The birth of the Epitranscriptome: deciphering the function of RNA modifications
© BioMed Central Ltd 2012
Accepted: 31 October 2012
Published: 31 October 2012
Recent studies have found methyl-6-adenosine in thousands of mammalian genes, and this modification is most pronounced near the beginning of the 3' UTR. We present a perspective on current work and new single-molecule sequencing methods for detecting RNA base modifications.
Keywordsepigenetics epigenomics epitranscriptome m6A methyl-6-adenosine methyladenosine N6-methyladenosine RNA modifications
Techniques for sequencing RNA and DNA pioneered by Fred Sanger and others in the 1960s  and 1970s  began to reveal the biochemical recipes for storing biological information in organisms and laid the foundation for modern genomics. Yet, decades before the first nucleic acid was sequenced, various chemical modifications of DNA had already been described, such as 5-methylcytosine  and 5-hydroxy-methylcytosine , now dubbed the 5th  and 6th  base of genetics; in total, several dozen DNA modifications have been reported . These modifications, along with histone modifications, are now recognized as important regulatory mechanisms for controlling gene expression and function .
Fortunately, it is now relatively easy to characterize these modified DNA bases, which form part of the 'epi'-genome (epi, on top), for any organism with a finished genome, given the widespread availability of high-throughput techniques, especially those based on next-generation sequencing (NGS). Various NGS approaches are being used in the National Institutes of Health (NIH)'s Epigenomics Roadmap  and in the BLUEPRINT Project . Similarly, cell-specific, post-translational modifications of proteins, sometimes referred to collectively as the 'epiproteome' , are essential mechanisms necessary for the regulation of protein activity, folding, stability and binding partners. Elucidating the roles of protein and DNA modifications has had a major impact on our understanding of cellular signaling, gene regulation and cancer biology .
However, our understanding of an additional regulatory layer of biology that rests between DNA and proteins is still in its infancy; namely, the multitude of RNA modifications that together constitute the 'Epitranscriptome'. There are currently 107 known RNA base modifications, with the majority of these having been reported in tRNAs or rRNAs . Outside the 5' cap, the role of modifications in mRNA is unclear [14, 15]. One RNA modification, N6-methyladenosine, or methyl-6-adenosine (m6A), has been observed in a wide variety of organisms, including viruses , yeast , plants , humans [19, 20] and mice [19, 20], and exhibits dynamic changes in response to a variety of stimuli in yeast . Older studies using purified polyadenylated RNA from mammalian cells showed that m6A was the most abundant post-transcriptional modification in polyadenylated RNA , which contemporary doctrine considered to be synonymous with mRNA. However, it is now known that polyadenylation occurs not only on mRNAs, but also in other RNAs, such as rRNAs and long intergenic noncoding RNAs (lincRNAs). Thus, it was historically unclear exactly how m6A existed in mRNAs and, if so, whether it was restricted to a select few transcripts or prevalent throughout the transcriptome.
Previous methods for investigating the prevalence of m6A were laborious and involved incubating cells with 14C-radiolabeled methionine (the precursor for the endogenous methyl donor, S-adenosylmethionine), following which the incorporation of methyl groups into RNAs could be quantified. These early studies detected methylated bases in ribosomal RNA (rRNA) , small RNA fractions [23–27] and in mRNAs . However, these methods were limited by their inability to identify the specific mRNAs that contained m6A. Indeed, m6A had previously been detected in vivo for only a single mammalian mRNA (bovine prolactin ), and the specific sites of m6A incorporation had been established for only two RNAs: prolactin  and Rous sarcoma virus RNA [30, 31]. The methods used to map these m6A sites were technically challenging and, more importantly, required a pre-ordained focus on a particular transcript, rather than a global approach that could detect sites of adenosine methylation in all mRNAs. Moreover, adenosine methylation is invisible, insofar as both methylated and non-methylated adenosines readily base pair with T or U, and both are reverse transcribed to T, further hindering the study of m6A and its role in biology.
However, a renewed interest in m6A has recently emerged, partially due to the finding that the fat mass- and obesity-associated (FTO) gene encodes a brain- and hypothalamus-enriched m6A demethylase that is responsible for converting m6A to adenosine . Defects in this enzyme result in significant alterations in energy use and metabolism, and mutations in FTO have recently been linked to a higher risk for Alzheimer's disease and decreased brain mass [33, 34]. These studies suggest that m6A may have a physiological role in cellular signaling and neurodegeneration. Recent advances in NGS technology, in addition to the availability of antibodies that recognize m6A, have enabled the development of global approaches for studying m6A. Recently, two groups have independently developed high-throughput methods for rapid characterization of m6A sites across the transcriptome. Methods such as methyl-RNA-immunoprecipitation-sequencing (MeRIP-seq)  or m6A-seq , which combine immunoprecipitation (IP) of methylated RNAs using an m6A-specific antibody, with NGS, have finally opened the door to global methods for studying the epitranscriptome and its dynamics.
