relatively high RNase If concentration used or other nucleases present during incubation of extracted nucleic acids samples at room temperature and at 37 °C in nuclease buffer may have destroyed any double stranded RNA present. We are not able to further look into this as our sample material is now exhausted. However, further studies could look at this in infected cell cultures.The two samples selected for this analysis represented two different types of samples. Sample GC-26/66 representing a good quality sample and sample GC-55/68, having been suspended in water rather than in PBS or transport medium, representing a sample with partly degraded RNA.23.As part of this protocol, half of the final 16 fractions obtained for each sample were treated with Triton X-100 to determine whether lipid membranes may be important in protecting any SARS-CoV-2 RNA present. As hypothesized, Triton X-100 treatment had a significant effect on final fractions. The 16 treated fractions became either negative or only borderline positive for all three targets in the commercial PCR used, i.e., with Cts in the mid to upper thirties very close to the detection limit of the PCR (see the Source Data file for details). Overall, this indicates that the Triton X-100 treatment, even without addition of external nucleases, results in degradation of any SARS-CoV-2 RNA present by at least 1000-fold or more, consistent with such RNA being protected by lipid membranes and consistent with what has been observed for SARS-CoV replication/transcription complexes in cell cultureg spin, non-Triton treated, nuclease treated and then supernatant from final 10,000 × g spin). This particular fraction was positive for the other PCR targets, see Supplementary Table 24. In contrast, sample GC55/68 that had been in water rather than PBS/transport medium, had most of the targets in the supernatant fraction from the first 1000 × g spin, and what was present in this supernatant was partly susceptible to nuclease treatment and could not be efficiently pelleted by the 10,000 × g spin. Furthermore, for this sample the target RNA present in the initial 1000 × g pellet was, in contrast to what was observed for sample GC-26/66, highly susceptible to nuclease treatment . For sample GC-26/66, this fraction had a difference of 5.5 Cts and for sample GC-55/68, a difference of 11 Cts possibly indicating a higher proportion of nuclease protected virion RNA as compared to subgenomic RNAs in this particular fraction (Supplementary Table Looking at the detected levels of PCR targets for the 7a subgenomic RNA target compared to the 7a genomic target (that detect both genomic and subgenomic targets), the difference was somewhat similar to what is shown in the section above for results directly on samples and corresponding to having around ten times more genomic than subgenomic targets Table . An exceTo look at this in more details, we also did the strand-specific PCR for these fractions. However, the sensitivity of the strand-specific assay (approximately 10-fold less sensitive than the non-strand specific assay) on fractionated samples was not sufficient to detect any negative sense SARS-CoV-2 RNA. However, we could detect positive sense genomic plus subgenomic RNA in most of the fractions in an amount consistent with expected levels detected in the non-strand specific PCRs . We present results from two different approaches to map subgenomic RNAs, one mapping directly to the expected sequences of the leader containing subgenomic RNAs and another approach, where reads already mapped to the virus reference genome are filtered based on whether they contain the partial leader sequence or not. We also analyse for the presence of subgenomic RNAs in selected read archives from the NCBI Sequence Read Archive. Furthermore, we extend our study to include semiquantitation of subgenomic and genomic SARS-CoV-2 RNAs by specific PCRs and quantitation of plus strand as compared to negative strand SARS-CoV-2 RNAs. Finally, we present results supporting that these subgenomic RNAs are associated with cellular membranes and are nuclease resistant. Aided by the current understanding of the cell biology of coronavirus infections including the known association of virus RNAs with cellular double-membrane vesicles (DMVs), we present an integrated interpretation of our results based on detailed analysis of relative abundance of the different subgenomic RNAs in samples collected early and late in infection, of different quality or subjected to membrane lysis by detergent treatment and fractionation. Our integrated interpretation