Current advances of the next-generation sequencing technology have revealed a large number of un-annotated RNA transcripts. Comparative study of the RNA structurome is an important approach to assess their biological functionalities. Due to the large sizes and abundance of the RNA transcripts, an efficient and accurate RNA structure-structure alignment algorithm is in urgent need to facilitate the comparative study. Despite the importance of the RNA secondary structure alignment problem, there are no computational tools available that provide high computational efficiency and accuracy. In this case, designing and implementing such an efficient and accurate RNA secondary structure alignment algorithm is highly desirable.O(n3) expected time complexity, where n is the average number of base pairs in the RNA structures. This complexity, which can be shown assuming the polymer-zeta property, is confirmed by our experiments. The resulting new RNA secondary structure alignment tool is called ERA. Benchmark results indicate that ERA can significantly speedup RNA structure-structure alignments compared to other state-of-the-art RNA alignment tools, while maintaining high alignment accuracy.In this work, through incorporating the sparse dynamic programming technique, we implemented an algorithm that has an ERA will significantly promote comparative RNA structure studies. The program, ERA, is freely available at http://genome.ucf.edu/ERA.Using the sparse dynamic programming technique, we are able to develop a new RNA secondary structure alignment tool that is both efficient and accurate. We anticipate that the new alignment algorithm  Drosophila melanogaster 3’-UTR suggests a cluster of ncRNA elements that can direct the localization of their upstream genes within the spermatids  denote a continuous sequence region in RNA A, which begins with the ith nucleotide and ends with the jth nucleotide. Define L(pA) as the sequence A[ l(pA)+1…r(pA)-1] (hairpin loop). If We also define loop regions  whose sequence similarities are assessed by the alignment. The loop regions can be viewed as the unpaired regions in the RNA sequence that are segregated by the paired nucleotides. Let A and B is the optimal matching between their base-pair sets A and B is a one-to-one binary relationA and B into two sets of loop regions, M that combines both structure and sequence similarities:The structure alignment between RNA Sstr) between the annotated base pairs. The structural similarity score for base-pair substitution is set using the RIBOSUM matrix  and B[ i′…j′] =0. The pseudo base pairs are only used for the sake of representational simplicity, and are not required for the implementation of the algorithm. Define a pseudo base pair pA= and a pseudo base pair pB=. In this case, the optimal alignment score between the regions A[ i…j] and B[ i′…j′], i.e. M, can be rewritten as M. Similarly, define pseudo base pairs To simplify the expression of the triangular inequality property, we define a number of pseudo base pairs to indicate specific regions of interest. A pseudo base pair is a void interaction, such that the structural similarity between any two pseudo base pairs is defined to be 0. For instance, let pA and pB are being aligned at the current stage. Let pA. Define pseudo base pairs B symmetrically . If the matching of pA and pB is an OPM, we denote this OPM as oA,B. Similarly, we represent the OPM formed by base pairs polymer-zeta property . In this case, we only need to compute the second alignment score. However, computing the second alignment score (assuming pA does not match pB) is difficult. Instead, we can compute the overall alignment score without assuming any restrictions. Apparently, the overall alignment score includes both cases disregarding whether pA matches with pB. Therefore, if M is greater than or equal to such an overall optimal alignment, it is guaranteed to be greater than the alignment score when assuming pA does not match pB, and ipso facto the matching of pA and pB is an OPM.Based on previous definition, the first alignment score is computed as M corresponds to the case where pA matches with pB, and therefore it can be decomposed as the sum of two parts: the structure similarity between the two base pairs themselves Sstr, and the optimal alignment score between the regions A[ l(pA)+1…r(pA)-1] and B[ l(pB)+1…r(pB)-1] without any restrictions. In this case, define two pseudo base pairs A[ l(pA)…r(pA)] and B[ l(pB)…r(pB)] without any restrictions. Note that Recall that the alignment score pA and pB as an OPM, and add the OPM oA,B to the OPM list pA and pB. However, such overhead will not raise the time complexity, and it is worthy as it will lead to a more significant speedup of the algorithm. In the following section, we will devise a new algorithm by assuming that the OPM list In this case, based on Lemma 1, if RNAscf algorithm based on Lemma 1 and has a time complexity of O(z(n2+l2)). Here, z is the size of the OPM list z∈O(n) when assuming polymer-zeta property  and Mh are boundary cases for the algorithm and are directly computed without referring to previous alignment results, the recursive functions for computing them are exactly the same as in the original algorithm:The new algorithm is developed based on the lgorithm . TherefoMl, on the other hand, refers to the previous alignment results that assumes The computation of Similarly, for the computation of Here, the set O(n4+n2l2). The first term O(n4) results from O(n2) computations by traversing all combinations of pA and pB (see Equation 2) and O(n2) time for computing Ml (see Equation 4). In the new algorithm, we introduce the OPM constraint to Equation 8 and Equation 9, and thus reduce the time complexity for computing Ml from O(n2) to O(z). In this case, the first term O(n4) of the original time complexity can be reduced to O(zn2).Recall that the time complexity of the original algorithm is O(n2l2) in the original time complexity results from computing the sequence similarities between all loop regions. Note that all loop similarities required for computing Ml (Equation 8) and Mc (Equation 9) are associated with OPMs. For example, in Equation 8, all the loops are defined according to O(n2) base-pair combinations, instead we only need to compute the loop similarities that are associated with the OPMs. In this case, the time complexity for computing the sequence similarities between all loops that are required by the computation of Ml and Mc can be finished in O(zl2) time.The second term Sseq(L(pA),L(pB)), which is required for computing Mh (Equation 7). The computation of Mh is not constrained by the OPM list, and therefore O(n2l2) time is still required. To resolve this issue, we observe that most of the RNA structure alignment algorithms emphasize the structure similarity other than sequence similarity (w1>w2 in Equation 1). In this case, if there exist some base pairs within the regions enclosed by pA and pB to be matched, we can expect that Ml>Mh in Equation 6. In this case, to avoid the unnecessary computation of Mh, we can derive an upper bound Ml>Mh by transition, and thus can save the computation of Mh. The upper bound The only exception for the sequence similarity computation is the hairpin loop similarity dmax is the highest score in the 4-by-4 nucleotide substitution matrix D, and I is a boolean variable that is set to 1 if |L(pA)|≠|L(pB)| and set to 0 otherwise. For the computation of each M, we first estimate the upper bound Ml in O(z) time. By comparing these two values, we will determine whether the computation of Mh is necessary. The computation of Mh is only necessary when there are only a few base pair enclosed by pA and pB to be matched. Such condition implies the scenarios that either pA or pB is a real hairpin loop in the RNA structures, whose number is bounded by O(n). Overall, the hairpin loop similarity matrix Mh can be computed in O(nl2) time, and the overall time complexity of this algorithm is thus O(z(n2+l2)).where O(z(n2+l2)) time is sufficient for this new algorithm. The size of the OPM list z, thus becomes an important factor that determines the efficiency of the novel algorithm. Under the current algorithmic setup, as well as other similar works that implement a candidate list  has just been computed and oA,B has been identified as an OPM, where oA,B  using GNU C++. In this section, we will show that (1) ERA has the expected O(zl2) time complexity; (2) ERA is as accurate as the other state-of-the-art RNA alignment tools; and (3) ERA runs much faster than the other RNA alignment tools. In addition to these goals, we have also benchmarked ERA to demonstrate its O(l2) space complexity. For details regarding the space complexity issues please refer to the Additional file We implemented the proposed general edit-distance RNA structural alignment algorithm into a program called how that  ERA has ERA with two other state-of-the-art RNA alignment tools: LocARNA as a representative of the general edit-distance RNA structure alignment algorithms and RNAforester as a representative of the tree-based RNA structure alignment algorithms. Note that although LocARNA is developed to compare RNA structure ensembles, its flexible parameter setup makes it easy to prune its input RNA ensembles (see Section ‘Running LocARNA’ for more details). However, the readers should note that LocARNA is used in a restricted case for fair comparison with ERA, and more potential applications of LocARNA should be recognized. We do not compare ERA with its predecessor RNAscf, because RNAscf is implemented to find consensus helical configurations that do not include individual base pairs [LocARNA and RNAforester were invoked using their default parameters.We benchmarked the se pairs . Both LoLocARNA was originally developed to compare two RNA structure ensembles [ERA to compare two fixed RNA structures. In this case, we need to prune the original inputs of LocARNA, so as to ensure that they only represent the fixed structures rather than any additional information.Note that nsembles . Due to LocARNA are represented using the base-pairing probability matrices, which can be computed using the McCaskill’s algorithm [LocARNA inputs for experiments mentioned in this article are preprocessed using this strategy.The input RNA ensembles for lgorithm ,41. In algorithm  annotatilgorithm  annotatiERA has the expected O(zl2) time complexity. To show the O(zl2) time complexity, we chose a number of RNA families from Rfam that have a wide range of sequence lengths. We then randomly selected two individual RNA structures from each family =O(l)), is plotted in Figure O(zl2) time complexity from the figure. In addition, we are also able to show that the speedup ratio, when comparing to the O(l4+n2l2)LocARNA algorithm, is strongly correlated with the efficiency of pair matching reduction due to the sparse DP technique .In this section, we expect to show that the proposed sparsification is successfully implemented, and to the Ol+n2l2LocAERA is as accurate as the other state-of-the-art general edit-distance RNA structure alignment tools. We used BraliBase II [ERA and LocARNA are nearly identical, since incorporating the sparse DP technique will not compromise global optimality. The benchmark results also show that ERA and LocARNA can produce more accurate alignments when compared to RNAforester. This is because ERA and the restricted version of LocARNA are both general edit-distance RNA alignment algorithms that are capable of flexibly handling incorrectly predicted base-pairs, while RNAforester is more sensitive to such errors, since it implements tree alignment.In addition to time complexity improvement, we also expect to show that iBase II  as the riBase II  and the iBase II . The benERA, we also expect to show that ERA is an efficient implementation and can run faster than other state-of-the-art RNA alignment tools. We compared the real running time of ERA, LocARNA, and RNAforester on the selected RNA structures from Rfam. The benchmark results are summarized in Table ERA is capable of speeding up LocARNA by a minimum of 5.2 fold and a maximum of 91.5 fold. ERA can also speedup RNAforester by a minimum of 2.8 fold and a maximum of 242.6 fold, with only one exception in which RNAforester is 9.6 times faster than ERA. This is because the RNA structures being aligned (snR86) contain only one stem-loop structure; and in such a special case, the time complexity of RNAforester becomes O(l2) [Finally, after benchmarking the time complexity and alignment accuracy of es O(l2) .ERA on randomly selected RNA structures, we compiled a much larger data set that contains 1,000 pairs of randomly selected RNA structures from Rfam. The benchmark results on this large data set are summarized in Figure ERA (blue triangle) runs much faster than LocARNA (red cross) and RNAforester (green star). In addition, we can also observe that the running time of ERA grows slower than those of LocARNA and RNAforester, which further confirms our previous time complexity analysis  time and O(l2). Such time and space complexity make ERA one of the most efficient RNA structure alignment tools that are currently available.In this article, we have presented a novel algorithm for efficient alignment of RNA secondary structures by incorporating the sparse DP technique. The major theoretical contribution