Human coronaviruses, which can cause a range of infectious diseases, have been studied for nearly 60 years. The field has gained renewed interest from researchers around the world due to the COVID-19 outbreak in late 2019. Despite a large amount of research, little is known about the knowledge structure and developing trends of this topic. Here, we apply bibliometric analysis along with visualization tools to analyze 15,207 publications related to human coronavirus from the Scopus database, using indicators on publication and citation, journal, country or territory, affiliation and international cooperation, author, and keyword co-occurrence cluster. The results show that research on human coronavirus is dominated by SARS-CoV. Although there have been many publications, only 626 publications  have more than 100 citations. The top 20 journals with most publications account for 20.6% of total publications and 41% of total citations. In addition to the United States and some European countries, many Asian and African countries are involved in this research, with China holding an important position in this area. Leading researchers from various fields of human coronavirus research are listed to facilitate collaboration and promote effective disease prevention and control. The keywords co-occurrence analysis reveals that the research focus on virology, public health, drugs and other hotspot fields, and uncovers changes in the direction of coronavirus research. The research map on human coronavirus obtained by our analysis are expected to help researchers to efficiently and effectively explore COVID-19. Nidovirales order, the Coronaviridae family and the Coronavirinae subfamily , but also a range of mammalian species , including humans  had not been considered as a highly pathogenic virus to humans until the Severe Acute Respiratory Syndrome (SARS) outbreak in 2002 , which provides a comprehensive collection of different types of scientific peer-reviewed literature , “human coronavirus OC43” (“HCoV-OC43”), “human coronavirus NL63” (“HCoV-NL63”), and “human coronavirus HKU1” (“HCoV-HKU1”) to search for publications related to these four currently known non-severely pathogenic human coronavirus; for SARS related publications, we have “severe acute respiratory syndrome-related coronavirus” (“SARSr-CoV” or “SARS-CoV”) and “severe acute respiratory syndrome” (“SARS”); for MERS related publications, we have “Middle East respiratory syndrome-related coronavirus” (“MERS-CoV”) and “Middle East respiratory syndrome” (“MERS”) as well as “MERS coronavirus Erasmus Medical Center/2012” , “novel coronavirus-2012” (“2012-nCoV”) and “camel flu”; as for the COVID-19 related publications, we have “severe acute respiratory syndrome coronavirus-2” (“SARS-CoV-2”), “coronavirus disease-2019” (“COVID-19”), “2019-novel coronavirus” (“2019-nCoV”), “2019-nCoV acute respiratory disease,” “novel coronavirus pneumonia” and “novel coronavirus-infected pneumonia.” Several writing formats that are not generally accepted, like “corona virus” and “HCoV229E,” were also taken into consideration after we had found them been used in some publications.Notably, when directly using the abbreviation “SARS” and “MERS” to search, we were provided with a massive number of unrelated results in other fields. We decided to add qualifiers as limitation to these terms to have the accuracy of search optimized .*CoV” OR *virus OR crisis OR outbreak OR epidemic OR pandemic) OR “SARSr-CoV” OR “middle east respiratory syndrome” OR ({MERS} AND “*CoV” OR *virus OR crisis OR outbreak OR epidemic OR pandemic) OR “camel flu” OR “EMC/2012” OR “coronavirus disease 2019” OR “COVID-19” OR (coronavirus OR “corona virus” OR “*CoV” AND 229E OR OC43 OR NL63 OR HKU1) OR HCoV229E OR HCoVOC43 OR HCoVNL63 OR HCoVHKU1)  .All search results, data and information that were essential for our analysis, including the count of citations and some indicators, were extracted from Scopus on February 15, 2020. Complete document lists were exported as CSV files, which were then imported into Microsoft Excel 2016 for ranking and counting. Bar charts were made by GraphPad Prism 8, and VOSviewer 1.6.12 was used to generate the visualization maps.In the analysis of country (or territory), affiliation and author, every co-author was counted equally. In this case, documents with too many authors are not comparable to documents with fewer authors. Since only documents with a maximum of 15 authors were counted by Scopus, we followed the same strategy when conducting the visualization analysis with VOSviewer.Our search yielded 15,979 results, based on their titles, abstracts and key words. Then we limited our results to publications in English and Chinese, which yielded 15,207 documents in total, including 9,182 articles, 2,170 reviews and 3,855 documents of other types .As expected, the annual publications count was naturally divided into four sections, due to three notable epidemic events in history. Before the outbreak of SARS, the annual publication amount remained low, reaching a maximum of 29 in 1998. It was not until the SARS crisis occurred in 2002 that the scientific publications in this field experienced explosive growth. The annual publication number rose suddenly to 1,729 in 2003 and continued to rise the following year, when it reached 1,754 publications. For the next few years, the publication count declined gradually, reaching a low of 488 publications in 2011. It was the first outbreak of MERS in the Middle East that led to renewed interest in coronavirus and an increase in publications. This lasted for 4 years, during which time the annual number reached another peak, 838 publications in 2015, coinciding with the second outbreak of MERS in South Korea. Notably, the annual publication number has continued to decline since then. For the publications on human coronavirus in 2020, only publications released prior to February 15 were counted. Interestingly, the trend for summed citation of annual publications closely aligned with annual publication production prior to 2013 .For our citation analysis, we counted the number of publications with different citation amounts. We found that more than half of the publications, up to 9,383 , had been cited no more than 10 times, including 3,336 that had gone uncited . Only 4.1% of publications  had more than 100 citations, and only 206 publications (1.4%) and 46 publications (0.3%) had been cited more than 200 and 500 times, respectively .The top 20 most cited articles and non-articles are listed separately in All English and Chinese documents came from 3,443 different journals. The 21 journals with the most publications on human coronavirus are listed in The United States has dominated this field with 143,960 citations on its 4,225 published documents. China ranked second with a total of 49,316 citations for its 2,720 documents from researchers in mainland China, 48,828 total citations on 1,411 documents from researchers in China Hong kong and 13,408 total citations on 646 documents from researchers in China Taiwan. The United Kingdom and Canada have also contributed significantly to this research area . The UniIt is worth noting that China has the top position in terms of our affiliation analysis, with the University of Hong kong publishing 703 documents, Chinese University of Hong kong contributing 499 documents, Chinese Academy of Sciences producing 407 documents, Prince of Wales Hospital Hong kong publishing 304 documents, etc. .In A co-occurrence relationship is formed between two keywords when they both occurred in the same paper. Keywords with strong co-occurrence relationship can reveal research hotspots more precisely than a single keyword. In our visual representation, strongly connected keywords were colored the same, indicating that they might share something in common. Keywords shown in red are roughly connected to public health, preventive medicine and epidemiology. According to these keywords, human coronavirus diseases like “SARS,” “MERS” and COVID-19 may have something worthwhile for comparison with other “infectious diseases” like “influenza” in their epidemiological characteristics; “healthcare workers,” “transmission,” “surveillance,” “quarantine,” or “isolation” may be the focuses of these studies, which can help to promote current disease control and prevention measures. Keywords in blue and yellow are related to virus detection and clinical diagnosis. Among them, “serology” and “RT-PCR” are likely to be the methods, the research objects are mainly some kinds of “respiratory viruses,” and the research purposes are mostly on “evolution,” “phylogeny,” and “diagnosis.” The words in green are mainly virology-related and also include some immunological and pharmaceutical research: “spike protein,” “nucleocapsid protein,” “receptor-binding domain,” “ACE-2” and “apoptosis” are mainly on “pathogenesis,” while “epitope,” “antibody,” “vaccine,” “inhibitor,” “interferon,” “ribavirin,” and “antiviral activity” are mainly about antiviral solutions . With keWith the outbreaks of SARS in 2002 and MERS in 2012, along with current COVID-19 pandemic  of the SARS-CoV-2 was estimated to be 2.2 approximately (Li et al., A large amount of meaningful information can be obtained from keyword co-occurrence analysis, which could enable the identification of hotspots and trends, and guide researchers to related topics in their field (Romero and Portillo-Salido, Although the keyword co-occurrence analysis shows the hotspots and trends within human coronavirus research, it also indicates that some fields remain unexplored or underexplored. For example, the relationship between human coronavirus and immune metabolism, the application of RNA-seq and single cell sequencing technology in coronavirus research, and the possibility of cocktail therapy in viral treatment have not been studied extensively. Due to the short duration of the recent COVID-19 outbreak, studies on SARS-CoV-2 were insufficient in various fields. Keyword co-occurrence analysis could help researchers studying SARS-CoV-2 by providing abundant potential directions in similar fields with other human coronaviruses and revealing the cross-disciplinary exploration potential.Today we are experiencing an information data explosion. Because researchers have been studying coronavirus for 60 years, there is a massive and complicated array of data. Thus, being able to benefit from such an unprecedented amount of data without being overwhelmed poses a significant obstacle for researchers, especially when facing the emergency of a novel infectious disease outbreak, such as COVID-19. A bibliometric analysis combined with data visualization is critical for exploring and communicating information effectively, and helping researchers to continue to progress Wong, . For thiDatasets generated for this study will be made available by the authors, to any qualified researcher upon request.TW designed the study. ZD and JC performed the search. TW, ZD, and JC analyzed the data and wrote the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


