The assumption that RNA can be readily classified into either protein-coding or non-protein–coding categories has pervaded biology for close to 50 years. Until recently, discrimination between these two categories was relatively straightforward: most transcripts were clearly identifiable as protein-coding messenger RNAs (mRNAs), and readily distinguished from the small number of well-characterized non-protein–coding RNAs (ncRNAs), such as transfer, ribosomal, and spliceosomal RNAs. Recent genome-wide studies have revealed the existence of thousands of noncoding transcripts, whose function and significance are unclear. The discovery of this hidden transcriptome and the implicit challenge it presents to our understanding of the expression and regulation of genetic information has made the need to distinguish between mRNAs and ncRNAs both more pressing and more complicated. In this Review, we consider the diverse strategies employed to discriminate between protein-coding and noncoding transcripts and the fundamental difficulties that are inherent in what may superficially appear to be a simple problem. Misannotations can also run in both directions: some ncRNAs may actually encode peptides, and some of those currently thought to do so may not. Moreover, recent studies have shown that some RNAs can function both as mRNAs and intrinsically as functional ncRNAs, which may be a relatively widespread phenomenon. We conclude that it is difficult to annotate an RNA unequivocally as protein-coding or noncoding, with overlapping protein-coding and noncoding transcripts further confounding this distinction. In addition, the finding that some transcripts can function both intrinsically at the RNA level and to encode proteins suggests a false dichotomy between mRNAs and ncRNAs. Therefore, the functionality of any transcript at the RNA level should not be discounted.
Citation: Dinger ME, Pang KC, Mercer TR, Mattick JS (2008) Differentiating Protein-Coding and Noncoding RNA: Challenges and Ambiguities. PLoS Comput Biol 4(11): e1000176. doi:10.1371/journal.pcbi.1000176
Editor: Johanna McEntyre, National Center for Biotechnology Information (NCBI), United States of America
Published: November 28, 2008
Copyright: © 2008 Dinger et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by the Australian Research Council and the New Zealand Foundation for Research Science and Technology. Neither of these funding agencies played any part in the design or conduct of this study, nor in the analysis of the data or the preparation of the manuscript. MED is funded by a Foundation for Research, Science, and Technology, New Zealand Fellowship. TRM is supported by an Australian Postgraduate Award. JSM is supported by an Australian Research Council Federation Fellowship, the University of Queensland, and the Queensland State Government.
Competing interests: The authors have declared that no competing interests exist.
Numerous studies have demonstrated that the true catalog of RNAs encoded within the genome (the “transcriptome”) is more extensive and complex than previously thought (reviewed in –). In humans and mice, for instance, it has become apparent that the vast majority of the genome is transcribed, often in intricate networks of overlapping sense and antisense transcripts, many of which are alternatively spliced , –. However, mRNAs account for only ~2.3% of the human genome ,, and therefore the vast majority of this unexpected transcription, sometimes referred to as “dark matter” ,, appears to be non-protein–coding.
Unsurprisingly, a great deal of attention is now focused on the noncoding transcriptome. Dominating this field of inquiry has been the discovery of thousands of small RNAs (<200 nt in length). Many of these have since been classified into novel categories (e.g., microRNAs, PIWI-associated RNAs, and endogenous small interfering RNAs) on the basis of function, length, biogenesis, structural/sequence features, and protein-binding partners (reviewed in ). Interestingly, however, long ncRNAs (>200 nt) appear to comprise the largest portion of the mammalian noncoding transcriptome. Tiling array studies of the human genome, for instance, revealed that the majority of transcription occurs as long ncRNAs , some of which may be precursors for smaller RNAs, but many of which are detected as relatively stable polyadenylated and non-polyadenylated transcripts ,.