of this work lies in two parts. First, to our knowledge, this is the first application of the sparse DP technique to RNA structure-structure alignment. Second, the novel online OPM pruning technique can provide insights for future algorithm designs that need to maintain a candidate list. The implementation of this novel algorithm is a tool called ERA in aligning individual RNA structures, with or without the online OPM pruning technique. We observed that by incorporating this technique, the running time of ERA was reduced by an average of 2.3 fold. Meanwhile, the speedup ratio is highly uniform (with 1.7 fold as the lowest and 3.1 fold as the highest) across RNA structures with different sizes, meaning that it reduces running time by a constant factor. The online OPM pruning technique can also be modified and incorporated into other related algorithms that implement the candidate list, such as the sparse DP algorithms for RNA folding [The online OPM pruning technique is newly developed from this work, which aims at deleting obsolete candidates as the DP proceeds. Although this technique cannot improve the computational complexity, it is efficient in reducing the real running time. In Additional file  folding , RNA con folding ,37, and  folding .ERA is most significant when the number of base pairs in the RNA structures is small. This is because the algorithm is indexed by base pairs and has a time complexity of O(z(n2+l2)). As n increases, the term O(zn2) will dominate the overall time complexity. In this case, an ideal application of ERA is to align fixed RNA structures, because it guarantees that n<l. Note that as a sparsified version of the SAF algorithm RNAscf[ERA, because one cannot guarantee n<l for RNA ensemble alignments. This would make the speedup of ERA less significant. Besides, there are other alternative tools [The speedup of hm RNAscf, the newve tools ,37 availERA, which can compare fixed RNA structure efficiently and accurately, becomes an ideal computational tool to evaluate the structural similarities of these RNA transcripts. ERA can be used to perform all-against-all alignments on these RNA transcripts, which will then be subsequently summarized as the distance matrix for clustering purposes. Various clustering algorithms [With the completion of the ENCODE  and modEgorithms ,39 can tThe authors declare that they have no competing interest.SZ contributed with the conception of the research. SZ and CZ designed the algorithm. CZ implemented the algorithm and performed benchmark analysis. SZ and CZ wrote the manuscript. Both authors read and approved the final manuscript.Supplementary information. This file contains four sections. In Section S1, we briefly discuss the space issue of ERA and provide related experimental results. In Section S2, we document the randomly selected RNA structures used for experiments mentioned in the main article. In Section S3, we evaluate the impact of the online OPM pruning technique in speeding up ERA. In Section S4, we give examples of the pruned base-pairing probability matrix for executing LocARNA.Click here for file


It has been demonstrated that urokinase-type plasminogen activator (uPA) is involved in tumor cell metastasis by degrading the extracellular matrix. However, there is little direct evidence of clinical uPA system expression in peritoneal metastatic tissues of gastric cancer. The objective of this study was to investigate uPA system expression in peritoneal tissues of peritoneal and nonperitoneal metastasis patients, and to explore the diagnostic value of the uPA system.Expressions of uPA, uPAR, and PAI-1 were measured by semi-quantitative RT-PCR and ELISA. uPA activity was detected using a uPA activity kit.P <0.05). Moreover, no statistical discrepancy of uPA activity was observed in various different tissues.There was no significant difference in uPA, uPAR, and PAI-1 expression in two types of peritoneal tissue in seven patients with peritoneal metastasis. However, uPA, uPAR, and PAI-1 expressions in peritoneal metastatic lesions were significantly higher than those in normal peritoneal tissues of 24 nonperitoneal metastasis patients (The expression of the uPA system positively correlates with peritoneal metastasis of gastric cancer. This expression difference in peritoneal or nonperitoneal metastasis patients may provide a reference for diagnosis of peritoneal metastasis. Although the incidence and mortality of gastric cancer have decreased in China over the last few decades, gastric cancer is still a big burden of the local health program . ImportaCancer invasion and metastasis are multifactorial processes  and an eet al. [et al. [It has been observed that the uPA system is well correlated with gastric cancers, by measuring the expression level of uPA, uPAR, and PAI-1 in gastric cancer and normal mucosal tissue and analyzing their correlation with various clinical pathological characteristics. For example, Plebani et al.  determin [et al.  studied  [et al. -15, and  [et al. ,17.However, there has been very little direct evidence to demonstrate the clinical significance of the uPA system in distinguishing peritoneal metastatic from normal peritoneal tissues in gastric cancer. Compared with the regular distribution and arrangement of lymph nodes, peritoneal tissue has a larger area, occupying the whole peritoneal cavity, thus entailing more randomness and unpredictability in cell peritoneal metastasis in gastric cancer. Thus, the objective of this study was to investigate further the expression difference of the uPA system between peritoneal metastatic tissues and normal peritoneal tissues of gastric cancer and to confirm the diagnosis significance of the uPA system by combining these results with clinical data.We assessed 31 patients  with a diagnosis of gastric cancer, who had been admitted to our department between July 2010 and December 2010. Their average age was 62.58 years . The patients were diagnosed with gastric cancer based on the preoperative or postoperative pathological analysis. Among them, 7 patients had peritoneal metastasis , while 24 patients had nonperitoneal metastasis .In the patients with peritoneal metastasis, we excised the peritoneal metastasis lesions, as well as a small amount of omentum majus, pelveoperitoneum, and diaphragmatic peritoneum without peritoneal metastasis. In the patients with nonperitoneal metastasis, a few resections of the omentum majus, pelveoperitoneum, and diaphragmatic peritoneum were also performed. The collected samples were stored at −80°C for further analysis.All patients were alive at the end of the research, and our study was approved by the ethics committee of Shanghai East Hospital, affiliated to Tong Ji University.2.Cells from a peritoneal mesothelial cell line (HMrSV5) were maintained in DMEM  supplemented with 10% fetal bovine serum. The cells were subcultured almost every day by 1:4 or 1:5 dilution in culture flasks at 37°C under an atmosphere of 5% CO2, 0.5 μl RNasin, 1 μl oligo(dT)18, 0.75 μl reverse transcriptase, and 1 μg RNA. The PCR condition was 70°C for 10 min, 42°C for 15 min, and 99°C for 5 min, after which the cDNA was stored at 4°C (within 3 months).Total RNA was isolated by cell precipitation using an RNeasy™ RNA extraction kit following the manufacturer’s instructions . The RNA purity and concentration were determined by spectrophotometric absorbance at 260 and 280 nm, respectively. Reverse transcription was performed on 1 μg of total RNA using oligo(dT) primers and AMV reverse transcriptase . The 20 μl PCR reaction mixture contained 2 μl 10× RT buffer, 2 μl dNTPs, 4 μl MgCl2, 0.5 μl Taq polymerase, 1 μl dNTP, and 17.5 μl Rnase-free H2O. Nested PCR was used for the amplification of carcinoembryonic antigen (CEA) with 1 μl of first PCR product used for the second PCR template. The amplification conditions were: (i) initial denaturation at 95°C for 5 min; (ii) 30 cycles of denaturation at 94°C for 1 min; (iii) annealing at 56°C for 30 s for uPA, 60°C for 1 min for uPAR, 55°C for 1 min for PAI-1, or 72°C for 2.5 min for CEA; and (iv) extension at 72°C for 1 min or 2.5 min. The PCR primers used were as follows: for uPA, A, 5′-AGAATTCACCACCATCGA GA-3′ and B, 5′-ATCAGCTTCACAACAGTCA T-3′; for uPAR, A, 5′-ACA GGAGCTGCCCTCGCGAC-3′ and B, 5′-GAGGGGGATTT CAGGTTT AGG-3′; for PAI-1, A, 5′-CTTTGGTGAAGGGTCTGC-3′ and B, 5′-CTC CACCTCTGAAAAGTCC-3′; for CEA, A, 5′-TCTGGAACTTCTCCTGGTCT CAGCTGG-3′, B, 5′-TGTAGCTGTTGCAAATGCTTTAAGGAAGAAGC-3′, and C, 5′-GGGCCACTGTCGGCATCATG ATTGG-3′; for β-actin, A, 5′-TTGAAGGTAGTTTC GGGAAT-3′ and B, 5′-GAA AATCTG GCACCACAC CTT-3′; for GAPDH, A, 5′-GAAGGTGAAGGCGGAGT C-3′ and B, 5′-GAAGATGGTGATGGGATTTC-3′. The A and B primers of CEA were used for the first PCR, while the B and C primers were used for the second amplification. The amplification product was 474 bp, 1046 bp, 409 bp, 131 bp, 591 bp, and 230 bp, respectively.The gene expression levels of uPA, uPAR, and PAI-1 were determined by comparison with β-actin gene or glyceraldehyde-3-phosphate dehydrogenase (GAPDH). The 25 μl RT-PCR reaction mixture contained 1 μl cDNA, sense and anti-sense primers (each 0.25 μl), 2.5 μl 10× PCR buffer, 2 μl 25% MgClg. The supernatant was transferred to a new tube and the protein concentration was determined by a bicinchoninic acid assay. Concentrations of uPA, uPAR, and PAI-1 antigen were determined using ELISA kits according to the manufacturers’ instructions . The reaction was stopped by the addition of 50 μl H2SO4, and the absorption was measured at 450 nm on an ELISA plate reader . Values of uPA, uPAR, and PAI-1 antigen were expressed as ng/mg protein.Tris-buffered saline  was added to frozen tissues (100 to 300 mg) and homogenization was performed in an ice-bath. Subsequently, 0.2 ml 10% Trixon X-100 was added to ensure a final concentration of 1% Trixon X-100 in the homogenate. The homogenate was shaken for 16 h and then centrifuged in a refrigerated centrifuge at 4°C for 1 h at 100,000 405, and the activity (units) was extrapolated from a standard curve.uPA activity was measured using an uPA activity assay kit (Chemicon). Briefly, tissue protein was mixed with assay buffer and incubated with a chromogenic substrate in 96-well plates at 37°C for 2 to 24 h. The absorbance was read at ODt test. P < 0.05 was considered statistically significant.All data were analyzed using SAS version 6.12 software and the results were recorded as average ± standard deviation. The significance of the differences between groups was evaluated by analysis of variance, followed by a paired CEA is an important marker for gastroenteric tumors. Thereby, we used a high sensibility nested RT-PCR method to detect the expression of CEA in gastric cancer tissues with or without peritoneal metastasis. As expected, the results were all positive for CEA in the peritoneal metastatic lesions of seven peritoneal metastasis patients Figure . Also, bP < 0.05).A quantitative ELISA method was used to determinate the uPA, uPAR, and PAI-1 protein contents in peritoneal metastatic lesions and CEA-negative nonperitoneal metastatic tissues of 7 peritoneal metastasis patients, and CEA-negative normal peritoneal tissues of 24 nonperitoneal metastasis patients. The results . Notably, there may be greater clinical significance in the increased uPA system protein content in CEA-negative peritoneal tissues of peritoneal metastasis patients. This finding indicated that these CEA-negative peritoneal tissues might be in a state of subclinical peritoneal metastasis and that progression of this disease might lead to peritoneal metastasis. Although uPA system changes in peritoneal tissues have not yet definitely been demonstrated in tumor cells, the changes in peritoneal tissues at least provides a reference value for diagnosis of peritoneal metastasis [et al. [P < 0.05). In brief, we suggest that increased uPAR and PAI-1 expressions in peritoneal tissues of nonperitoneal metastasis patients may be regarded as a reference indicator for peritoneal metastasis.The uPA system is widely distributed in various cells; our RT-PCR showed that uPA, uPAR, and PAI-1 could even be expressed in mesothelial cells. To avoid unspecific results, further quantitative ELISA was used to detect expression of the uPA system proteins. We found that uPA, uPAR, and PAI-1 protein contents were significantly higher in CEA-positive lesions or CEA-negative peritoneal tissues of peritoneal metastasis patients when compared with normal peritoneal tissues of nonperitoneal metastasis patients  in this study. Further study with higher numbers of subjects is still needed to obtain clearer and more convincing results.CEA: Carcinoembryonic antigen; DMEM: Dulbecco’s modified Eagle’s medium; ELISA: Enzyme-linked immunosorbent assay; GAPDH: Glyceraldehyde-3-phosphate dehydrogenase; PCR: Polymerase chain reaction; RT: Reverse transcriptase; RT-PCR: Reverse transcriptase polymerase chain reaction; uPA: Urokinase-type plasminogen activator; uPAR: Urokinase-type plasminogen activator receptor.The authors declare that they have no financial or nonfinancial competing interests.YD and ZZ designed the study, and performed RT-PCR, ELISA, and activity detection with HZ and ZZhou. YD, MZ, and XW participated in the sequence alignment and drafted the manuscript. ZZ helped to conduct statistical analysis. All authors read and approved the final manuscript.