More than a hundred chemical modifications in coding and non-coding RNAs have been identified so far. Many of the RNA modifications are dynamic and reversible, playing critical roles in gene regulation at the posttranscriptional level. The abundance and functions of RNA modifications are controlled mainly by the modification regulatory proteins: writers, erasers and readers. Modified RNA bases and their regulators form intricate networks which are associated with a vast array of diverse biological functions. RNA modifications are not only essential for maintaining the stability and structural integrity of the RNA molecules themselves, they are also associated with the functional outcomes and phenotypic attributes of cells. In addition to their normal biological roles, many of the RNA modifications also play important roles in various diseases particularly in cancer as evidenced that the modified RNA transcripts and their regulatory proteins are aberrantly expressed in many cancer types. This review will first summarize the most commonly reported RNA modifications and their regulations, followed by discussing recent studies on the roles of RNA modifications in cancer, cancer stemness as wells as functional RNA modification machinery as potential cancer therapeutic targets. It is concluded that, while advanced technologies have uncovered the contributions of many of RNA modifications in cancer, the underlying mechanisms are still poorly understood. Moreover, whether and how environmental pollutants, important cancer etiological factors, trigger abnormal RNA modifications and their roles in environmental carcinogenesis remain largely unknown. Further studies are needed to elucidate the mechanism of how RNA modifications promote cell malignant transformation and generation of cancer stem cells, which will lead to the development of new strategies for cancer prevention and treatment. The canonical concept of RNA structure made merely of A, C, G and U nucleotides has proven overly simple with the discovery of modifications in these basic building blocks. In recent years extensive research in RNA biology has revealed diverse modifications of RNA post-transcriptionally. The history of studying RNA modification is as old as six decades. The first RNA modification was discovered in 1956 by a group of scientists in the University of California School of Medicine, Barkeley; where they isolated, purified and characterized the modified RNA in a NaCl soluble form Precursor mRNA (pre-mRNA) undergoes a series of intricate processing of capping, splicing, polyadenylation to become mature mRNA and transport to cytoplasm in order for a gene to be translated into protein. This complex array of processes could be affected by a number of RNA binding proteins (RBPs). The binding specificity of RNA to RBPs is determined by the post-transcriptional modifications of the RNAs which are accomplished by addition or removal of certain chemical groups to the pertaining nucleotides Recent advances in the RNA mapping technology has revealed the detailed map of several RNA modifications with the help of specific antibody coupled immunoprecipitation technique or chemical modifications followed by next generation sequencing As a key player in the cellular processes, RNA plays critical roles in carrying the genetic information, catalyzes various biochemical reactions, carries vital information for protein synthesis by acting as an adapter and making the necessary scaffold for attaching the amino acids in proper sequential orders. The eukaryotic RNA contains different kinds of modifications as documented by several literatures. RNA modifications occur in all four nucleotide building blocks of RNA: A, C, G and U Figure . By far 6A RNA methylation is the most abundant mRNA modification in eukaryotic cells , which plays crucial roles in RNA fate and metabolism 6A is present in high abundance in mRNA although it can also be found in noncoding RNAs such as tRNAs, rRNAs 6A sites 6A methylation in mRNA, the whole field of RNA research was focused mainly on tRNAs, rRNAs, and snRNAs which are highly expressed and catalytically active types of RNAs. The underlying reason for this lacking is the technical difficulties in mapping the m6A modifications in mRNA transcripts, the expression level of which is very low  The m6A modification is that, the m6A modification is confined to the consensus motif RRACH; where, R represents G or A, A is m6A and H represents A, C or U. This consensus sequence may occur every 85 nucleotides, thus on an average each mRNA may contain approximately 30 of such sequence sites. Since only 3-5 m6A sites are assumed to be present per mRNA, adenosine in most of the consensus sequences probably are not modified or may be modified in special circumstances. m6A modifications are not equally distributed throughout the entire transcriptome, instead they are preferentially enriched in the sites of consensus motifs which are located mostly near the stop codons at 3' UTRs and long internal exons. The m6A modifications are conserved throughout the entire human and mouse embryonic stem cell transcriptome 6A through immunoprecipitation of m6A enriched mRNA with m6A-specific antibody followed by sequencing has enabled us to identify the more abundant m6A regions on the gene. In 2012 two research groups independently described the methods to determine the genome-wide distribution of m6A methylated mRNA 6A-rich fragments using m6A targeted antibody followed by a high-throughput sequencing (MeRIP-seq). The mRNA fragments thus isolated were then reversely transcribed and the resulting cDNAs were amplified by PCR and sequenced to determine the extent of mRNA containing the methylation. This method which detects the m6A in a ~100-nucleotide resolution, reveals that m6A RNA modification is distributed mainly in the coding and the 3' untranslated region (3'-UTR) . Based on the mutational studies and methyltransferase substrate preferences, the m6A contents in the mammalian cells were found to be an estimate of 0.1-0.4% of the total adenine present One of the most important features of the m6A has been widely used to quantify the overall transcriptome wide distribution of m6A. To determine the presence of m6A in the individual nucleotide resolution level, newer methods have been developed with higher resolution. One such method is called SCARLET (site-specific cleavage and radioactive-labeling followed by ligation-assisted extraction and thin-layer chromatography) which can site-specifically quantify the m6A in individual nucleotide resolution level 6A in the individual-resolution level. Using the antibody-specific recognition technique Linder et al. developed a method called 'm6A individual-nucleotide-resolution cross-linking and immunoprecipitation (miCLIP)'. The basis of specificity of this method is UV-induced crosslinking of the antibody and RNA which upon reverse transcription results in mutated or truncated cDNA at the site of crosslinking. Thus the position of the modification can be detected from the presence of antibody-bound nucleotide 6A in individual level include the use of m6A-sensitive reverse transcriptase 6A. Using the m6A sensitive RNA endoribonuclease, MazF and ChpBK, they developed a new method called m6A-sensitive RNA-endoribonuclease-facilitated sequencing or m6AREF-seq. This newly developed method facilitated to quantify the methylation level with single-base resolution in order to identify the m6A sites at specific motifs 6A detection method is the 'deamination adjacent to RNA modification targets followed by sequencing (DART-seq)'. This method takes advantage of the cytidine deaminase APOBEC1, which is in the CRISPR-Cas9 genome editing technique that causes alterations of cytosine to uracil (C to U) at the target sites in the single-stranded DNA. In this technique APOBEC1 is fused with the m6A-binding YTH domain of YTHDF2 protein which is then subjected to the RNA sample. APOBEC1 deaminates the cytosine present immediately following the m6A residue in the consensus sequence which is detected by reverse transcription followed by high-throughput sequencing 6A mapping techniques have shown that these m6A modified regions are versatile in all over the transcriptome with widespread biological roles in the living systems.The antibody-based identification method of m5C was first detected in mRNA almost at the same time with m6A in 1970s although in much less abundance 5C could be quantitatively determined with bisulfite sequencing technology. Taking advantage of this technique, transcriptome-wide m5C modification map could also be determined in RNAs. m5C modification was found more abundantly in tRNA among all other RNAs where the modification most commonly occurs in the variable region and the anticodon loop. While in rRNA, m5C could be found in different ribosomal subunits which is catalyzed by the SAM-dependent methyltransferase enzymes 5C modification has also been identified with mammalian mRNA and non-coding RNAs. Over ten thousands m5C modification sites have been identified in RNA isolated from HeLa cells with bisulfite conversion followed by high throughput sequencing. The principle behind this method is the chemical conversion of unmodified cytosine residues with the treatment of sodium bisulfite to uracil and identifying the intact cytosine as m5C candidate. The modified cytosine is identified as C by reverse transcription while the unmodified cytosine is identified by presence of T in the resulting cDNA 5C from other cytosine modifications that are resistant to bisulfite treatment and is prone to include false positive results. Third, bisulfite treatment causes significant RNA degradation and therefore unable to pre-enrich m5C RNA fragments for sequencing 5C modification in both eukaryotic, prokaryotic and archaeal RNA. This method was validated by direct immunoprecipitation of m5C RNA fragments followed by cDNA library preparation and massive parallel sequencing. This method not only identified novel m5C sites within the rRNA and tRNA along with the existing sites but also identified a consensus motif AUC(m5C)GANGU for m5C modification in archaeal mRNA The 5-cytosine methylation is a widespread DNA modification in eukaryotes but is also reported to be present in different types of RNAs, mostly in noncoding RNAs- tRNAs and rRNAs. It was first discovered in DNA in early 1950s and was later identified in RNA. m5C positions in RNA, a mechanism-based approach named 5-azacytidine mediated RNA immunoprecipitation (Aza-IP) has been developed. This method exploits the catalytic activity of the m5C methyltransferases to link with its RNA substrate and subsequent methyl transfer from S-Adenosyl Methionine (SAM) , miRNA, lncRNA etc.To accurately determine the m) Figure a. This ms Figure b 10. In s Figure , particu1A, another frequently occurring RNA modification was first discovered in 1961 and was isolated from the RNA sample in the following year by the same research group 1A which diminishes the Watson-Crick base pairing of A to T or U because of the steric hindrance by the methyl group. This hindrance of base pairing in the m1A-T/U interface may result in a misincorporation of the base by unstable Hoogsteen base pairing with other nucleotides or truncation of the nucleotide chain. N1-methylation of A produces a positive charge on the m1A molecule which can