of the results overall, is that both virion and subgenomic RNAs are most likely rather stable in vivo and that detection of subgenomic RNAs in clinical samples, importantly, do not necessarily signify active virus replication/transcription, but instead is due to such RNAs being part of membrane vesicles, most likely so-called double-membrane vesicles, and thus relatively stable.9. While some samples, in particular those with a high virus load, had very few reads mapped to cellular RNA control amplicons, other samples, in particular the negative sample and those with a low virus load, had many reads mapped to these amplicons. When comparing the number of reads from SARS-CoV-2 genomic amplicons to the number of reads mapped specifically to the SARS-CoV-2 subgenomic RNAs, we found that the ratio of genomic to subgenomic RNA reads varied from around 2–20 depending on the specific comparison made. This finding was consistent with the results from PCR with specific targets, which indicated a ratio of genomic to 7a subgenomic RNA of around 4–20 fold. Further testing of these samples using so-called strand-specific PCRs indicated that for the positive sense RNA, the ratio of the 7a genomic to subgenomic RNA is around 14–28 fold while the positive to negative sense ratio for the 7a subgenomic RNA is around 20-fold and around 150-fold or higher for the genomic RNA. Although the presence of both negative and positive sense RNA in some samples indicated that double-stranded forms of these RNAs may be present, the limited sample volumes available and the lower sensitivity of these methods did not allow us to detect that.Our mapping of specific subgenomic RNAs indicated that samples had variable levels of eight of the predicted ten subgenomic RNAs while no or very low levels of subgenomic RNA for Orf7b was detected and a subgenomic RNA for Orf10 was absent. This is consistent with what has been described for SARS-CoV-2 in cell culture23. With a slight modification of this protocol, we were able to show, similar to the original cell culture study of SARS-CoV, that the SARS-CoV-2 RNAs were, at least in part, protected from nucleases, could be pelleted by centrifugation and that detergent treatment would greatly reduce the nuclease protection and ability to be pelleted by centrifugation. Interestingly, even after fractionation, the ratio of genomic to subgenomic 7a RNA was still around 10-fold except for a fraction thought to mainly include nuclease protected virion RNA, for which the ratio may be as high as 600-fold more genomic to subgenomic RNA.Another aspect evaluated in this study, was whether the SARS-CoV-2 RNAs detected in the diagnostic samples were protected from nucleases, and whether such protection was likely to be facilitated by cellular membranes as hypothesized. The protocol for this part of the study was based on a study of SARS-CoV replication/transcription complexes in cell culture that showed that such membrane complexes could be pelleted by centrifugation and protected the virus RNA from nucleases unless disrupted by mild detergent treatment25. Detection of subgenomic RNAs in late infection, as described here up to 11 and 17 days after first detection by PCR and NGS, respectively, although in contrast to the studies by Wölfel et al.16, is consistent with the recent findings described by van Kampen et al.17, which detected the E gene subgenomic RNA by PCR in respiratory swabs up to 22 days after first day of onset of clinical symptoms. The participants in their study likely had more severe disease than the ones included in our study, as their study focused on hospitalized patients, many of which were in intensive care units, while our study subjects only had minor clinical symptoms and all self-isolated at home26. Nevertheless, although their study detected the E gene subgenomic RNA by PCR while we focused on the 7a subgenomic RNA by PCR and all the subgenomic RNAs by NGS, these studies support each other and were also supported by results presented in additional studies using the same subgenomic E gene PCR21. The detection of subgenomic RNA is therefore not direct evidence of active infection, instead its presence at lower levels than virion genomic RNA results in detection for a shorter period of time unless using e.g., highly sensitive NGS.The results described here fully support that SARS-CoV-2 genomic and subgenomic RNAs are present in diagnostic samples even in late infection/after active infection. Subgenomic RNAs, like virion RNA, are rather stable and are likely protected from nucleases by cellular membranes, for the