The biological significance of these long ncRNAs is controversial. Despite an increasing number of long ncRNAs having been shown to fulfill a diverse range of regulatory roles (reviewed in ,), the functions of the vast majority remain unknown and untested. And while this is also true of small RNAs to some extent, long ncRNAs—unlike their smaller counterparts—lack obvious features to allow a priori functional categorization or prediction. Furthermore, the exact prevalence of long ncRNAs remains subject to significant interpretation and debate. For instance, the FANTOM and H-Invitational consortiums annotated comprehensive full-length cDNA collections in mouse and human, respectively ,. Despite using similar methods of cDNA library construction, the two groups came up with very different prevalence estimates for long ncRNAs within the mammalian genome: in mouse, 33% of transcripts (34,030/102,281) were annotated as noncoding; by comparison, only 7% of human transcripts (1,377/21,037) were identified as ncRNAs. That such divergent estimates exist highlights how difficult it has become to discriminate between long ncRNAs and mRNAs. To better understand the nature of these challenges, the approaches used to distinguish noncoding from protein-coding are considered below.
Strategies to Discriminate between ncRNAs and mRNAs
Open reading frame length.
One of the most fundamental criteria used to distinguish long ncRNAs from mRNAs is ORF length. Since short putative ORFs can be expected to occur by chance within long noncoding sequences, minimum ORF cutoffs are usually applied to reduce the likelihood of falsely categorizing ncRNAs as mRNAs. For instance, the FANTOM consortium originally used a cutoff of 300 nt (100 codons) to help identify putative mRNAs . This somewhat arbitrary threshold is consistent with the observation that >95% of proteins in public databases such as Swiss-Prot and the International Protein Index are >100 aa in length , and has subsequently been shown to display a high level of concordance with more sophisticated discrimination methods . This length is also approximately two standard deviations above the average length of ORFs in a one kilobase random sequence (Figure 1).
Twenty thousand transcripts of varying length and random nucleotide composition were computationally generated and scanned for ORFs. The maximum ORF and transcript lengths were plotted and fitted to a logarithmic curve. The shaded regions represent incidences of randomly occurring ORFs at 1, 2, or 3 standard deviations from the mean. The red line indicates the 300 nt ORF threshold that is commonly used to distinguish protein-coding genes in transcript classification pipelines. Therefore, this plot illustrates that for transcripts longer than ~1000 bp, such a threshold may define transcripts as protein-coding that would be expected to occur by chance. The function y = 91.Ln(x)−330, which approximates random ORF incidence according to transcript length at two standard deviations above the mean (i.e., 95% confidence interval, indicated in green), could be used to discriminate noncoding from protein-coding transcripts in a transcript-length–dependent manner.
Using putative ORF length alone, although straightforward to apply across large datasets, is problematic for various reasons. First, bona fide long ncRNAs will by chance contain putative ORFs that are quite long. For instance, H19, Xist, Mirg, Gtl2, and KcnqOT1 all have putative ORFs >100 codons, but have been characterized as functional ncRNAs . Applying a traditional ORF cutoff of 300 nt will therefore misclassify many ncRNAs as mRNAs, and this is especially true for very long ncRNAs, as illustrated in Figure 1. For example, murine Xist is ~15 Kb in size  and contains a putative ORF of 298 aa, which led to the erroneous conclusion that it was a protein-coding gene when first discovered . Second, with a cutoff of 300 nt, proteins <100 aa in size may also be incorrectly classified as ncRNAs. The potential scale of such errors is significant, given recent estimates that the mammalian proteome contains ~3,700 proteins below this size . To minimize such errors, ORF length cutoffs can be reduced, which is exactly what the H-Invitational consortium did in applying a threshold of 60 nt (20 codons) in their annotation pipeline . However, such a low threshold will falsely underestimate the number of ncRNAs, which probably explains to a large extent why the numbers of H-Invitational ncRNAs are so small. Finally, it is notable that even at very low cutoffs, some atypical proteins will still be missed. The tarsal-less (tal) gene, for example, controls tissue folding in Drosophila and encodes a ~1.5 Kb transcript , whose putative ORFs are all extremely short. Tal was therefore initially classified as an ncRNA , but it has subsequently been shown that it is actually translated into multiple 11 aa peptides that fulfill the function of the gene . With examples such as this that highlight the perils of dismissing even the shortest of putative ORFs, one wonders how many other presumed ncRNAs encode real albeit very short proteins.