dramatically alter the RNA structure or alter the RNA-protein interaction 1A has been identified in rRNA, tRNA and most recently in mRNA 1A is versatile in almost all organisms from prokaryotes like bacteria, archaea to humans. This modification is most commonly found in the tRNAs at positions 9 in metazoan mitochondrial tRNAs, position 14, and 58 in metazoan tRNA T-loop 1A was identified in position 9 and 58 of the tRNAs in cytoplasm and mitochondria whereas at position 1322 on 28S subunit of rRNA The m1A RNA modification in mammalian mRNA so far has seen very slow progress. The main reason behind this is, the abundance of mRNA is much less compared to the other types of RNA and the m1A modification in mRNA is even lower than that of tRNA  1A methylation in the transcriptome with adequate sensitivity and reproducibility. Similar to the detection of m6A in mammalian mRNA, Dominissini et al. adopted an m1A-specific antibody binding approach where they separated the antibody bound m1A-enriched mRNA fragments and carried out reverse transcription. They characterized the m1A-carrying mRNA by the nature of the modification to introduce mismatched nucleotide and truncation and identified the mismatch rate created by m1A using Dimroth rearrangement (m1A converted to m6A) 1A-specific antibody for detecting m1A in mRNA has been adopted by Li et al., but they used the demethylase enzyme ALKBH3 to convert m1A to A instead of Dimroth rearrangement. Exploiting the tendency of m1A to produce truncated RT products, they were able to calculate the truncation positions 1A -ID-Seq' was introduced which also relies on m1A immunoprecipitation and the ability of the m1A to stall the RT reaction 1A was found to be highly enriched in the 5'UTR regions and close to the start codons . This method made use of m1A -induced misincorporation in cDNA to identify mutational signature combined with pre-enrichment of m1A and in vitro demethylation. These all together ensured mapping m1A in human transcriptome with a higher level of sensitivity and confidence 1A detection follows the basic m1A containing mRNA enrichment step with m1A immunoprecipitation and Dimroth rearrangement followed by TGIRT mediated reverse transcription Research on ms Figure . To idenXenopus laevis) The Adenosine to Inosine (A-to-I) modification was first discovered in developing Xenopus embryo, the South African clawed toad (1-cyanoethylinosine (ce1I) which prevents I to base pair with C. This avoids false positive G peaks in the cDNA chromatogram The adenosine usually base-pairs with uridine, but modification to inosine confers it structural similarity to guanosine (G). The inosine produced by the A-to-I modification is misread as G and thus base pairs with cytosine (C). The enzymes that recognize G, also read I as G and the protein thus translated changes the sequence read of the modified RNA. This mismatched base pairing also brings about structural conformational changes of the edited RNA, creating additional bulge or removing the existing ones from the dsRNA. This is because the modification can increase or decrease the base pairing through misreading of I as G 0 rotation of the base gives rise to its isomeric form, Ψ. Thus, in Ψ structure a C-C bond appears in place of typical N-C bond between the ribose sugar and the base as seen with other nucleosides. This conformational change creates an extra H-bond donor on N1 position of the Ψ molecule. This additional N1-H forms H-bond with a water molecule and contributes to base stacking due to more bonding interaction with other molecules. This unique feature provides the molecule with enhanced conformational rigidity and thermodynamic stability Pseudouridine (Ψ) is the first discovered nucleoside present in RNA and is the most abundant RNA modification, the ratio of Ψ/U being 7-9% in the entire transcriptome. Structurally it is the 5-ribosyl isomer of the uridine base of RNA which is often termed as “the fifth nucleoside” Ψ is most abundantly present in noncoding RNAs such as rRNAs, tRNAs, snRNAs and small nucleonar RNAs (snoRNAs). However, recent studies revealed the presence of Ψ in lncRNA and mRNA transcripts as well 5C and m1A Since Ψ is the most abundant RNA modificantion which has been identified in almost all forms of RNAs, the functional consequences of this modification could be better understood with accurate and reliable transcriptome-wide mapping. Until now the most widely used method for detection of Ψ modification relies on its ability to form a stable complex with 1-cyclohexyl-(2-morpholinoethyl) carbodiimide metho-p-toluene sulfonate (CMCT). CMCT reacts with specific N atoms on the G and U where it forms carbodiimide (CMC) adduct with the N1 on G, N3 on U and both N1 and N3 on Ψ. After the alkaline treatment at a pH ~10.4, all the CMC adduct is hydrolyzed except N3 on Ψ. The bulky CMC group attached to N3 on Ψ hinders the reverse transcription and thus results in a cDNA truncation. This facilitates the detection of Ψ in a single nucleotide resolution level 6A), 5‑methylcytosine (m5C), N1‑methyladenosine (m1A), Adenosine to Inosine (A to I) and pseudouridine (Ψ) modifications that are sparse and versatile in the entire transcriptome, there are hundreds of other RNA modifications and more are likely to be identified. The modifications exhibit either simple addition of a methyl or acetyl group or rearrangement of the molecule giving rise to a modified isomer. However, more complex modifications involving multiple chemical modifications in the same molecule also exist.Besides N6‑methyladenosine  which they named m6Am. The additional methylation occurred at the N6 position of Am7G cap. The nucleotides next to the m7G of the transcribed mRNA are usually methylated on the ribose sugar at the 2'-OH position. The Am present at that position may undergo an additional methylation at N6 to form m6Am. m6Am is thus located mainly at the beginning of the transcribed nucleotide positioned in mRNA which is in vicinity of m7G. While m6A is identified as the most common internal modification of mRNA, m6Am is discovered as the second nucleotide from beginning of the 5' UTR of mRNA (m7GpppNm). The abundance of m6Am is very low in the 5' UTR of mRNA, the ratio of m6Am to m6A ranges between 1:10 to 1:15 Shortly after the discovery of m6A, it is very difficult to chemically identify m6Am. MeRIP-Seq approach for transcriptome wide mapping of m6A does not work for identification of m6Am since this approach uses m6A-specific antibody which binds with both m6A and m6Am. To identify m6Am with high resolution, a miCLIP method has been introduced which is the modified version of individual-nucleotide resolution cross-linking and immunoprecipitation (iCLIP) approach. Similar to iCLIP, this method uses UV crosslinking of anti-m6A antibodies to RNA and a miCLIP library is prepared which is then used for subsequent steps to map m6A and m6Am residues in a single nucleotide resolution level. Without miCLIP the peak signal within the 5′ UTR would refer to m6A instead of m6Am which is found only in the transcription start site. In miCLIP technique the m6Am present in 5′ end of the RNA is protected through library preparation which is not ensured in other techniques. By this way the ambiguity for misannotation of m6A for m6Am is eliminated Being very similar in chemical structure with m7G is present in the cap structure of almost all eukaryotic mRNAs which is installed during the transcription initiation and is involved in regulating mRNA stabilization, transport and splicing. This is also one of the internal modifications of non-coding RNAs such as tRNAs, rRNAs, miRNAs etc. occurring most commonly in position 46 of the tRNAs in both yeast and human and position 1639 of the 18S subunit of the human rRNA 6A, m5C) m7G-MeRIP technique has been used taking advantage of the m7G-specific antibody for transcriptome-wide mapping of m7G 7G with higher resolution, a chemical modification based approach called tRNA reduction and cleavage sequencing (TRAC-Seq) has been developed recently m6A and m5C in the RNA transcriptome globally, however; is associated with major drawbacks and is not immune to error. One of the major disadvantage of this technique is the requirement of large amount of RNA input. Another drawback for this method is the non-specific binding to the modified nucleotides that possess structural similarity, such as misrecognition of m6A for m6Am by m6A specific antibody. The overall process is costly and time consuming, involving a lot of efforts for library preparation prior to sequencing With the advent of novel detection techniques, the overall transcriptomic mapping of these modifications is slowly being revealed. One of the techniques that brought about unprecedented insight in posttranscriptional RNA modification and revolutionized our understanding in this field is the antibody-based detection method. This method, although emerged as an efficient technique in recent years for detecting mRNA modification is a dynamic process, which at the beginning was thought to be irreversible. With the discovery of the RNA modifying proteins  it has become evident that, this process is reversible and well regulated. Although there are more than hundred different RNA modifications that has been discovered until now, the regulatory proteins for all of the modifications have not been identified yet. For some of the modifications, only the proteins that are installing the modifications have been identified Table . In this6A modifications in gene transcription was a major breakthrough in molecular biology. The posttranscriptional installment of m6A is accomplished by a multicomponent methyltransferase complex which are commonly termed as m6A 'writers'. In mammalian cells this complex contains METTL3 , METTL14, and WTAP Discovery of the involvement of m6A methylation which further by proteomic profiling and co-immunoprecipitation was found to be associated with METTL3. Both of the enzymes are capable of catalyzing the transfer of a methyl group from a co-factor called S-adenosyl methionine (SAM) to the GGACU and GGAUU sequences located in a single stranded or a stem-loop form of RNA 6A installation which binds to Wilms' tumor protein 1, hence the name WTAP. Initially it was considered as a splicing factor regulating RNA splicing to control cell cycle progression and embryonic development. The involvement of WTAP in RNA methylation was discovered by gel filtration experiment where it was found to form a large molecular weight complex with METTL3-METTL14. With photoactivable ribonucleoside-enhanced crosslinking (PARCLIP) assay it was found that METTL3, METTL14 and WTAP share the same RNA binding motif by which all of them recognize the same m6A consensus sequence on RNA. Thus, in the mammalian cells, METTL3 and METTL14 complex perform the methylation on m6A whereas WTAP join the complex to affect the m6A deposition inside the cell 6A deposition, which established the notion that the m6A modification is dynamic and reversible in nature which further associated its link to human health 6A level in mRNA. RNA-binding motif protein 15 (RBM15) and its paralogue RBM15B was also reported to act as a mediator of m6A methylation which