We also compared the rpoH sequences from 81 mesophilic proteobacteria. Although both rpoH and ibpA show a higher rate of melting at their RBS compared with the mean of non-thermometers, contrary to our expectations these higher rates are not significant. Surprisingly, we also do not find any significant differences between rpoH thermometers from other E. coli non-thermometers.A large number of studies have been dedicated to identify the structural and sequence based features of RNA thermometers, mRNAs that regulate their translation initiation rate with temperature. It has been shown that the melting of the ribosome-binding site (RBS) plays a prominent role in this thermosensing process. However, little is known as to how widespread this melting phenomenon is as earlier studies on the subject have worked with a small sample of known RNA thermometers. We have developed a novel method of studying the melting of RNAs with temperature by computationally sampling the distribution of the RNA structures at various temperatures using the RNA folding software Vienna. In this study, we compared the thermosensing property of 100 randomly selected mRNAs and three well known thermometers -  Many microorganisms live in a variable environment. They have evolved a variety of mechanisms to sense changes in their environment and alter their gene expression in response to these changes. Regulatory proteins often play a role in controlling the level of transcription and translation of other genes. However, in certain cases post-transcriptional mechanisms, such as changes in mRNA conformation, are known to influence gene expression. In some prokaryotes, reaction to changes in the temperature is thought to be mediated by one such class of mRNAs called RNA thermometers Previous work on RNA thermometers has focused primarily on understanding and identifying their sequence based features and residues important for thermosensing Although these studies provide insights into the mechanisms by which specific thermometers function, little is known as to how widespread these mechanisms are. The fraction of genes in a genome that possess an ability to regulate their translation by thermosensing or a similar mechanism is unknown. More importantly, because the above studies do not include non-thermometers as controls, it is difficult to ascertain if RNA thermometers are a special class of molecules different from other RNAs. Since it is not feasible to perform mutational or spectral studies on every gene to identify whether it behaves as an RNA thermometer, computational tools need to be developed to provide these insights. We here propose a computational approach to characterize RNA thermometers and ask how they differ from non-thermometers in their ability to melt with increasing temperature. Understanding the melting potential of non-thermometers should aid in understanding the adaptive features of RNA thermometer sequences. We focus specifically on the ability of genes to change their expression by modifying the accessibility of RBS, or in other words, ‘RBS exposure’.Earlier attempts to identify potential RNA thermometers have focused on search patterns based on similarities in the secondary structure of the mRNAs Here we propose a novel method of quantitatively studying secondary structures of RNAs that addresses all of the above shortcomings. This method explores the ability of mRNAs to change their rate of translation initiation with temperature. We see this approach as complementary to experimental studies in the field of RNA structures.probabilities equal to their Boltzmann weights via stochastic backtracking in the partition function We used the RNAsubopt package from RNA folding software Vienna E. coli genome  structures by constraining the RBS in the open conformation In order to compare the probability of openness across temperatures, we fitted a logistic model to the fraction of open windows as a function of temperature.We find that the logistic model serves as a reasonable descriptor of RNA melting . At veryrpoH gene sequence of E. coli. The rpoH gene is a rpoH shows a much higher fold-change in openness as compared to the regions flanking it. The openness of the RBS at 50ibpA in E. coli, resulted in a loss of thermosensing activity. rpoH and ibpA have a higher rate of increase in their RBS exposure compared to the mean of the randomly selected 100 E. coli genes. However, the MLE of ibpA gene sequence, as we did not observe a single open window in 1000 runs at all temperatures between 25To show that our method is capable of capturing the increase in openness of the RBS of an RNA thermometer, we used the Shapiro-Wilk test, rpoH and ibpA had a higher Wilcox test, agsA had a When the rate of openness of RBS, ATG) see , stabiliInterestingly, the median transition temperature, given by rpoH of 81 mesophilic rpoH sequences, 17 (21%) showed no significant change in their Kolmogorov-Smirnov testE. coli genes with significant rpoH genes of 64 mesophilic In order to show the generality of the above results, we compared the distribution of We present here a novel method of studying the melting of RNAs with temperature by incorporating the entire distribution of the RNA structures at a given temperature. This approach is more holistic as it takes into account the probability of finding the RNA in a sub-optimal structure based on its free energy as opposed to previous studies which have looked at structures with the least free energies only As one would expect, we find that mRNAs have an inherent tendency to melt with an increase in temperature. This tendency varies with the sequence and the difference in temperatures. Contrary to our expectations, we find that RNA thermometers are not unique with respect to their ability to increase their RBS exposure with temperature. Since it is difficult and expensive to demonstrate the effect of temperature on the RNA secondary structure in the laboratory, researchers have focused primarily on known RNA thermometers. However, due to a lack of such studies on non-thermometers, it has been hard to ascertain whether thermosensing properties are unique to a special class of RNAs. Our results call for further experimental exploration of ‘non-thermometers’ with changes in temperatures, before firm conclusions can be drawn regarding the uniqueness of RNA thermometers.Physiological similarities between RNA thermometers and non-thermometers with respect to their melting with temperature, raise an important question that if a large number of mRNAs show an extensive increase in RBS exposure with temperature, why don't we see corresponding changes in their protein expressions. In other words, why do physiological similarities not lead to functional similarities? This discrepancy could be explained, in part, by the fact that the amount of protein expression depends on a variety of factors such as mRNA abundance and stability, amount of regulatory proteins, the stability of the protein itself, and factors apart from the accessibility of the RBS of the mRNA to the ribosome. Hence, although temperature may not result in significant phenotypic effects of certain genes in terms of protein expression, it does not preclude the possibility of changes in its RBS exposure. Thus, the above results indicate that increased RBS exposure does not solely define as to what constitutes an RNA thermometer.One of the key challenges in such studies is to devise appropriate measures that quantify the structural features in analyzing the distribution of secondary structures. Here, we use a simple measure of openness to quantify the changes in the structure with temperature. In order to quantify complex structural features like stems and loops in a distribution of RNA structures, more sophisticated measures could be developed. Our analysis based on the current state of RNA folding algorithms is also limited by the simple energy model as well as parameter estimates used in most algorithms.Another key limitation of this study is the fact that current RNA folding algorithms do not take into account the effect of presence of ribosome on the mRNAs secondary structure. The secondary structure of an mRNA becomes a constantly changing environment due to the presence and movement of ribosomes along the mRNA affecting the openness of a window both upstream and downstream of its current position. Hence, including the effect of ribosomes on the mRNA on translation initiation in the folding algorithm may be important in identifying RNA thermometers computationally. This is likely to be a non-trivial task both mathematically and computationally. However, we believe that incorporating the movement of ribosomes in RNA folding routine would open new avenues of research in investigating and understanding not only the effect of ribosome on the RNA structure and in translation initiation but also on the effect of any RNA-protein interactions on the secondary structure of the RNA.Table S1Escherichia coli genes used in the study, their functions and methods used to identify their lengths and ribosome binding sites.List of (0.03 MB XLS)Click here for additional data file.Table S2rpoH gene sequences were used in the study.List of Mesophilic γ-proteobacteria whose (0.02 MB PDF)Click here for additional data file.Figure S1E. coli. rpoH, ibpA and agsA genes show an increase in openness with temperature with b values 0.109, 0.137 and 0.0, respectively. However, none of these values are significantly higher than the mean b = 0.158, of the distribution . In addition, when the base G71 is removed from ibpA sequence, the MLE estimate of b reduces to 0.The distribution of MLE estimates of b of the 75 genes that differed significantly from zero in (0.35 MB EPS)Click here for additional data file.