Given the problems of relying solely upon ORF size, an alternative approach to discriminating long ncRNAs from mRNAs is to assess putative ORFs for similarity to known proteins or protein domains, since such homology provides indirect evidence of function as an mRNA. Indeed, the vast majority of putative human ORFs without cross-species counterparts is likely to be random occurrences , and many studies of individual ncRNAs now cite a lack of ORF conservation to argue against function as an mRNA. Several tools and resources are available for such analysis, including BLASTX , rsCDS , Pfam , and SUPERFAMILY .
A few methods designed to detect ORF conservation can be used to distinguish ncRNAs from mRNAs on a transcriptome-wide scale. These comparative approaches include the programs CSTminer , and CRITICA , both of which exploit the tendency for protein-coding sequences to favor synonymous base changes (i.e., changes that do not result in amino acid substitution) over non-synonymous ones, but are limited by the numbers of genomes available for comparison and the rapid evolution of many ncRNAs , making it difficult to detect orthologous sequences.
There are also other problems in using ORF conservation to identify protein-coding RNAs. First, these approaches are limited by the comprehensiveness and accuracy of current protein annotations. For instance, Xist was annotated as a protein-coding gene in public databases (Swiss-Prot accession: P27571) for almost fifteen years after its characterization as a functional ncRNA , which led to inadvertent misclassification by a computational pipeline in one recent study . Second, some ncRNAs have evolved from protein-coding genes ,, and so will retain remnant signatures of and homologies to mRNAs. Finally, particularly in less complex eukaryotes, such as yeast, absence of ORF conservation even with closely related species, does not guarantee an absence of function. Indeed, a recent study of orphan ORFs in Saccharomyces cerevisiae that had been initially annotated as spurious showed that many produced detectable transcripts and/or translated products .
The approaches described above are primarily designed to identify mRNAs. Consequently, long ncRNAs are typically defined indirectly through an absence of mRNA-like characteristics. In contrast, a number of studies have used the presence of conserved predicted RNA secondary structure to identify ncRNAs imputed to have functional properties. These include the programs QRNA , RNAz , and EvoFOLD . However, using these programs to classify transcripts as ncRNAs is likely to lead to significant false positive and false negative discoveries, since conserved secondary structures are also commonly found in mRNAs (especially 3′ UTRs), and functional ncRNAs may contain secondary or tertiary structures with non-canonical base interactions  that are not considered by structural prediction programs.
As well as computational methods, several experimental strategies have also been used to try to distinguish mRNAs and ncRNAs. For instance, in vitro translation assays have been performed in individual cases to test whether a putative ORF is translated into protein ,,. Positive translation results gives an indication that a transcript is an mRNA, but one needs to interpret such results with caution, since spurious ORFs have previously been translated in vitro and antibodies have even been generated against the resultant protein . Meanwhile, negative translation results also tend to be inconclusive. Another experimental method is to assess whether a transcript is associated with polysomes , as would be expected for mRNAs that are actively translated, but again such a method is far from definitive.
Reliable classification of novel transcripts into mRNAs or ncRNAs is predicated on the assumption that they represent genuine, full-length transcripts. However, incomplete reverse transcription, internal priming of pre-mRNAs, and genomic contamination can all result in the generation of spurious or truncated transcripts, many of which are likely to masquerade as ncRNAs . To help address this issue, various approaches have been used to filter out potential experimental artifacts. The FANTOM3 consortium, for instance, whittled down their original list of 34,030 ncRNAs to a shortlist of 3,652 confidently full-length ncRNAs by requiring that transcripts have stringent support at their 5′ and 3′ ends . Another method excluded ncRNAs that mapped to the same genomic strand and locus as an mRNA, in the belief that such transcripts were likely to represent spuriously truncated mRNAs . Notably, both these approaches are likely to filter out genuine ncRNAs, since not only are very long (>5 Kb) ncRNAs such as Xist and Air unable to be successfully captured as full-length transcripts using current cloning and sequencing approaches , but many genomic loci are also now known to harbor overlapping and/or interleaving mRNAs and ncRNAs on the same strand .