recruits the methylation complex in specific modification sites in RNA 6A methyltransferase associated , HAKAI and Zinc finger CCCH domain-containing protein 13 (ZC3H13) have been discovered. With RNA co-immunoprecipitation followed by mass spectrometry, these proteins were discovered as the new components of the complex interacting with METTL3-METTL14-WTAP core catalytic complex. In this large multi-component complex, VIRMA is assumed to serve as a scaffold to hold the WTAP, HAKAI, ZC3H13 together, which combinedly create a pocket for accommodating the METTL3 and METTL14 catalytic subunits  as the methyltransferase that generates m6Am in mRNA. In this study they showed that, the methylation of adenosine is dependent on the presence of m7G in the adjacent position. This methyltransferase is incapable of methylating other adenosine outside the context of m7G presence in the 5'cap. The discovery of PCIF1 as the methyltransferase for m6Am also facilitated the identification of alternative transcription start site (TSS) on mRNA since m6Am is only located in this region in vitro and in vivo models discovered PCIF1 as the only methyl transferase for generating m6AmThe methytransferases that bring about methylation in A5C modification in E. coli rRNA is catalyzed by SAM-dependent methyltransferase enzyme, RsmB and RsmF 5C modification. DNMT2 was identified as a specific methylation agent for cytosine at the 38 position on the tRNA anticodon loop 5C modification; whereas in humans, several other related proteins  are found to mediate the m5C modification 5C at the 911 position of the human rRNA 12S. NSUN3 on the other hand is responsible for methylation of C34 in the mitochondrial tRNA wobble position 5C modification was found to be catalyzed mainly by NSUN2 and is localized close to the translation initiation sites m1A is most commonly found in tRNA and conserved throughout the process of evolution. Since m1A modification is more abundant at position 58 of tRNA, m1A methyltransferase at this position is most extensively studied. The methyl group present on the m1A in tRNA is introduced by tRNA methyltransferase (MTase) using the S-adenosylmethionine (SAM) as the donor of the methyl group. Although the presence of the methyltransferase enzyme was first detected in 1962, the first purified MTase was isolated in 1976 from rat liver; the molecular weight of which has been determined as 95 kDa TRMT6 and TRMT61TRMT6 and TRMT61 form a homoteramer complex, α2β2 of the two subunits where Trmt61 acts as the catalytic subunit to transfer the methyl group from SAM to A. Although Trmt61 acts as the catalyst for methyl transfer, both Trmt6 and Trmt61 subunits are essential for binding to RNA. In human, Trmt61A and Trmt6 subunits are orthologues of Trmt61 and Trmt6 subunits in S. cerevisiae. Human Trmt61 has two isoforms: Trmt61A and Trmt61B. Trmt61A is cytoplasmic tRNA specific whereas the Trmt61B is mitochondrial tRNA specific 1A modification in human mitochondrial tRNA is at position 9 (m1A9). The enzyme for m1A9 modification is yet to be identified. Vilardo et al. reported the possibility of involvement of tRNA methyltransferase 10 C (TRMT10C) in m1A9 modification. TRMT10C is the human homolog of S. cerevisiae Trm10p. TRMT10C is a methyltransferase for m1G9 in human, however its role in m1A9 modification is yet to be confirmed Another widely occurring RNA modification m2CR) are the most common targets for ADAR1 and ADAR2 where several specific editing sites are targeted by these two enzymes in these receptors The family of proteins that catalyzes the deamination of adenine in A-to-I editing is broadly named as adenosine deaminases acting on RNA or ADARs. ADARs catalyze the A-to-I conversion by hydrolytic removal of amide group from the 6-position of adenosine. In all organisms ADARs share common structural features: double stranded RNA binding motifs (dsRBMs), the number of which varies from species to species and a C-terminal catalytic domain which is highly conserved in all species The most abundant RNA modification psedouridne is introduced in RNA by the enzyme family named pseudouridine synthases or PUS enzymes. Six pseudouridine synthase families are known so far: TruA, TruB, TruD, RsuA, RIuA, and Pus10b. TruA, TruB, TruD, RsuA, RIuA are present in almost all organisms and extensively studied in bacteria whereas Pus10b is present only in humans and other eukaryotes. Although there is very little sequence similarity among them, all of the enzymes share the same structural features in their catalytic domains and hence a common mode of catalytic activity. Their substrate specificity is determined by differences in their N and C terminal domains 7G is carried out by the heterodimeric enzyme complex METTL1-WDR4 in mammals which is the yeast homolog of Trm8 and Trm82 enzyme complex 7G modification in the tRNA and rRNA is carried out by the enzyme, WBSCR22 The common eukaryotic mRNA cap modification m6A by oxidative demethylation to revert it to adenosine in polyadenylated mRNAs and lncRNAs. As a member of the demethylating enzyme AlkB subfamily of Fe2+/α-ketoglutarate dependent dioxygenases, FTO catalyzes the oxidative demethylation of substrates from biological origin. The demethylating ALKB enzyme family recognizes the methylation on adenine and cytosine nucleotide bases in both single stranded DNA and RNA and to a smaller extent in the double stranded DNA which block DNA replication 6A to two different intermediates: N6-hydroxymethyladenosine (hm6A) and N6-formyladenosine (f6A) by stepwise oxidative demethylation. This function of FTO is similar to that of ten eleven translocation (TET) enzyme which demethylates 5-methylcytosine (m5C) to 5-hydroxymethylcytosine (hm5C) and 5-formylcytosine (f5C) further to 5-carboxylcytosine (ca5C) in genomic DNA. This stepwise process is very important in active demethylation of DNA but the importance of similar demethylation in RNA is yet to be revealed. Like their methylating counter parts METTL3 and METTL14, FTO also co-localizes with them in the nuclear speckles which emphasizes the dynamicity and reversibility of the m6A modification process in mRNA 6A demethylase in mammalian cells shortly after FTO. The human AlkB enzyme family has eight members which are designated as ALKBH1-ALKBH8. ALKBH5 and FTO share the similar substrate specificity (i.e. both target ssRNA) and demethylase activity on m6A, but unlike FTO, ALKBH5 demethylase directly converts m6A to adenosine without any intermediate. ALKBH5 also co-localizes in the nuclear speckles and controls the transport of mRNA between cytoplasm and nucleus, at the same time affecting the RNA metabolism In 2008 the first demethylating enzyme fat mass and obesity-associated (FTO) protein was discovered which brought new dimension in the RNA methylation study in mammals. This enzyme specifically removes methyl group from m6Am is demethylated by FTO in the same way as m6A and reverted to Am . With in vivo and in vitro experiments Mauer et al. showed that m6Am is a more preferred substrate for FTO than m6A. They also found that, m6Am transcripts have more stability compared to mRNA transcripts with their non-methylated counterpart Am or any other nucleotide. This enhanced stability is due to their resistance to mRNA decapping enzyme DCP2. This study suggests that m6Am modification is a cap-associated dynamic and reversible mRNA modification m6A, certain ALKBH family proteins also remove methyl group from m1A. In one study it was shown that human AlkB homologue hABH3 can act as RNA demethylase. While another study showed ALKBH1can catalyze the demethylation of m1A. ALKBH1 demethylase can revert the methylation of m1A at 58 position on tRNA. The presence of a tRNA-binding motif has been identified on ALKBH1 which preferentially binds to the m1A on the tRNA stem loop structure 1A is also reported to be reversed by another member of DNA/RNA demethylase family ALKBH3 5C, A-to-I and Ψ; no eraser is identified yet.Similar to m6A and bind to the methylated RNA to exhibit the functional outcomes are known as m6A readers. These proteins were identified for the first time using the antibodies targeting the methylated RNA and co-immunoprecipitation followed by mass spectrometry analysis 6A in the nuclear region. The YTH domain contains a hydrophobic binding pocket for methyl group in the m6A at the recognition motif GG(m6A)C. These reader proteins have diverse profile of binding specificity for the cellular target mRNA which can be recognized by profiling their binding sites. YTHDF2 selectively binds to the target mRNA at G(m6A)C motif and mediates the mRNA decay. YTHDF1 and 3 coordinate each other in processing the m6A bearing mRNA for translation. However, the YTHDC2 was found to enhance the translational efficiency of m6A containing targets and decrease their abundance 6A readers in a recent study. One such protein hnRNPC was found to bind to an m6A altered stem-loop RNA structure which subsequently directs an alternative splicing of RNA. In absence of m6A the hnRNPC cannot bind with the RNA which affects its pattern of splicing 6A in mRNAs as well as miRNAs 6A at 5' UTR of the mRNA is recognized as another reader protein playing vital role in protein translation under the stress condition 6A reader protein family. Three members of this family, IGF2BP 1-3 have been identified which can recognize the m6A consensus sequence in mRNA The ultimate outcome of the game of dynamic and reversible phenomenon 'methylation-demethylation' is exhibited by a third family of proteins called 'readers'. The family of proteins which specifically recognize m1A is similar to the readers of m6A. The m6A readers YTH domain containing family proteins YTHDF1-3 and YTHDC1-2 also act as m1A readers. But unlike the other members of the family, YTHDC2 cannot directly interact with the m1A 5C, therefore was the only known m5C reader protein which is localized mainly in the nucleus 5C reader, Y-box binding protein 1 (YBX1) has been discovered in an attempt to find a potential cytoplasmic m5C reader The readers of mIn the entire cellular transcriptome, RNA modification is versatile and observed in almost all types of cellular RNAs. In normal cellular processes, the extent of RNA modification is well regulated maintaining the homeostasis of RNA stability and decay. However, in cancer there is abnormal up/down regulation of modified RNA transcripts and their regulatory proteins that are correlated with the process of carcinogenesis Table .6A RNA modification as an epigenetic regulator in cancer is emerging now. RNA methylation at N6-methyladenosine (m6A) plays a central role in development of cancer which has been evidenced by several recent studies 6A modification. In normal hematopoietic stem/progenitor cells (HSPCs), METTL14 is reported to be highly expressed while it is downregulated during the differentiation to myeloid cells. In acute myeloid leukemia (AML), METTL14 is also found to be highly expressed, silencing of which led to inhibition of cellular proliferation and reduced survival of AML cells. METTL14 knockdown also reduced the self-renewal capacity of leukemia stem cells. The oncogenic effect of METTL14 was due to an increase in the m6A level and