The 5′ end of the IgH locus contains a cluster of DNaseI hypersensitive sites, one of which (HS1) was shown to be pro-B cell specific and to contain binding sites for the transcription factors PU.1, E2A, and Pax5. These data as well as the location of the hypersensitive sites at the 5′ border of the IgH locus suggested a possible regulatory function for these elements with respect to the IgH locus. To test this notion, we generated mice carrying targeted deletions of either the pro-B cell specific site HS1 or the whole cluster of DNaseI hypersensitive sites. Lymphocytes carrying these deletions appear to undergo normal development, and mutant B cells do not exhibit any obvious defects in V(D)J recombination, allelic exclusion, or class switch recombination. We conclude that deletion of these DNaseI hypersensitive sites does not have an obvious impact on the IgH locus or B cell development. H segments and multiple D and JH segments but an individual IgH V(D)J exon is assembled from only one VH, one D, and one JH segment.The variable region of an immunoglobulin heavy chain (IgH) is assembled from V (variable), D (diversity), and J (joining) gene segments that lie upstream of several IgH constant (C) region exons in a process called V(D)J recombination H recombination precedes VH to DJH recombination H rearrangement efficiently occurs on both alleles, however, allelic exclusion ensures that VH to DJH recombination results in expression of a functional heavy chain (HC) from only one of the two alleles V(D)J recombination of the IgH locus takes place in pro-B cells in an ordered way such that D to JH exons replaces the Cµ exons and the intervening sequence is deleted from the chromosome, which results in expression of a new C region without changing the specificity of the IgH variable region Mature B-cells can undergo further alterations of their HCs. IgH class switch recombination (CSR) causes expression of different immunoglobulin isotypes which confer different effector functions. During this recombination process one of several sets of downstream CH – CH intron and was shown to be necessary for efficient V(D)J recombination by promoting both D to JH and VH to DJH recombination H genes at the very 3′ end of the IgH locus a cluster of DNaseI hypersensitive sites was described, termed 3′ IgH regulatory region (3′IgH RR). So far two main functions have been assigned to this regulatory region: the 3′IgH RR plays an important role in promoting CSR to most IgH isotypes, and the 3′IgH RR was shown to be necessary for high level expression of the functionally assembled HC gene from the promoter 5′ of the VHDJH exon A large effort has been made to elucidate mechanisms of IgH locus regulation and a number of cis-regulatory elements have been described and characterized. The IgH intronic enhancer (Eµ) resides in the JH germline transcripts as so far no cis-regulatory element has been identified that controls activity of the bulk of unrearranged VH promoters. Furthermore, it is not known how it is achieved that proximal and distal VH segments are activated independently or why usage of distal versus proximal VH gene families varies significantly.An additional potential regulatory region was identified at the 5′ end of the IgH locus, consisting of four DNaseI hypersensitive sites Here we report the targeted deletion of the pro-B cell specific 5′IgH HS1 as well as combined deletion of HS1, HS2, HS3a,b in mice. We analyzed potential implications on B cell development, V(D)J recombination, and IgH CSR.5′ GTCGACGGATTTAGGAGGATACACAAC 3′ and 5′ GTCGACCTTGGATAACACAGAACTCTG 3′ containing a SalI site at their 5′ ends, which facilitate cloning of the PCR product into the SalI site of pLNTK. As a 3′ homology arm a 7.3 kb AatII – ApaI fragment was blunt end cloned into the XhoI site of pLNTK. The R3′HSs targeting vector was generated by blunt end cloning a 4.4 kb EcoRI fragment into the SalI site of pLNTK as a 5′ homology arm, and a 7.0 kb KpnI fragment into the XhoI site as the 3′ homology arm. Correct targeting events and cre – loxP deletion events were confirmed by Southern blotting . The RHS1 targeting vector was assembled in pLNTK blotting . Probe 1a/b embryonic stem (ES) cells, generated in the Alt laboratory, were transfected with PvuI linearized RHS1 targeting vector to obtain RHS1/+ ES cells. To obtain ΔHS1/+ ES cells, the PGK-NeoR cassette was deleted by applying a Cre – expressing adenovirus vector. ΔHS1/+ ES cells were transfected with PvuI linearized R3′HSs targeting vector to obtain R3′HSs/+ ES cells. R3′HSs/+ ES cells were selected for homozygocity of the targeted allele through increasing concentration of G418 to obtain R3′HSs/R3′HSs ES cells. Cre – loxP mediated deletion of the PGK-NeoR cassette resulted in ΔHSs/ΔHSs ES cells. Targeted ES cells were injected into Rag2-/- blastocysts to obtain RDBC chimeras EF1 heterozygous IgH- splenocytes were isolated by MACS, stimulated with LPS (20 µg/ml), and fused to NS-1 plasmacytoma cells  as described previously H4, a 0.38 kb SacI - ApaI fragment 3′ of DHQ52, and a 0.75 kb PCR product 5′ of DHFL16.1 generated with oligonucleotides 5′ GAACAGCAACCCTTGACTGACTCTG 3′ and 5′ GATTGGTTCTTATGGAATGGGTGG 3′.CD43- B220+ CD43hi), pre B-cells (IgM- B220+ CD43lo), and double positive T-cells (B220- CD4+ CD8+) were isolated by FACS on a FACSAria (BD Biosciences) and genomic DNA was extracted. 50 ng DNA or 5-fold dilutions were analyzed by PCR for DH–JH, VH-DJH, Vκ–Jκ, and Vλ–Jλ rearrangements with primers listed in Pro-B cells . 200 ng–1 µg of total RNA was reverse transcribed for one hour at 50°C using random hexamers (Roche) and Superscript III (Invitrogen) reverse transcriptase. PCR was performed at 94°C for 4′, 30–39 cycles of 94°C for 30″, annealing temperature  for 30″,Single cell suspensions from spleen, thymus, or bone marrow were stained in PBS 2% FBS with various antibodies: FITC-αIgM, PE-Cy5-αB220, PE-αCD8a, PE-αCD43, FITC-αLy9.1, APC-αIgM, APC-Cy7-αB220 (BD Pharmingen), PE-αAA4.1, FITC-αCD4 (eBioscience). FACS analysis was performed on a FACSCalibur (BD Biosciences) and a FACSAria (BD Biosciences) apparatus. Cell sorts were performed on a FACSAria (BD Biosciences) apparatus.in vivo function of the cluster of DNaseI hypersensitive sites described at the 5′ end of the IgH locus R cassette. All targeting experiments were performed in heterozygous IgHa/b EF1 ES cells which have the advantage that IgHa (129 strain) and IgHb (C57BL/6 strain) alleles can be distinguished by antibodies against the different allotypes or by detection of restriction fragment length polymorphisms (RFLP). Targeting vector homology arms were cloned from 129 strain genomic DNA, resulting in correct targeting events only on the IgHa allele. In heterozygous targeted ES cells, the IgHb allele always remained in the untargeted wildtype configuration.To determine the RHS1 allele and, upon cre/loxP deletion, the ΔHS1 allele. Correct targeting events (Rag2-/- blastocysts to obtain Rag-deficient blastocyst complementation (RDBC) chimeras, and into wildtype blastocyts to generate mice that carry the RHS1 or ΔHS1 allele in their germline. In order to delete all four hypersensitivity sites , ES cells containing the ΔHS1 allele were targeted with the R3′HSs targeting vector to obtain the R3′HSs allele J recombination leads to an accumulation of pro-B cells and to reduced numbers of pre-B cells +/CD43+ events in the total lymphocyte gate were 14±2 and 50±20 for wildtype, 9±3 and 56±6 for ΔHS1, and 9±3 and 42±11 for RHS1 mice  and mature (65.3%–72.7%) B-cell compartments similar to wildtype were identified; whereas, in spleens from RDBC chimeras generated from homozygous ΔHSs ES cells strongly reduced numbers of transitional B-cells were observed (6.44%). This reduction in the transitional B-cell compartment compared to the mature B-cell compartment (75%) might be due to overall reduced numbers of developing B cells in the obtained RDBC chimeras and to the accumulation of mature B-cells in the periphery of these mice and not to a defect in B cell development. Finally, we observed normal development of T-lymphocytes in the thymi of wildtype, homozygous RHS1, and homozygous ΔHS1 mice as well as RDBC chimeras generated from homozygous ΔHSs ES cells  and pre-B cells (IgM- B220+ CD43lo) from bone marrow and double positive (DP) T-cells (B220- CD4+ CD8+) from thymus were analyzed for D to JH, VH to DJH, Vκ to Jκ and Vλ to Jλ rearrangements. Intensities of PCR bands for DHQ52 to JH J recombination at the IgH locus 52 to JH  and DSP ngements  were comLG5 gene .H families H7183 family . In 50–60% of wildtype B-cells the nonproductive allele is in DJH configuration; whereas in 40–50% the nonproductive allele is in VHDJH configuration H alleles could indicate less efficient VH to DJH recombination: in contrast, an increased percentage of VHDJH alleles might indicate a break in allelic exclusion. IgMa expressing hybridomas generated from B-cells heterozygous for RHS1, ΔHS1, and ΔHSs were analyzed for their rearrangement status of the wildtype IgMb allele and show ratios of DJH (56%–61%), and VHDJH alleles (39%–44%) in the expected range  rearrangement ratios compared to wt alleles, as 52%–69% of mutant alleles were in DJH configuration while 31%–48% were in VHDJH configuration.Wildtype B cells undergo D to Jed range . IgMb exRHS1, ΔHS1, and ΔHSs ES cells. IgMa expressing populations, representing the targeted allele, and IgMb expressing populations, representing the wildtype allele, were of similar size both in bone marrow and in spleen from RHS1, ΔHS1, and ΔHSs chimeras, suggesting that the RHS1, ΔHS1, and ΔHSs alleles can undergo V(D)J recombination at the IgH locus at similar efficiencies as wildtype alleles.FACS analysis was performed on B-cells from spleens  and bonea or IgHb, but intact allelic exclusion prevents the appearance of an obvious IgHa, IgHb double producing population. Similarly, RDBC chimeras generated from heterozygous RHS1, ΔHS1, and ΔHSs ES cells exhibited IgHa or IgHb single positive B-cell populations of similar size in spleen  or deletion of the entire cluster of hypersensitive sites (ΔHSs) in mice or in their lymphocytes, respectively. A potential regulatory element at the 5′end of the IgH locus was speculated to regulate processes such as IgH allelic exclusion, VH germline transcription, differential accessibility or usage of distal versus proximal VH gene families. Furthermore, it was suggested that the 5′end of the IgH locus might play a role in positioning the IgH locus in distinct subnuclear compartments This study aimed for elucidating the potential regulatory functions of a cluster of recently described DNaseI hypersensitive sites at the 5′ end of the IgH locus ΔHS1, RHS1, and ΔHSs alleles appear to proceed through lymphocyte development in an unimpaired way. Data from RDBC chimeras generated from heterozygous ΔHS1, RHS1, and ΔHSs ES cells indicated that allelic exclusion is not affected in mutant B-cells and that mutant IgH alleles can undergo efficient V(D)J recombination of their IgH locus. Furthermore, data from PCR assays to analyze V(D)J recombination efficiency in mice with HS1 deleted on both alleles supports the notion that HS1 is not necessary for neither the D to JH nor the VH to DJH recombination step. Both proximal and distal VH families as well as the distal most VH segment VHJ558.55 rearrange as efficiently as on wildtype alleles. Similarly, IgL loci in HS1 deleted B-cells rearrange at the same efficiency as wildtype IgL loci. Analysis of IgH V(D)J rearrangement status in hybridomas generated from heterozygous ΔHS1, RHS1, and ΔHSs B-cells also strengthens the idea that the deleted DNAseI hypersensitive sites would not regulate IgH V(D)J recombination. We tested for potential alterations associated with DNA end processing during V(D)J recombination by examining the CDR3 sequence obtained from homozygous ΔHS1 B cells and found a distribution in length that was similar to wildtype B cells B- and T-lymphocytes homozygous for the ΔHSs B-cells let us conclude that the cluster of 5′IgH DNaseI hypersensitive sites does not play a crucial role in CSR.We tested a potential effect of the cluster of DNaseI hypersensitive site on the process of IgH CSR. Assaying class switching upon different in vitro stimulations in wildtype and homozygous ΔHS1 mice and seem to be independent of the IgH locus. ΔHS1 mice show abnormal limp grasping indicating a neurological abnormality, ΔHS1 mice can develop severe hydrocephalus and exhibit retinal impairments. A possible explanation for these phenotypes is a potential defect in regulation of the zinc finger protein Zfp386. ΔHS1 deletes a 340 bp region from intron 1 of Zfp386 which might result in different splice forms, impaired expression levels, or expression patterns of this gene.The only observed phenotypes so far occurred in homozygous Overall, our analysis of the deletion of the pro-B cell specific site HS1 or the whole cluster of 5′IgH DNaseI hypersensitive sites did not support the existence of a cis-regulatory function of these elements regarding the IgH locus.Figure S1Single integration of the RHS1 targeting vector. The targeting vector (targeting vector RHS1), the targeted locus (RHS1), and the wildtype (wt) IgH locus with its 5′ flanking region are shown. VH, DH, JH indicate representative IgH V, D, and J segments. Exons 1, 2, and 3 of Zfp386 are shown as grey rectangles, DNaseI hypersensitive sites HS1, HS2, HS3a, and HS3b are shown as black ovals, the NeoR specific Southern probe as a black rectangle. X - XbaI. Southern analysis of XbaI digested genomic DNA from the targeted RHS1 clones 5 (lane 1) and 23 (lane 2) utilizing the NeoR specific probe shows a single 16.0 kb band. No bands are visible from untargeted wildtype ES cell DNA (lane 3). M - Fermentas 1 kb ladder.(0.17 MB TIF)Click here for additional data file.Figure S2ΔHS1 B cells. Peripheral B cells were isolated from one ΔHS1 mouse and a wildtype mouse and amplified for V558-JH4 rearrangements. Heavy Chain CDR3 lengths were calculated as the number of nucleotides between the consensus Cys residue and the Trp residue. 23 individual sequences were analyzed from ΔHS1 B cells and 10 from wildtype.CDR3 length in (0.05 MB PDF)Click here for additional data file.Table S1(0.08 MB DOCX)Click here for additional data file.