Many of the strategies described above are complementary, and can be combined to good effect. For instance, CRITICA uses statistical techniques in addition to its comparative approach  and was the best-performing of ten bioinformatic methods used to discriminate ncRNAs and mRNAs from the FANTOM cDNA collection . A number of other programs use sophisticated statistical approaches based on integrating a range of characteristic protein-coding signatures, including splice acceptor/donor sites, polyadenylation signals, ORF length, and sequence homology. For example, to discriminate coding regions, DIANA-EST employs a combination of artificial neural networks and statistical approaches , and ESTScan uses a hidden Markov model approach .
Two recently described tools, CPC and CONC, use supervised learning algorithms known as support vector machines to distinguish mRNAs from ncRNAs ,. These algorithms take into consideration multiple features such as peptide length, amino acid composition, protein homologs, secondary structure, and protein alignment information. Both showed high levels of accuracy when cross-validated against reference protein and ncRNA datasets, and are likely to represent the vanguard of future discrimination methods.
Bifunctional RNAs and the False Dichotomy
Despite recent advances such as support vector machines to distinguish ncRNAs from mRNAs, large numbers of novel transcripts remain ambiguous and difficult to definitively categorize. CONC, for instance, estimated that ~28,000 FANTOM cDNAs were ncRNAs, but >50% of these predictions fell outside the reliable range . Are these ncRNAs or mRNAs? Currently, we cannot really say, but perhaps the question is itself flawed. After all, reports are now emerging of transcripts that can not only be translated into protein but also function independently as RNA, and the very existence of such bifunctional RNAs challenges the assumption that the RNA world can be neatly parsed between mutually exclusive protein-coding and noncoding categories.
The first report of a bifunctional RNA was the human Steroid Receptor Activator (SRA). Originally, SRA was characterized as an ncRNA, which functioned at the transcript level to co-activate steroid hormone receptors . Remarkably, SRA transcripts have now been shown to also encode a functional protein (SRAP) , which appears to act antagonistically to SRA RNA at steroid hormone receptors . This raises the intriguing possibility that bifunctional transcripts can negatively regulate their own functions, although just how such a process operates and is controlled requires further study.
Additional examples of bifunctional RNAs have also recently emerged. The VegT RNA has been known for many years to encode a protein needed to establish the primary germ layers in Xenopus . However, VegT RNA has since been shown to also fulfill a separate structural role in the cytokeratin network of primordial germ cells . In Drosophila, Oskar RNA was first characterized for its ability to be translated into one of two proteins important for oocyte development ,. Recently, it has been found that Oskar mRNA (specifically, its 3′ UTR) functions independently of the Oskar protein and is also essential for oogenesis . Moreover, some 3′UTRs can regulate cell proliferation and differentiation in mammals, independently of their associated protein-coding sequences, with perturbations of this information in certain cancers –. There are also examples in bacteria. SgrS is a 227 nt transcript from Escherichia coli that functions to relieve the effects of glucose–phosphate stress. It functions as an RNA by base-pairing with the glucose transporter ptsG mRNA to negatively regulate its translation and stability  but also encodes a 43 aa protein, which acts to further reduce glucose uptake by inhibiting ptsG transporter activity . In this way, the SgrS RNA functions through two distinct mechanisms to protect cells from glucose–phosphate stress. Moreover, these two functions appear to be physiologically redundant , which indicates that in some situations bifunctionality represents an inbuilt failsafe.
The number of documented cases of bifunctional RNAs is limited. However, as mentioned earlier, conserved secondary structures are commonly found in mRNAs, which suggests that bifunctional RNAs might be widespread. Indeed, it was recently predicted that in yeast as much as 5% of mRNAs function independently as RNA, and it was estimated that this proportion is likely to be significantly greater in higher eukaryotes . To further confound this dichotomy, recent studies show that mRNAs that form duplex RNA structures within themselves, or with other antisense RNAs or pseudogenes, may be processed into endogenous siRNAs, therefore providing mRNAs with an additional fate –. Even synonymous sites in codons, often thought to be fully redundant, can encode additional subtle information. For instance, “silent” mutations in synonymous sites can affect both splicing and co-translational folding, and thereby alter protein function ,. This suggests that RNA carries much more cis- and trans-acting information than previously imagined, both within and beyond protein-coding sequences.