Mapping the epitranscriptome
Comparison of MeRIP-seq and m6A-seq
Synaptic Systems, NEB
IP rounds (n)
RNA fragment size
RNA sequencing platform
Illumina GAII and HiSeq2000
Fisher's exact test of IP read enrichment
Computed Winscore >2 (4 × enrichment) + filtering 
Peaks reported (n)
Stop codon, internal exons
Stop codon, TSS, internal exons, AS exons
In addition to sequencing, the MeRIP-seq study also used immunoblotting to investigate m6A, demonstrating that m6A is present in mouse heart, lung, brain, liver and kidney tissues, with a particular enrichment in brain, liver and kidney. High levels of m6A were found in HepG2 and MCF7 cells, in contrast to lower levels detected in other human cancer cell lines (PC3 and PC9). The dynamic nature of m6A was confirmed by comparing embryonic with adult tissue, which showed that m6A levels increase over the course of development. The m6A-seq study also found m6A to be a dynamic modification, finding that its distribution changed in response to a variety of external stimuli (ultraviolet, interferon gamma, hepatocyte growth factor and heat shock), although as many as 70 to 95% of the peaks were static.
Experiments leveraging the depletion of the METTL3 subunit responsible for methylating adenosines were used in the m6A-seq study to explore the modification's function. A statistically significant increase in the abundance of alternatively spliced transcripts was observed as a result of this depletion, with the alternatively spliced exons and introns showing an enrichment for m6A peaks. However, a permutation analysis of splice junction-localized m6A sites in the MeRIP-seq study data did not find a statistically significant enrichment of m6A peaks in the proximity of splice junctions . Moreover, an analysis of the total mapped bases from the MeRIP-seq samples versus the control, non-IP RNA samples showed that fewer bases mapped to splice junctions in the IP samples (Additional file 1). Elucidating whether m6A functions in splicing and, if so, whether this is direct or indirect through the regulation of splicing factor-encoding transcripts, will require further investigation. In light of the MeRIP-seq data, we suggest that m6A is not likely to cause an overall increase in the global amount of transcript splicing, but it may modify splicing for certain classes of genes, and particularly for genes with alternative, internal exons .
Challenges of epitranscriptomic site detection
There are many factors to consider when computing the m6A enrichment for a site. For example, the definition of gene regions, the gene isoform used, the presence of secondary structure, the alignment method and the read depth can all impact the degree of enrichment discovered. Given that epitranscriptomics is a nascent field, computational analysis methods are only now emerging. Here, we explore the impact of these factors on detecting and quantifying m6A.
Overlap of genes with m6A peaks
Total genes with m6A
Overlap of commonly expressed genes with m6A sites
Genes expressed (RPKM ≥0.2)
Expressed genes with m6A
We re-analyzed the datasets from both studies in order to determine the effect of the peak-calling method on the apparent m6A distribution in the transcriptome and found two discrepancies. By comparing peak-calling methods, we observed that the presence of the 5' UTR peak in the m6A-seq dataset was attenuated when that study's peak-caller was replaced by MeRIPPeR  from the MeRIP-seq study. This reduction indicates that each of the two peak-calling algorithms may have different sensitivities and specificities.
m6A mapping is heavily impacted by the read depth and by the choice of peak detection and alignment methods
To further investigate the analytic dependency of m6A peak detection, we examined the m6A site detection as a function of alignment method, antibody and read depth. Part of the challenge of MeRIP-seq analysis is a reliance on other IP-seq analysis methods, developed for chromatin IP-seq (ChIP-seq). ChIP-seq experiments are designed to characterize DNA-histone and DNA-transcription factor interactions. Existing ChIP-seq peak-finders take advantage of inherent properties of the data to assist in finding peaks, many of which do not apply in the case of finding m6A sites in RNA. For example, each fragmented RNA molecule pulled-down by an m6A antibody has the potential to harbor far more methylation sites than the maximum number of protein binding sites expected for the equivalent ChIP-seq fragment, and so the m6A sites are more challenging to resolve. ChIP-seq peak finders use different methods and heuristics to find peaks, attempting to balance finding weak peaks with maintaining a low FDR and resulting in a diverse group of peak sets [36, 37]. The same is true for m6A peaks, as the MeRIP-seq study used Fisher's exact test and the m6A-seq study derived a window score based on peak enrichment.
Each of the multiple methods for aligning reads to a transcriptome has its own set of advantages and challenges. A genome-based aligner, such as BWA , can be used when a genome sequence is available, but introduces added complexity when reads map to multiple transcript variants, and suffers from being unable to align reads to genomic regions that are absent from a pre-defined reference. Alternatively, a gap-based aligner, such as TopHat  or GSNAP , can be used, with the advantage that these algorithms are designed for transcriptomes and so can map reads across both known and novel splice junctions. However, these methods tend to be slower and can introduce many false splice sites, leading to poorly aligned reads. The ability of an aligner to handle errors typical of RNA-seq, which differ to those seen in DNA sequencing, is another factor to consider. A common source of error in RNA-seq is the random hexamer priming used in cDNA synthesis, which introduces a bias in the nucleotide distribution at the beginning of reads . One possible solution to this particular error is to trim the reads, an approach that was employed in the m6A-seq study.
To examine the effect of aligner on the detection of m6A peaks, we analyzed processed HEK293T MeRIP-seq data using three aligners (BWA , TopHat 2  and GSNAP ), and then called peaks with MeRIPPeR . We observed a slight increase in the number of 5' UTR peaks when using the transcriptome aligners GSNAP and TopHat 2 relative to the number called when using BWA (Additional file 2). More importantly, there was a significant increase in the number of individual peaks: MeRIPPeR found 19,617 peaks using BWA, 45,738 with GSNAP and 135,706 using TopHat 2, all at the same FDR (0.05). These results indicate that the alignment method selected has a significant impact on the number of peaks identified in a MeRIP-seq dataset.