subgenomic RNAs possibly the so-called double-membrane vesicles known to support coronavirus RNA replication and transcription25. In contrast, the same authors found that extracellular and partly purified coronavirus virion preparations from late cell culture infection, while being RNase resistant and susceptible to detergents, have a much higher genomic to subgenomic RNA ratio of 10–30 or higher and at least 100-fold more positive than negative sense RNA25. Consequently, our findings based on NGS, specific PCR assays and fractionation together with nuclease and detergent treatment, are fully consistent with what has been shown from cell culture infection and fractionation of coronavirus replication/transcription complexes in cellular membrane structures, most likely double-membrane vesicles (DMVs). Thus, SARS-CoV-2 RNAs in diagnostic swab samples are likely found as a mixture of virion genomic as well as subgenomic RNAs, both protected from nucleases by virus/cellular membranes and at a ratio of around 10-fold more genomic/virion RNA than subgenomic RNA and a plus to minus sense RNA ratio of around 150-fold or more for genomic/virion RNA and around 20 for subgenomic RNA. This stability of subgenomic RNAs together with the variability observed for different amplicons at low target levels, may at least in part help explain variability/discrepancies of PCR results reported for different diagnostic PCR assays detecting targets in different parts of the SARS-CoV-2 genome28. For example, our analysis indicated that some subgenomic RNAs may be more abundantly amplified in poor samples, possibly because of partly degraded RNA in such samples and the increased ability of PCR, including most diagnostic PCRs as well as NGS employing various amplification steps, to amplify short targets. This notion is supported by our findings in two diagnostic samples that for unrelated reasons had been suspended in water rather than in PBS or transport medium as for our other samples. These particular two samples (sample GC-25/65 and GC-55/68), in which cells and membrane vesicles were almost certainly partly disrupted29, and thus any coronavirus RNAs exposed to the environment and likely to RNases, were dominated by short reads, i.e., short amplicons, and strikingly, with a very high abundance of reads mapped to some subgenomic RNA amplicons, in particular the Orf7a RNA. Clearly, original sample abundance cannot change just because cellular membranes are lysed by the hypotonic treatment, so the observed increase in these reads may almost certainly be caused by preferred amplification of shorter or more efficient amplicons in such samples.Overall, our results fit well with what would be expected from a coronavirus infection based on what is known from cell culture studies. The caveat is that samples from even relatively early infection in vivo, as assessed by upper respiratory swab samples, are more alike late infection cell culture supernatant or partly purified virion preparations and less like what is found for early intracellular coronavirus RNAs in cell culture. Consequently, when looking at what is known for other coronaviruses and cell culture studies, intracellular subgenomic RNAs may dominate over genomic RNA very early on, with 8–70 times more intracellular subgenomic than genomic RNA at 6–8 h after infection for infectious bronchitis virus, a gammacoronavirus, and for bovine coronavirus, a betacoronavirus, and with at least ten times more plus sense than minus sense RNANidovirales. That coronaviruses, and their RNA, may be extremely resistant when part of a membrane matrix is well known, and was demonstrated for example when porcine epidemic diarrhea virus, also a coronavirus, entered and infected pigs in Canada in early 2014 by feed containing spray‐dried porcine plasma31. Detergent treatment and ultracentrifugation indicated that this coronavirus RNA was initially bound to membranes, but could be pelleted by ultracentrifugation after detergent release. Other studies, including our own using yet another coronavirus, the avian infectious bronchitis virus, further support that detergent treatment will release the coronavirus RNA and make it susceptible to nuclease degradation supporting the fact that the majority of such coronavirus RNA is membrane bound24. Consequently, we believe that the methods described here to detect and look at relative abundance of SARS-CoV-2 RNAs in clinical samples together with insights in what is known about coronavirus cell biology overall, will have general interest and applicability not only for SARS-CoV-2, but