activation of SPI1-METTL14-MYB/MYC signaling pathway 6A writer METTL3 was reported to upregulate in hepatocellular carcinoma (HCC). Knockdown of METTL3 suppressed tumorigenicity and metastasis whereas overexpression promoted the HCC growth. The tumorigenic effect of METTL3 was due to METTL3-mediated m6A modification and suppression of SOCS2 mRNA expression. High expression of METTL3 is identified in patients with HCC which is correlated with tumorigenesis, vascular invasion and poor prognosis 6A in p53 pre-mRNA transcript was observed in the p53 mutant colon cancer cells 6A modification by METTL3 was shown to promote HBXIP expression which in turn rescued the miR let-7g-mediated METTL3 suppression in breast cancer cells. This mutual regulation of METTL3 and HBXIP promotes breast cancer progression 6A methylation in AXL mRNA, thereby increasing translation of the protein 6A methylation and activation of NF-κB signaling molecules  and induces MYC transcriptional activity. METTL3 also modifies AFF4 transcript which binds to the promoter region of MYC mRNA and induces its transcription. This multiple transcriptional activation of MYC by METTL3 induces progression of BCa In normal tissue development, the role of DNA methylation as a vital epigenetic factor is well established; however, association of m6A in mRNA transcript and downregulation of Ankyrin repeat and SOCS box-containing 2 (ASB2) and retinoic acid receptor-α (RARA). During the normal process of hematopoiesis, ASB2 and RARA are upregulated and act as regulators of ATRA-induced cell differentiation. FTO inhibits the expression of ASB2 and RARA thus decreasing the differentiation of leukemic cells in AML 6A level and thus performs a vital oncogenic function by inhibiting ATRA-mediated AML cell differentiation 6A methyltransferase WTAP and demethylase FTO are increased in AML suggesting that, in different subtypes of AML m6A modification is tightly regulated by the mRNA methylation machinery 6A demethylase FTO was also found as a prognostic factor in lung squamous cell carcinoma (LUSC). Decreased m6A modification by FTO is associated with tumorigenesis of LUSC. FTO induces tumor progression by targeting MZF1, reducing the m6A in MZF1 transcript, increasing the stability and thus increased expression of MZF1 mRNA. Knockdown of FTO effectively reduced cell proliferation, invasiveness and increased apoptotic death 6A demethylation of the protumorigenic mRNA transcripts  accelerates the growth of melanoma cells and leads to reduced sensitivity against anti-PD-1 blockade immunotherapy. In absence of FTO, m6A in the target transcripts are recognized by YTHDF2 leading to their decay thereby sensitizing the cells towards interferon gamma (IFNγ) and anti-PD-1 treatment The demethylase FTO was found to enhance the cellular transformation and leukemogenesis and at the same time inhibit all-trans-retinoic acid (ATRA)-induced AML cell differentiation. The possible mechanism behind it is that, FTO caused demethylation of m6A methylation by FTO is also associated with breast cancer cell proliferation and metastasis. FTO-induced demethylation of the tumor suppressor BNIP3 mRNA in its 3'UTR causes degradation of the transcript thus eliminating the tumor suppressive effect of BNIP3 in breast cancer in vitro. An inverse correlation of FTO expression with the expression of CA19-9 and micro-vessel density (MVD) and stability of oncogenic TEAD2 mRNA was also reported Decreased m6A-containing site to promote EGFR and TAZ mRNA translation indicating that, increased m6A level may enhance the translation of proteins which promote lung cancer progression 6A mRNA level both in vivo and in vitro. Also, downregulation of METTL14 in HCC acts as a prognostic marker for recurrence of the disease. In this study, it was confirmed that METTL14 interacts with the microprocessor protein DGCR8 and has a positive modulatory effect on primary miR-126 in an m6A dependent manner. This study provides evidence of the involvement of m6A status in the cellular mRNA to the tumorigenesis in HCC through the regulation of microRNA signaling by METTL14 6A immunoprecipitation followed by high-throughput sequencing confirmed the decrease in the overall level of m6A in ADAM19 mRNA 6A demethylase ALKBH5 overexpression was observed in glioblastoma cells. Knockdown of ALKBH5 inhibited the proliferative capacity of GBCs. Silencing of ALKBH5 also altered the expression level of several ALKBH5 target genes which was evidenced from the integrated transcriptome and m6A immunoprecipitation followed by the sequence analysis of the mRNAs. Gene expression analysis in ALKBH5 knockdown cells have revealed forkhead box M1 (FOXM1) as the target gene for ALKBH5 which is the regulator of downstream genes controlled by ALKBH5. At the same time ALKBH5 knockdown increases m6A level in FOXM1 mRNA which lacks the regulation by HuR thus decreased FOXM1 expression In human lung cancer, METTL3 acts as a translation promoter for mRNAs of epidermal growth factor and Hippo pathway effector TAZ by recruiting the eukaryotic initiation factor 3 (eIF3). In addition to this, mRNA-ribonucleoprotein complex may replace the reader protein YTHDF1 for METTL3 at the m5C modifying enzyme in mammalian cells. Its association with tumorigenesis has been reported in several studies. In one study it has been shown that NSUN2 is over-expressed in tumors  compared to normal cells. In this study NSUN2 was found as a downstream target for MYC which directly regulates cell proliferation 5C methylation and associated decrease in cell proliferation and migration of HEK293 cells. The genes associated with proliferation, migration and cell-cell adhesion were also affected by the NSUN2 knockdown 5C modification is reported in human urothelial carcinoma of bladder (UCB). Increased m5C methylation in several oncogenes is identified in UCB. The methylation is carried out by NSUN2 and is recognized by the m5C reader YBX1 which stabilizes the modified oncogenic mRNAs. Thus, NSUN2 and YBX1 in cooperation drive the tumorigenesis in UCB by hypermethylation of the mRNA targets 5C methyltransferase is associated with the regulation of various developmental processes through regulation of gene expression. Knockdown of TRDMT1 in human HEK293 cells resulted in inhibition of cellular proliferation and migration ability. The inhibitory effect of TRDMT1 knockdown was associated with the alteration of genes controlling cell cycle progression, RNA transport and degradation NSUN2 (Misu) is the most common mSince A-to-I modification is more abundant in human brain than any other organs, the modification itself and the modification machinery are associated with various human neurological disorders including cancer. Grade III and IV astrocytomas, which are the most aggressive types of brain tumors, are associated with decreased level of editing activity by ADAR2 enzyme. The levels of ADAR2 was found significantly decreased in pediatric patients with aggressive astrocytomas, connecting it as a potential diagnostic marker in patients with higher grade astrocytoma or the recurrence of the disease 1A demethylase ALKBH3 which is originally known as prostate cancer antigen-1 (PCA-1) is present in high abundance in prostate cancer. Knockdown of ALKBH3 was found to decrease the proliferation of prostate cancer cell proliferation and tumor growth in vivo. ALKBH3 has also been found to be highly expressed in other cancer types such as pancreatic cancer, renal cell carcinoma and non-small-cell lung carcinoma etc.The specific min vitro studies with prostate cancer cells Mutation in the snoRNA-dependent pseudouridine synthase DKC1 is associated with a heriditary bone marrow failure syndrome Dyskeratosis Congenita (DC) which leads to various cutaneous and noncutaneous abnormalities including premature ageing and cancer 7G in mRNA and internal modification in other non-coding RNAs, is found upregulated in hepatocellular carcinoma (HCC). Overexpression of METTL1 is correlated with poor overall survival in HCC patients. METTL1 overexpression also showed increased cell proliferation and migration in in vitro studies. The oncogenic effect of METTL1 is exhibited via the PTEN/AKT signaling pathway. METTL1 downregulation activates the PTEN signaling with subsequent attenuation of AKT activity. Combined downregulation of METTL1 and upregulation of PTEN improved the overall patient survival. Therefore, METTL1 serves as a potential prognostic marker for HCC 5C and m7G, NSUN2 and METTL1 are both tRNA modifiers which are found concurrently overexpressed in human cancers. Combined knockdown of both NSUN2 and METTL1 in HeLa cells increased their sensitivity to chemotherapeutic drug 5-fluorouracil (5-FU) METTL1, writer of the common 5'-cap modification m6A Writer protein METTL3 plays a vital role in regulating pluripotency of the normal stem cells. Depletion of METTL3 in naïve embryonic stem cells (ESCs) resulted in defective differentiation of embryonic bodies and neuronal stem cell maturation. Thus, lack of METTL3 maintains the pluripotency in naïve ESCs. The opposite effect of METT3 depletion was observed in primed pluripotent stem cells. In this case METT3 depletion diminished self-renewal capability and accelerated the stem cell differentiation 6A methylation catalyzed by METTL3 and METTL14 has been found to be involved in embryonic stem cell formation in mouse 6A RNA modification and its regulators also play crucial roles in maintaining pluripotency in porcine induced pluripotent stem cells (piPSC). piPSCs maintain pluripotent attributes through METTL3 regulated activation of JAK2-STAT3 signaling pathway. METTL3 mediates the m6A modification in both JAK2 and the negative regulator of the JAK2-STAT3 pathway, SOCS3 which causes decreased SOCS3 expression resulting in rescue of the repressive effect of SOCS3 on this pathway. In this case, m6A modified JAK2 mRNA is recognized and stabilized by YTHDF1, whereas the SOCS3 mRNA is degraded by YTHDF2 6A modified JAK2 mRNA is recognized and degraded by YTHDF2 6A modification to JAK1 mRNA which is recognized by YTHDF2, leading to JAK1 degradation and inhibition of STAT5 phosphorylation. Failure of STAT5 phosphorylation inhibits STAT5 mediated activation of C/EBPβ mRNA transcription resulting in failure to adipocyte differentiation to adipocytes Cells with special capabilities of self-renewal and the ability to differentiate into diverse progenies in response to specialized signals are termed stem cells. Different RNA modifications and their regulators have been reported to be associated with pluripotent attribuites of the normal stem/progenitor cells In addition to their roles in maintaining pluripotency in the normal stem cells, RNA modifications and their regulatory proteins are also found to contribute to the stemness attributes of the cancer cells Table . In canc6A RNA modification and its regulators play crucial role in CSC generation and maintenance. METTL14 has been found highly expressed in normal hematopoietic stem/progenitor cells (HSPCs) which is downregulated in the process of normal myelopoiesis. In acute myeloid leukemia (AML), the m6A writer METTL14 was found upregulated. Ablation of METTL14 reduced the immortalization of the cells in