To effectively gauge the influence of read depth on m6A site detection, we used a sub-sampling titration analysis of the aligned reads. We found that peak detection is heavily dependent on read depth (Additional file 3a), with some aligners showing a nearly linear increase in peaks as a function of depth. The number of genes in which these peaks were found also increased with read depth, albeit less dramatically (Additional file 3b), with the number of genes continually increasing as a function of depth. While a specific point in a transcript might be correctly called as an m6A site, it is not known if the site is methylated in all copies of that transcript . The percentage of transcripts at which a site is methylated may be quantified as the stoichiometry of m6A. It is likely that the new peaks detected with increasing read depth are low in m6A stoichiometry and hence more challenging to detect at lower read depths. From these data, we extrapolate that, given enough tissues, cell types and conditions, it is possible that almost all genes may be marked, at some point, by m6A.
We next sought to establish whether m6A peak calls vary with the antibody used, by separately plotting peaks obtained with the two different antibodies in the MeRIP-seq study. Both antibodies had the same peak distribution across gene bodies (Additional file 4), indicating that the choice of antibody, at least for the two tested, should not impact the global distribution of m6A sites.
m6A motif analysis
A primary motif [AG]ACU was discovered within m6A peaks by both studies, each of which used a different motif-finding algorithm, and both analyses suggest that the A in the canonical motif is the methylated site - agreeing with prior work in m6A sequence specificity [42, 43]. Both groups found the motif to be highly enriched in peak regions compared with negative control regions. If the A in the motif is indeed the m6A, then application of this information to m6A-seq or MeRIP-seq datasets could enable the mapping of m6A sites at single base pair resolution. We used a motif pattern-matching algorithm from FIRE  to find the [AG]ACU motif in the MeRIP-seq mouse dataset (Methods), and subsequently applied the assumption that the A in each motif is equivalent to an m6A site, to identify m6A sites in all the datasets. We identified 21,004 m6A sites from 10,488 m6A-seq HepG2 peaks, 46,293 from 17,071 MeRip-seq HEK293T peaks, 9,124 from 4,054 m6A-seq mouse liver peaks, and 37,459 from 12,664 MeRIP-seq mouse brain peaks. Only about 5 to 15% of the peaks lacked the motif sequence and the distribution of these putative single base-resolution m6A sites across gene bodies is very similar to the peak distribution (Figure 1a).
However, we did not observe an enrichment of m6A sites in the 5' UTR, and the coding sequence profile is fairly flat until the peak reaches the proximity of the stop codon. This could indicate that the identified [AG]ACU motif is specific to those peaks near the stop codon, or that the peak enrichment near the 5' UTR does not reflect a true increase in the number of actual m6A sites. To test whether the motif was specific to stop codon-proximal regions, we performed a FIRE  motif finder analysis of the 5' UTR peaks that were present in the MeRIP-seq mouse liver dataset, since this dataset was not enriched for this motif in this genomic region. Nonetheless, FIRE found a [CG]ACU motif, though not the strongest motif, indicating that it is not specific to the stop codon peaks, and thus likely a global motif for m6A, but perhaps weakly represented in the 5' UTR.
Single-molecule approaches to RNA modifications
Single-molecule sequencing has the potential to provide base-level resolution of m6A sites, without the need for motif-based inference. The most commonly found platform for this method of sequencing currently on the market is the single-molecule, real-time (SMRT) technology (Pacific Biosciences). SMRT sequencing uses thousands of zero-mode waveguides (ZMWs) to capture an enzyme in real time, traditionally a DNA polymerase, as it incorporates fluorescent nucleotides into a polymer . This method of molecular monitoring has the advantage of detecting both genetic and epigenetic information simultaneously, since the patterns of base incorporation by the polymerase are contingent upon the steric and sequence contexts of the bases present in the template . Specifically, if a modified base is present on the template, the biophysical dynamics of DNA polymerase movement and base incorporation are affected, creating a unique kinetic signature before, during and after base incorporation, and thus enabling identification of specific DNA modifications .
Similarly, Oxford Nanopore Technologies (ONT) and other companies are developing nanopore-based sequencing technologies, which use nanopore-forming proteins to sequence DNA by attaching an application-specific integrated circuit to the membrane upon which the nanopore rests. In principle, observations of any modified DNA or RNA base could be made during transit of the molecule through the nanopore, and some observations have already been made with nanopores that allow detection of 5hmC . While all of these technologies are still under development, we note that all direct-observation methods, in principle, have the potential to detect m6A and other epitranscriptomic modifications.
Beyond m6A: mapping the full epitranscriptome
Interestingly, the enzyme commonly known as DNA methyltransferase-2 (DNMT2) [Swiss-Prot: O14717] was shown to methylate cytosine 38 of tRNAAsp , and with such high specificity that it was renamed tRNA aspartic acid methyltransferase 1 (TRDMT1). More recently, two more tRNAs were found to be methylated by TRDMT1, and it was also observed that the methylation protects the tRNA from stress-induced cleavage and improves its stability [50, 51]. Several tRNA nucleoside modifications have been shown to control frame shifting and codon binding during translation. These types of modifications often occur in the crucial 7 bp anticodon stem and loop (ASL) region that binds to mRNA codons in ribosomes, and are hypothesized to affect the stability and codon binding affinity during translation by controlling the overall shape of the loop and its dynamics [52–54]. Taken together, a pattern emerges in which RNA modifications in multiple RNA species act as a critical regulatory layer of RNA biology.