also for other coronaviruses and related viruses.In conclusion, by combining knowledge of general coronavirus cell biology and replication/transcription with careful mapping of NGS reads to SARS-CoV-2 subgenomic RNAs and by PCR on clinical samples taken at different times of infection and of different quality, we present information that helps understand prolonged and sometimes inconsistent PCR-positivity. This information may in turn pave the way for development of better diagnostic PCRs and NGS strategies to define active SARS-CoV-2 infection as opposed to extended presence of what most likely represent highly stable virus genomic and subgenomic RNAs present in, and at least in part protected by, cellular membranes; for the subgenomic RNAs most likely so-called double-membrane vesicles (DMVs). Our findings are likely to be relevant also for other coronaviruses and possibly also other viruses in the Order 26. Summary details of the samples included are shown in Table We here describe detailed analysis of samples subjected to next generation sequencing (NGS) at the Geelong Center for Emerging Infectious Diseases (GCEID). The study included combined nasopharyngeal and oropharyngeal swab samples collected from individuals in the region of Greater Geelong, Victoria, Australia between the 28th of January to the 24th of April 2020. The study included NGS of 12 SARS-CoV-2 PCR-positive samples and 1 negative sample as control. The 12 PCR-positive samples were obtained from eight individuals as NGS was done on samples taken at three time points from two individuals to monitor their infectionhttps://www.ncbi.nlm.nih.gov/sra/]) and used selected SRA studies to support the findings from our own samples.In addition to analysis of the NGS reads obtained from the samples mentioned above, we also searched the National Center for Biotechnology Information (NCBI) Sequence Read Archive 34 and a commercially available SARS-CoV-2 Ampliseq panel [https://www.thermofisher.com/au/en/home/life-science/sequencing/dna-sequencing/microbial-sequencing/microbial-identification-ion-torrent-next-generation-sequencing/viral-typing/coronavirus-research.html] kindly provided by Thermofisher Scientific, Victoria, Australia. In addition, two of the samples (GC-11 and GC-14) that yielded a low virus coverage by this method, were amplified separately (GC-11/38 and GC-14/37) using essentially the same method, but with the Ampliseq HiFi mix replaced with Amplitaq Gold 360 Master mix for the amplification step. Amplification was done for either 21, 27, or 35 cycles depending on the estimated virus load in the samples26 and libraries prepared and run on Ion Torrent 530 chips on an Ion S5 XL genetic sequencer (Thermofisher Scientific) at a concentration of 50 pM as per the manufacturer’s protocols35. Generated sequence reads were then mapped to a SARS-CoV-2 reference genome (Wuhan-Hu-1-NC_045512/MN908947.3 [https://www.ncbi.nlm.nih.gov/nuccore/NC_045512.2/ and https://www.ncbi.nlm.nih.gov/nuccore/MN908947.3]) using the TMAP software included in the Torrent Suite 5.10.136, and virus genomic consensus sequences generated using additional Torrent Suite plugins supplied by Thermofisher Scientific, and visualized in Integrative Genomic Viewer37 (IGV 2.6.3) . Near complete and partial SARS-CoV-2 genomes were aligned using Clustal-W38 in MEGA 7 software39 and near full length sequences 26 submitted to the EpiCoVTM database of the Global Initiative on Sharing All influenza Database (GISAID)41 (https://www.gisaid.org/) and to NCBI GenBank accession numbers MW192766, MW192771, MW192772, MW193406, MW193407, MW193408.Nucleic acid extraction and cDNA synthesis was performed by heating extracted nucleic acids at 70 °C for 5 min and rapid cooling on ice before cDNA synthesis using SuperScript™ VILO™ Master Mix  as per manufacturers’ instructions26. Close inspection of all primers included in the panel, indicated that two of the forward primers  have their 3′-end at SARS-CoV-2 (NCBI Accession Wuhan-Hu-1-NC_045512/MN908947.3 [https://www.ncbi.nlm.nih.gov/nuccore/NC_045512.2/ and https://www.ncbi.nlm.nih.gov/nuccore/MN908947.3])2 nucleotide 42 and 52, respectively, and consequently have a perfect match to a sequence included in the SARS-CoV-2 leader sequence with an estimated 27 or 17 nucleotides downstream of these primers also being part of the leader sequence16.Although the SARS-CoV-2 Ampliseq panel used for the NGS has been designed to generate near full length SARS-CoV-2 genomic sequences, it uses simultaneous amplification of sample cDNA with a total of 242 primer pairs of which 237 