As the number of protein-coding genes continues to be revised downward , there appears to be an ever-growing catalogue of ncRNAs. Nevertheless, there is an ongoing lack of clarity regarding the true number of ncRNAs within the genome. This is at least partly due to the inherent difficulties in discriminating ncRNAs from mRNAs and artifacts, especially amongst the thousands of long transcripts that defy categorization by even the most sophisticated of today's classification methods. This situation is further complicated by the emerging realization that the transcriptome may not consist of discrete separable species, but in reality comprise a series of overlapping clusters, of which many span large genomic regions ,,, potentially comprising an information continuum. Looking ahead, we must also be prepared to cast off our historical biases toward what appears now to be an increasingly false dichotomy, and instead embrace the likelihood that RNA is a molecular multi-tasker, whose roles can simultaneously bridge both protein-coding and noncoding domains, and not only have more than one embedded function but also produce multiple products.
- 1. Frith MC, Pheasant M, Mattick JS (2005) The amazing complexity of the human transcriptome. Eur J Hum Genet 13: 894–897.
- 2. Kapranov P, Willingham AT, Gingeras TR (2007) Genome-wide transcription and the implications for genomic organization. Nat Rev Genet 8: 413–423.
- 3. Mattick JS, Makunin IV (2006) Non-coding RNA. Hum Mol Genet 15 Spec No 1: R17–R29.
- 4. Birney E, Stamatoyannopoulos JA, Dutta A, Guigo R, Gingeras TR, et al. (2007) Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447: 799–816.
- 5. Carninci P, Kasukawa T, Katayama S, Gough J, Frith MC, et al. (2005) The transcriptional landscape of the mammalian genome. Science 309: 1559–1563.
- 6. Kapranov P, Cawley SE, Drenkow J, Bekiranov S, Strausberg RL, et al. (2002) Large-scale transcriptional activity in Chromosomes 21 and 22. Science 296: 916–919.
- 7. Bertone P, Stolc V, Royce TE, Rozowsky JS, Urban AE, et al. (2004) Global identification of human transcribed sequences with genome tiling arrays. Science 306: 2242–2246.
- 8. Cheng J, Kapranov P, Drenkow J, Dike S, Brubaker S, et al. (2005) Transcriptional maps of 10 human chromosomes at 5-nucleotide resolution. Science 308: 1149–1154.
- 9. Consortium IHGS (2004) Finishing the euchromatic sequence of the human genome. Nature 431: 931–945.
- 10. Riddihough G (2005) In the forests of RNA dark matter. Science 309: 1507.
- 11. Johnson JM, Edwards S, Shoemaker D, Schadt EE (2005) Dark matter in the genome: Evidence of widespread transcription detected by microarray tiling experiments. Trends Genet 21: 93–102.
- 12. Farazi TA, Juranek SA, Tuschl T (2008) The growing catalog of small RNAs and their association with distinct Argonaute/Piwi family members. Development 135: 1201–1214.
- 13. Kapranov P, Cheng J, Dike S, Nix DA, Duttagupta R, et al. (2007) RNA maps reveal new RNA classes and a possible function for pervasive transcription. Science 316: 1484–1488.
- 14. Kiyosawa H, Mise N, Iwase S, Hayashizaki Y, Abe K (2005) Disclosing hidden transcripts: Mouse natural sense–antisense transcripts tend to be poly(A) negative and nuclear localized. Genome Res 15: 463–474.
- 15. Prasanth KV, Spector DL (2007) Eukaryotic regulatory RNAs: An answer to the “genome complexity” conundrum. Genes Dev 21: 11–42.
- 16. Amaral PP, Dinger ME, Mercer TR, Mattick JS (2008) The eukaryotic genome as an RNA machine. Science 319: 1787–1789.