Many RNA modifications would benefit from a more global and cross-species characterization than is present in the existing literature. For example, studies in Escherichia coli and yeast have shown that nucleotide modifications in rRNA lie in functionally significant regions, with a possible role in the regulation of translation . Another example is methylation in plant rRNAs, where the modification is thought to help maintain rRNA stability, possibly in order to sustain ribosomal function during dramatic changes in temperature . Interestingly, rRNA modifications in trypanosomes were shown to be mediated by small nucleolar RNAs (snoRNAs) , and changes in pseudouridylation of rRNA in mice, induced by mutations in DKC1 [Swiss-Prot: Q9ESX5], led to the onset of dyskeratosis congenital, resulting in an increase in tumor susceptibility .
Taken together, these studies demonstrate the possible significance and functional importance of (r/t/m/mi/sno/linc)RNA modifications and begin to sketch out what might be called a transcriptomic regulome, where various species of coding and noncoding  RNAs, as well as their modified epitranscriptomic variants, compete with, coordinate and control each other during normal cellular processes, from the birth of a transcript until the production of its subsequent protein product or localization of its cellular target.
Characterizations of m6A across the transcriptome show that m6A is present in the majority of mammalian genes, and is highly enriched at the beginning of the 3' UTR and near the stop codon. Yet, many peaks exist in intergenic regions or in introns, and there is some evidence that m6A functions in the regulation of splicing or other modifications that take place in the processing of RNA into a mature transcript. Since m6A distribution has already been shown to undergo developmental changes and differences in cancer cell lines, it is also possible that epitranscriptomic signatures may be used to stratify various states of disease, just as in epigenetics . Despite these advances, the complete purpose and molecular function of m6A is still unknown.
Nonetheless, some reasonable hypotheses can be proposed from the existing data. The enrichment of m6A sites near the stop codon suggests that the modification could play some role in regulating translation termination, potentially by altering translation efficiency or ribosome occupancy. In addition, m6A may mark transcripts for shuttling to RNA granules or for other mechanisms that will preserve the RNA for later use. Just as the number of known modifications of RNA has rapidly expanded (currently 107), the number of known RNA-binding proteins similarly keeps growing, and it is possible that some of these may be responsible for altering the function of m6A within RNAs, either directly or through the regulation of FTO or METTL3. Such interactions could occur at any point of transcription, post-transcriptional modification or translation, with different consequences at each stage in the life of an mRNA. Finally, it is also possible that some RNA binding proteins may be m6A site scanners that bind selectively to either methylated or unmethylated RNA, and as such would be regulated by the epitranscriptomic state of an RNA.
Two additional avenues warrant consideration when discussing possible regulatory functions of m6A. First, even though an inverse spatial relationship was observed between m6A peaks and microRNA (miRNA) binding sites in 3' UTRs , it is notable that brain tissue is enriched for both highly expressed miRNAs and m6A-containing genes, which suggests that miRNAs might influence the methylation of a targeted mRNA. In addition, recent work has shown an interplay of mRNA methylation and the reduction of Dicer activity, thus decreasing miRNA maturation rates . Second, m6A has already been shown to inhibit RNA editing in certain cases , implying that m6A may serve as the long-sought balancing mechanism for the prevention of RNA editing . If it is the case that m6A prevents RNA editing from occurring, then evidence for this should be apparent in a diminished overlap between m6A and the target RNA editing sites. So far, this appears to be true , but the number of sites examined is too low to be definitive yet. If upheld with additional experiments, these feedback and regulatory loops may help explain the genesis and changes in RNA editing sites and miRNA levels, and provide additional mechanisms for controlling gene expression and RNA function.
In summary, the high-throughput and single-molecule methods described here represent the dawn of new research into a novel, RNA-based regulatory layer in cells, which adds yet another component of regulatory complexity to the central dogma of molecular biology (Additional file 5). The high conservation of specific m6A sites across mouse and humans, as well as the general increase in PhyloP conservation scores of the m6A sites themselves , both indicate that m6A is under strong evolutionary selection pressure, and thus may represent a critical modification for many organisms. Even though previous evidence indicates that m6A is an RNA modification present in all species, it has so far only been examined on a transcriptome-wide basis in two species (human and mouse), and observed in mRNAs only in eukaryotes, leaving open a wide area of research for many eukaryotic and prokaryotic systems. Just as the protein translation code and epigenetic code have slowly accreted into a cogent framework for information transfer and regulation within the cell, and between generations, these data indicate that an important epitranscriptome code is emerging. Notably, this dynamic code already appears to greatly expand the function and regulatory potential of all information contained within the many species of RNA present in a cell.
Sequence data were realigned to the genome using BWA , TopHat 2  or GSNAP . BWA was run using default parameters, and GSNAP and TopHat 2 were inputted with known RefSeq transcript definitions and run with novel splice junction finding turned on. The aligned files were converted to bam files using SamTools , filtering out reads with Phred quality scores under 20. BEDTools  was used to compute genome properties, such as coverageBed to compute genome coverage and RPKM (using a Perl script) and intersectBed to determine peak overlaps. Subsampling was accomplished using Picard's DownSampleSam .