primer pairs cover the near full genome of SARS-CoV-2 and an additional 5 amplicons targeting cellular genes in two primer pools26, a close inspection of reads around expected subgenomic RNA Transcription Regulatory Sites (TRS)10 indicated that a significant number of NGS reads may have been amplified from subgenomic RNAs rather than from virus genomic RNA. To analyse this in more detail, we first assembled an exploratory composite reference for remapping using the Torrent Suite T-map reanalyze function. This initial assembled reference consisted of a composite reference with one sequence containing the first 21,500 nucleotides of the SARS-CoV-2 reference genome used for the initial assembly (NCBI Accession (Wuhan-Hu-1-NC_045512/MN908947.3 [https://www.ncbi.nlm.nih.gov/nuccore/NC_045512.2/ and https://www.ncbi.nlm.nih.gov/nuccore/MN908947.3])2 to map reads most likely corresponding to the virus genome while we in addition, assembled ten tentative subgenomic RNA sequences containing 28 nucleotides from the 3′end of the leader sequence  and this leader then followed by the assumed TRS and gene specific sequence for the next 72 nucleotides. Consequently, this reference contained the first 21,500 nucleotides of the virus genome (Wuhan-Hu-1-NC_045512/MN908947.3 [https://www.ncbi.nlm.nih.gov/nuccore/NC_045512.2/ and https://www.ncbi.nlm.nih.gov/nuccore/MN908947.3]) as well as 10 composite references corresponding to the assumed 5′-end of the ten potential subgenomic RNAs; S, Orf3a, E, M, Orf6, Orf7a, Orf7b, Orf8, N, and Orf10/1510. This initial analysis indicated that this was an efficient way of mapping reads corresponding to subgenomic RNAs, and for our final analysis we updated the subgenomic RNA sequences in this composite reference to include the full leader sequence from nucleotide 1–69 and extended the gene specific sequences to ensure that they would include a reverse primer from each primer pool without extending into the next specific gene sequence. This final composite reference used for mapping then included the first 21,500 nucleotides of the SARS-CoV-2 genome and the ten subgenomic RNA specific sequences, each including the leader and gene specific sequences and having a length of 233–364 nucleotides  written in BASH 4.4.20 and AWK 4.1.4  using samtools 1.742. This sequence corresponded to nucleotides 52–67 within the SARS-CoV-2 leader sequence in GenBank sequence Wuhan-Hu-1-NC_045512/MN908947.3 [https://www.ncbi.nlm.nih.gov/nuccore/NC_045512.2/ and https://www.ncbi.nlm.nih.gov/nuccore/MN908947.3]2. Reads with this sequence immediately upstream from the mapped region of the read, or within the 5′ end of the mapped region, were retained. The script then generated a spreadsheet giving the nucleotide position of where the leader sequence of each read finished in relation to the reference genome. These reads were then inspected in IGV. To assign the reads to the corresponding subgenomic RNA, the reads were grouped by the nucleotide position at the end of the leader sequence and tallied in Excel. Based on the end position of the leader mapped to the reference genome, each read was assigned to the corresponding subgenomic TRS. Typically, the leader sequence sat within a soft-clipped portion of each read, although depending on the reference sequence, the Ion Torrent TMAP algorithm did occasionally include the start of the leader sequence within the mapped portion of some reads, and at times included spurious insertions or deletions within this section of the mapping in its attempt to map the leader to the reference. Therefore, any read with the leader ending within 10nt of the start of the known subgenomic TRS sequences were assigned to the respective TRS. Some reads did not map to any known TRS, and these were assigned to an “Unknown TRS”. Violin plots of the number of subgenomic reads were created as described above.The next step in the analysis was to look at whether such subgenomic RNAs could be detected by a somewhat different type of analysis based on using reads already mapped to the full virus sequence, and then filter these to only look at reads containing part of the leader sequence. This type of analysis should give an unbiased view as to where reads containing the leader may be located on the genome, whether those sites correspond to the assumed position of the genomic leader and proposed TRS of the leader-containing subgenomic RNAs, whether the abundance somewhat correspond to that found by the method mentioned above and finally, whether any additional subgenomic RNAs or cryptic TRS sites may be detected. Reads within