We found little overlap between the sets of transcripts with altered expression in the trf4Δ or the trf5Δ mutants, suggesting that Trf4p and Trf5p target distinct groups of RNAs for degradation. Surprisingly, most RNAs the expression of which was altered by the trf4 deletion were restored to wild-type levels by overexpression of TRF4(DADA), showing that the polyadenylation activity of Trf4p is dispensable in vivo. Apart from previously reported Trf4p and Trf5p target RNAs, this analysis along with in vivo cross-linking and RNA immunopurification-chip experiments revealed that both the TRAMP4 and the TRAMP5 complexes stimulate the degradation of spliced-out introns via a mechanism that is independent of the polyadenylation activity of Trf4p. In addition, we show that disruption of trf4 causes severe shortening of telomeres suggesting that TRF4 functions in the maintenance of telomere length. Finally, our study demonstrates that TRF4, the exosome, and TRF5 participate in antisense RNA–mediated regulation of genes involved in phosphate metabolism. In conclusion, our results suggest that paralogous TRAMP complexes have distinct RNA selectivities with functional implications in RNA surveillance as well as other RNA–related processes. This indicates widespread and integrative functions of TRAMP complexes for the coordination of different gene expression regulatory processes.Trf4p and Trf5p are non-canonical poly(A) polymerases and are part of the heteromeric protein complexes TRAMP4 and TRAMP5 that promote the degradation of aberrant and short-lived RNA substrates by interacting with the nuclear exosome. To assess the level of functional redundancy between the paralogous Trf4 and Trf5 proteins and to investigate the role of the Trf4-dependent polyadenylation  S. cerevisiae, Trf4p and Trf5p are alternative subunits of the so-called TRAMP complex, which degrades aberrant and short-lived RNAs. They add short poly(A) tails to their substrate RNAs that function as landing pads for exonucleases mediating RNA decay. Although alternate compositions of TRAMP complexes exist, the RNA substrate specificities and the processes controlled by them have not been determined. Applying a genome-wide approach, we describe overlapping yet distinct functional implications of different TRAMP complexes, and we demonstrate strong connections between RNA quality control and other RNA–related processes such as telomer length maintenance. Moreover, our study shows that the degradation of specific target RNAs is not strictly dependent on the polyadenylation activity of Trf proteins in vivo. These results suggest novel and integrative functions of TRAMP complexes for RNA regulation.The discovery that most regions of the genome are actively transcribed into non-coding RNAs has dramatically increased interest in their function and regulation. Recent data from us and others have shed light on the molecular machinery that promotes the decay of such transcripts. In the yeast  Gene expression in eukaryotes depends on highly complex mechanisms for production of mature RNA molecules. Precursors of mRNAs, ribosomal RNAs (rRNAs), transfer RNAs (tRNAs), small nucleolar RNAs (snoRNAs), and small nuclear RNA (snRNAs) undergo stepwise processing and maturation, which includes 5′-capping, splicing, 3′-polyadenylation, endo- and exonucleolytic trimming, and base modifications. All these processes are error-prone and thus, RNA maturation has to be monitored by nuclear and cytoplasmic RNA quality control pathways to remove potentially harmful aberrant RNAs Saccharomyces cerevisiae, nuclear RNA surveillance is mediated by the combined action of the Trf4/5-Air1/2-Mtr4 (TRAMP) complex and the exosome that promote rapid degradation of nonfunctional RNAs In the budding yeast iMet) rrp6Δ. Originally detected in some intergenic regions (IGRs) Initially, Trf4 protein was identified as a key player in the surveillance and degradation of hypomodified initiator tRNA  suppressed most of the altered expression pattern as seen in the trf4Δ mutant cells suggesting that the TRAMP polyadenylation activity is not essential for RNA regulation. We further demonstrate that Trf4p and to a lower extent Trf5p promotes the degradation of a group of introns through an exosome-dependent but polyadenylation-independent mechanism. Moreover, Trf4p but not Trf5p stimulates RNA degradation mechanisms that are functionally linked to telomere maintenance and to antisense RNA-mediated regulatory pathways of gene expression. These results suggest widespread and distinct roles of different TRAMP complexes in the regulation of gene expression.Although the above mentioned studies revealed some substrates and functions for complexes containing Trf4 (TRAMP4) and Trf5 (TRAMP5), a comprehensive view of the substrate specificities and potential functional implications of the different TRAMP complexes is still lacking. We therefore wished to obtain a global picture of the RNA substrates that are regulated by the TRAMP4 and TRAMP5 complexes. To this end, we have used DNA microarrays to systematically map the RNA targets of Trf4p and Trf5. Surprisingly, we found that the different TRAMP complexes S. cerevisiae cells lacking either trf4 (trf4Δ) or trf5 (trf5Δ) compared to wild-type (WT) cells using yeast oligo microarrays that contained features representing all annotated yeast ORFs, ncRNAs, introns, rRNA precursors, as well as some intergenic regions (IGRs) and tiled regions downstream of a few genes , we arbitrarily selected those features that changed relative expression at least 2-fold  with false discovery rates (FDRs) of less than 5% p-value of less than 0.05 p-values see trf4Δ and trf5Δ mutants, we searched for common Gene Ontology (GO) annotations among the 550 transcripts for which GO annotations were available at the Saccharomyces Genome Database , which is in agreement with the idea that Trf4p and Trf5p promote RNA degradation and their depletion hence leads to the accumulation of RNAs that are normally targeted by these proteins  or enzymes involved in glucose metabolism . These results suggest that trf4 and trf5 specifically affect very different groups of transcripts in vivo. However, we wish to point out that the comparison of gene expression profiles of single trf4 and trf5 deletion mutants may not reveal the full spectra of the in vivo targets; particularly in cases where either functional Trf4 or Trf5 proteins can fully substitute the absence of the other paralog.Not surprisingly, most of the 715 selected features showed increased steady-state levels in proteins . More inficantly . Only 33ncreased  and reprtrf4Δ mutant were ncRNAs, such as snoRNAs (27 features) and RNAs derived from intergenic regions , or autonomously replicating sequences  and unchanged in cells lacking the exosome subunit Rrp6p (rrp6Δ)  in the trf4Δ mutant and decreased (∼1.5-fold) in the trf5Δ mutant  . Ty1 traΔ mutant . Since bTRF5 caused the accumulation of only 11 ncRNAs (4%) out of the 269 features (representing 220 GO annotated genes) for which we measured significantly altered expression levels (SNR68), and four IGRs likely representing two CUTs  and the stable untranslated transcript SUT180 trf5-affected transcripts can be assigned to protein-coding mRNAs. A GO analysis among these messages revealed overrepresentation of mRNAs coding for cytoplasmic proteins involved in translation  or act in diverse metabolic processes such as glycolysis , sulphate assimilation , and nitrogen metabolism , these themes could not be seen among the mRNAs that were affected in the trf4Δ mutant which preferentially encode nuclear proteins . In conclusion, it appears that deletion of trf4 or trf5 affected the steady-state level of a large variety of mRNAs that function in diverse cellular pathways and may reflect in part the pleiotropic defects of trf4Δ and trf5Δ mutants Deletion of n levels . This intrf4Δ/TRF4-DADA mutant strain, where the polyadenylation defective allele TRF4-DADAtrf4Δ cells under the control of the NOP1 promoter .We next aimed at identifying the set of transcripts that require the polyadenylation activity of Trf4 for efficient degradation. For this purpose we constructed a oter see . Trf4p-Dtrf4Δ mutant cells were almost fully restored to WT levels by the overexpression of the TRF4-DADA allele  that were also enriched in rrp6Δ mutants in vivo and thus, a substantial fraction of the total RNA turnover mediated by Trf4p may depend upon Trf4 catalytic activity.Surprisingly, the expression levels of more than 90% of transcripts that were significantly altered in A allele . Only 57ed to WT , 18 of ted to WT . Althougnt cells , 31 ncRNnt cells . Similartrf4Δ mutant (Trf4-dependent introns) or the trf5Δ mutant  in either the  mutants . Thus, trrp6Δ exosome mutant and in trf4Δ/TRF4-DADA mutant cells. Similarly to what has been observed in the trf4Δ mutant, introns accumulated in cells lacking rrp6 but no significant change in the expression of the pre-mRNAs and mature mRNAs of RPS9A, RPL7B, and GCR1 was detected  the WT levels .To test whether Trf5p could efficiently target spliced-out introns in the absence of Trf4p, we analyzed intron accumulation by qRT-PCR experiments upon conditional TRF5 in trf4Δ cells caused intron accumulation in vivo, we infer that Trf4p and Trf5p are functionally redundant for