Peak-finding was accomplished using MeRIPPeR  and transcriptome profile plots were generated using Jenotator: Java Genome Annotator . A custom R script was used for plotting the transcriptome profile plot and Excel 2013 was used to plot the other bar charts. r-make was used to generate genome annotation plots . Motif regions were extracted using ChIPseeqer's ChIPseeqerMotifMatch  and individual m6A sites were extrapolated with a Perl script.
false discovery rate
methylated RNA immunoprecipitation and sequencing
reads per kilobase of exon model per million mapped reads
We wish to acknowledge the invaluable contribution of the WCMC Epigenomics Core Facility. Supported by a Starr Cancer Consortium grant (I4-A442) (CEM, YS), National Institutes of Health grants I4-A411, I4-A442, and 1R01NS076465-02 (CEM) and NINDS NS56306 (SRJ), and the Tri-Institutional Training Program in Computational Biology and Medicine (YS).
- Brownlee GG, Sanger F, Barrell BG: Nucleotide sequence of 5S-ribosomal RNA from Escherichia coli. Nature. 1967, 215: 735-736. 10.1038/215735a0.PubMedView ArticleGoogle Scholar
- Sanger F, Air GM, Barrell BG, Brown NL, Coulson AR, Fiddes JC, Hutchison CA, Slocombe PM, Smith M: Nucleotide sequence of bacteriophage phi X174 DNA. Nature. 1977, 265: 687-695. 10.1038/265687a0.PubMedView ArticleGoogle Scholar
- Johnson TB, Coghill RD: Researches on pyrimidines. C111. The discovery of 5-methyl-cytosine in tuberculnic acid, the nucleic acid of the tubercle bacillis. J Am Chem Soc. 1925, 47: 2838-2844. 10.1021/ja01688a030.View ArticleGoogle Scholar
- Wyatt GR, Cohen SS: A new pyrimidine base from bacteriophage nucleic acids. Nature. 1952, 170: 1072-1073. 10.1038/1701072a0.PubMedView ArticleGoogle Scholar
- Ryan L, Ecker JR: Finding the fifth base: genome-wide sequencing of cytosine methylation. Genome Res. 2009, 19: 959-966. 10.1101/gr.083451.108.View ArticleGoogle Scholar
- Münzel M, Globisch D, Carell T: 5-Hydroxymethylcytosine, the sixth base of the genome. Angewandte Chemie International Edition. 2011, 50: 6460-6468. 10.1002/anie.201101547.View ArticleGoogle Scholar
- Korlach J, Turner SW: Going beyond five bases in DNA sequencing. Curr Opin Struct Biol. 2012, 22: 251-261. 10.1016/j.sbi.2012.04.002.PubMedView ArticleGoogle Scholar
- Miyanari Y, Torres-Padilla M-E: Control of ground-state pluripotency by allelic regulation of Nanog. Nature. 2012, 483: 470-473. 10.1038/nature10807.PubMedView ArticleGoogle Scholar
- Chadwick LH: The NIH Roadmap Epigenomics Program data resource. Epigenomics. 2012, 4: 317-324. 10.2217/epi.12.18.PubMedPubMed CentralView ArticleGoogle Scholar
- Blueprint Epigenome. [http://www.blueprint-epigenome.eu]
- Dai B, Rasmussen TP: Global epiproteomic signatures distinguish embryonic stem cells from differentiated cells. Stem Cells. 2007, 25: 2567-2574. 10.1634/stemcells.2007-0131.PubMedView ArticleGoogle Scholar
- Akalin A, Garrett-Bakelman FE, Kormaksson M, Busuttil J, Zhang L, Khrebtukova I, Milne TA, Huang Y, Biswas D, Hess JL, Allis CD, Roeder RG, Valk PJM, Löwenberg B, Delwel R, Fernandez HF, Paietta E, Tallman MS, Schroth GP, Mason CE, Melnick A, Figueroa ME: Base-pair resolution DNA methylation sequencing reveals profoundly divergent epigenetic landscapes in acute myeloid leukemia. PLoS Genet. 2012, 8: e1002781-10.1371/journal.pgen.1002781.PubMedPubMed CentralView ArticleGoogle Scholar
- Agris P, Crain P, Rozenski J, Fabris D, Vendeix F: The RNA Modification Database. [http://rna-mdb.cas.albany.edu/RNAmods/]
- Desrosiers RC, Friderici KH, Rottman FM: Characterization of Novikoff hepatoma mRNA methylation and heterogeneity in the methylated 5' terminus. Biochemistry. 1975, 14: 4367-4374. 10.1021/bi00691a004.PubMedView ArticleGoogle Scholar
- Wei C-M, Gershowitz A, Moss B: Methylated nucleotides block 5' terminus of HeLa cell messenger RNA. Cell. 1975, 4: 379-386. 10.1016/0092-8674(75)90158-0.PubMedView ArticleGoogle Scholar
- Moss B, Gershowitz A, Stringer JR, Holland LE, Wagner EK: 5' -Terminal and internal methylated nucleosides in herpes simplex virus type 1 mRNA. J Virol. 1977, 23: 234-239.PubMedPubMed CentralGoogle Scholar