Drosophila melanogaster has two silencing activities that target transposons: endogenous siRNAs (esiRNAs or endo-siRNAs) and Piwi-interacting small RNAs (piRNAs). The biogenesis of endo-siRNAs involves the Dicer-2 co-factors Loqs-PD, which acts predominantly during processing of dsRNA by Dcr-2, and R2D2, which primarily helps to direct siRNAs into the RNA interference effector Ago2. Nonetheless, loss of either protein is not sufficient to produce a phenotype comparable with a dcr-2 mutation. We provide further deep sequencing evidence supporting the notion that R2D2 and Loqs-PD have partially overlapping function. Certain transposons display a preference for either dsRBD-protein during production or loading; this appeared to correlate neither with overall abundance, classification of the transposon or a specific site of genomic origin. The endo-siRNA biogenesis pathway in germline operates according to the same principles as the existing model for the soma, and its impairment does not significantly affect piRNAs. Expanding the analysis, we confirmed the occurrence of somatic piRNA-like RNAs (pilRNAs) that show a ping-pong signature. We detected expression of the Piwi-family protein mRNAs only barely above background, indicating that the somatic pilRNAs may arise from a small sub-population of somatic cells that express a functional piRNA pathway.Transposable elements are a serious threat for genome integrity and their control via small RNA mediated silencing pathways is an ancient strategy. The fruit fly  Drosophila, these diverse tasks are distributed among different branches, with miRNAs serving to regulate endogenous gene expression, siRNAs mediating antiviral protection and the mostly germline restricted piRNAs preventing the spread of selfish genetic elements The small RNA silencing system is essential for gene regulation, antiviral defense and the control of transposable elements in the genome dcr-2 mutant flies Although the biogenesis of transposon-targeting piRNAs and endo-siRNAs is mechanistically quite distinct, transcripts in sense- and antisense-orientation are required in both cases. This potentially leads to a competition for precursor molecules in the germline, where both pathways are active concomitantly. While it has been previously demonstrated that the piRNA system is unperturbed in loqs gene generates several isoforms with specialized functions  loqs locus that carried a transgene to re-introduce a Loqs-PB cDNA and restore miRNA biogenesis loqs-D mutants in the remainder of this manuscript for simplicity. For r2d2, a mutant allele generated by imprecise excision of a P-element was used  from which homozygous mutant animals were selected for RNA isolation and deep sequencing. Heterozygous controls were obtained by crossing balanced flies with 1118w and selecting the non-Cy offspring thus carrying a mutant and a wild-type, rather than a balancer, chromosome. As a somatic sample, we prepared RNA from the head and thorax portion of female flies while dissected ovaries were used as a predominantly germline derived sample.To further elucidate the contributions of R2D2 and Loqs-PD in transposon defense we took a genetic approach. Since the ed r2d21. Since oO-methyl modified ago2 null mutant flies Drosophila genome and the amount of reads that mapped to a collection of transposon sequences (EMBL/FlyBase collection) or miRNAs. No mismatches were allowed during mapping of the reads. A first analysis of all transposon-matching endo-siRNAs and all miRNAs in the somatic RNA samples revealed that as expected in control animals, miRNAs are sensitive and siRNAs resistant to β-elimination. In r2d2 mutants, the endo-siRNAs have become sensitive to the chemical treatment but are still distinguishable from miRNAs in this respect. In the case of loqs-D mutants, transposon-matching endo-siRNAs have also become somewhat sensitive to β-elimination but less than in the case of r2d2  could be attributed to only four transposable elements   and other transposons  . Diagramd blood) . The amot levels . CompareF-element, 412 and Doc were only slightly affected by loss of Loqs-PD  that have escaped chemical treatment. In this case, we would expect that the 5’-nucleotide preference does not change with or without β-elimination as the same RNA pool would be sequenced in both cases. In the untreated RNA samples, loss of r2d2 led to a small increase of transposon-matching siRNAs that begin with adenosine at their 5’-end, accompanied by a corresponding reduction of guanosine and cytosine in comparison to heterozygous controls. In contrast, upon β-elimination transposon-matching small RNAs from homozygous 1r2d2 animals showed a reduction of adenosine and a corresponding increase of uridine, clearly demonstrating that this small RNA population is distinct from the one that was sequenced without β-elimination  in the absence of r2d2.To exclude that incomplete chemical oxidation/β-elimination may be a technical reason that could explain these ed rules . Second,mination . FinallyIs there any common principle that could explain why certain transposons differ from the bulk in their requirements for Loqs-PD and R2D2? This distinction is not based on their abundance since preference of Loqs-PD for biogenesis or R2D2 for Ago2-loading does not correlate with the absolute amount of small RNAs . Furtherr2d2 in contrast to other clusters. This difference was due to a unique sequence with 23275 counts at a single location. We consider this sequence to be a technical artifact (e.g. particularly high ligation efficiency) and removed it from the analyzed data set . Loss of Loqs-PD, in contrast, only resulted in de-repression of 412, roo, INE-1 and nof . Apparently, the redirection of endo-siRNAs into Ago1 in the absence of R2D2 represents a more severe loss of function than the reduced endo-siRNA biogenesis upon loss of Loqs-PD. In the germline, a high abundance of piRNAs and the severe phenotype of piRNA pathway mutations suggest a predominance of the piRNA system in transposon repression. Nonetheless, we detected significant changes for mdg1, het-A and F-element upon loss of R2D2 in ovarian RNA samples. Mdg1 is expressed predominantly in the somatic follicle cells HeT-A as well as the F-element are considered mostly germline expressed 412 element with preferential expression in the follicle cells is unchanged in 1r2d2 mutants and slightly hyper-repressed in loqs-D mutant ovaries. Taken together, we could demonstrate that a certain extent of transposon de-repression can be observed if the endo-siRNA pathway is impaired in the germline. We examined whether the changes in steady-state transposon mRNA levels correlate with the effects of 1r2d2 or loqs-D mutants on endo-siRNA abundance. The corresponding scatter plots comparing the fold change of mRNA levels against the fold change in siRNA levels from homozygous versus heterozygous mutants indicate that in both soma and germline, a reduction of transposon targeting endo-siRNAs can occur without necessarily affecting the corresponding steady-state mRNA levels , a possible explanation is that a small amount of mis-targeting already occurs in the absence of Loqs-PD despite the presence of R2D2. A direct comparison of the sensitivity to β-elimination with miRNAs in heterozygous and homozygous loqs-D mutants also indicates that loqs-D is required to achieve the full extent of Ago2-loading  with 23 to 27 nt length in soma matching transposons but present in significantly smaller quantity than germline piRNAs or somatic endo-siRNAs . These skoloqsr2d2 mutants 1118w stock. Details are provided in the Transposons are a major source of genome variability and their activity and genomic distribution may differ between fly strains. To facilitate our comparative analysis of the Dissected ovaries or the head & thorax portion were ground in Trizol  using a micro-pistil. RNA was then extracted and precipitated according to the manufacturer’s instructions. For qRT-PCR analysis, the background genomic DNA was removed by digestion with DNaseI  followed by digestion with proteinase K . Reverse transcription was primed with random hexamers, then qRT-PCR was performed with the Dynamo Sybr Green System  using the primers detailed in the section.2O and incubated with 12 µl 5x borate buffer  and 7.5 µl NaIO4 (200 mM feshly dissolved in H2O) for 10 min at RT. The oxidation was quenched by addition of 6 µl 100% glycerol . Elimination of the oxidized last nucleotide was performed by elevating the pH with 2M NaOH . After 90 min at 45°C the sample was transferred to a Mini quick spin oligo column  for purification and centrifuged . 20 µg glycogen were added and RNA was precipitated with three volumes of 100% ethanol . The RNA pellet was washed three times with 70% ethanol  and dissolved in 20 µl 2x formamide gel loading buffer. A small aliquot of the samples were analyzed on a 15% acrylamide-urea and stained with sybr gold to verify complete β-elimination and RNA quality.40 µg total RNA dissolved in 40.5 µl Hreads per kilobase of target sequence and million genome-matching sequences in library) in analogy to a commonly used approach for RNAseq.Small RNAs were enriched by size-selection on 20% acrylamide-urea gels, the size range of 19–29 nt was cut out and eluted from the gel slice. Linker ligation, library preparation and deep sequencing on the Illumina GAIIx platform was performed as previously described The sequences obtained in this study were submitted to the NCBI GEO database under the accession number GSE45290.File S1koloqs mutant in 1118w genetic background. Figure S2: Fly stock mapping of koloqs mutants. Figure S3: Backcrossing schema of 1r2d2 mutant in 1118w genetic background. Figure S4: Fly stock mapping of r2d2 mutants. Figure S5: Verification of β-elimination efficiency. Figure S6: Comparison of read counts in untreated and β-eliminated deep sequencing libraries (somatic RNA samples). Figure S7: Read length distribution of roo, TNFB, blood and roo transposon mapping small RNAs in r2d2 and loqs-D mutants.. Figure S8: The length distribution of 412, F-element, doc transposon mapping small RNAs in r2d2 and loqs-D mutants. Figure S9: Analysis of endo-siRNAs classified in LTRs, LINEs and IRs transposons in r2d2 and loqs-D mutants. Figure S10: Comparison of endo-siRNA abundance changes with changes in steady-state levels of transposons. Figure S11: Analysis of ping-pong signature of pilRNAs and piRNAs. Figure S12: Transcript levels of ago3, aub and piwi.Table S1: Primer sequences.Figure S1: Backcrossing scheme of (DOCX)Click here for additional data file.