intron decay . Cells expressing recombinant tandem-affinity purification (TAP)-tagged Trf4 protein were cross-linked with formaldehyde, and Trf4-containing ribonucleoprotein complexes were recovered by affinity selection on IgG-coupled beads  and from the immunopurified samples and labeled cDNAs derived from the RNAs with Cy3 and Cy5 fluorescent dyes, respectively. The differentially labeled samples were mixed and competitively hybridized on yeast oligo arrays. In this assay, the ratio of the two RNA populations at a given array element provides a measurement for enrichment of the respective RNA with the TRAMP4 complex p = 2.5×10−5; p = 0.12; trf4Δ mutant  compared to the controls, were mostly unchanged in either trf4Δ or trf5Δ mutants  but for which expression levels were not significantly changed in neither trf4 nor trf5 mutants, by Northern blot analysis with intron specific probes. As expected, these introns were detectable in total RNA derived from the rrp6Δ exosome mutant and from the mutant of the debranching enzyme dbr1Δ, which was used as a positive control for intron detection trf4Δ or the trf5Δ single mutant strains, but they were strongly accumulated in RNA samples isolated from conditional trf4(ts)trf5Δ double mutants. This result is reminiscent of our finding for the RPS9A, RPL7B, and RPL40A introns that became increasingly enriched in conditional double mutants  were highly abundant in the trf4Δ and the rrp6Δ mutants  or lack (TEL11L) the Y′ sequence element  was also found in strains deficient of trf5 as shown by qRT-PCR experiments  or negatively (SAS5) regulate chromatin silencing (trf5Δ mutant reflects defects in pathways other than RNA turnover.Yeast telomeres (TEL) consist of a complex mosaic of telomeric and subtelomeric sequences, where the X element sequence is the only region common to all chromosome ends. Some subtelomeric regions contain a conserved helicase-encoding repetitive sequence (Y′ sequence) located within terminal telomeric repeats TR;  [40]. It mutants . In part element . SimilarWT cells . This de in vivo . Increaseriments . Interesilencing . Thus, iXhoI digested genomic DNA to determine the length of Y′ containing telomeres (trf4Δ mutant and ∼40 bp in the rrp6Δ exosome mutant (trf5Δ mutant or in trf4Δ mutant cells complemented with a plasmid (pNOPPATA1L) carrying the WT allele of TRF4  of factors such as EST3, CST6, and MET18, which participate in the maintenance of telomeres in vivo  were significantly decreased  polymerases that activate RNA turnover and quality control pathways by targeting aberrant and short-lived RNA substrates to the nuclear exosome for degradation WT cells . We founnscripts . Such hetrf4Δ and the trf5Δ mutants differed in the number of ncRNAs that accumulated in the cell. NcRNAs represented 17% and 4% of the transcripts that were selectively increased  in trf4Δ and trf5Δ mutants, respectively. However, our microarrays cover only a fraction of the experimentally defined CUTs derived from intergenic regions (IGRs) PHO genes  can also activate degradation of RNAs by the exosome trf4 mutation can rescue the lethality of trf4 and trf5 double mutants trf4Δ mutant upon overexpression  RNAs trf4Δ mutants overexpressing TRF4-DADA  tails to facilitate exosome-mediated degradation. In agreement with this idea is the finding that deletion of trf5 in the polyadenylation-defective trf4-236 mutant enhanced the defect in the degradation of CUTs compared to either single mutant trf4 trf5 double mutants However, additional mechanisms may account for the suppression of the in vivo , YPGal , or in synthetic minimal medium  at 30°C. Temperature shifts to 37°C were done in a shaking water bath. Yeast strains are described in trf5Δ and rrp6Δ mutant strains were purchased from Open Biosystems. Replacement of the trf5 and the rrp6 genes with the kanMX6 cassette was confirmed by PCR following the manufacturer's instructions. The strain with C-terminal TAP-fusion of FPR1 (YNL135C) was purchased from BioCat . The correct integration of the TAP-tag was verified by PCR. The trf4Δ strain is a derivative of BY4741 in which the trf4 gene was replaced with the natMX4 marker by homologous recombination as previously reported Manipulations of trf4Δ with the wild type allele of trf4 the coding region of trf4 was PCR amplified from S. cerevisiae BY4741 genomic DNA with primers XmaI-trf4-Fw (CCCGGGAAATATGGGGGCAAAGAGTGTAAC-3′5′-GT) and trf4-Rev-SalI (GTCGACTTATTAAAGGGTATAAGGATTATAT-3′5′-AC) (restriction sites are underlined). The insert was cloned in pGEMT-easy vector (Promega), digested with XmaI and SalI restriction enzymes and ligated into the same sites on the pNOPPATA1L vector to generate pNOPPATA1L(pNOP1::TRF4). Trf4Δ cells were transformed with pNOPPATA1L(pNOP1::TRF4) and transformants were selected for leucine prototrophy in synthetic medium at 30°C. The correct integration of the fragment was verified by sequencing. Control trf4Δ/pNOPPAT1L and BY4741/ pNOPPATA1L strains were selected for leucine prototrophy onto synthetic medium after transformation of trf4Δ and BY4741 cells with the pNOPPAT1L plasmid.To complement trf4 or trf5 from an inducible pGAL1 promoter the coding regions of trf4 and trf5 were PCR amplified from S. cerevisiae BY4741 genomic DNA with primers SacI-trf4-Fw (GAGCTCAAATATGGGGGCAAAGAGTGTAAC-3′5′-GT) and trf4-Rev-XhoI (CTCGAGTTATTAAAGGGTATAAGGATTATAT-3′5′AC) or BamHI-trf5-Fw (GGATCCCATAATGACAAGGCTCAAAGCAAAATA-3′5′-AA) and trf5-Rev-XhoI (5′-AGCTCGAGTTATTAAAGAGCCTGGCCTTTAGAG-3′). The fragments were cloned into pGEMT-easy vector, digested with SacI-XhoI or with BamHI-XhoI and ligated into the same sites of pYC6/CT (Invitrogen) to generate pSAL1 (pGAL1::TRF4) or pSAL2 (pGAL1::TRF5), respectively. trf4Δ trf5Δ mutant cells complemented with wild type TRF4 by the pRS416-TRF4 plasmid were successively transformed with either pSAL1 or pSAL2. Transformants were selected onto YPD supplemented with blasticidin (InvivoGen) 150 µg/ml at 30°C. To induce the loss of the pRS416-TRF4 plasmid, blasticidin resistant colonies were inoculated three times on synthetic medium supplemented with 2% galactose, blasticidin (15 µg/ml) and 5-fluoro-orotic acid  at 30°C. Transformation with pSAL1 or pSAL2 and loss of pRS416-TRF4 was confirmed by restriction digestion with SacI-XhoI or with BamHI-XhoI of plasmid DNA preparations purified from clones that were uracil auxotrophic, blasticidin resistant, and glucose sensitive.To express 600 of 0.1 in 50 ml of fresh YPD or YPGal medium without drugs and grown at 30°C to an OD600 of 0.7. Cells were collected by centrifugation for 5 min at 3,000 g and resuspended in AE buffer  with 1% SDS. After adding one volume of phenol (pH 5.3), the suspension was vigorously shaken for 1 min and incubated at 65°C for 4 min in a heating block . The aqueous phase was separated from the phenol phase by centrifugation at 20,000 g and extracted again with phenol (pH 5.3) and then with chloroform. Total RNA was precipitated from the aqueous phase by the addition of 2.6 volumes of ice-cold ethanol and of 1/10 volume of 1 M Na-acetate (pH 5.3) for 20 min on ice. The precipitated RNA was recovered by centrifugation at 20,000 g for 30 min at 4°C, the pellet was washed with 70% ethanol and resuspended in DEPC-treated water.Total RNA was extracted with the hot phenol extraction method. Single yeast colonies were inoculated in 5 ml YPD or YPGal medium supplemented with the appropriate amount of selective drug  and incubated overnight at 30°C (pre-cultures). Pre-cultures were diluted to an ODfree™ (Ambion) according to the manufacturer's instructions. The integrity of RNA samples was routinely checked by gel electrophoresis  in 1× HEPES buffer  and RNA was quantified by UV-spectrophotometry (A260) with a Nanodrop device (Witeg AG).To remove contaminating DNA, RNA preparations were treated with DNA-trf4Δ trf5Δ/pSAL1 or of the trf4Δ trf5Δ/pSAL2 mutant strains were inoculated in YPGal liquid medium supplemented with blasticidin  at 30°C and grown overnight. The following day, cultures were diluted to an OD600 of 0.1 in 300 ml of fresh YPGal medium and grown at 30°C to an OD600 of 0.7. Cultures were equally split into three tubes and cells were collected by centrifugation at RT for 5 min at 3,000 g. After centrifugation, one-third of the culture was rapidly frozen in liquid nitrogen and stored at −70°C (0t time point); one-third was resuspended in YPGal and incubated at 30°C for 1 h , while the rest of the culture was inoculated in YPD at 30°C for 1 h (1t Glc time point). Cells were collected by centrifugation and stored at −70°C. Total RNA was purified from the cell pellet as described above.Single colonies of the S. cerevisiae ORFs, and the Yeast Brown Lab Oligo Extension Version (YBOX vers. 1.0) with 3,456 probes to detect ncRNAs, rRNA precursors, introns, exon-intron and exon-exon junctions, other sequences predicted to be expressed, additional probes for genes with high cross-hybridization potential and controls for array quality measurements and normalization. Details of oligonucleotide selection and probe sequences are available from the Operon website (www.operon.com). All microarray data are available at the Stanford Microarray Database (SMD) or at the Gene Expression Omnibus (GEO) with accession number GSE16107.DNA microarrays contained 10,944 oligo probes (70-mers) from the Array-Ready Oligo Set Version 1.1 representing 6,388 The probes were printed on epoxy coated glass slides (Nexterion slide E) at the Center for Integrative Genomics, University of Lausanne, Switzerland. Oligo arrays were blocked in 5× SSC, 0.1 mg/ ml BSA, 0.1% SDS for 1 h at 42°C, and subsequently washed three times in 0.1× SSC for 5 min at RT, rinsed in water for 30 s, and dried by centrifugation (500 g for 2 min). The slides were used the same day.3 (pH 9.0) and covalently linked to either fluorescent Cy3 or Cy5 NHS-monoester . Thereby, cDNAs derived from wild-type control cells were labeled with Cy3, the ones derived from mutant cells with Cy5. Unincorporated dyes were removed with the QIAquick PCR Purification Kit (Qiagen). The samples were mixed in standard formamide based hybridization buffer  supplemented with 1 mg/ml poly(A) in a final volume of 20 µl, and competitively hybridized to yeast oligo arrays in a sealed hybridization chamber (Corning) at 42°C for 12–16 h. Arrays were successively washed in three buffer chambers filled with 2× SSC , 0.2% SDS; 2× SSC; and 0.2× SSC. The first wash was performed at 42°C for 12 min, the subsequent washes at RT for 12 min. After briefly rinsing in ethanol, microarrays were scanned with an Axon Instruments Scanner 4200A (Molecular Devices). Scanning parameters were adjusted to give similar fluorescent intensities in both channels. Data were collected with GenePix Pro 5.1 (Molecular Devices) and spots with abnormal morphology were excluded from further analysis. Array data were exported to Acuity 4.0 (Molecular Devices) and normalized to the mean of ratio of medians = 1 excluding the signals from control features. We collected three biological replicates each for determining the relative changes of transcript levels in the trf4Δ, trf5Δ and trf4Δ/TRF4-DADA mutant cells compared to the respective wild-type cells . Data were filtered in Acuity for regression correlation (Rgn2>0.5), signal to noise ratio >2.5 in both channels, and only features that met these criteria in >60% of arrays were considered for further analysis  to employ statistical analyses based on regularized t-tests that use a Bayesian estimate of the variance among gene measurements within an experiment Microarray analysis was performed by competitive hybridization of Cy3 and Cy5 fluorescently labeled cDNA. Total RNA (25 µg) was reverse transcribed with SuperScript RT II (Invitrogen) in the presence of 2.5 mM aminoallyl-dUTP (Sigma) and dNTPs, with a 1∶1 mixture of dT20V and random nonamer (N9) primers . After first strand cDNA synthesis, RNA was hydrolyzed with 0.1 M NaOH and 0.1 M EDTA at 65°C for 15 min, and samples were neutralized with 0.35 M HEPES (pH 8.0). Clean up of the reaction mix was performed in Microcon-YM30 (Millipore) filled with distilled water. Amino-allyl containing cDNA was eluted with 100 mM NaHCOtrf4Δ, trf5Δ, or trf4Δ/TRF4-DADA replicates were compiled .The 715 unique features  that were on average at least 2-fold changed with an FDR<5% in either the compiled . The gencompiled . Commonl20, 0.1 µM random hexamers and SuperScript RT III (Invitrogen). RNA was subsequently hydrolyzed with 125 mM NaOH and 10 mM EDTA at 65°C for 15 min. The mix was neutralized with 400 mM Tris-HCl (pH 8.0) and loaded onto a Microcon-YM30 (Millipore) concentrator column filled with 10 mM Tris-HCl (pH 8.0). After centrifugation for 8 min at 13,500 g the microcon was filled again with 10 mM Tris-HCl (pH 8.0). This step was repeated twice. After elution, the cDNA was used as template for PCR with the following conditions: 95°C for 10 min; 40 cycles at 95°C for 15 s, and 60°C for 1 min. Transcript abundance was calculated as log2 of normalized ratios with the Pfaffel method of relative quantification qRT-PCR was performed with an ABI Prism 7000 Sequence Detection System (ABI Prism) and the Power SYBR Green PCR Master Mix (Applied Biosystems) according to the manufacturer's instructions. The first strand cDNA was synthesized with 5 µg of total RNA, 50 µM oligo(dT)600 = 0.1) that were further cultured at 30°C to an OD600 = 0.7. RNAs and proteins were cross-linked with 1% formaldehyde that was added directly to the culture for 7 min at RT. The cross-linking reaction was subsequently quenched by the addition of 125 mM glycine (pH 7.0) for 5 min at RT. During cross-linking and quenching reactions the cultures were maintained under constant shaking at 100 rpm. Cells were harvested by centrifugation  at 4°C, washed twice with 50 ml of ice-cold PBS, and lysed in 5 ml of ice-cold lysis buffer  by grinding in mortar filled with liquid nitrogen. Contaminating DNA was digested with 20 U/ml RNAse-free DNase I (Promega). The WCE was centrifuged twice at 13,000 g for 10 min at 4°C to remove cell debris. The protein concentration of the extract was determined with the Bradford method . To purify total RNA from extracts for microarray analysis (input RNA sample), 100 µl of WCE was digested with Proteinase K (0.4 mg/ml) for 30 min at 37°C, heated up to 70°C for 45 min to reverse the formaldehyde cross-linking, and proteins were extracted with phenol-chloroform-isoamyl alcohol . RNA was precipitated with 1.5 M LiCl at −20°C overnight and collected by 30 min centrifugation  at 4°C. The RNA pellet was washed twice with 70% ethanol and resuspended in DEPC-treated water.1 L of fresh YPD medium was inoculated with an overnight pre-culture of yeast cells (ODCross-linked TAP-tagged proteins were captured from the WCE as follows: 300 µl rabbit IgG-coupled agarose beads (Sigma) were equilibrated at 4°C in lysis buffer supplemented with 5% BSA. WCE was added to the blocked IgG beads and mixed on a rotator overnight at 4°C. TAP-tagged proteins were then recovered by spinning down the IgG beads at 72 g for 2 min at 4°C. Beads were thoroughly washed three times with ice-cold lysis buffer supplemented with increasing concentrations of NaCl . RNP complexes were digested in 1 ml of elution buffer  with 100 µl Proteinase K (4 mg/ml) for 20 min at 37°C. Formaldehyde cross-linking was reversed by incubation of the eluate at 70°C for 45 min in a gently shaken heating block. Immunopurified RNA (IP-RNA) was isolated by extraction with PCI and isopropanol precipitation. The RNA pellet was washed twice with 70% ethanol and resuspended in DEPC-water.2 of the Cy5/Cy3 ratio in each experiment with Excel  and 500 ng of IP-RNA were converted into Cy3 and Cy5 fluorescently labeled cDNA, respectively, and samples were competitively hybridized on yeast oligo arrays as described above. We collected data from three biological replicates with Trf4-TAP, from two replicates with Fpr1-TAP and from one untagged control (BY4741 strain) sample. Array data were filtered in Acuity for signal to noise ratio >3 for the channel with the input RNA (Cy3), and percentile ranks for filtered data were calculated based upon the logth Excel .Northern blotting experiments were performed as previously described 32P] dATP with the Random Prime DNA Labeling Kit (Roche). Unincorporated α-[32P] dATP was removed by MicroSpinTM G-25 Columns . DNA templates for the preparation of the randomly labeled probes were produced by PCR amplification with primer pairs rps24A-int-Fw (5′-AGAAATGGTATGTTAAAAAGTGCTCAGATG-3′) and rps24A-int-Rev (5′-CAGCGTCAGACTGAGAAAAAAC-3′) or rpl2B-int-Fw (5′-CGCATAATTATGGCAAATGTTATGAAGG-3′) and rpl2B-int-Rev (5′-CGAATAACTCTACCTGTTTAAATGAGG-3′) to detect the RPS24A and RPL2B introns, and primer pairs rps24A-ex-Fw (5′-TCTGACGCTGTTACTATCCGTACTA-3′) and rps24A-ex-Rev (5′-AATCGGCGTTACGACGAGCAACCT-3′) or rpl2B-ex-Fw (5′-CACACCAGATTAAGACAAGGTGCT-3′) and rpl2B-ex-Rev (5′-GAACCACGTAGTAAACCGGTTCTTCT-3′) to detect the RPS24A or RPL2B mRNAs. The DNA template for the preparation of the randomly labeled PGK1 probe was previously described 2PO4, 5 mM EDTA, pH 7.4), 5× Denhardts solution, 1% SDS, and 200 µg/ml salmon sperm DNA according to standard procedures. The result of the hybridization was visualized with a Phosphor Imager.DNA probes for hybridization were prepared by random incorporation of α-[Escherichia coli strain BL21. The resulting C-terminal Air2p fragment contained a [His]6 tag fusion on its C-terminus. The protein was expressed in LB medium according to the manufacturer (Novagen) and affinity purified on Ni2+-NTA agarose (Sigma) under denaturing conditions as described Two µl of protein samples were directly spotted onto the nitrocellulose membrane (Whatman) for dot blot analysis. For Western blots, proteins were separated on 12% polyacrylamide gels, transferred to nitrocellulose membranes, and incubated with antibodies indicated in the figure legends. The anti-TAP antibodies were previously described XhoI and fractionated by 1% agarose gel electrophoresis in 1× TBE buffer  at 40 Volts for 15 h. DNA was transferred to a Hybond-N+ membrane (Amersham) and Southern blot was performed by hybridization with a telomeric probe  that was end-labeled with γ-[32P] ATP and T4 polynucleotide kinase (Biolabs). All hybridizations were done in 200 mM Na2HPO4, 1 mM EDTA, 2% SDS, 1% BSA and 50 µg/ml salmon sperm DNA. The average telomeric length for each lane was estimated by a 1 kb DNA ladder (peqLab) that was run in a lane next to the XhoI digested genomic DNA. The ladder was probed with the same DNA ladder after γ-32P end-labeling.Telomere length