- 17. Imanishi T, Itoh T, Suzuki Y, O'Donovan C, Fukuchi S, et al. (2004) Integrative annotation of 21,037 human genes validated by full-length cDNA clones. PLoS Biol 2: e162. doi:10.1371/journal.pbio.0020162.
- 18. Okazaki Y, Furuno M, Kasukawa T, Adachi J, Bono H, et al. (2002) Analysis of the mouse transcriptome based on functional annotation of 60,770 full-length cDNAs. Nature 420: 563–573.
- 19. Frith MC, Forrest AR, Nourbakhsh E, Pang KC, Kai C, et al. (2006) The abundance of short proteins in the mammalian proteome. PLoS Genet 2: e52. doi:10.1371/journal.pgen.0020052.
- 20. Frith MC, Bailey TL, Kasukawa T, Mignone F, Kummerfeld SK, et al. (2006) Discrimination of non-protein–coding transcripts from protein-coding mRNA. RNA Biol 3:
- 21. Brockdorff N, Ashworth A, Kay GF, McCabe VM, Norris DP, et al. (1992) The product of the mouse Xist gene is a 15 kb inactive X-specific transcript containing no conserved ORF and located in the nucleus. Cell 71: 515–526.
- 22. Borsani G, Tonlorenzi R, Simmler MC, Dandolo L, Arnaud D, et al. (1991) Characterization of a murine gene expressed from the inactive X chromosome. Nature 351: 325–329.
- 23. Galindo MI, Pueyo JI, Fouix S, Bishop SA, Couso JP (2007) Peptides encoded by short ORFs control development and define a new eukaryotic gene family. PLoS Biol 5: e106. doi:10.1371/journal.pbio.0050106.
- 24. Tupy JL, Bailey AM, Dailey G, Evans-Holm M, Siebel CW, et al. (2005) Identification of putative noncoding polyadenylated transcripts in Drosophila melanogaster. Proc Natl Acad Sci U S A 102: 5495–5500.
- 25. Clamp M, Fry B, Kamal M, Xie X, Cuff J, et al. (2007) Distinguishing protein-coding and noncoding genes in the human genome. Proc Natl Acad Sci U S A 104: 19428–19433.
- 26. Gish W, States DJ (1993) Identification of protein coding regions by database similarity search. Nat Genet 3: 266–272.
- 27. Furuno M, Kasukawa T, Saito R, Adachi J, Suzuki H, et al. (2003) CDS annotation in full-length cDNA sequence. Genome Res 13: 1478–1487.
- 28. Finn RD, Tate J, Mistry J, Coggill PC, Sammut SJ, et al. (2008) The Pfam protein families database. Nucleic Acids Res 36: D281–D288.
- 29. Gough J, Karplus K, Hughey R, Chothia C (2001) Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structure. J Mol Biol 313: 903–919.
- 30. Mignone F, Grillo G, Liuni S, Pesole G (2003) Computational identification of protein coding potential of conserved sequence tags through cross-species evolutionary analysis. Nucleic Acids Res 31: 4639–4645.
- 31. Castrignano T, Canali A, Grillo G, Liuni S, Mignone F, et al. (2004) CSTminer: A Web tool for the identification of coding and noncoding conserved sequence tags through cross-species genome comparison. Nucleic Acids Res 32: W624–W627.
- 32. Badger JH, Olsen GJ (1999) CRITICA: Coding region identification tool invoking comparative analysis. Mol Biol Evol 16: 512–524.
- 33. Pang KC, Frith MC, Mattick JS (2006) Rapid evolution of noncoding RNAs: Lack of conservation does not mean lack of function. Trends Genet 22: 1–5.
- 34. Furuno M, Pang KC, Ninomiya N, Fukuda S, Frith MC, et al. (2006) Clusters of internally primed transcripts reveal novel long noncoding RNAs. PLoS Genet 2: e37. doi:10.1371/journal.pgen.0020037.
- 35. Allen E, Xie Z, Gustafson AM, Sung GH, Spatafora JW, et al. (2004) Evolution of microRNA genes by inverted duplication of target gene sequences in Arabidopsis thaliana. Nat Genet 36: 1282–1290.