- Bodi Z, Button JD, Grierson D, Fray RG: Yeast targets for mRNA methylation. Nucleic Acids Res. 2010, 38: 5327-5335. 10.1093/nar/gkq266.PubMedPubMed CentralView ArticleGoogle Scholar
- Nichols JL: N6-methyladenosine in maize poly(A)-containing RNA. Plant Sci Lett. 1979, 15: 357-361. 10.1016/0304-4211(79)90141-X.View ArticleGoogle Scholar
- Meyer KD, Saletore Y, Zumbo P, Elemento O, Mason CE, Jaffrey SR: Comprehensive analysis of mRNA methylation reveals enrichment in 3' UTRs and near stop codons. Cell. 2012, 149: 1635-1646. 10.1016/j.cell.2012.05.003.PubMedPubMed CentralView ArticleGoogle Scholar
- Dominissini D, Moshitch-Moshkovitz S, Schwartz S, Salmon-Divon M, Ungar L, Osenberg S, Cesarkas K, Jacob-Hirsch J, Amariglio N, Kupiec M, Sorek R, Rechavi G: Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature. 2012, 485: 201-206. 10.1038/nature11112.PubMedView ArticleGoogle Scholar
- Clancy MJ, Shambaugh ME, Timpte CS, Bokar JA: Induction of sporulation in Saccharomyces cerevisiae leads to the formation of N6-methyladenosine in mRNA: a potential mechanism for the activity of the IME4 gene. Nucleic Acids Res. 2002, 30: 4509-4518. 10.1093/nar/gkf573.PubMedPubMed CentralView ArticleGoogle Scholar
- Iwanami Y, Brown GM: Methylated bases of ribosomal ribonucleic acid from HeLa cells. Arch Biochem Biophys. 1968, 126: 8-15. 10.1016/0003-9861(68)90553-5.PubMedView ArticleGoogle Scholar
- Epstein P, Reddy R, Henning D, Busch H: The nucleotide sequence of nuclear U6 (4.7 S) RNA. J Biol Chem. 1980, 255: 8901-8906.PubMedGoogle Scholar
- Desrosiers R, Friderici K, Rottman F: Identification of methylated nucleosides in messenger RNA from Novikoff hepatoma cells. Proc Nat Acad Sci USA. 1974, 71: 3971-3975. 10.1073/pnas.71.10.3971.PubMedPubMed CentralView ArticleGoogle Scholar
- Levis R, Penman S: 5'-Terminal structures of poly(A)+ cytoplasmic messenger RNA and of poly(A)+ and poly(A)− heterogeneous nuclear RNA of cells of the dipteran Drosophila melanogaster. J Mol Biol. 1978, 120: 487-515. 10.1016/0022-2836(78)90350-9.PubMedView ArticleGoogle Scholar
- Wei CM, Gershowitz A, Moss B: 5'-Terminal and internal methylated nucleotide sequences in HeLa cell mRNA. Biochemistry. 1976, 15: 397-401. 10.1021/bi00647a024.PubMedView ArticleGoogle Scholar
- Bringmann P, Lührmann R: Antibodies specific for N6-methyladenosine react with intact snRNPs U2 and U4/U6. FEBS Lett. 1987, 213: 309-315. 10.1016/0014-5793(87)81512-0.PubMedView ArticleGoogle Scholar
- Horowitz S, Horowitz A, Nilsen TW: Mapping of N6-methyladenosine residues in bovine prolactin mRNA. Proc Nat Acad Sci USA. 1984, 81: 5667-5671. 10.1073/pnas.81.18.5667.PubMedPubMed CentralView ArticleGoogle Scholar
- Chen-Kiang S, Nevins JR, Darnell JE: N-6-methyl-adenosine in adenovirus type 2 nuclear RNA is conserved in the formation of messenger RNA. J Mol Biol. 1979, 135: 733-752. 10.1016/0022-2836(79)90174-8.PubMedView ArticleGoogle Scholar
- Beemon K, Keith J: Localization of N6-methyladenosine in the Rous sarcoma virus genome. J Mol Biol. 1977, 113: 165-179. 10.1016/0022-2836(77)90047-X.PubMedView ArticleGoogle Scholar
- Kane SE, Beemon K: Precise localization of m6A in Rous sarcoma virus RNA reveals clustering of methylation sites: implications for RNA processing. Mol Cell Biol. 1985, 5: 2298-2306.PubMedPubMed CentralView ArticleGoogle Scholar
- Jia G, Fu Y, Zhao X, Dai Q, Zheng G, Yang Y, Yi C, Lindahl T, Pan T, Yang Y-G, He C: N6-Methyladenosine in nuclear RNA is a major substrate of the obesity-associated FTO. Nat Chem Biol. 2011, 7: 885-887. 10.1038/nchembio.687.PubMedPubMed CentralView ArticleGoogle Scholar
- Benedict C, Jacobsson JA, Rönnemaa E, Sällman-Almén M, Brooks S, Schultes B, Fredriksson R, Lannfelt L, Kilander L, Schiöth HB: The fat mass and obesity gene is linked to reduced verbal fluency in overweight and obese elderly men. Neurobiol Aging. 2011, 32: 1159.e1-5. 10.1016/j.neurobiolaging.2011.02.006.View ArticleGoogle Scholar
- Keller L, Xu W, Wang HX, Winblad B, Fratiglioni L, Graff C: The obesity related gene, FTO, interacts with APOE, and is associated with Alzheimer's disease risk: a prospective cohort study. J Alzheimers Dis. 2011, 23: 461-469.PubMedGoogle Scholar