measurement was carried out as previously described Figure S1trf4Δ, trf5Δ, and trf4Δ/TRF4-DADA mutants. Pie chart classifying the transcripts with more than 2-fold (FDRs<5%) increased expression in in the trf4Δ (A), the trf5Δ (B), and the trf4Δ/TRF4-DADA (C) mutant strains as determined by microarray analysis. Microarrays contained 10,944 oligo probes (70-mers) representing 6,388 S. cerevisiae ORFs and 3,456 probes to detect ncRNAs (e.g. snRNAs/snoRNAs), rRNA precursors, INTs, Ty1 retrotransposon elements, exon-intron and exon-exon junctions, and 242 IGRs/CUTs. We infer that the fraction of CUTs and ncRNAs in the three mutants is underestimated as our microarrays do not fully cover all the genome's intergenic regions (including both strands) as well as antisense RNAs.Classes of RNAs that accumulate in (2.19 MB TIF)Click here for additional data file.Figure S2trf4Δ, trf5Δ, and trf4Δ/TRF4-DADA (DADA) mutants showing relative changes for a sample of snoRNAs  and IGRs/CUTs the steady-state levels of which were >2-fold increased (FDRs<5%) in trf4Δ mutant cells. snoRNAs and IGRs/CUTs strongly accumulated in the trf4Δ mutant, but not in the trf5Δ mutant. In addition, most of the snoRNAs  and some IGRs  still showed an increase of more than 1.5-fold relative to WT cells upon overexpression of Trf4-DADA in trf4Δ mutant cells. Microarrays are the same as shown in Expression profiles of SnoRNAs and IGRs (CUTs) in RNA surveillance mutants. Microarray analysis of (1.66 MB TIF)Click here for additional data file.Figure S3trf4Δ Mutant and are restored to wt levels by Trf4p-DADA overexpression. (A) Bar diagram representing relative changes of Ty1 retrotransposon RNAs as found by microarray analysis of the trf4Δ, the trf5Δ and the trf4Δ/TRF4-DADA (DADA) mutants. The values are averages of the levels of Ty1 retrotransposon transcripts as displayed by 68 out of 96 Ty1 retrotransposon probes showing more than 2-fold increase (FDR<5%) in the trf4Δ mutant. Microarrays are the same as shown in trf4Δ, trf4Δ/TRF4-DADA, trf5Δ, and rrp6Δ). The scheme above the bar diagram represents the Ty1 retrotransposon locus: grey arrow (UP) indicates the position of the UP-αTy1 primer used for strand specific synthesis of anti-sense-Ty1 (RTL) cDNA; convergent solid arrows (αTyF and αTyR) indicate the primer pairs anti-Ty-Fw and anti-Ty-Rv used for the quantification of the RTL cDNA; convergent grey arrowheads (TyF and TyR) show the location of Ty-Fw and Ty-Rv primers used for the quantification of the TyA/B cDNA. Consistent with the microarray analysis the expression of Ty1 retrotransposon is restored to WT levels by the overexpression of Trf4p-DADA in trf4Δ mutant cells. RNA amounts were normalized to ACT1 mRNA and are compared to the isogenic WT strain. Relative changes of transcript abundances  represent averages from two independent qRT-PCR analyses. The RNA was also used for the microarray analysis presented in Retrotransposon Ty1 elements accumulate in the (1.39 MB TIF)Click here for additional data file.Figure S4RPS9A-INT; RPL16A-INT, RPL7B-INT1, GCR1-INT, and RPL40A-INT) in RNA surveillance mutants . qRT-PCR analysis was performed with intron-specific primers. Overexpression of Trf4p-DADA in trf4Δ mutant cells abolished the accumulation of the first intron of RPL7B (RPL7B-INT1) and of GCR1 (GCR1-INT) and reduced by 3.6-fold (log2) the abundance of the intron of RPS9A (RPS9A-INT). RNA amounts were normalized to ACT1 mRNA and are compared relative to the isogenic wild-type strain. Relative changes of transcript abundances  represent averages from two independent qRT-PCR analyses. The RNA was also used for the microarray analysis presented in Intron expression profiles in RNA surveillance mutants. Bar diagrams show the results of qRT-PCR analysis for a group of introns ((0.76 MB TIF)Click here for additional data file.Figure S5HML/HMR cassettes and of genes involved in chromatin silencing. (A) Bar diagram representing the results of the qRT–PCR analysis for HMLα1 and ARS318 in RNA surveillance mutants . Whereas overexpression of Trf4p-DADA restored the abundance of HMLα1 to WT levels, ARS318 transcripts still exhibited a 2-fold increase in trf4Δ/TRF4-DADA mutant cells. Both HMLα1 and ARS318 RNAs strongly accumulated in the rrp6Δ mutant strain. RNA levels were normalized to ACT1 mRNA and compared to the relative expression in isogenic wild-type strain. Relative changes of transcript levels  correspond to the average from two independent experiments. The RNA was also used for the microarray analysis presented in SIR2, SIR3, SIR4, SAS5, and MCM10 mRNAs in the trf4Δ and the trf5Δ mutant strains quantified qRT-PCR. RNA amounts were normalized to ACT1 mRNA and are compared relative to the isogenic WT strain. Relative changes of transcript abundances  represent averages from two independent qRT–PCR analyses. The same RNA was used for the microarray analysis shown in Expression profiles of transcripts derived from the silenced (1.14 MB TIF)Click here for additional data file.Figure S6PHO5, PHO89, anti-PHO5, and anti-PHO89 RNAs in RNA surveillance mutants. Strand-specific qRT–PCR analysis examining the steady-state levels of PHO5, anti-PHO5 (A), PHO89, and anti-PHO89 (B) RNAs in RNA surveillance mutants . RNA amounts were normalized to ACT1 mRNA and compared relative to the isogenic wild-type strain. Relative changes of transcript abundances  represent averages from two independent qRT–PCR analyses. The RNA was also used for the microarray analysis presented in Expression profiles of (1.15 MB TIF)Click here for additional data file.Table S1trf4 or trf5 or by overexpression of Trf4p-DADA in the trf4Δ mutant strain. Columns indicate the following (from left to right): Clone ID; gene name; systematic name; Probe sequence on the array (70-mer); GO annotations for biological process, function, and cellular compartment; Operon description of gene product; average log2 ratio in trf4Δ mutants; average log2 ratios in trf4Δ/TRF4-DADA mutants; average log2 ratio in trf5Δ mutants; FDRs trf4Δ; p-value trf4Δ; FDR trf4Δ/TRF4-DADA; p-value trf4Δ/TRF4-DADA microarrays; FDR trf5Δ; p-value trf5Δ; p-value trf4Δ vs. trf4Δ/TRF4-DADA; cyberT test of trf4Δ vs. trf5Δ microarrays; FDR trf4Δ vs. trf4Δ/TRF4-DADA; FDR trf4Δ vs. trf5Δ microarrays.List of genes with 2-fold altered expression (FDR<5%) by deletion of either (5.12 MB XLS)Click here for additional data file.Table S2p<0.05); number of features exhibiting 2-fold up- or downregulation after SAM analysis (FDR<0.05%).Statistics of the 2-fold changed features. Columns indicate the following (from left to right): mutant strain; number of features exhibiting 2-fold up- or downregulation; number of features exhibiting 2-fold up- or downregulation after CyberT test ((0.02 MB XLS)Click here for additional data file.Table S3trf4Δ and the trf5Δ mutants. Columns indicate the following (from left to right): Mutant strain; category; gene ontology (GO) term; number of genes with annotations; number of genes in the genome with annotation; p-value.Significantly enriched GO terms among transcripts that are significantly increased in the (0.02 MB XLS)Click here for additional data file.Table S42 ratios) in trf4Δ and trf5Δ mutants are shown in separate columns Click here for additional data file.Table S5S. cerevisiae strain; genotype; reference.Yeast strains used in this work. Columns indicate the following (from left to right): (0.03 MB XLS)Click here for additional data file.Table S6Oligonucleotide primer sequences for qRT–PCR analyses. Columns indicate the following (from left to right): Oligonucleotide name; target RNA; oligonucleotide sequence in the 5′ to 3′ direction.(0.03 MB XLS)Click here for additional data file.Dataset S1trf4Δ, trf5Δ, and trf4Δ/TRF4-DADA mutants. Columns indicate the following (from left to right): Oligo ID (Operon); gene name; systematic name; yeast ORF (compatible with SGD); GO annotations for process, function, and cellular compartment; Operon description of the gene product; log2 ratio trf4Δ mutants (triplicates); average log2 ratio trf4Δ mutants; log2 ratio trf4Δ/TRF4-DADA mutants (triplicates); average log2 ratio trf4Δ/TRF4-DADA mutants; log2 ratio trf5Δ mutants (triplicates), average log2 ratio trf5Δ mutants; FDRs trf4Δ microarrays; p-values trf4Δ microarrays; FDRs trf4Δ/TRF4-DADA microarrays; p-values trf4Δ/TRF4-DADA microarrays; FDRs of trf5Δ microarrays; p-values trf5Δ microarrays; p-values trf4Δ vs. trf4Δ/TRF4-DADA microarrays; p-values trf4Δ vs. trf5Δ microarrays; FDRs trf4Δ vs. trf4Δ/TRF4-DADA microarrays; FDRs trf4Δ vs. trf5Δ microarrays. An annotation key to the oligos and a comparison of IGRs to recently mapped CUTs Normalized data from DNA microarray experiments with (4.36 MB XLS)Click here for additional data file.Dataset S2Percentile ranks from X-RIP-Chip data. Columns indicate the following (from left to right): Oligo ID (Operon); gene name; yeast ORF (compatible with SGD); GO annotations for process, function, and cellular compartment; Operon description of the gene product; percentile rank in Trf4 affinity isolations ; average percentile rank of Trf4 affinity isolations; percentile rank of Fpr1 control affinity isolations ; percentile rank of WT mock control isolations (BY4741); average percentile rank of control isolates .(1.99 MB XLS)Click here for additional data file.