- 36. Duret L, Chureau C, Samain S, Weissenbach J, Avner P (2006) The Xist RNA gene evolved in eutherians by pseudogenization of a protein-coding gene. Science 312: 1653–1655.
- 37. Li QR, Carvunis AR, Yu H, Han JD, Zhong Q, et al. (2008) Revisiting the Saccharomyces cerevisiae predicted ORFeome. Genome Res 18: 1294–1303.
- 38. Rivas E, Eddy SR (2001) Noncoding RNA gene detection using comparative sequence analysis. BMC Bioinformatics 2: 8.
- 39. Washietl S, Hofacker IL, Stadler PF (2005) Fast and reliable prediction of noncoding RNAs. Proc Natl Acad Sci U S A 102: 2454–2459.
- 40. Pedersen JS, Bejerano G, Siepel A, Rosenbloom K, Lindblad-Toh K, et al. (2006) Identification and classification of conserved RNA secondary structures in the human genome. PLoS Comput Biol 2: e33. doi:10.1371/journal.pcbi.0020033.
- 41. Leontis NB, Lescoute A, Westhof E (2006) The building blocks and motifs of RNA architecture. Curr Opin Struct Biol 16: 279–287.
- 42. Kohtz JD, Fishell G (2004) Developmental regulation of EVF-1, a novel non-coding RNA transcribed upstream of the mouse Dlx6 gene. Gene Expr Patterns 4: 407–412.
- 43. Lanz RB, McKenna NJ, Onate SA, Albrecht U, Wong J, et al. (1999) A steroid receptor coactivator, SRA, functions as an RNA and is present in an SRC-1 complex. Cell 97: 17–27.
- 44. Glasgow E, Ryu SL, Yamashita M, Zhang BJ, Mutsuga N, et al. (2005) APeg3, a novel paternally expressed gene 3 antisense RNA transcript specifically expressed in vasopressinergic magnocellular neurons in the rat supraoptic nucleus. Brain Res Mol Brain Res 137: 143–151.
- 45. Ravasi T, Suzuki H, Pang KC, Katayama S, Furuno M, et al. (2006) Experimental validation of the regulated expression of large numbers of non-coding RNAs from the mouse genome. Genome Res 16: 11–19.
- 46. Hatzigeorgiou AG, Fiziev P, Reczko M (2001) DIANA-EST: A statistical analysis. Bioinformatics 17: 913–919.
- 47. Lottaz C, Iseli C, Jongeneel CV, Bucher P (2003) Modeling sequencing errors by combining Hidden Markov models. Bioinformatics 19: Supplement 2ii103–ii112.
- 48. Liu J, Gough J, Rost B (2006) Distinguishing protein-coding from non-coding RNAs through support vector machines. PLoS Genet 2: e29. doi:10.1371/journal.pgen.0020029.
- 49. Kong L, Zhang Y, Ye ZQ, Liu XQ, Zhao SQ, et al. (2007) CPC: Assess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Res 35: W345–W349.
- 50. Chooniedass-Kothari S, Emberley E, Hamedani MK, Troup S, Wang X, et al. (2004) The steroid receptor RNA activator is the first functional RNA encoding a protein. FEBS Lett 566: 43–47.
- 51. Chooniedass-Kothari S, Hamedani MK, Troup S, Hube F, Leygue E (2006) The steroid receptor RNA activator protein is expressed in breast tumor tissues. Int J Cancer 118: 1054–1059.
- 52. Zhang J, Houston DW, King ML, Payne C, Wylie C, et al. (1998) The role of maternal VegT in establishing the primary germ layers in Xenopus embryos. Cell 94: 515–524.
- 53. Kloc M, Wilk K, Vargas D, Shirato Y, Bilinski S, et al. (2005) Potential structural role of non-coding and coding RNAs in the organization of the cytoskeleton at the vegetal cortex of Xenopus oocytes. Development 132: 3445–3457.
- 54. Ephrussi A, Lehmann R (1992) Induction of germ cell formation by oskar. Nature 358: 387–392.