Zero-mode waveguides (ZMWs) are photonic nanostructures that create highly confined optical observation volumes, thereby allowing single-molecule-resolved biophysical studies at relatively high concentrations of fluorescent molecules. This principle has been successfully applied in single-molecule, real-time (SMRT®) DNA sequencing for the detection of DNA sequences and DNA base modifications. In contrast, RNA sequencing methods cannot provide sequence and RNA base modifications concurrently as they rely on complementary DNA (cDNA) synthesis by reverse transcription followed by sequencing of cDNA. Thus, information on RNA modifications is lost during the process of cDNA synthesis.6A) in oligonucleotides and in a specific mRNA extracted from total cellular mRNA. Furthermore, the real-time reverse transcriptase dynamics informs about RNA secondary structure and its rearrangements, as demonstrated on a ribosomal RNA and an mRNA template.Here we describe an application of SMRT technology to follow the activity of reverse transcriptase enzymes synthesizing cDNA on thousands of single RNA templates simultaneously in real time with single nucleotide turnover resolution using arrays of ZMWs. This method thereby obtains information from the RNA template directly. The analysis of the kinetics of the reverse transcriptase can be used to identify RNA base modifications, shown by example for N6-methyladenine (mOur results highlight the feasibility of studying RNA modifications and RNA structural rearrangements in ZMWs in real time. In addition, they suggest that technology can be developed for direct RNA sequencing provided that the reverse transcriptase is optimized to resolve homonucleotide stretches in RNA. The function and dynamic regulation of all forms of RNA are critically dependent on their sequence, structure, and post-transcriptional modifications . Many diWe set out to adapt a technique originally developed for single-molecule, real-time (SMRT) DNA sequencing towards utilizing an HIV reverse transcriptase (HIV RT) for concurrent determination of RNA sequence, base modifications, and structure. Analogous to SMRT DNA sequencing , the RT A development of direct SMRT RNA sequencing requires a processive RNA-dependent polymerase that can utilize phospholinked nucleotides. Reverse transcriptases represent one such class of enzymes which initiate polymerization on a DNA-primed RNA template and synthesize a complementary DNA strand. We evaluated an in-house HIV, and commercially available AMV as well as MMLV RTs for their ability to incorporate phospholinked nucleotides during first strand synthesis. A FAM-labeled DNA primer (FAM-P5) was hybridized to a synthetic RNA template . For some RNA molecules, more random phospholinked nucleotide binding events continued beyond this expected termination point  is the most abundant modification in mRNAs of multicellular organisms and is also present in viral RNAs that replicate in the nucleus [6A in mRNA includes modulations of mRNA stability, splicing, and export from the nucleus. m6A modification has also been shown to confer innate immune tolerance of transfected exogenous RNA [6A methyltransferase led to cell apoptosis [6A modification in mRNA, to date only one modification site is known in a specific mRNA due to the extremely laborious biochemical assays for mapping m6A sites in mRNA [6A because Watson-Crick base pairing is not affected by the methyl group during cDNA conversion, thus causing the information about the base modification to be erased in that step. Recently, a combination of immunoprecipitation and next-generation sequencing enabled a localization of m6A in mRNAs within ~100 nucleotide long transcriptome regions [N nucleus -7. The pnous RNA . siRNA k regions ,10.6A and an unmodified control template of the same sequence  showed that m6A decreased the binding rate of the complementary phospholinked T nucleotide in the active site of the enzyme by ~5-fold  obtained on native mRNA revealed a slower binding kinetics compared to the in vitro transcribed control, while the dissociation kinetics remained unaffected . BL21 E. coli were transformed with each expression vector and cultured overnight. After 100-fold dilution into fresh LB media, the cultures were grown further at 37°C. The incubator temperature was reduced to 18°C when the E. coli density reached 0.9 OD600 followed by the addition of 1 mM IPTG for induction. The cultures were allowed to grow overnight before harvesting.Methods for expression and purification of HIV reverse transcriptase (HIV RT) were adapted from previously published procedures ,23. E. cCell pellets from the p66 and p51 cultures were combined in 2:1 weight ratio and re-suspended in lysis buffer . The cell suspension was passed through a microfluidizer device 3 times at 80 psi  to lyse the cells. HIV RT was released from bacterial DNA by adding 0.5 M NaCl to the lysate and stirring for 30 min at 4°C. PEI was then slowly added to the stirring lysate to 0.3% final concentration and stirred for another 30min. Precipitated bacterial DNA was removed by centrifugation. Ammonium sulfate was added at 0.37 g/ml to the cleared lysate and precipitated proteins were collected by centrifugation. Protein pellets were re-suspended with 30 mM HEPES pH 7.5 and buffer content adjusted to Buffer A . The HIV-RT was then purified using a HisTrap FF crude column  and a 10 mM to 500 mM imidazole linear gradient. Collected fractions containing HIV-RT heterodimer were pooled and concentrated using a 30k MWCO Vivaspin 4 device . A Superdex 200 column was used for the final purification step. Purified HIV RT was stored at −20°C in a buffer containing 47.5% glycerol, 30 mM HEPES pH 7.5, 60 mM NaCl, 0.1 mM EDTA, and 1 mM DTT. The final concentration was determined by UV absorbance and extinction coefficients calculated from the amino acid sequences.All nucleic acids used in the study are listed in Additional file 6A and m6A control templates were synthesized as described previously [in vitro run-off transcription using T7 RNA polymerase and the template obtained with PCR of the plasmid used in the transfection.meviously . DNA seqE. coli was purchased from Applied Biosystems as solution of total RNA from E. coli. mRNA template was synthesized by in vitro run-off transcription using cDNA of 216 nt long 3’-end section of the human 40S ribosomal protein S17 gene and T7 promoter, as described previously [Ribosomal RNA from eviously .-1 and cooled down to 4°C before storing at −20°C. All hybridization pairs used in this study are listed in Additional file All complementary nucleic acids were hybridized in 10 mM Tris, pH 7.5, 75 mM KCl and incubated at 65°C for 5 min followed by gradual cooling to 25°C at 0.1°C sWe have tested in-house HIV RT and commercially available AMV RT , and MMLV RT  for their ability to incorporate phospholinked nucleotides. To this purpose, we have hybridized synthetic RNA template and FAM-P5 DNA primer , and denatured for 10 min at 65°C. Denatured samples were run on a denaturing 15% PAGE, and analyzed on a Typhoon imager .k1, nucleotide dissociation rates k−1, and incorporation rates (k2) for phospholinked nucleotides using pre-steady state bulk kinetic assays . Changes in FAM fluorescence upon mixing of the nucleotide analog with RT-DNA template complex were recorded with a SF-2004 stopped-flow instrument . k−1 and k2 were determined by nonlinear regression either by the stopped-flow software or by the KaleidaGraph software using double-exponential and hyperbolic equations with data collected at various nucleotide concentrations.We have determined nucleotide binding rates k−1 in Additional file k−1 was determined from the increase in fluorescence signal upon addition of unlabeled nucleotides.To measure the dissociation rate of phospholinked nucleotides ) at 37°C for 5 min and stored on ice. DNA primer-RNA template-streptavidin complex was immobilized in ZMWs by incubation on ZMW arrays for 5 min at room temperature in the Immobilization Buffer. Excess complex was removed by washing with Wash Buffer . Next, 30 μl Reaction Buffer  was applied to the array and placed in the instrument. The recording of the fluorescent signals was started and reverse transcription on the chip was initiated by adding 10 μl of Enzyme Buffer (Reaction Buffer supplemented with 2 nM HIV RT) at room temperature. The movie acquisition was stopped after 15 min. In the case of experiments carried out at 37°C, the above Reaction Buffer was replaced by Reaction Buffer 2 . In addition, when the experiments were carried out at 37°C, the fluorescent signal was measured in 10 successive 5 min long movies with ZMW array alignment steps after each movie to correct for thermal drift.Phospholinked dNTPs were generated as described previously ,26. ZMW Data were collected on a highly parallel confocal fluorescence detection instrument, as previously described ,28. PulsmRNA template during SMRT reverse transcription was defined as stalling of reverse transcription for longer than 5 min. The pausing probability for a given sequence position was then defined as a fraction of reverse transcription events pausing at that position relative to the total number of reverse transcripts containing this position.A pause along mRNA template when it is present in the active site of HIV RT. In the Kinetic Trap Model (KTM), an ensemble of full length mRNA folds was first generated using the program mfold[mRNA during the first nucleotide incorporation event:We have applied two different models to calculate the basepairing probability for each sequence position of ram mfold with thePbp is the basepairing probability for j-th nucleotide when the first nucleotide is bound in the RT active site, n is the number of mfold structures where j-th nucleotide is in a basepaired state, m is the number of all calculated mfold structures, ΔGk is a free energy of folding a k structure, ΔGl is a free energy of folding an l structure, R is the gas constant (8.314 J·(mol·K)-1), and T is reaction temperature (310.15 K). After the first nucleotide incorporation, RT translocates to the second nucleotide. The base pairs of the initial ensemble of mRNA folds are retained except for those involving the first base that has been reverse transcribed and is now forming part of the RNA/cDNA hybrid product. A set of basepairing probabilities Pbp was then determined using the readjusted ensemble of mRNA folds. This procedure was subsequently repeated for every mRNA sequence position. The calculated basepairing probabilities were corrected since the stability of a predicted base pair can depend on the basepairing state of its sequence neighbors [where eighbors . We havePbp’ is the average basepairing probability for j-th nucleotide when it is bound in the active site of HIV RT, and w+1 is the width of the sliding window. We have calculated Pbp’ values using w values of 0, 2, 4, 6, and 8  values and experimentally measured pausing