- Saletore Y, Mason CE: MeRIPPeR. [http://meripper.sourceforge.net/]
- Rye MB, Sætrom P, Drabløs F: A manually curated ChIP-seq benchmark demonstrates room for improvement in current peak-finder programs. Nucleic Acids Res. 2011, 39: e25-10.1093/nar/gkq1187.PubMedPubMed CentralView ArticleGoogle Scholar
- Wilbanks EG, Facciotti MT: Evaluation of algorithm performance in ChIP-Seq peak detection. PLoS One. 2010, 5: e11471-10.1371/journal.pone.0011471.PubMedPubMed CentralView ArticleGoogle Scholar
- Li H, Durbin R: Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics. 2010, 26: 589-595. 10.1093/bioinformatics/btp698.PubMedPubMed CentralView ArticleGoogle Scholar
- Trapnell C, Pachter L, Salzberg SL: TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009, 25: 1105-1111. 10.1093/bioinformatics/btp120.PubMedPubMed CentralView ArticleGoogle Scholar
- Wu TD, Nacu S: Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics. 2010, 26: 873-881. 10.1093/bioinformatics/btq057.PubMedPubMed CentralView ArticleGoogle Scholar
- Hansen KD, Brenner SE, Dudoit S: Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Res. 2010, 38: e131-10.1093/nar/gkq224.PubMedPubMed CentralView ArticleGoogle Scholar
- Harper JE, Miceli SM, Roberts RJ, Manley JL: Sequence specificity of the human mRNA N6-adenosine methylase in vitro. Nucleic Acids Res. 1990, 18: 5735-5741. 10.1093/nar/18.19.5735.PubMedPubMed CentralView ArticleGoogle Scholar
- Wei C-M, Moss B: Nucleotide sequences at the N6-methyladenosine sites of HeLa cell messenger ribonucleic acid. Biochemistry. 1977, 16: 1672-1676. 10.1021/bi00627a023.PubMedView ArticleGoogle Scholar
- Elemento O, Slonim N, Tavazoie S: A universal framework for regulatory element discovery across all genomes and data types. Mol Cell. 2007, 28: 337-350. 10.1016/j.molcel.2007.09.027.PubMedPubMed CentralView ArticleGoogle Scholar
- Eid J, Fehr A, Gray J, Luong K, Lyle J, Otto G, Peluso P, Rank D, Baybayan P, Bettman B, Bibillo A, Bjornson K, Chaudhuri B, Christians F, Cicero R, Clark S, Dalal R, Dewinter A, Dixon J, Foquet M, Gaertner A, Hardenbol P, Heiner C, Hester K, Holden D, Kearns G, Kong X, Kuse R, Lacroix Y, Lin S, et al: Real-time DNA sequencing from single polymerase molecules. Science. 2009, 323: 133-138. 10.1126/science.1162986.PubMedView ArticleGoogle Scholar
- Flusberg BA, Webster DR, Lee JH, Travers KJ, Olivares EC, Clark TA, Korlach J, Turner SW: Direct detection of DNA methylation during single-molecule, real-time sequencing. Nat Methods. 2010, 7: 461-465. 10.1038/nmeth.1459.PubMedPubMed CentralView ArticleGoogle Scholar
- Song CX, Clark TA, Lu XY, Kislyuk A, Dai Q, Turner SW, He C, Korlach J: Sensitive and specific single-molecule sequencing of 5-hydroxymethylcytosine. Nat Methods. 2011, 9: 75-77.PubMedPubMed CentralView ArticleGoogle Scholar
- Wanunu M, Cohen-Karni D, Johnson RR, Fields L, Benner J, Peterman N, Zheng Y, Klein ML, Drndic M: Discrimination of methylcytosine from hydroxymethylcytosine in DNA molecules. J Am Chem Soc. 2011, 133: 486-492. 10.1021/ja107836t.PubMedPubMed CentralView ArticleGoogle Scholar
- Goll MG, Kirpekar F, Maggert KA, Yoder JA, Hsieh C-L, Zhang X, Golic KG, Jacobsen SE, Bestor TH: Methylation of tRNAAsp by the DNA methyltransferase homolog Dnmt2. Science. 2006, 311: 395-398. 10.1126/science.1120976.PubMedView ArticleGoogle Scholar
- Schaefer M, Pollex T, Hanna K, Tuorto F, Meusburger M, Helm M, Lyko F: RNA methylation by Dnmt2 protects transfer RNAs against stress-induced cleavage. Genes Dev. 2010, 24: 1590-1595. 10.1101/gad.586710.PubMedPubMed CentralView ArticleGoogle Scholar
- Tuorto F, Liebers R, Musch T, Schaefer M, Hofmann S, Kellner S, Frye M, Helm M, Stoecklin G, Lyko F: RNA cytosine methylation by Dnmt2 and NSun2 promotes tRNA stability and protein synthesis. Nat Struct Mol Biol. 2012, 19: 900-905. 10.1038/nsmb.2357.PubMedView ArticleGoogle Scholar
- Urbonavièius J, Qian Q, Durand JMB, Hagervall TG, Björk GR: Improvement of reading frame maintenance is a common function for several tRNA modifications. EMBO J. 2001, 20: 4863-4873. 10.1093/emboj/20.17.4863.View ArticleGoogle Scholar