RNA molecules have been discovered playing crucial roles in numerous biological and medical procedures and processes. RNA structures determination have become a major problem in the biology context. Recently, computer scientists have empowered the biologists with RNA secondary structures that ease an understanding of the RNA functions and roles. Detecting RNA secondary structure is an NP-hard problem, especially in pseudoknotted RNA structures. The detection process is also time-consuming; as a result, an alternative approach such as using parallel architectures is a desirable option. The main goal in this paper is to do an intensive investigation of parallel methods used in the literature to solve the demanding issues, related to the RNA secondary structure prediction methods. Then, we introduce a new taxonomy for the parallel RNA folding methods. Based on this proposed taxonomy, a systematic and scientific comparison is performed among these existing methods. This new scientific application has grown rapidly and nowadays it is becoming a cornerstone for each molecular biological study.It is important to study the folding of RNA molecules to understand their roles and functionalities.X-ray crystallography. Primarily, it is the faster and popular purification experimental methods, to determine the RNA tertiary structures. The researchers fixed a pure crystal of single RNA molecule. They needed to obtain sufficient amounts of pure RNA crystal, a milligram of RNA quantity is necessary,Nuclear Magnetic Resonance (NMR). In this NMR experimental method,Due to these constraints and difficulties from the experimental physical sides, computational methods from computer scientists and bioinformatic researchers have become more demanding to do the RNA structure prediction process. Predicting the RNA 3D structure from the primary sequence is difficult to accomplish. Hence, the bioinformatic researchers first detect and predict the RNA secondary structure from a given RNA primary sequence. Then, the result of this RNA secondary prediction process will assist the biologists to determine the RNA tertiary structure.Thus, solving the RNA secondary structure problem is becoming a main issue among bioinformatic researchers.O(3n) time complexity. Later, several stable algorithms for the RNA secondary structures were proposed like Zuker’s algorithm.O(3n), where n is the length of the RNA sequences, which are implemented in the ViennaRNA package.14The earliest prediction methods for solving the RNA secondary structure problems were presented by Waterman and SmithBasically, most computational algorithms, approaches and methods for solving the RNA secondary structures problems were restricted to the length of the bases, which were only a few hundreds of characters. Computational methods become much more desirable to solve traditional RNA cases and original RNA algorithms.16i) literature and background of the RNA predictions, (ii) RNA research models, (iii) the discussion and evaluation of existing methods with a comparative taxonomy. The main objective of this research is to present a comprehensive summary of the state-of-the-art researches on the RNA secondary structure predictions methods. Thus, we combined and joined among these all parts as follows:The First part is on the literature and background of the RNA prediction work. It explains the cause of the formation of folds and helices in each RNA molecule, in the chemical context. Next, the authors elucidated the RNA primary and the RNA secondary structure as a base step to clarify the RNA problem domain and the research statement on the computer side.The next part was for the RNA research models and methods. The researchers presented the existing RNA methods including the sequential and the parallel structural predictions. Then, the researchers made a comparison among either of the group. Lastly, in the discussion and comparison section, the authors highlighted the results of this study. In the discussion, the researchers concluded with the most suitable efficient paradigm that could be used for parallelizing the RNA prediction algorithms. Also, the reasons that guided to choose this decision were given.Finally, there is the discussion and the evaluation of existing methods with a comparative taxonomy. The researchers proposed and described a general taxonomy for RNA parallel methods. By using this taxonomy, they applied a comparative process for the previous existing parallel RNA prediction methods. This process enabled the researchers in this field to distinguish among the parallel RNA prediction approaches. Future researchers will be able to choose the suitable parallel paradigm that will fit their interests. This selection will depend on the group of the RNA molecules that the bioinformatic researchers working on or needs.We begin this comparative taxonomy study for the RNA parallel prediction methods by compromising and organizing this paper into three main research issues;  structures. Next, the authors presented the fundamental concept for the RNA secondary structure problem statement. Then, they listed and investigated the sequential RNA prediction methods and categorized the RNA parallel techniques based on the parallelization hardware architecture which are listed as follows: (i) An individual RNA parallel algorithmii) A one solitary RNA parallel method on the GPU.iii) One work of the RNA parallel algorithm on the Beowulf cluster.iv) Three different parallel RNA algorithms were implemented on the FPGA.i process to discover new medications as a treatment for dangerous virus diseases.21The RNA molecules had been confirmed to be very resourceful materials by.ribose”, the RNA molecule acquired its nickname “Ribonucleic Acids”. The nitrogenous bases in the RNA group were made up of four different bases Adenine (A), Cytosine (C), Guanine (G), and Uracil (U). These bases attached to the five-carbon sugar in 1′ position and they give RNA molecules characteristic possessions and properties.To understand the roles and functions of the RNA molecules, the biomedical engineers and researchers need to determine and scrutinize the RNA tertiary structures. As the first step to reach this goal, they ought to know the RNA molecules chemical structure and the motives that forced them to make these folds of 3D structures. Accordingly, the RNA was the common name for ribonucleic acids; they are made of long chains (single-stranded) of nucleotide units. There are three different components of the RNA nucleotides: the nitrogenous base, the sugar, and the phosphate group. While, the RNA backbone is made up of ribose five atom carbon-sugar counted from 1′ through 5′ and it is attached by two phosphate groups in 3′ and 5′, respectively. From the sugar group “Veritably, the phosphate groups in the backbone of the RNA have a negative charge, which makes the RNA a charged molecule.22Both the phosphate groups are attached to the 3′ and 5′ positions from ribose sugar in the backbone of RNA molecules. Due to this fact and based on the convention and general agreement among biologists, the RNA primary structure is a string series of bases reported from the 5′ end to the 3′ end, as shown in Watson-Crick and Wobble “WW”: Watson-Crick base pairs (G ≡ C) and (A = U) and a Wobble base pair (G-U). By applying these rules, the single-stranded RNA secondary structure forms two large groups: [Stem-loops and Pseudoknots (PK)], as shown in The RNA secondary structure derives from the pairing up of these four nucleotides according to the rules of Recently, RNA molecules have been confirmed to be very resourceful materials in the medical process and biological systems.The RNA folding recognition methods attempt to predict an accurate and more stable RNA folding structure based on the Minimum Free Energy (MFE) models. As shown in n characters, xi = x1x2n…x where xi∈ {A or a, C or c, G or g, U or u} the four bases in uppercases or lowercases letters, as well 1≤i≤n, as seen in the RNA primary sequence structure is a string of i, xjx). These pair of letters is called a complementary base pair, according to the “WW ” rules, in WW” rules are:  =  or ,  =  or  for the Watson-Crick rule. Also,  =  or  along with the Wobble rule later.These “i, xjx) for RNA secondary structure where that:first, 1≤i<j≤n.It should be at Second, j–i>t where t is a small constant, i.e. j–i≥2.In addition, these base pairs  and  in X, i = i′, if and only if j = j′, , ∈ R: i = i′ ⇔ j = j’) as depictured in For all base pairs , ∈ X (i<i′) such that i<i′<j<j′, as seen in the simple or recursive, those depictured in The components of the RNA involved in understanding the RNA functions, which are extracted from,From the energy stability point of view, the phosphate groups in the backbone of RNA molecule have a negative charge. Thus, the RNA molecules inside the living cells are not stable.Then, the main goal of the RNA secondary structure prediction computational method is to arrive at more stable equilibrium of the RNA folding form, based on the free-energy model. Hence, to calculate the RNA free energy stabilities, it is necessary to predict RNA secondary structure by calculating the MFE, which is named as an optimal RNA structure.28The summation of energies for all loops is the energy of RNA secondary structure , which e20et al in.In reality, to the best knowledge of the authors, thermodynamic prediction approach for calculating the energy of the optimization RNA, had been expressed and introduced for the most time by Lyngso NP-Complete Problem.NP-hard problem, particularly by applying the standard nearest-neighbour energy function.These MFE calculative motifs complicate the general pseudoknotted RNA secondary structure prediction algorithms. The pseudoknots type turns the RNA prediction algorithms to be First, the RNA secondary structure prediction with the pseudoknots is computationally intricate and difficult to carry out.NP-complex problem.Second, almost majority of RNAs computational methods have analyzed nested “Stem-Loops” RNA secondary structure prediction,Lastly, most of the existing methods for RNA structure prediction are not acceptable at levels of accuracy, reliability and robustness.33Consequently, researchers of pseudoknotted RNAs faced with three problems: i) Energy thermodynamic models to predict RNA secondary structures; i.e. Mfold,ii) Comparative analysis methods to predict the RNA secondary structures from multiple homologous sequence alignment.iii) Stochastic Context-Free Grammar methods (SCFS) are a comparative sequence analysis for prediction consensus of the RNA secondary structures from multiple sequence alignment.37Basically, one of the important tasks in front of bioinformatics and computer application researchers is the RNA secondary structure prediction dilemma. From a biological point of view, there are various complexities and constraints that are faced the biomedical researchers in experimental methods to determine the RNA tertiary structures.non-pseudoknotted RNA secondary structures “Stem-Loop of RNA”, while the other group is solving the RNA secondary structure prediction problem including pseudoknots type “RNA with Pseudoknots”.The original existing methods that have been proposed in solving the RNA secondary structures are divided into two major groups. The first group predicts the Then, the researchers in this paper classified these sequential RNA prediction methods in a schematic diagram, as shown in i) RNA prediction methods based on a thermodynamic energy minimization model. (ii) RNA structural comparative approaches from multiple homologous sequences. (iii) A comparative prediction consensus of the RNA structures by using SCFG methods. (iv) The Genetic Algorithm (GA) for predicting RNA structures.18The experimental methods are completely accurate for determining the RNA 3D folding structures. But due to their time consuming and expensive nature, many computational approaches have been proposed to predict the RNA secondary structure, which includes: (i) The scalable program using the parallel multi-core.ii) An implementing computer algorithms to run on the graphics hardware like GPU.iii) A parallel implementation on the Beowulf cluster by using Message Passing Interface (MPI) library.iv) The fine-grained hardware implemented on FPGA.Lately, many different parallel methods were introduced, in order to face the computational complexities of the RNA