the mapped reads BAM files were filtered on whether they had a MAPQ of 32 or higher and contained the partial leader sequence GTAGATCTGTTCTCT, using a custom script , are then subsequently also removed. Consequently, all read numbers mentioned in this study are reads that have already satisfied these criteria and remained for further analysis/mapping.43 with a minimum mapping quality (MAPQ) of 20 and requiring the mapped reads to cover more than 90% of an amplicon, and for the amplicons to cover no less than 70% of the read to be included in the count. This ensured reads were not counted more than once, as amplicons targeted partially overlapping regions of the SARS-CoV2 genome, and some of the smaller virus amplicons, were completely overlapped by a larger amplicon. This was done on all samples and in the same way for all the included 237 SARS-CoV-2 amplicons and the five control gene amplicons included in the Ampliseq panel. The five control gene amplicons span an intron of each of the following cellular genes, and thus amplify mRNAs for TATA-box binding protein (TBP NM_003194), LDL receptor related protein 1 (LRP1 NM_002332), hydroxymethylbilane synthase (HMBS NM_000190), MYC proto-oncogene (MYC NM_002467) and integrin subunit beta 7 (ITGB7 NM_000889). These control cellular gene amplicons are part of the Thermofisher AmpliseqTM panel, and are automatically mapped as part of the SARS-CoV-2 mapping on the Ion Browser as described above.We then checked reads mapped to the SARS-CoV-2 Ampliseq panel employed for the NGS that uses simultaneous amplification of sample cDNA with a total of 242 primer pairs of which 237 primer pairs cover the near full genome of SARS-CoV-2 and an additional 5 amplicons targeting cellular genes . Checking of mapped reads indicated that they were all mapped uniquely and thus counted only once. Counting/abundance of reads mapped to the individual amplicons were then done using BEDTools Version 2.27.1We designed primers for specific detection of the 7a subgenomic RNA by creating a forward primer in the leader sequence and a reverse primer within the 7a sequence itself. A second PCR targeting the Orf7a  was also developed. Two PCRs specifically targeting the 5′-UTR were developed, one including part of the leader sequence and the other targeting the 5′-UTR downstream of the leader sequence. These two assays were specifically designed to only detect SARS-CoV-2 genomic RNA and not subgenomic RNAs. The primers are listed in Table 44.These PCR assays were all performed using the same cDNA preparations as those used for the NGS; however, as we had limited cDNA volumes remaining for most samples, cDNA was diluted 2.5-fold except for sample GC13/35 for which we had more cDNA available and used undiluted cDNA. The PCRs all employed 2 µl of cDNA and 1 × AmpliTaq Gold 360 PCR Mix, 1 µM of each primer, 2 µM Syto 9  and a PCR protocol of 95 °C for 10 min, 40 cycles of 95 °C for 30 s, 58 °C for 30 s, 72 °C for 30 s, and a final 72 °C step for 3 min. A melt curve analysis was performed immediately post PCR with the reaction conditions of 95 °C for 15 s, then 60 °C for 1 min followed by a continuous temperature ramp between 60 °C and 95 °C increasing at 0.05 °C/s. Positive results were called based on threshold cycle and the correct peak melt temperature of the product. For the initial assay set up, amplicon identity was further confirmed by gel electrophoresis followed by Sanger sequencing of the PCR productsTM COVID-19 RT-PCR Kit  using 1.5 µl of the same diluted cDNA samples mentioned above (except for sample GC-13/35 for which we had more cDNA available and used 2.5 µl of undiluted cDNA) and employing the TaqPathTM 1-Step Multiplex Master Mix without ROX  together with the TaqPathTM COVID-19 RT-PCR Kit 26 although skipping the initial reverse transcription step. This assay simultaneously detects three targets; a target in the Orf1 only detecting the virus genomic RNA, a target in the S gene detecting genomic and S subgenomic RNA and a target in the N gene detecting genomic RNA as well as all full length subgenomic RNAs except Orf10 if present. Samples were identified as having a high or low virus load based on the Ct obtained from the COVID-19 RT-PCR kit assay or the Ct reported from the original diagnostic laboratory26.In addition to the in-house PCR assays described above, we also used the commercial TaqPathEfficiency, slope and theoretical sensitivity of each PCR were determined by using a dilution series of gel purified amplicons for the in-house assays and a dilution series of a positive control included in the