- Agris PF: Bringing order to translation: the contributions of transfer RNA anticodon-domain modifications. EMBO Rep. 2008, 9: 629-635. 10.1038/embor.2008.104.PubMedPubMed CentralView ArticleGoogle Scholar
- Dao V, Guenther R, Malkiewicz A, Nawrot B, Sochacka E, Kraszewski A, Jankowska J, Everett K, Agris PF: Ribosome binding of DNA analogs of tRNA requires base modifications and supports the "extended anticodon". Proc Natl Acad Sci USA. 1994, 91: 2125-2129. 10.1073/pnas.91.6.2125.PubMedPubMed CentralView ArticleGoogle Scholar
- Decatur WA, Fournier MJ: rRNA modifications and ribosome function. Trends Biochem Sci. 2002, 27: 344-351. 10.1016/S0968-0004(02)02109-6.PubMedView ArticleGoogle Scholar
- Brown JW, Echeverria M, Qu LH: Plant snoRNAs: functional evolution and new modes of gene expression. Trends Plant Sci. 2003, 8: 42-49. 10.1016/S1360-1385(02)00007-9.PubMedView ArticleGoogle Scholar
- Liang X-H, Uliel S, Hury A, Barth S, Doniger T, Unger R, Michaeli S: A genome-wide analysis of C/D and H/ACA-like small nucleolar RNAs in Trypanosoma brucei reveals a trypanosome-specific pattern of rRNA modification. RNA. 2005, 11: 619-645. 10.1261/rna.7174805.PubMedPubMed CentralView ArticleGoogle Scholar
- Ruggero D, Grisendi S, Piazza F, Rego E, Mari F, Rao PH, Cordon-Cardo C, Pandolfi PP: Dyskeratosis congenita and cancer in mice deficient in ribosomal RNA modification. Science. 2003, 299: 259-262. 10.1126/science.1079447.PubMedView ArticleGoogle Scholar
- Stolc V, Gauhar Z, Mason C, Halasz G, van Batenburg MF, Rifkin SA, Hua S, Herreman T, Tongprasit W, Barbano PE, Bussemaker HJ, White KP: A gene expression map for the euchromatic genome of Drosophila Melanogaster. Science. 2004, 306: 655-660. 10.1126/science.1101312.PubMedView ArticleGoogle Scholar
- Akalin A, Garrett-Bakelman FE, Kormaksson M, Busuttil J, Zhang L, Khrebtukova I, Milne TA, Huang Y, Biswas D, Hess JL, Allis CD, Roeder RG, Valk PJ, Löwenberg B, Delwel R, Fernandez HF, Paietta E, Tallman MS, Schroth GP, Mason CE, Melnick A, Figueroa ME: Base-pair resolution DNA methylation sequencing reveals profoundly divergent epigenetic landscapes in acute myeloid leukemia. PLoS Genet. 2012, 8: e1002781-10.1371/journal.pgen.1002781.PubMedPubMed CentralView ArticleGoogle Scholar
- Xhemalce B, Robson SC, Kouzarides T: Human RNA methyltransferase BCDIN3D regulates microRNA processing. Cell. 2012, 151: 278-288. 10.1016/j.cell.2012.08.041.PubMedPubMed CentralView ArticleGoogle Scholar
- Veliz EA, Easterwood LM, Beal PA: Substrate analogues for an RNA-editing adenosine deaminase: mechanistic investigation and inhibitor design. J Am Chem Soc. 2003, 125: 10867-10876. 10.1021/ja029742d.PubMedView ArticleGoogle Scholar
- Li M, Wang IX, Li Y, Bruzel A, Richards AL, Toung JM, Cheung VG: Widespread RNA and DNA sequence differences in the human transcriptome. Science. 2011, 333: 53-58. 10.1126/science.1207018.PubMedPubMed CentralView ArticleGoogle Scholar
- Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R: The sequence alignment/map format and SAMtools. Bioinformatics. 2009, 25: 2078-2079. 10.1093/bioinformatics/btp352.PubMedPubMed CentralView ArticleGoogle Scholar
- Quinlan AR, Hall IM: BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010, 26: 841-842. 10.1093/bioinformatics/btq033.PubMedPubMed CentralView ArticleGoogle Scholar
- Picard. [http://picard.sourceforge.net/]
- Saletore Y, Mason C: Jenotator: Java Genome Annotator. [http://jenotator.sourceforge.net/]
- Zumbo P, Li S, Mason C: r-make. [http://physiology.med.cornell.edu/faculty/mason/lab/r-make/]
- Giannopoulou E, Elemento O: An integrated ChIP-seq analysis platform with customizable workflows. BMC Bioinformatics. 2011, 12: 277-10.1186/1471-2105-12-277.PubMedPubMed CentralView ArticleGoogle Scholar
- Novocraft: Novoalign. [http://www.novocraft.com/]
- Langmead B, Trapnell C, Pop M, Salzberg S: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009, 10: R25-10.1186/gb-2009-10-3-r25.PubMedPubMed CentralView ArticleGoogle Scholar