secondary structure prediction problem such as: (MfoldRNAfold.O(n4) execution time steps and the O(n2) spatial storage complexities. Having these complexities, the prediction of the large RNA sequences would not be feasible, particularly in the sequential implementation. Lately, one RNA research presented a parallel and scalable design “GTfold” in.Mfold and RNAfold algorithms together, in the parallel blueprint. This proposed re-implemented parallel method “GTfold”Recently, the mainly accepted and accurate approaches for predicting the RNA secondary structures sequentially are GTfold is in predicting the accuracy of the large RNA sequences. This accurate result when compared to many other existing RNA approaches such as the Mfold,RNAfold.O(n4) to O(n3). This improvement was calculated in computing the internal loop in the RNA fold, by improving the Internal Loop Speedup Algorithm “ILSA”. Basically, this ILSA enhancement enabled the execution steps of the GTfold algorithm, to run in a shorter time.In fact, the value and the significance of the ILSA enhancement gave the opportunity for the GTfold to predict Homo sapiens 23S ribosomal with 5,184 nucleotides of the RNA sequence only in minutes compared to the nine hours before.GTfold calculated the HIV-1 viral RNA genomes in 84 seconds . This algorithm has shown enhancements in both efficiency and performance when compared to the existing sequential algorithms,Therefore, the GPUs) started like a specific processor for accelerating and manipulating 3D computer graphical operations and games. Fortunately, due to the GPU’s highly parallel structure, extraordinary powerful and common function computing engines, this technique open a promising way in the parallel bio-computing sector. Recently, the General Purpose of GPUs (GPGPU) also has been growing very fast. The GPUs are going to become the cornerstone for the high computational complexity algorithms,NP-hard problem.30The Graphics Processing Units (hybrid-ss-minO(n3). Consequently, the new parallel designBasically, the researchers utilized the latest modern GPUs to speed-up the algorithms in solving RNA secondary structure problems. Rizk and Lavenier inThe significance of the research results in,The computer hardware Beowulf cluster showed some strong parallel features, based on the master-slave paradigm.CompPknotsPknots-REPknots-RE method requires O(n6) in time and O(n4) memory space, where n is the length of the RNA sequences.Pknots-RGPknots-RE. The authors of Pknots-RG utilized the MFE model, to achieve better performance on the complexity, when comparing with the Pknots-RE.Pknotes-RG, which enhanced the execution time complexity from O(n6) to O(n4). Also, Pknots-RG improved the storage complexity to O(n2) from O(n4) comparing with the Pknots-RE method.A prominent work has opened a new epoch in pseudoknotted RNA secondary structure prediction research. The CompPknots,CompPknots parallel design was implemented on the Beowulf cluster based on the master-slave architecture. This parallel design obtained the good results in the pseudoknotted RNA secondary structure detection with the higher accurate prediction. Also, the method ran in a shorter execution time. Moreover, the authors in50The main contribution of the Different researches found and confirmed that the traditional RNA prediction methods can be put into operation by using fine-grained hardware implemented on the FPGA. Recently, the bioinformatic researchers found that the modern computers, parallel or multi-core, do not show a greater than 50% parallel usefulness and efficiency.20(i)RNAalifold folding method, which was presented by Hofacker et al in.RNAalifold was used for computing the consensus of RNA structures and it had been implemented as an extension of Zuker’s algorithm.RNAalifold algorithm was considered the thermodynamic energy minimization stability with an average energy matrix and sequence co-variation score matrix together. The RNAalifold approach has the worst case execution time and storage space complexities are respectively O(m × n4 + n3) and O(n2), as n is the length of RNA sequence and m is the number of RNA sequences in the alignment. Due to these strong reasons Xia et al inRNAalifold algorithm and re-implemented it on FPGA chips.The most popular computational approach for RNA secondary structures based on using the MFE-model is the RNAalifold algorithm. They proposed a systolic array structure with one master Processing Element (PE) and many slave PEs for fine-grained hardware implementation on the FPGA. Their goals from this re-implementation, was to parallelize the original RNAalifold algorithm. The master PE loads the energy matrices from the outer memory “DRAM”, while the other slave PEs remain waiting to take data from the master PE. The execution time of the parallel prediction algorithm was grown to be more than 12× on one FPGA with 16PEs. This results compared with the previous results for ViennaRNA-1.6.5 software,RNAalifold algorithm.The researchers in(ii)O(n3) time and requires O(n2) storage space complexities.Many RNA folding methods utilized empirical models for predicting the RNA secondary structures based on the MFE estimation by using the Dynamic Programming (DP) algorithm. Jacob et al inVirtex-II 6000 FPGA) for implementing the normal Nussinov’s algorithm. Also, the researchers calculated and built it on two classic two-dimensional (2D) systolic arrays to achieve optimal string parenthesization and to deal with the maximum length of RNA primary sequence. Hence, Jacob et alVirtex-II 6000 FPGA. This output was compared with the same implemented method on the modern x86 CPU. The results obtained better factor up to 39× speed-ups in execution time.Jacob et al in(iii)O(n4) execution time and resides in the O(n2) storage memory requirements complexities. Recently, different developments and many optimal algorithms have been derived to simplify and reduce the Zuker’s algorithm computational complexities. Also, most biology researchers have proved that, the RNA molecule existing in large sequences is more than what was expected before. In other words, biological experiments have shown that RNA molecules fold in thousands of bases.O(n4). This complexity could not be able to deal with these sophisticated instances introduced earlier, in a sequential manner.The most well-liked and admired computational approach for the RNA secondary structure based on using the MFE is the Zuker’s algorithm.ViennaPackage.51The promising way to make Zuker’s algorithm tolerable and balanced in calculation is a parallel structural design implementation. Recently, Dou et al inWW-rules).24The Input Checker Agent (INCA) receives the RNA primary sequence from the input device or reads it from a text file. Subsequently, the INCA checks the validity of this RNA by applying the valid nucleotides RNA bases in Watson-Wobble rules (P-RNAmfe), which starts calculating the RNA secondary structure by using WW-rules to combine the valid RNA base-pairs. The P-RNAmfe algorithm simultaneously predicts the optimal and most stable RNA secondary structure by using the MFE lookup tables (mfe-LT).mfe-LTs are loaded to the shared cache memory by loader just before the P-RNAmfe algorithm start calculating and predicting the RNA secondary structures. Essentially, based on this investigation, there are four parallel techniques was used to re-implement the previous RNA prediction methods. These four popular parallel RNA prediction methods were illustrated by zooming in the “P-RNAmfe”, as shown in First, a parallel design to harness the power of the multi-core CPUs for predicting the RNA secondary structures. The GTfold is a parallel multi-core algorithm.Second, a parallel paradigm on the latest modern GPU-NVIDIA cards. This parallel design was utilizing the CUDA programming code in the C-language environment. This new scheme was used inAnother parallel design is a combination of two existing pseudoknotted RNA secondary structures prediction methods.The last parallel scheme elaborated the 2D systolic array parallel by using FPGA in fine-grained hardware. This parallel paradigm was illustrated in INCA transfers the valid RNA sequence to the parallel prediction RNA MFE algorithm (The Output Decision Maker Agent (OTDMA) checks the accuracy of the initial Output RNA Secondary Structure Production (ORSSP). It performs this checkable process by testing and comparing the first output “ORSSP” with the diverse known RNA structures  in the RNA Databases (RNA-Db).The FR2CA compares the final RNA secondary structure result with the known and existing structures in RNA-Db, to report the accuracy scale of predicted RNA.In particular, the most popular RNA secondary structure prediction methods are the dynamic programming algorithms based on the MFE models. Herein, we proposed and described a general taxonomy to apply on the existing RNA parallel methods. This proposed taxonomy for the RNA parallel secondary structure methods is using four main phases. Three of them working as a different agents and the fourth one is the main RNA prediction algorithm, as shown in By using this proposed comparative taxonomy, as a classified comparison procedure for the existing parallel RNA prediction methods. The authors found that some of these RNA parallel prediction methods ran through all the comparative taxonomy phases. While, the other RNA prediction methods went through some steps of this proposed comparative taxonomy.From the methodological point of view, the researchers explained and investigated a comparison procedure among these RNA parallel methods according to the proposed taxonomy in Scores of RNA researchers have introduced several evolutionary parallel blueprints for solving the RNA secondary structure problem. In this paper, the researchers discussed and compared these parallel RNA methods, as shown in Then, in this paper, the authors focused their analysis on discussing and comparing the latest parallel RNA prediction efforts that have been pioneered in the RNA secondary structure folding domain. Consequently, from the comparison of the parallel RNA methods in Finally, the authors concluded that, implementing the RNA secondary structure prediction methods on parallel architectures has several significant benefits. The main advantage is reduced complexity, in both time and memory storage, comparing with the original RNA structure prediction algorithms. Also, the parallel RNA algorithms provided accurate results with better performance.In this paper for the solving RNA secondary structure prediction problem, the authors presented the state-of-the-art of the RNA parallel methods. They introduced an intensive investigation on exhaustive up to date parallel RNA secondary structure prediction algorithms. Indeed, recently various methods and techniques for predicting RNA secondary structures have emerged. Most of these computational methods have faced with some complexities, from sequential implementation viewpoint. Therefore, re-implementing the existing RNA prediction methods by using parallelization architectures would result and contribute in solving the faced difficulties. The study concluded and showed that all of RNA parallel methods obtained better results, when compared to the sequential methods in terms of accuracy in one side and time/space complexities, in another side.i) RNA in biological context. (ii) RNA computational prediction methods. (iii) A comparative taxonomy of RNA parallel methods. In the first part, the researchers explained the experimental method’s difficulties that the biologists are facing, in determination of RNA 3D structures. Exploratively, the researchers concluded the reasons that force and allow the RNA molecule to fold and pair back on itself forming a double helices, in a chemical and biological context. Secondly, in the methods and research findings, the authors listed the sequential RNA secondary structure methods, in a schematic classification diagram. Then, they compared their contributions and complexities. Also, the authors investigated comprehensively the state-of-the-art of the RNA parallel prediction methods.This research study was comprised in three trends:  in a high level language in C environment could be considered as a positive point. These parallelizing RNA secondary structure prediction methods, showed a promising area for future RNA studies and for computational RNA bioinformatic researches.