vitro and decreased the tumorigenicity of the AML cells in vivo. The tumorigenicity and stemness of AML cells were due to METTL14-mediated methylation and increased stability of the MYB and MYC mRNA transcripts. Knockdown of MYB and MYC resulted in myeloid differentiation whereas ectopic expression of these two genes restored the self-renewal properties of the leukemic stem cells (LSCs) in AML 6A reader protein YTHDF2 is also reported to increase the leukemic stem cells (LSCs) in AML. This reader protein was found to decrease the half-life of the target transcripts (e.g TNF receptor 2 (TNFR2)) thus eliminating their repressive effect on LSCs 6A writer METTL3 was also reported to be overexpressed in AML leading to accelerated leukemia progression. Increased m6A methylation of c-MYC, BCL2 and PTEN mRNAs by METTL3 increased translation of these proteins and downregulated the phosphor-Akt contributing to the stemness behavior of AML cells 6A modification. Silencing METTL3 sensitized the GSCs to γ-irradiation and reduced the neurosphere formation 6A methylation in glioblastoma was observed in other studies. The increased expression of METTL3 and METTL14 or decreased demethylase FTO resulting in increased m6A methylation suppressed the growth and self-renewal potential of GSCs. This is evidenced by knockdown of METTL3 and METTL14 and subsequent increase in several oncogenes  and a decrease in tumor suppressor genes  6A demethylase enzyme ALKBH5 is overexpressed in GSCs. ALKBH5 mediated the cancer progression 6A demethylation of NANOG mRNA enhanced the breast cancer stem cell (BCSC) in hypoxic condition. ALKBH5 upregulation showed decreased m6A level in pluripotency induction marker NANOG mRNA in breast cancer stem cells (BCSCs). In this case, ALKBH5 expression is induced by hypoxia which reduces the adenosine methylation in NANOG mRNA transcript and increases its stability and expression of protein level, ultimately increasing the stemness behavior in BCSCs. Inhibition of ALKBH5 expression can be a potential therapeutic strategy to downregulate the NANOG expression and block the recurrence of breast cancer by BCSCs mADAR1 is the modification enzyme for the A to I modification and ADAR1 overexpression in oral squamous cell carcinoma (OSCC) contributes to the promotion and maintenance of cancer stem cells. In OSCC cells ADAR1 was found to physically interact with the miRNAs processing machinery Dicer and six miRNAs to promote oncogenesis and stemness 5C and pseudouridine modifications in mRNA are also reported to be associated with pluripotency of the mammalian cells. With the transfection of m5C- and pseudouridine-modified synthetic mRNAs encoding the four pluripotency factors (Yamanaka factors) KLF4, c-MYC, OCT4, and SOX2, Warren and colleagues were able to reprogram the differentiated human cells to pluripotent cells 5C modification was identified in higher abundance in the undifferentiated pluripotent embryonic stem cell (ESC) mRNAs which are associated with cell cycle regulation, RNA and chromatin modification etc. 5C writer NSUN2 is a downstream target for c-Myc which is a well-known regulator of stem cell differentiation. It is found to be a determinant for the self-renewal and differentiation of epidermal hair follicular stem cells. In epidermal cells the NSUN2 expression is maintained in a low level; however when the cells are committed to differentiation, the level is increased 5C modification by down regulation of NSUN2 promotes cancer stem cell generation and tumorigenesis The mRNA pseudouridylation has a profound effect on cellular protein synthesis and determination of cell fate. The Pseudouridine writer, PUS7 is found to be a critical regulator of protein translation controlling stem cell growth and differentiation. PUS7 dysfunction is associated with perturbed protein synthesis in embryonic stem cell (ESC) resulting in increased hematological disorders. Impaired PUS7 function retards ESC differentiation and promotes malignant transformation to generate aggressive acute myeloid leukemia (AML) stem cells The knowledge of base modifications in RNA molecule is relatively old but their role in vital cellular processes and disease conditions has only been revealed in recent years. Especially the implications of RNA modifications and their regulatory machinery in cancers are still largely unknown. This has triggered a tremendous surge of research efforts in last couple of years to reveal the involvement of RNA posttranscriptional modifications in cancer and a handful of literatures have been published on this issue. Based on the published reports, the modified RNA bases and their regulators have been found to be up/down regulated in a number of cancer cases which contribute to overall cancer progression and poor patient survival Table . This ha6A, m1A, m5C, m7G and other methylations fall under Class I category which is the largest among all other groups 6A-methyltransferase complex METTL3/METTL14/WTAP, Selberg et al. used in silico-based drug discovery method and designed molecules that can specifically bind the complex. They further tested the molecules for their binding affinity and kinetics with the complex as well as their effects on the enzyme activity 6A RNA methylation The approaches for targeting the RNA modification machinery as anticancer drug targets are still in its infancy and most of the effort is focused mainly on the proteins involved in RNA methylations . The RNA methyltransferases are mostly S-adenosyl-L-methionine (SAM) dependent methyl transferases which catalyze the transfer of the methyl group using SAM as the substrate. There are at least five distinct classes of SAM-dependent methyltransferases (Class I to V), but the majority of the enzymes belong to class I and IV. The methyltransferases responsible for m2+/α-ketoglutarate dioxygenase family of enzymes (FTO and ALKBH family). All the members of this family share a common jellyroll β‑sheet coordinating Fe2+ binding which is critical for their optimal functionality. Apart from this, individual members of the family have additional domains 2+ as a co-factor and 2-oxoglutarate (2OG) as a co-substrate to carry out their demethylation functions 2+ and 2OG. With biochemical and crystallographic analysis several small molecules targeting the 2OG binding site has been identified which upon optimization can give rise to molecules with higher potency and substrate selectivity 1A demethylase ALKBH3 ) is derive from screening of a number of 1-aryl-3,4-substituted-1H-pyrazol-5-ol derivatives. This specific inhibitor showed promising anticancer activity against prostate cancer cells in culture and mouse tumor xenografts Rheum palmatum) was found as an FTO inhibitor which occupies the Fe2+ and 2OG along with the nucleotide binding sites Most of the known RNA demethylases belong to the FeThe known reader proteins are the RNA binding proteins which belong to YTH domain containing family of proteins. The YTH domain is well conserved in all three domains of life; the unique features of which may be exploited in inhibitor design in the approach of new drug discovery RNA modifications have been established as key players of diverse cellular processes which are determined by the factors like - subcellular localization of the modifying enzyme, the enzyme that recognizes the modification etc. Depending on the modifying enzyme and its subcellular localization as well as the modification reader, the modification causes changes in the translational efficiency of the transcripts, regulates their shuttling between cytoplasm and nucleus, determines their stability or recruits splicing factors thereby determining splice variants In addition to their roles in regulating normal cellular processes, RNA modifications are also found to play roles in carcinogenesis and confer stemness to the cancerous cell sub populations. On one hand, RNA modifications can contribute to cancer progression by decreasing the stability of the suppressors of the oncogenes thereby eliminating their inhibitory effects, on the other hand they can enhance the stability and expression of the protooncogenic transcripts


Background: Seasonality is a characteristic of some respiratory viruses. The aim of our study was to evaluate the seasonality and the potential effects of different meteorological factors on the detection rate of the non-SARS coronavirus detection by PCR. Methods: We performed a retrospective analysis of 12,763 respiratory tract sample results  for non-SARS, non-MERS coronaviruses . The effect of seven single weather factors on the coronavirus detection rate was fitted in a logistic regression model with and without adjusting for other weather factors. Results: Coronavirus infections followed a seasonal pattern peaking from December to March and plunged from July to September. The seasonal effect was less pronounced in immunosuppressed patients compared to immunocompetent patients. Different automatic variable selection processes agreed on selecting the predictors temperature, relative humidity, cloud cover and precipitation as remaining predictors in the multivariable logistic regression model, including all weather factors, with low ambient temperature, low relative humidity, high cloud cover and high precipitation being linked to increased coronavirus detection rates. Conclusions: Coronavirus infections followed a seasonal pattern, which was more pronounced in immunocompetent patients compared to immunosuppressed patients. Several meteorological factors were associated with the coronavirus detection rate. However, when mutually adjusting for all weather factors, only temperature, relative humidity, precipitation and cloud cover contributed independently to predicting the coronavirus detection rate. Respiratory tract infections (RTIs) are an important contributor to overall morbidity and mortality, with lower RTI being the fourth most frequent cause of death worldwide . RespiraSeasonality is a prominent characteristic of many viral RTIs. In temperate climate regions, viral RTIs reach their peak in winter, and in tropical areas during the rainy season ,6,7,8. IInterestingly, meteorological factors seem to play a part in the seasonal distribution of some respiratory viral pathogens ,7. PreprThe aim of the present study was to evaluate the seasonality and the potential effect of different meteorological factors on the detection rate of the non-SARS, non-MERS coronaviruses. Furthermore, we focused on potential differences in the seasonality of coronaviruses in immunocompetent vs. immunosuppressed hosts. We performed a retrospective analysis of 12,763 samples , including nose/throat swabs, tracheal aspirates and bronchoalveolar lavages, tested in the Institute for Virology of the University Hospital Essen, Germany from June 2013 to December 2019. The samples were tested with the respiratory viral panel  according to the manufacturer’s instructions, for the detection of non-SARS, non-MERS coronavirus . The analysis included all tested samples, meaning that a patient could contribute more than once over time, with the following exception: repeated positive samples from the same individual collected within two weeks of each other were removed. The term “coronavirus” corresponds to non-SARS, non-MERS coronaviruses in our manuscript, unless specifically otherwise noted . Nucleic acid extraction was performed using MagNA pure . Demographic and clinical data were taken from patient charts. A quarter of our cases  and a third of our positive cases  were generated by immunosuppressed individuals .Meteorological data were obtained for each day of the study period from the weather station of Essen Bredeney, Germany, through the server of “Deutscher Wetterdienst”. The data included daily average temperature, daily average relative humidity, precipitation, daily average wind speed, sunlight hours, daily average cloud cover and daily average atmospheric pressure. We expressed the association between continuous weather variables and the coronavirus detection rates by increments of 5 units on the corresponding scales of the variables if not otherwise specified. The meteorological factors in Essen, Germany for the duration of the study are presented in the p-value below 0.157 ) were combined into a single year-specific seasonal score. The formula can be found in the The virus detection rate was modeled via logistic regression with a seasonal effect and/or weather factors as explanatory variables. Seasonal variation in virus detection rate was fitted for each year separately with a cosinus function, with 1-year frequency length together with a sinus curve, and all variables with a Weather was represented as daily average values locally assessed 10 days prior to the virus measurement to account for incubation time ,20,21 anThe effect of single weather factors on the coronavirus detection rate was fitted in a logistic regression model with and without adjusting for other weather factors and seasonality. Variable selection was applied to the weather factors with and without adjustment for season, such as backward, forward, and stepwise selection, targeting statistical model optimization, such as the improvement of AIC. Selected effects were also estimated in patients stratified according to their immune status (immunocompetent vs. immunosuppressed). The monthly coronavirus detection rate (as the number of positive samples per tested samples per month) was compared with the corresponding sum of predicted values from the logistic model. Additionally, the coronavirus detection rate was compared with the sum of predicted values within deciles of the predicted values in a calibration plot. As our study does not aim to test or confirm predefined associations, we display the statistical precision of our parameter estimates in graphical presentations of odds ratio estimates with confidence intervals, including a reference line at OR = 1 (as indicating absence of a risk modifying effect). We calculate and report confidence intervals to assess the precision of our estimates, because our goal is estimation and non-significance testing. We wish to avoid publication bias by the preferential reporting of significant results. Instead, we judge the value of our estimates by their precision and validity ,23. All Since 2013, we have generated 12,763 test results for the detection of the non-SARS, non-MERS coronaviruses  by multiplex PCR technology. Coronavirus infections followed a clear seasonal pattern among our patients. Although coronaviruses were detected throughout the years, detection rates were at their peak from December to March and at their nadir from July to September. In October and November, the number of positive cases was low in most years, usually lower than in May. As shown in The coronavirus detection rate could be predicted well with a mathematical model including the seasonal effect alone, as well as a model only including seven weather factors  of our patients consisted of immunosuppressed individuals, generating a third (31.9%) of our positive cases. The group of immunosuppressed patients included patients with hematological or oncological malignancies under chemotherapy, solid organ transplant recipients and patients after allogeneic human stem cell transplantation. To evaluate the potential impact of immunosuppression on the seasonality of coronavirus detection, we divided our patients into immunosuppressed and immunocompetent patients, and used logistic regression models to calculate the seasonal effect and the effect of weather factors through a multivariable logistic regression model . As demoThe coronavirus detection rate demonstrated a pronounced seasonal pattern, but even in the time period with the lowest detection rate , we could detect some coronavirus-positive cases. Thus, we aimed to characterize these cases from a clinical point-of-view. As shown in Among our patients, data on viral persistence were limited, since follow-up was not consistently performed in most cases. In six cases, we observed viral persistence lasting for more than a month (from 34 to 116 days). Of theses 6 patients, 5 were immunosuppressed.Coronavirus infections with the viruses NL63, 229E, OC43 and HKU1 followed a seasonal pattern among our patients. Although coronaviruses were in principle detected throughout the year, the detection rate was at its peak from December to March, and at its nadir from July to September. “Off-season” detection of coronavirus was more frequent in immunosuppressed patients. Low ambient temperature, few sunlight hours per day, high relative humidity, wind speed, cloud cover and precipitation were each individually associated with high detection rates of coronaviruses. In a multivariable model including all weather factors, temperature, relative humidity, precipitation and cloud cover were independently associated with the coronavirus detection rateSeasonality is commonly observed in respiratory infections. Its form and impact depend on the individual pathogen and the climate zone under evaluation ,6,7,8. OSeasonality is obviously a characteristic of coronavirus infections. Among our patients, its detection rate was at its peak from December to March and at its nadir from July to September. This pattern has similarities but also differences compared to influenza virus detection. Both viruses peak in the winter, but the influenza virus has a markedly narrower time interval of detection. It is highly prevalent from January to March, but disappears in late spring , summer and autumn (apart from very few isolated cases), as shown in previous studies ,7 and inA seasonal effect on coronavirus detection rates was present in both immunocompetent and immunosuppressed patents, but it was less pronounced in the immunosuppressed group. “Off-season” detection of coronavirus was more frequent in immunosuppressed patients. Focusing on the “off-season” coronavirus cases, we found that 66% of them were observed in immunosuppressed individuals, although immunosuppressed patients accounted for only 26% of our cases. The difference in the effect of different weather parameters on the coronavirus detection rate in immunosuppressed vs. immunocompetent individuals is minimal. We observed, however, a slightly larger difference in the effect of temperature on the coronavirus detection rate in immunosuppressed vs. immunocompetent hosts, suggesting that temperature indeed may contribute to a greater degree than other weather parameters to coronavirus seasonality, depending on the host’s immune status. It is possible that the observed difference is associated with the fact that temperature demonstrates greater variability throughout the year compared to the other parameters in the model. Since, however, the difference in the above-mentioned effect is very small, caution is warranted in attributing the difference to any particular pathophysiological or behavioral parameter. We have only limited data on the persistence of coronaviruses, but prolonged viral shedding was observed among our patients and it involved mainly immunosuppressed individuals. This is consistent with current knowledge about respiratory virus shedding in humans ,26, and We also evaluated the association of meteorological conditions and coronavirus detection rates. Taken individually, low ambient temperature, minimum sunlight hours per day, high relative humidity, wind speed, cloud cover and precipitation were associated with higher detection rates of coronaviruses 10 days later. Similar weather effects, to a lesser or greater degree, have been described for other respiratory viruses, such as influenza virus and respiratory syncytial virus . In a peExperimental data on the link between coronavirus transmission and weather factors are largely missing. However, experiments performed with another respiratory virus, e.g., influenza viruses, provide some insight into the possible mechanistic explanation of these associations. We found a clear correlation between coronavirus detection rates and cloud cover or sun light hours. How can this be explained? Solar radiation, which is dependent on cloud cover and sunlight at a given location, has been shown to act in a virucidal way against influenza virus . The virHigh precipitation was associated with increased coronavirus detection rates among our patients, which seems counterintuitive when one considers the association of lower relative humidity with higher coronavirus detection rates. Interestingly, the factors of precipitation and relative humidity are not as closely linked see  as one wBoth seasonality and the combined effects of weather factors are reliable predictors of coronavirus detection rates, as shown in the models depicted in A limitation of our study was that we used a convenience sample, namely, patients seeking treatment at our hospital. This leads to the exclusion of infected but asymptomatic individuals, and probably to a marked underestimation of mildly symptomatic individuals, meaning that the true number of coronavirus-infected individuals in the general population at any given time is probably much higher. Furthermore, due to the nature of our study we cannot offer mechanistic explanations for the associations we observed. However, data on the association of weather factors and coronavirus detection rates are very limited, but very important in order to predict the ongoing SARS-CoV-2 pandemic. Our study describes, for the first time, the effect of the interplay of seasonality and several weather factors on the non-SARS, non-MERS coronaviruses’ detection rate, and indicates that some but not all weather factors are independently associated with it, providing valuable insight into the seasonal pattern of coronaviruses in general. This information is relevant for the calculation of projections for disease development according to the time of year and certain weather conditions. An association between meteorological factors and the SARS-CoV-2 detection rate has been suggested ,16,17, bIn conclusion, coronavirus infections followed a clear seasonal pattern among our patients, which was more pronounced in immunocompetent patients compared to the immunosuppressed. Several meteorological factors were associated with the coronavirus detection rate. However, after multiple adjustments for all weather factors, only temperature, relative humidity, precipitation and cloud cover remained independently associated with the coronavirus detection rate.