probability on mRNA was evaluated using the Spearman correlation coefficient , the initial ensemble of mRNA folds and the basepairing probabilities were determined in the same way as described above for KTM. However, after the first nucleotide incorporation and RT translocation to the second base, the untranscribed mRNA now shortened by one nucleotide was allowed to re-equilibrate to a new ensemble of stable folds. A set of basepairing probabilities Pbp was then obtained using the re-equilibrated ensemble of mRNA folds. This re-equilibration procedure was subsequently repeated for every mRNA sequence position, and basepairing probabilities averaged as explained above for KTM using w = 4  in the presence of phospholinked nucleotides (PL-Ns). Figure S2 (a)-(j). Examples of SMRT reverse transcription traces obtained with synthetic RNA template. Figure S3. Determination of binding, dissociation and incorporation rates for HIV RT using phospholinked nucleotides. Figure S4. Cumulative distributions of block widths during SMRT reverse transcription for incorporations of A (yellow), C (red), G (green), and T (blue) phospholinked nucleotides by HIV RT in ZMWs. Figure S5. Basepairing probabilities on mRNA template calculated using the Kinetic Trap Model (KTM). Figure S6. Basepairing probabilities on mRNA calculated using the Equilibrium Model (EM). Figure S7. Activity of RNA-dependent RNA polymerase from bacteriophage ϕ6 (ϕ6 RdRP) in the presence of a phospholinked rCTP. Table S1. RNA templates and DNA primers used in SMRT reverse transcription. Table S2. Sequences of DNA templates and corresponding DNA primers used in the bulk measurements of HIV RT transcription kinetics . Table S3. Bulk kinetics data obtained with stopped-flow experiments  for all four phospholinked nucleotides used in SMRT reverse transcription. Table S4. Collapsed sequences of 16S rRNA and mRNA. Table S5. A list links to the raw data.Reaction mechanism of reverse transcriptase. Click here for file


Genotyping circulating rotaviruses before and after introduction of rotavirus vaccine is useful for evaluating vaccine-associated changes in genotype distribution. We determined frequency of rotavirus genotypes among 61 rotavirus-positive children hospitalized in Israel during the 2005–06 rotavirus season. Accurate molecular epidemiologic data were recovered from affinity-concentrated rotavirus immobilized in rotavirus-positive bands from air-dried, diagnostic rotavirus rapid test strips (dipstick) stored at room temperature from 1 week to 5 years. G genotypes were identical for 21 paired dipsticks and suspensions, whereas dipsticks or suspensions detected an additional G genotype in 2 samples. RNA sequences from 7 pairs were identical. Phylogenetic analysis suggested previously unreported G2 sublineages and G9 lineages. The ease with which dipsticks can be stored at local facilities and transported to central reference laboratories can reverse increasing difficulties in obtaining geographically representative stool samples and expand surveillance to regions lacking adequate laboratory facilities. Dipsticks are dipped into saline stool suspensions. If rotavirus is present, a colored band appears when indicator-linked rotavirus antibodies bind to the virus, and this complex is trapped on a band of membrane-bound rotavirus antibody on the dipstick. With their growing use at point-of-care facilities and in local hospital laboratories, fewer samples are available for genotyping by centralized reference laboratories. Obtaining geographically representative stool samples and expanding surveillance to regions lacking adequate laboratory facilities is becoming increasingly difficult. The ease with which dipsticks can be stored at local facilities and transported to central reference laboratories can help overcome these difficulties. We hypothesized that rotavirus trapped on rotavirus-specific antibody bands, equivalent to affinity concentrated rotavirus, might yield genotyping-quality RNA from dipsticks that are easily archived locally and transportable to centralized laboratories. In this study, our main objective was to determine the feasibility of recovering molecular epidemiologic data from rotavirus immobilized on air-dried diagnostic rotavirus dipsticks to compensate for the decreasing number of fecal samples reaching central reference laboratories.Stool samples from 311 children hospitalized with acute gastroenteritis at Sheba Medical Center, Tel Hashomer, Israel, during May 15, 2005–May 15, 2006, were suspended in saline. Rotavirus dipsticks  were used to identify rotavirus-positive stool samples.  The section of the dipstick containing the rotavirus-specific bands from 14 rotavirus-positive dipsticks that had been stored at room temperature for at least 1 week were excised and placed into 120 µL saline.Rotaviral RNA was extracted from these rotavirus-specific bands and from the corresponding saline stool suspensions by using QIAmp Viral RNA Mini kits . RNA amplified from rotavirus-specific bands from dipsticks archived at room temperature 5 years earlier was similarly extracted. RNA was amplified by using generic primers for reverse transcription–PCR (RT-PCR)  followed by heminested PCR  with genotype-specific primers to identify G and P genotypes as described by Gouvea et al. . Nearest-neighbor phylogenetic trees were constructed from these sequences after the data were bootstrapped 1,000 times with Clustal X .This study was approved by the institutional review board of Chaim Sheba Medical Center (SMC-7606-09). Experiments were not performed on humans. All personal identification was removed from the remnants of fecal samples sent to the National Center for Viral Gastroenteritis for rotavirus analysis. The viral RNA used for this study was obtained from these anonymous samples.To assess the suitability of RNA extracted from dipsticks for determining the VP7 genotype, we first determined the VP7 G genotype of rotavirus in rotavirus-positive clinical samples from children hospitalized during May 15, 2005–May 15, 2006. Of 311 stool samples, 61 were determined to be rotavirus positive by using diagnostic rotavirus dipsticks. The G genotype and P genotype of RNA in 54 of the 61 samples was determined by the size of the heminested PCR amplification product as described. Mixed infections, e.g., >1 G (6 samples) and P genotypes (2 samples) in the same fecal suspension, were found in 8 (15%) of the 54 positive samples. Taking into account mixed infections, we found G1, G2, G3, and G9 G genotypes in 79%, 5%, 2%, and 15%, respectively, of the 62 rotaviruses identified in the 54 clinical samples. The frequencies of associated G and P genotypes for isolates from the 2005–2006 rotavirus season were 3% G1P[4], 76% G1P[8], 5% G2P[4], 2% G3P[8], 3% G9P[4], and 11% G9P[8].We chose 18 rotavirus-positive clinical samples with which to study the feasibility of recovering molecular information from dipsticks. Thirteen were from children infected with 1 G genotype of rotavirus and 5 from children simultaneously infected with rotaviruses belonging to 2 different G genotypes. Of the 13 infections by a single G genotype, 8 were G1, 3 were G2, and 1 each were G3 and G9. All 5 mixed infections, RoV-24_ISR05, RoV_41_ISR05, RoV_56_ISR06, RoV_57_ISR06, and RoV_60_ISR06, were simultaneous infections with G1 and G9 genotypes. Mixed infections were identified by specific product size on gels and confirmed by partial sequencing of the G1 product for all 4 patients whose G9 sequences appear in the >1 week at room temperature. The quality of the RNA from all 18 extractions was sufficient to enable determination of VP7 G genotypes by heminested RT-PCR. The genotypes obtained from the RNA extracted from dipsticks were identical to those from RNA extracted from corresponding saline suspensions for 14 of the 18 rotavirus-positive stool samples, including 3 samples from children with dual infections. In 2 mixed G1–G9 infections, G9, but not G1 RNA, was recovered, and G1 RNA was recovered from dipstick RNA in addition to G9 from a G9-infected child. A fourth dipstick from a G3 sample yielded a G3 and an equivocal G1 that did not appear in subsequent replicate heminested amplifications. In other words, 21 (92%) of 23 genotypes identified from RNA extracted from 18 fecal suspensions were also identified from RNA extracted from dipsticks, including 8 of 10 genotypes from 5 samples simultaneously infected with rotaviruses of 2 different genotypes.RNA was extracted from rotavirus-positive bands of dipsticks from the 18 clinical samples. The dipsticks had been stored for Rotaviral VP7 RNA suitable for genotyping was also recovered from all 5 air-dried rotavirus-positive dipsticks that had been stored at room temperature for 5 years. The G genotypes of the rotavirus in the fecal suspensions from the 5 archived dipsticks from 2002 were unknown, and the suspensions were no longer available for comparison. One of the dipsticks was manufactured by Hy Laboratories; the remaining 4 were manufactured by Novamed. Initial amplification with generic G-type primers yielded generic G genotype amplification products of expected size  upon gel electrophoresis. Three of the dipsticks yielded G3 RNA; the other 2 yielded G1 RNA. As proof of concept that P genotypes could be determined in addition to G genotypes, the P genotypes of RNA recovered from 2 of the 5-year-old rotavirus dipsticks with G3 RNA were also determined by direct sequencing of the RT-PCR products by using the generic P-type primers. Both were P[8] by sequence analysis .http://blast.ncbi.nlm.nih.gov/Blast.cgi)   were conFar East . G9 isol,,The G1P[8] genotype predominated in central Israel during the 2005–06 rotavirus season as it did throughout Israel during 1991–1994 (In conclusion, air-dried affinity-concentrated virus from rotavirus-positive bands of dipsticks yielded RNA suitable for G genotyping and sequencing, even for dipsticks stored at room temperature for 5 years. Even though P genotyping and sequencing was performed only on RNA isolated from 2 of the 5-year old dipsticks, it is reasonable to assume that P genotyping and sequencing would have been possible for the rest based on the equivalent quality of the RNA extracted from the dipsticks and RNA extracted from stool suspensions. Thus, dipsticks can be used for recovery of molecular epidemiologic data by reference laboratories from diagnostic tests routinely performed in many clinical laboratories and point-of-care facilities. RNA extracted from these archived dipsticks will enable centralized laboratories to easily recover epidemiologically useful data from the samples of other laboratories and from regions lacking adequate laboratory facilities. In addition, centralized laboratories will be able to assess whether the introduction of universal rotavirus vaccination changed the distribution of rotavirus genotypes associated with severe rotavirus-associated acute gastroenteritis.