- 55. Markussen FH, Michon AM, Breitwieser W, Ephrussi A (1995) Translational control of oskar generates short OSK, the isoform that induces pole plasma assembly. Development 121: 3723–3732.
- 56. Jenny A, Hachet O, Zavorszky P, Cyrklaff A, Weston MD, et al. (2006) A translation-independent role of oskar RNA in early Drosophila oogenesis. Development 133: 2827–2833.
- 57. Rastinejad F, Blau HM (1993) Genetic complementation reveals a novel regulatory role for 3′ untranslated regions in growth and differentiation. Cell 72: 903–917.
- 58. Rastinejad F, Conboy MJ, Rando TA, Blau HM (1993) Tumor suppression by RNA from the 3′ untranslated region of alpha-tropomyosin. Cell 75: 1107–1117.
- 59. Fan H, Villegas C, Huang A, Wright JA (1996) Suppression of malignancy by the 3′ untranslated regions of ribonucleotide reductase R1 and R2 messenger RNAs. Cancer Res 56: 4366–4369.
- 60. Jupe ER, Liu XT, Kiehlbauch JL, McClung JK, Dell'Orco RT (1996) Prohibitin in breast cancer cell lines: Loss of antiproliferative activity is linked to 3′ untranslated region mutations. Cell Growth Differ 7: 871–878.
- 61. Jupe ER, Liu XT, Kiehlbauch JL, McClung JK, Dell'Orco RT (1996) The 3′ untranslated region of prohibitin and cellular immortalization. Exp Cell Res 224: 128–135.
- 62. Amack JD, Paguio AP, Mahadevan MS (1999) Cis and trans effects of the myotonic dystrophy (DM) mutation in a cell culture model. Hum Mol Genet 8: 1975–1984.
- 63. Vanderpool CK, Gottesman S (2004) Involvement of a novel transcriptional activator and small RNA in post-transcriptional regulation of the glucose phosphoenolpyruvate phosphotransferase system. Mol Microbiol 54: 1076–1089.
- 64. Wadler CS, Vanderpool CK (2007) A dual function for a bacterial small RNA: SgrS performs base pairing–dependent regulation and encodes a functional polypeptide. Proc Natl Acad Sci U S A 104: 20454–20459.
- 65. Warden CD, Kim SH, Yi SV (2008) Predicted functional RNAs within coding regions constrain evolutionary rates of yeast proteins. PLoS ONE 3: e1559. doi:10.1371/journal.pone.0001559.
- 66. Tam OH, Aravin AA, Stein P, Girard A, Murchison EP, et al. (2008) Pseudogene-derived small interfering RNAs regulate gene expression in mouse oocytes. Nature 453: 534–538.
- 67. Ghildiyal M, Seitz H, Horwich MD, Li C, Du T, et al. (2008) Endogenous siRNAs derived from transposons and mRNAs in Drosophila somatic cells. Science 320: 1077–1081.
- 68. Watanabe T, Totoki Y, Toyoda A, Kaneda M, Kuramochi-Miyagawa S, et al. (2008) Endogenous siRNAs from naturally formed dsRNAs regulate transcripts in mouse oocytes. Nature 453: 539–543.
- 69. Kimchi-Sarfaty C, Oh JM, Kim IW, Sauna ZE, Calcagno AM, et al. (2007) A “silent” polymorphism in the MDR1 gene changes substrate specificity. Science 315: 525–528.
- 70. Komar AA (2007) Silent SNPs: Impact on gene function and phenotype. Pharmacogenomics 8: 1075–1080.
- 71. Denoeud F, Kapranov P, Ucla C, Frankish A, Castelo R, et al. (2007) Prominent use of distal 5′ transcription start sites and discovery of a large number of additional exons in ENCODE regions. Genome Res 17: 746–759.
- 72. Manak JR, Dike S, Sementchenko V, Kapranov P, Biemar F, et al. (2006) Biological function of unannotated transcription during the early development of Drosophila melanogaster. Nat Genet 38: 1151–1158.