One commonly used method is the alkaline cleavage-based RiboMethSeq protocol, where positions of reads' 5'-ends are used to distinguish nucleotides protected by ribose methylation. This method was successfully applied to detect and quantify Nm residues in various RNA species such as rRNA, tRNA, and snRNA. Such applications require adaptation of the initially published protocol(s), both at the wet bench and in the bioinformatics analysis. In this manuscript, we describe the optimization of RiboMethSeq bioinformatics at the level of initial read treatment, alignment to the reference sequence, counting the 5′- and 3′- ends, and calculation of the RiboMethSeq scores, allowing precise detection and quantification of the Nm-related signal. These improvements introduced in the original pipeline permit a more accurate detection of Nm candidates and a more precise quantification of Nm level variations. Applications of the improved RiboMethSeq treatment pipeline for different cellular RNA types are discussed.A major trend in the epitranscriptomics field over the last 5 years has been the high-throughput analysis of RNA modifications by a combination of specific chemical treatment(s), followed by library preparation and deep sequencing. Multiple protocols have been described for several important RNA modifications, such as 5-methylcytosine (m Several recent publications have demonstrated the presence of numerous RNA modifications, not only in rather well studied species such as tRNA/rRNA/sn(sno)RNA, but also in coding RNAs (mRNA), in all living organisms studied to date. Thus, several high-throughput methods for the identification of RNA modifications have been developed and successfully applied for mapping m7G/m3C/D .The major impediment to applying high-throughput screening methods resides in various experimental and bioinformatics biases, which are only partially controlled and may affect the precision of the final result or even lead to a nonnegligible number of false-positive identifications. Taking into account the size of eukaryotic transcriptomes, thousands of false-positive signals may appear at the transcriptome-wide level, even with extremely strict criteria for candidate site selection . Thus, every step in bioinformatic data treatment, conversion and manipulation should be optimized in order to minimize the number of potential false-positive signals.Recently, we published a high-throughput deep sequencing-based approach, named RiboMethSeq, for mapping of 2′-O-methylations in highly abundant RNAs, mostly in rRNA , with poIn this work, we report the comprehensive optimization of every step of the bioinformatic treatment used for the detection and quantification of ribose 2′-O-methylation by the RiboMethSeq protocol. We systematically evaluated the importance and the impact of 5′- and 3′-trimming strategies, parameters for alignment to the reference sequence, as well as the use of specific calculated scores for 2′-O-Me mapping and quantification. Our results demonstrate that a reduced calculation interval is favorable for the general discrimination of 2′-O-Me signals from potential false-positive hits. We propose new, optimized scores  that provide better FDR values and also improve the relative quantification of 2′-O-methylation in RNA.Saccharomyces cerevisiae and human HeLa cell rRNA 2′-O-methylation analysis, as well as additional samples for hTERT immortalized human mammary epithelial cell line (HME)  ; accessiThe RiboMethSeq protocol  consistsAdapter removal in this study was performed using the Trimmomatic utility . With thFor the selected datasets, we first applied the RiboMethSeq pipeline under standard conditions, and the previously described scores  were calculated . Score vAs described above, the high-throughput mapping of 2′-O-Me residues in RNA is based on random fragmentation of the phosphodiester bonds under mild alkaline conditions. The presence of a 2′-O-Me group protects the 3′-adjacent phosphodiester bond from nucleolytic cleavage, thus generating the characteristic gap in the 5′-end (as well as 3′-end) coverage profile of the sequencing library prepared from the fragmented RNA  mismatched nucleotides in the seed. The following parameter combinations were tested: seed lengths of 22 nt , 16 nt, 12 nt, and 8 nt, allowing (or not) mismatched nucleotides in the seed.The data in In the originally published RiboMethSeq protocols , 12 neigIn the original RiboMethSeq protocol , the MetWe noticed that RiboMethSeq detection scores behave better with a reduced calculation interval (+/−2 nt); therefore, we also explored variations of the MethScore. To select the best calculation interval, we compared the MethScore for intervals from two to eight neighboring nucleotides for naturally modified yeast rRNA and for unmodified synthetic rRNA transcripts. The MethScore values were expected to reach maximum for naturally modified rRNA and minimum for the unmodified counterpart. The MethScore demonstrates the maximal cumulative difference between rRNA and synthetic transcript for the shortest interval of +/−2 nt; the other tested intervals gave roughly the same results. On the basis of this observation, we suggest calculating the MethScore for two neighboring nucleotides (referred to as MethScore2).A detailed analysis, position by position, for 18S and 25S rRNA .The originally used ScoreMAX6  was desiCalculation of the max MCC value demonstrated that ScoreMAX-MAX is less performant than the original ScoreMAX2, while ScoreMEAN2 shows better discrimination of FP hits. For the same dataset, Sample 3 – HeLa application of the ScoreMEAN2 allows attainment of the maximal MCC value of 0.7954 and reduces the FDR from 27% to 4%. A similar tendency was observed for two other datasets . Despite the fact that ScoreA2 and ScoreMEAN2 generally pick out different FP hits, ScoreD does not further improve the performance (max MCC and FDR) compared to ScoreMEAN2 alone. In conclusion, the best results for detecting 2′-O-methylated residues were obtained with a calculation window of 4 nt (+/−2 nt) using ScoreA2 and ScoreMEAN2.S. cerevisiae rRNA RiboMethSeq dataset. To compare improvements associated with the use of ScoreMEAN2 and ScoreA2, we used independent human RiboMethSeq datasets obtained for other HeLa samples, human mammary epithelial cells (HME), human fibroblasts and Wharton's jelly MSC . Comparison of max MCC and FDR values obtained for Scores MAX6/MEAN2 and Scores A6/A2 calculated with different scoring schemes is shown on The results of this optimization suggest that RiboMethSeq analysis of rRNA can be performed using short trimming lengths (10–12 nt) and adapted Bowtie2 alignment parameters, which allows a gain in sequencing information. Calculations of RiboMethSeq scores for short calculation intervals (4 neighboring nucleotides) and ScoreMEAN2 and ScoreA2 can be used for mapping of the modified positions. Quantification of 2′-O-Me is accomplished using MethScore2, also calculated for four neighbors. A minimal of 4–6 mln of raw reads (~400 reads/nt on average) can be used to evaluate the methylation level for known rRNA methylation sites, but at least 15 mln reads  should be used to discover new methylation candidates. For different RNA species such as tRNAs, the trimming/seed length can be further reduced (up to 8 nt), but the calculation of RiboMethSeq scores with four neighbors can be maintained. The optimal read coverage is somewhat similar .S. cerevisiae and human HeLa cells rRNA 2′-O-methylation analysis as well additional samples (We used previously published datasets obtained for wild type yeast  samples , accessiFP—designed and optimized treatment pipeline. VM, LA and VB-I—performed RiboMethSeq library preparation and analysis. VM, MH and YM—wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.