S. cerevisiae is a known component of the NRD complex implicated in transcription termination of nonpolyadenylated as well as some polyadenylated RNA polymerase II transcripts. We now show that Sen1 helicase possesses a wider function by restricting the occurrence of RNA:DNA hybrids that may naturally form during transcription, when nascent RNA hybridizes to DNA prior to its packaging into RNA protein complexes. These hybrids displace the nontranscribed strand and create R loop structures. Loss of Sen1 results in transient R loop accumulation and so elicits transcription-associated recombination. SEN1 genetically interacts with DNA repair genes, suggesting that R loop resolution requires proteins involved in homologous recombination. Based on these findings, we propose that R loop formation is a frequent event during transcription and a key function of Sen1 is to prevent their accumulation and associated genome instability.Sen1 of  ► Nascent RNA forms hybrids with underwound DNA upstream of elongating Pol II ► Single-stranded DNA so formed is prone to damage which results in genome instability ► Sen1 helicase acts to remove R loops by resolving RNA:DNA hybrids ► Sen1 function in Pol II elongation and termination may relate to R loop resolution S. cerevisiae nascent transcripts formed by RNA polymerase II (Pol II) on protein-coding genes are immediately processed, packaged, and exported to the cytoplasm  and somatic hypermutation (SHM) in clonally expanding B cells . Both pr mutants .Many events during transcription are orchestrated by proteins binding to the carboxy-terminal domain (CTD) of the Pol II largest subunit. CTD consists in yeast of 26 hepta-peptide repeats (YSPTSPS) that are dynamically modified during transcription. In particular, serine phosphorylation occurs during early  and late (ser2-P) elongation phases to allow stage specific binding of elongation and RNA processing factors . TranscrTermination of many noncoding RNAs requires an additional component, the NRD complex , in which Nrd1 is bound to ser5-P CTD . NRD-depSEN1 codes for a 240 kDa superfamily I helicase (S. pombe homolog possesses 3′-5′ nucleic acid unwinding activity (sen1-1 mutant (helicase domain G1747D), we set out to characterize the molecular role of Sen1 in transcription termination. We now identify broad functions for Sen1 during Pol II transcription in reducing R loop formation and consequent prevention of transcription-associated genome instability.helicase , and itsactivity . The essactivity . The Senactivity . Mutatioactivity . Althougactivity , they haGAL10 pA signal (sen1-1 mutants grown for 150 min at nonpermissive temperature (37°C) show a strong termination defect ] or additionally the C-terminal helicase domain [Sen1(1212)] , but not Sen1(323). Similarly, coexpression of Sen1(1212) restored wild-type levels of endogenous PMA1 mRNA, also previously shown to display mild termination defects in sen1-1 ] . As showonstruct B. mRNA ln sen1-1 .CYC1 3′ flanking RNA as the pA substrate  had no effect on mRNA 3′ end formation but stabilized the 3′ end cleavage product, indicative of loss of exonuclease “torpedo” function . Lack of THO elicits TAR in LNA and consequent restoration of LEU2, as previously shown , recombination levels in the hpr1Δ strain were reduced to background WT levels, presumably because Pol II termination restricts R loop formation. Similar analysis of LNA and LNAT transformed rat1-1 and rna14-1 showed no detectible increase in recombination, confirming that defects in CPF/Rat1 dependent transcription termination per se do not promote recombination  further stimulated recombination may reflect an increase in R loop formation downstream of pA signals.Since ubstrate , we consubstrate . RNA:DNAubstrate . We therly shown A (Prado ly shown . In contbination . In markreported . Moreovenab3 and nrd1 CTD-interacting domain mutants (but not the RNA binding domain mutant nrd1-102) transformed with LNA, they were reduced to background levels in LNAT  with plasmid-encoded LACZ::URA3 chimeric gene . Although mutation rates were very low in WT and sen1-1 cells grown at semipermissive temperature, AID expression in sen1-1 significantly increased mutation rates  or the NTS and only 30%  occur on G or the TS  sequence . In regie pLAUR, B and selon rates B. DNA ser the TS . The smar the TS . Since Asen1-1 were sensitive to RNase H overexpression as previously observed with THO and THSC/TREX2 mutants and indicative of increased R loop formation  but not over LGZ5  in all tested strains and may indicate a region that is particularly prone to form RNA:DNA hybrids.The S9.6 antibody was also employed in a regular ChIP analysis of endogenous ver PMA1 A, althouhromatin . Of notePMA1, we developed an independent assay for R loop formation over this same gene  or downstream of the pA signal (P7). As shown in gel fractionation of these amplified DNAs, sen1-1 chromatin associated RNA is highly sensitive to RNase H treatment. Quantitative PCR (qPCR) values  show that although in sen1-1 chromatin associated RNA is nearly fully RNase H sensitive, in WT RNase H sensitivity increases downstream of the PMA1 pA signal. This suggests that in sen1-1 a higher fraction of RNA forms R loop structures with genomic DNA, whereas in the WT R loops are particularly prone to forming downstream of the pA site.In view of the only modest RNA:DNA hybrid accumulation observed over ame gene B. Genomisen1-1 in a transcription-dependent manner. Furthermore, they imply that WT-transcribed and subsequently packaged RNA still forms some level of RNA:DNA hybrids. The amount of RNA in R loop conformation appears to differ throughout the transcribed PMA1 gene, with a particular prevalence over the pA site.From these combined analyses, we conclude that R loops accumulate in sen1-1 elicit TAR. However, to substantiate this conclusion and further exclude the possibility that sen1-1 TAR is an indirect consequence of its transcription termination defect, we employed genomic analysis. A comparison of RNA steady-state levels of sen1-1 to WT cells grown for 150 min at 30°C was performed by hybridization to strand-specific tiling arrays (data available at http://www.ebi.ac.uk/arrayexpress/ with accession number E-TABM-863)  genes. DDCC genes were underrepresented, and among those changed, no clear trend was observed  and others downregulated .Experiments presented so far suggest that R loops formed in ABM-863) . This reABM-863) . Importaobserved , with sosen1-1, we searched for synthetic genetic interactions of sen1-1 with mutants of candidate genes involved in either NHEJ or homologous recombination (HR). At permissive temperature (25°C) or in presence of replicative stress, we observed genetic interaction with various factors involved in HR but not in NHEJ . In contrast, sen1-1 yku70Δ double mutants showed neither growth defects nor increased sensitivity to HU, suggesting that NHEJ is not required for cell survival of sen1-1 cells  or nonpermissive (37°C) temperature for 3 hr. In contrast, only about 1% of WT cells formed foci under these conditions. Importantly, steady-state RNA analysis from the same cell populations showed that Rad52 foci formation correlated with accumulation of a bicistronic transcript from the SNR13 snoRNA gene, which results from termination at the next available pA when NRD-dependent termination is defective     C, we couSEN1 genetic interaction with HR genes. The occurrence of Rad52 foci, as a marker for ongoing recombination, shows that recombination is related to transcription, as well as to the presence of a functional Sen1 helicase domain. In summary, we suggest that R loop formation is more frequent than hitherto anticipated and requires active removal by helicases such as Sen1.The deleterious effects of transcription on genome integrity have been suggested by various observations . Whenevesen1-1 extend to many CUT loci, then their accumulation, even if transient, would cover substantial regions of the genome. In both CUTs and mRNA coding genes, R loops could interfere with DNA replication, induce ssDNA breaks, or be recognized as recombination intermediates. Any of these possibilities could explain the essential need for DSB sensing proteins in sen1-1  cause the neurodegenerative diseases, Ataxia with Oculomotor Apraxia Type II (AOAII), and juvenile amyotrophic lateral sclerosis (ALS4). Like sen1-1 these SETX mutants show defects in transcription, RNA processing, and DNA damage repair  with HR and NHEJ mutants employed standard conditions. Recombination frequencies were scored by counting of LEU+ cells or by FACS analysis of GFP+ cells. GFP-Rad52 nuclear foci were detected by epifluorescent microscopy.Yeast strains, plasmids, and primers are listed in The transcription run on (TRO) method and probes for pKGG are as described .For RNA isolation, strains were grown in minimal (selective) media at indicated temperatures. After acidic phenol RNA isolation, RNA (15 μg) was separated by 1% formaldehyde agarose gel electrophoresis. RNA immobilized on nitrocellulose membranes was detected with random primed probes.ChIP employed real-time qPCR as previously described . S9.6 puSequential RNase H  and DNase I  digestion of genomic DNA (5 μg) isolated from logarithmic phase cells cultivated for 1 hr at 37°C with yeast breaking buffer , phenol, and glass beads. Isolated RNA was reverse transcribed (Invitrogen Superscript III) according to the manufacturer.These are presented in the


Biometrics 2011).RNA sequencing (RNA-seq) has become a major tool for biomedical research. A key step in analyzing RNA-seq data is to infer the origin of short reads in the source genome, and for this purpose, many read alignment/mapping software programs have been developed. Usually, the majority of mappable reads can be mapped to one unambiguous genomic location, and these reads are called unique reads. However, a considerable proportion of mappable reads can be aligned to more than one genomic location with the same or similar fidelities, and they are called "multireads". Allocating these multireads is challenging but critical for interpreting RNA-seq data. We recently developed a Bayesian stochastic model that allocates multireads more accurately than alternative methods , the most popular read alignment format, as the standard input; then based on the Bayesian model, it calculates mapping probabilities of multireads for competing genomic loci; and BM-Map generates the output by adding mapping probabilities to the original SAM file so that users can easily perform downstream analyses. The program is available in three common operating systems, Linux, Mac and PC. Moreover, we have built a dedicated website, We have developed a stand-alone, efficient, and user-friendly software package for accurately allocating multireads, which is an important addition to our previous methodology paper. We believe that this bioinformatics tool will greatly help RNA-seq and related applications reach their full potential in life science research. In recent years RNA-seq has become a popular and powerful approach for transcriptome profiling ,2. UsingA key step in the analysis of RNA-seq data is read mapping, the goal of which is to infer the origin of short reads in the source genome. For this purpose, many software programs have been developed such as Bowtie , BFAST  and MAQ Therefore, we consider allocating multireads is a critical issue in the RNA-seq analysis, which has received relatively little attention in literature. One pioneering and influential method is "the proportional method" developed by Mortazavi et al. (2008)  in whichFigure The details about the underlying probability model and algorithm are depicted in our previous study , and herq). We assume that the observed mismatches (e for multiread and g for unique reads) come from two resources. The first one is sequencing error (α), and we estimate quality-score specific error rate by considering all the unique reads in the dataset. The second source is called hidden nucleotide variations (β), which is mainly due to that RNA-seq reads are typically mapped to a public reference genome rather than the actual sample genome. Hence, variations between the two genome versions  may cause some observed mismatches between the reads and the reference genome. Given the unique reads associated with a competing location, we model the uncertainty of hidden nucleotide variations using Markov chain Monte Carlo (MCMC) simulations. The prior distribution of reads mapping (Z) is set in proportion to the numbers of unique reads associated with competing loci, which essentially follows the main idea in Mortazavi et al. (2008) [In our model, the likelihood can be calculated based on the probability of mismatch at each nucleotide position of the multiread (. 