commercial COVID-19 kit. For in house assays using either Syto 9 or SYBR Green (see below), the amplification slopes of the assays were very similar with around 3.9-4.0 cycles between each 10-fold dilution and a lower Ct sensitivity/threshold of 30-32 while the commercial probe-based assay, as anticipated, was more sensitive and efficient with around 3.4 cycles between each 10-fold dilution and a lower Ct sensitivity/threshold of 39 for all three targets included.44.For strand specific PCR detection, we used the original nucleic acids extracted for NGS and using an initial step to denature any double-stranded RNA by first heating at 95 °C for 3 min followed by snap-freezing at −20 °C. The samples were then tested using real-time SYBR Green PCR assays with the Power SYBR Green RNA-to-CT 1 step kit  using the 7a PCRs described above and adapted so that we initially added a single primer for the reverse transcription step of the protocol at 48 °C for 30 min, then inactivated the reverse transcription enzyme by incubation at 95 °C for 8 min before adding the other primer and continuing the protocol by initially heating to 95 °C for 2 min to further activate the PCR enzyme before conducting 40 cycles of 95 °C for 30 s, 58 °C for 30 s, 72 °C for 30 s and a final 72 °C step for 3 min. A melt curve analysis was performed and positive results as described above. The initial assay set up was further confirmed by gel electrophoresis of the products followed by Sanger sequencing to confirm amplicon identity45, but with a slight modification to promote annealing of extracted RNA before digestion. This was done by adding 1/10th volume of the 10 × RNase If buffer (NEBuffer 3) and incubating at room temperature for 10 min before adding 50 units of RNase If and then incubating at 37 °C for 10 min, heating at 95 °C for 3 min followed by snap-freezing at −20 °C to heat inactivate the enzyme and denature any double-stranded RNA before PCR.To analyse if samples testing positive for minus sense SARS-CoV-2 RNA potentially contained double stranded RNA, we attempted treatment of samples with RNase If (New England Biolabs (NEB), Victoria, Australia) to preferentially remove single stranded RNA before PCR. This was performed essentially as described23. This protocol was followed with the following minor modifications. To allow analysis of swab sample material that had already been frozen and thawed at least twice, we started the protocol without an initial Dounce homogenizer step and starting with the swab material in PBS without any additional chemicals or RNase inhibitors. The first step of fractionation consisted of centrifugation at 1000 × g for 5 min and taking the pellet  and the supernatant fraction . The P1 and S1 fractions were then each divided into two aliquots, of which one was treated with 0.5% of the nonionic detergent Triton X-100 for 15 min at 4 °C. These fractions were then again split into two aliquots of which one was treated with nucleases, first adding a 20 × nuclease buffer and then benzonase and micrococcal nuclease and incubation at room temperature for 30 min32. Fractions were then centrifuged at 10,000 × g for 10 min and the pellet fraction (designated the P10 fraction) and the supernatant (designated S10) collected. In effect, this resulted in a total of 16 fractions from each sample of which 8 came from each of the P1 and S1 fractions and of which half had been treated with Triton and the other half not, and then half of these fractions treated with nucleases or not. Final fractions were designated P1P10, P1S10, S1P10 and S1S10 and including aliquots that had been treated or not with Triton (T+ or T−) and treated or not with nuclease (N+ or N−). These fractions were then subjected to nucleic acids extraction and cDNA preparation as described for NGS, and tested by the 7a subgenomic and genomic PCRs as well as the commercial kit as described above. The obtained PCR values were normalized to the final volume of sample in each of the fractions in order to compare the results. In addition, we also tested the nucleic acids from these fractions in the strand-specific PCRs for 7a genomic and subgenomic RNA.To study a potential membrane association and nuclease resistance of SARS-CoV-2 RNAs, we modified a protocol described for analysis of SARS-CoV replication/transcription complexes in cell cultureFurther information on research design is available in the Nature Research Reporting Summary linked to this article.Supplementary InformationPeer Review FileDescription of Additional Supplementary InformationSupplementary Data 1Reporting summary