2008) . Figure 008 . FigBecause the amount of RNA-seq data is large and our Bayesian method relies on extensive numerical computations for statistical inference, we implemented our method in a C++ programming written software package. To reduce the computer memory requirement for the calculations, we carefully examined the aligned data to minimize the size of the data structures that were held in memory simultaneously. To reduce lengthy execution times, we optimized the search algorithms used to process the reads. Finally, we achieved a further reduction in wall-clock runtime by running the calculations on multiple parallel threads of execution utilizing the capabilities typical of a modern desktop computer . Currently, for an RNA-seq dataset with 8 million post-aligned short reads, our BM-Map program takes 3~4 hours to complete allocating the multireads in a single Windows 64-bit desktop machine , which is generally comparable to the computing time of mapping short reads by other methods.http://bioinformatics.mdanderson.org/main/BM-Map. The website also contains detailed tutorials and references about the BM-Map software.We have made BM-Map a user-friendly software package. BM-Map takes SAM format (Sequence Alignment/Map) , the mosBased on a Bayesian stochastic model we previously proposed, we have developed an efficient and user-friendly software package for allocating RNA-seq multireads, which takes full advantage of the information stored in the unique reads, including sequencing error profiles, the likelihood of hidden nucleotide variations , and the expression level of competing locations. After reads are aligned by a general aligner, a mapping refinement step on multireads using our program would further improve the accuracy of gene expression quantification.Obviously, the effect of allocating multireads on gene expression is related to next-generation sequencing techniques. With the increase of read length, whether to include multireads in expression quantification would become less important. Nevertheless, through our analysis on RNA-seq datasets with different read lengths (from 36~76 nt), the effect of multireads on certain genes  appear to be non-negligible given current sequencing technologies  organisms underwent several rounds of whole-genome duplications such as plants , (ii) geWe have developed a stand-alone and user-friendly software package, BM-Map, which can accurately allocating a large amount of RNA-seq multireads in an efficient way. We expect that this useful bioinformatics tool would help RNA-seq and its related applications reach their full potential in life sciences and biomedical research.• Project name: BM-Map• Project home page: http://bioinformatics.mdanderson.org/main/BM-Map• Operating system(s): Linux, Mac and PC• Programming language: C++• Other requirements: No• License: No• Any restrictions to use by non-academics: NoCPU: central processing unit; MCMC: Markov Chain Monte Carlo; nt: nucleotide; RNA-seq: RNA sequencing; RPKM: reads per kilobase of exon model per million mapped reads; SAM: sequence alignment map; SNP: single-nucleotide polymorphism; RIP-seq: RNA immunoprecipitation sequencing; CLIP-seq: cross-linking immunoprecipitation sequencing; 3-seq: 3'-end sequencing.The authors declare that they have no competing interests.Y.J. and H.L. designed the research and drafted the manuscript. Y.Y. and C.N. wrote the programming code for the software and built the website. Y.X. and K.-W.T. optimized the algorithm for efficiency. All authors read and approved the final manuscript.Effect of multireads on the expression quantification of different gene groups.Click here for file


There is an error in the authors' affiliation. The correct affiliation is: Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China


Tobacco Necrosis Virus D (TNV-D), in the genus Betanecrovirus (family Tombusviridae), possesses a single-stranded, positive-sense RNA genome containing six open reading frames (ORFs). Two 5'-proximal ORFs (1 and 2) encode overlapping polypeptides of 22 and 82 kDa  which are both required for replication. The p22 auxiliary protein contains no replication motifs but the C-terminal region, downstream of a leaky stop codon, encodes a 60 kDa polypeptide (p60) which contains conserved RNA-dependent RNA polymerase (RdRP) motifs. Here we have expressed and purified recombinant p60 and show that in vitro it binds and efficiently synthesises both TNV-D RNA and Satellite tobacco necrosis virus C RNA. Alanine scanning mutagenesis of conserved amino acids in characteristic motifs in p60 revealed that some mutations significantly reduced RNA synthesis but mutating the second asparagine residue in the conserved GDD box was lethal. The effects of mutating identical amino acids in p60 on virus replication in vivo were examined in Nicotiana benthamiana plants following infection with RNA transcribed from wild type (wt) and mutant constructs. In inoculated leaves the behaviour of the mutants paralleled the in vitro data but systemic infection was precluded in all but one mutant which had reverted to wt. This study is the first to demonstrate the nucleic acid-binding and synthetic capabilities of a betanecrovirus polymerase. Betanecrovirus in the family Tombusviridae -labelled ss satC- RNA probe (28.4 pmol) was mixed with different amounts of MBP-p60 protein (0-120 pmol=0-13 μg) in 20 µL binding buffer , and 0.5 µ of [32P] UTP, 30 pmol template RNA and MBP-p60 protein in a 50 µL total volume. After phenol-chloroform extraction and ammonium acetate-isopropanol precipitation, half the amount of the RdRP products was treated with S1 nuclease as described before [RdRP reactions were carried out as previously described  in the pd before . Subsequin vitro from wt clone pTNVD and the seven mutant constructs were synthesised as described above and mechanically inoculated onto N. benthamiana plants (three leaves per plant employing approximately 10 μg of transcript per leaf) as described before [N. benthamiana leaves were isolated using the TRI Reagent according the manufacturer’s instructions (Sigma) and northern blot analysis was performed with oligolabelled [32P]-radioactive DNA probes derived from the complete TNV-D genome as described previously [Transcripts synthesised d before . Plants eviously . Any mutE. coli as described in the Materials and Methods. After affinity-based purification of the MBP-p60 fusion protein . RNA binding by the MBP-p60 fusion protein was also tested in competition assays in which increasing amounts of unlabelled competitor RNAs, including positive-strand STNV-C (satC+) RNA produced from pSTNVC following linearisation with Leek white stripe virus, BBSV and TBSV  [Following the successful expression and purification of a recombinant TNV-D p60 enzyme, which binds and replicates both TNV-D and STNV-C RNA, we wished to understand more of the involvement of the RdRP A-F motif sequence motifs ,12 in thnt RdRPs . Interes2 (p92C) , which wAs anticipated the TNV-D C domain mutation D327A was lethal for both RNA synthesis and plant infectivity  strand RNA templates. (TIF)Click here for additional data file.Figure S2Primers used for alanine scanning site-directed mutagenesis of TNV-D p60.(TIF)Click here for additional data file.Figure S3Betanecrovirus and TBSV which are most closely related to TNV-D.Amino acid alignment of the putative polymerase domains from members of the genus Accession numbers of each virus are shown in brackets: Leek white stripe virus  TNV-D (D00942), Beet black scorch virus  and TBSV (M21958). The amino acid sequences depicted show the readthrough domain of the putative RdRPs immediately downstream of the amber termination codon and extend to the termination codon of the ORFs. The sequences were aligned using the Fast Fourier Transform MAFFT program L9INS-1 [ L9INS-1 . The con L9INS-1  are unde L9INS-1  are unde(TIF)Click here for additional data file.


With the advent of Next Generation Sequencing (NGS) technologies, the ability to generate large amounts of sequence data has revolutionized the genomics field. Most RNA viruses have relatively small genomes in comparison to other organisms and as such, would appear to be an obvious success story for the use of NGS technologies. However, due to the relatively low abundance of viral RNA in relation to host RNA, RNA viruses have proved relatively difficult to sequence using NGS technologies. Here we detail a simple, robust methodology, without the use of ultra-centrifugation, filtration or viral enrichment protocols, to prepare RNA from diagnostic clinical tissue samples, cell monolayers and tissue culture supernatant, for subsequent sequencing on the Roche 454 platform.de novo algorithms. Furthermore, genome sequences were generated from considerably less than 200 ng RNA, indicating that manufacturers’ minimum template guidance is conservative. In addition to obtaining genome consensus sequence, a high proportion of SNPs (Single Nucleotide Polymorphisms) were identified in the majority of samples analyzed.As representative RNA viruses, full genome sequence was successfully obtained from known lyssaviruses belonging to recognized species and a novel lyssavirus species using these protocols and assembling the reads using The approaches reported clearly facilitate successful full genome lyssavirus sequencing and can be universally applied to discovering and obtaining consensus genome sequences of RNA viruses from a variety of sources. RNA viruses have small, simple genomes, which have a high level of diversity, due to the low-fidelity viral polymerase used for replication. Traditionally, due to their small genomes, ‘genome-walking’ was used to obtain a reference sequence, from which primer pairs can be designed for down-stream use on similar viruses -6. HowevThe advantages of NGS technologies in virology are numerous, and its use is becoming more commonplace , particuRhabdoviridae) have negative-sense, single-stranded RNA genomes approximately 12 kb long. Traditionally, partial gene sequence  was utilized for viral speciation and phylogenetic analysis. However, limitations on the information obtained from these partial sequences and a requirement to use full genome sequence as part of the ICTV  criteria to propose new lyssavirus species, in combination with recent advances in high-throughput sequencing, have resulted in an increase of full genome sequences available on Genbank. From the submission of the first full length lyssavirus genome sequence of RABV (prototype) in 1988 [Lyssaviruses  observed in the sequences obtained.Two different extraction methodologies were compared to investigate the suitability of the RNA obtained. Three brain tissue samples  were extracted, using the same amount of starting material, either by TRIzol® extraction followed by isopropanol precipitation, or RNeasy® plus mini kit (see Table de novo assembly on two of the three samples (RV2516 and RV2627) failed to align viral reads into contigs for further analysis resulting in only host contigs being identified  the concentration of the TRIzol® extracted RNA after gDNA depletion was significantly less than the original extract RNA sample Table . The larWe investigated the requirement to deplete gDNA and rRNA in cultured viral samples after RNA extraction, since the amount of cellular material would be minimal in these supernatant preparations. Comparison at the RiboGreen stage determined that RV20 and RV1787 depleted samples are 45-fold and 10-fold less than the RV20 and RV1787 non-depleted RNA samples respectively. Indeed, for RV20 the total amount of RNA was too low to obtain 200 ng RNA for fragmentation. The virus titer of RV1787 and RV20 has been calculated previously ,24 with de novo assembly, is illustrated in Table The success of the methodology for both tissue material and cell cultured material, particularly the ability to detect viral sequence after The depth of viral read coverage of samples from which a single genome contig was obtained was investigated Figure . Brain t92) . Even with this dataset, we were able to observe minimum SNPs (equating to one read with a SNP) at 1% of the population in cultured material, and 3% in brain tissue samples Table 9 , was genome sequenced using Sanger sequencing as part of a larger dataset [1982  only had G9523. To investigate this variation further, specific primers were designed to amplify this region from the original brain sample and the passaged TCSN. The original brain material PCR amplicon had a read depth of 3783 reads, 18.4% G9523 and 81.6% A9523. The TCSN PCR amplicon had a read depth of 3280 reads, 97.5% G9523 and 2.5% A9523, confirming the consensus sequence data, but indicating the A variant is still present after 6 passages, just at a much lower level. This deep sequence analysis is a useful tool to investigate certain SNPs of interest indicated from the consensus data.RV2508 was sequenced both from the host brain tissue directly and from cell culture supernatant after 6 passages in BHK cells. Only one SNP was observed at position 9523 (L-gene) a synonymous coding change A/G92 and Leu/Phe186) and one synonymous change at genome position 2781. The remaining two were both silent, one in the glycoprotein gene (genome position 4403) and the other in the L-gene (genome position 7855)  and also had the most SNPs observed within a tissue sample . This simple modification will be used for future genome sequences.The genomic termini sequences from 454 data were obtained with varying success. The 5′ UTR was usually represented by reads more often than the 3′ UTR, the reasons for which are unclear Table . Often dFor samples where the genomic termini were still absent, RT-PCR was performed on the depleted RNA, using primers designed against the highly conserved genomic termini to obtain PCR products which were sequenced directly using Sanger sequencing. Although this approach resulted in a genome sequence still lacking the sequencing relating to the primer sites, for known lyssavirus species isolates this is acceptable. However, for novel lyssavirus species prototypes the ends must be deduced either by RACE  or circuWhenever possible, consensus sequences where aligned together with genome sequences from the same lyssavirus species. Any potential insertions or deletions (indels) were investigated by analysis of the flowgrams in the GS program suite. A limitation of pyrosequencing processing of raw data is runs of homopolymers such as the GAAAAAAA poly A termination signal found at the end of each lyssavirus gene sequence Figure a and b. Our goal was to establish a methodology which enabled sequencing from brain tissue, and cell cultured samples without the use of specific viral enhancement, ultracentrifugation or filtration to identify novel viruses, or obtain genome sequence for known virus species. It was important to develop a methodology which would not require prior knowledge of the virus being extracted and to use a methodology which is widely used to extract viral RNA. Instead of enriching for virus specific RNA, we depleted host genomic DNA and rRNA to increase the percentage of viral specific RNA in the sample. There are a large number of RNA extraction methodologies and kits available which can be broadly divided into two categories; 1) Phenol/choloform based extraction and 2) column or bead based extraction. We decided to compare TRIzol® as a phenol-based system and RNeasy® plus mini kit (Qiagen) as a column-based system. In our hands, the use of spin columns to extract the RNA from tissue, removed the majority of RNA in process, resulting in less than the 200 ng minimum quantity of total RNA recommended for fragmentation and more importantly, too few viral reads were obtained. The RNeasy® plus mini kit (Qiagen) was successfully used as part of the depletion protocol after initial extraction with TRIzol® with good results. In this study, less than 200 ng of RNA was used for a number of samples, and depending on the sample type less than 200 ng of RNA can provide full genome sequence. TRIzol® extracted tissue samples RV50 153 ng RNA) and RV2772 (30 ng RNA), not only obtained sufficient reads to cover the genome, but also obtained enough depth of coverage to identify SNPs . Although for this example there is no functional relevance for the variation, this approach is useful to determine high proportion SNP changes within passaged virus samples using consensus sequencing which can then be targeted directly by deep sequencing the relevant genomic regions, rather than deep sequencing the entire genome.The requirement to deplete tissue culture supernatant derived RNA was investigated using two 100 ml supernatant samples. Half the RNA was subjected to gDNA and rRNA depletion, whereas the other was not. The additional preparation time required to deplete the sample was not rewarded with a significant improvement of either proportion of viral-specific reads or read depth, therefore 454 sequencing directly from a TRIzol® extracted RNA is the simplest, yet most effective way to obtain viral genome sequence from PEG precipitated tissue culture supernatant samples. However, it is not always possible, or practical to culture viruses es Table . Furtheres Table . The RV2de novo and mapping programmes as well as biological influences such as mRNA over-representing the viral RNA population relative to genomic RNA and the effect of defective interfering (DI) particles which are often truncated. The use of primers situated near the genomic termini during the cDNA synthesis stages have been shown to improve the number of termini sequences, but have not completely resolved the problem.The genomic termini are often under-represented in the viral reads obtained. It is likely this is due to a variety of reasons including the process of trimming reads before assembly, the assembly parameters in Sequences generated on the 454 platform, due to the way the incorporation of bases are detected by the intensity of light emission, inherently have issues with long homopolymeric repeats. On the whole, a consensus sequence with 10 or more reads, when mapped using an available genome from the same species, can be confidently confirmed by checking the flowgram data. However, novel viruses, or viruses with a limited number of reads may require additional confirmation by Sanger sequencing PCR products, which span the regions in question.The application of this methodology to lyssaviruses from brain material and cell cultured samples has been shown to be highly successful. Moreover, we have successfully sequenced the majority of a flavivirus and hantavirus, demonstrating the applicability of the method to other families of viruses (data not shown). There is no reason why any cultured virus cannot be PEG precipitated, extracted and sequenced using the same methodology. We have shown for both a high titer virus (RV20) and a low titer virus (RV1787) 100 ml of supernatant is more than enough to obtain genome sequence, and to begin to investigate the presence of viral heterogeneity. Further investigations using 5-10 ml of tissue culture supernatant for a number of other cultured lyssaviruses have proved equally as successful (data not shown). Furthermore, extraction from the cell monolayer (often discarded when clarifying harvested supernatant) is an excellent source of viral RNA. The only disadvantage is the potential presence of non-functional virus variants presumably present in the cells as partially synthesized viral derivatives. In this study however, it was virus in the supernatant which appeared to contain defective virion particles, albeit at a low level.The platform used in this study (Roche 454) has been shown to be reliable and consistent, however, the RNA can be prepared for any NGS platform. Indeed we have obtained full genome sequences from a small number of RNA samples prepared as described here from brain material on the Illumina platform successfully (data not shown). The application of NGS sequencing of viruses has begun a new era of virus discovery and characterization of novel viruses and will revolutionize phylogeographic studies in all fields of virology in the coming years.Original clinical specimens, or once passaged mouse brain samples Table , confirmConcentration of virus in tissue culture supernatant (TCSN) over 1 ml  was achieved using 1 volume of PEG-it (System Biosciences) to 4 volumes of TCSN and following manufacturer’s instructions. Pellets were combined and resuspended in tissue culture media to a final volume of 1 ml.TCSN samples were clarified to remove cell debris by centrifugation at 1,200 rpm for 5 mins and were extracted either directly (250 μl) or after PEG precipitation (see above) using TRIzol® LS following manufacturer’s instructions and resuspended in 10 μl molecular grade water. All other samples were extracted using TRIzol® following manufacturer’s instructions and eluting in 10 μl molecular grade water. For RNA extraction method comparisons, duplicate samples were extracted using RNeasy® plus mini kit (Qiagen), including the DNase treatment (see 2.4).Genomic host DNA was depleted from the extracted RNA samples using the on-column DNase treatment in RNeasy® plus mini kit (Qiagen) following manufacturer’s instructions, eluting in 30 μl molecular grade water.Ribosomal RNA was depleted from the gDNA-depleted RNA samples using Terminator™ 5′-Phosphate-Dependent Exonuclease (Epicentre Biotechnologies). The reaction was performed according to the manufacturer’s instructions, briefly, 30 μl of RNA was mixed with 3 μl of Buffer A, 0.5 μl RNAsin® Ribonuclease inhibitor  and 1 μl Terminator (1 U/μl). The mixture was incubated at 30°C for 60 minutes. Thereafter the samples were subjected to subsequent round of purification using RNeasy® plus mini kit (Qiagen), without DNase digestion, following manufacturer’s instructions, eluting in 30 μl molecular grade water.After extraction and then gDNA depletion, the RNA was quantified using NanoDrop® spectrophotometer (results in Table 2) at 70°C for 30 s. Where less than 200 ng was available the maximum volume (20 μl) was used. The fragmented RNA was then purified using RNAclean XP (Beckman Coulter) magnetic beads and used as template for double-stranded cDNA synthesis using random hexamers (Roche) and a cDNA Synthesis System Kit (Roche) according to the manufacturer’s instructions. To improve the population of viral reads at the genomic termini, two panlyssavirus specific primers were included in the random RT reaction: N165-146 (GCAGGGTAYTTRTACTCATA) previously described [200 ng of RNA was fragmented using divalent cations  and sequenced on the Roche 454 GS FLX + instrument according to the manufacturer’s instructions. Each sample was loaded onto 16th picotitre 454 plate.de novo assembly software (Roche). All contigs were exported from the software and were aligned with a reference genome using Seqman (DNAStar) whenever possible. The resulting consensus sequence was subsequently used in GS Reference Mapper (Roche) to obtain further sequence reads from the original raw data. If a suitable reference sequence was available, this was used in the GS Reference Mapper program with the 454 reads to obtain a consensus sequence without the initial use of the GS de novo assembly software. Where no suitable genome reference sequence was available (RV2508) a BLAST search with the contigs provided evidence for which contigs were viral sequence, using the megablast algorithm on the nucleotide database (http://www.ncbi.nlm.nih.gov/blast/Blast.cgi?CMD=Web&PAGE_TYPE=BlastHome). Due to the increased difficulty of mapping reads at the ends of contigs, it was occasionally necessary to splice the 3′ and/or 5′ UTR sequences from a reference genome sequence onto the termini of the contig sequence of interest. Using the spliced consensus sequence in the GS Reference Mapper program resulted in extra reads being mapped to the contig resulting in a complete, or almost complete genome sequence.Initial assembly of the 454 reads was achieved with Newbler (V2.6) using the GS The lyssavirus genomic termini are extremely conserved, in particular the first and last 9 nucleotides have complete complementarity across all lyssaviruses ,2,5,22, Primers flanking the SNP identified for IKOV: RV2508-L-SNP-F (5′ CTGAAGCTTCGAGACTCTAC 3′) and RV2508-L-SNP-F (5′ CAGATGGATGACCCTATCAG 3′) were used in a standard RT-PCR reaction, using high fidelity enzymes in methods described previously . The ampNGS: Next generation sequencing; BHK: Baby hamster kidney; SNP: Single nucleotide polymorphism.The authors declare they have no competing interests.Funding was obtained by DM, LM, XDEL and ARF. DM carried out sequence analyses. DM, EW, SL carried out and optimised the experiments. DM, LM, RE, DH, XDEL and ARF designed the study. DD and RE provided reagents and tools. DM drafted the manuscript. All authors contributed to and approved the final manuscript.