Bcheck, a wrapper tool which predicts RNase P RNA genes by combining the speed of pattern matching and sensitivity of covariance models. The core of Bcheck is a library of subfamily specific descriptor models and covariance models.Effective bioinformatics solutions are needed to tackle challenges posed by industrial-scale genome annotation. We present Bcheck predictions have more intact structure and are automatically classified by subfamily membership. For eukaryotic chromosomes Bcheck could identify the known RNase P RNA genes in 84 out of 85 metazoan genomes and 19 out of 21 fungi genomes. Bcheck predicted 37 novel eukaryotic RNase P RNA genes, 32 of which are from fungi. Gene duplication events are observed in at least 20 metazoan organisms. Scanning of meta-genomic data from the Global Ocean Sampling Expedition, comprising over 10 million sample sequences (18 Gigabases), predicted 2909 unique genes, 98% of which fall into ancestral bacteria A type of RNase P RNA and 66% of which have no close homolog to known prokaryotic RNase P RNA.Scanning all microbial genomes in GenBank identifies RNase P RNA genes in 98% of 1024 microbial chromosomal sequences within just 4 hours on single CPU. Comparing to existing annotations found in 387 of the GenBank files, The combination of efficient filtering by means of a descriptor-based search and subsequent construction of a high-quality gene model by means of a covariance model provides an efficient method for the detection of RNase P RNA genes in large-scale sequencing data.Bcheck is implemented as webserver and can also be downloaded for local use from http://rna.tbi.univie.ac.at/bcheck In recent years, biological sequence databases have grown exponentially. These data include a rapidly increasing number of completely sequenced genomes as well as large-scale metagenomic data sets that await annotation. For instance, the Global Ocean Sampling Expedition (GOS) deposited more than 18G metagenomic sequences already .Infernal 1.0 [tRNAscan-SE[Non-protein-coding RNA (ncRNA) genes are abundant in genomic sequences, playing diverse important biological roles . The genrnal 1.0  outperfoNAscan-SE in termsNAscan-SE reach sutRNAscan-SE is not a single algorithm but rather a wrapper tool that combines a series of increasingly complex and expensive filters. Similarly, the major searching strategy of Rfam [blast-based filter followed by Infernal. The pre-filtering at sequence level with blast, however, is not ideal in particular in applications to distant homologs [RNAmot [PatScan[HyPaL[RNAMotif[rnabob[ of Rfam  is a comhomologs . Another [RNAmot , PatScan [PatScan, HyPaL[1can[HyPaL, RNAMoti[RNAMotif, and rnaif[rnabob, which irnpB in bacteria. A variable number of protein components [Nanoarchaeum equitans [Aquifex aeolicus [Pyrobaculum aerophilum [Methanothermobacter thermoautotrophicus may be a transition towards the loss of RNase P RNA, which is catalytically inactive in this organism but can be "repaired" by a few substitutions [RNase P RNA, possibly a remnant of the RNA world , is an imponents  facilitamponents . RNase Pmponents . So far,equitans . It is nequitans , some baaeolicus ,25) and rophilum ) have loitutions .The length of most RNase P RNA lies between 250 nt and 550 nt. It is divided into two structural domains: The S-domain repsonsible for binding and the catalytically active C-domain , see FigrnpB genes, forming diverse sets in terms of both sequence and structure variation. The derived types arcM and bacB, in contrast, have more uniform members. The diversity is largest among eukaryotic RNase P RNA genes. In eukaryotes, RNase P RNA is transcribed by polymerase III [The RNase P RNA structures can be broadly assigned to five subfamilies: the bacterial types A and B (bacA and bacB), the archaeal types A and M (arcA and arcM), and a single eukaryote group (nucA) . In addirase III . The humrase III  to contarnpB gene prediction has been proposed. It is not applicable to large-scale database searches in practice, however. Here, we present Bcheck, a wrapper, to perform efficient rnpB gene prediction by combining the fast filtering with rnabob[Infernal. The construction of such a method entails two tasks: the design of an efficient yet sensitive descriptor model (DM) that acts as a filter, and the derivation of a sensitive statistics covariance model (CM). Both components are based on a careful analysis of published RNase P RNA sequences and structures. The success of Bcheck depends on the efficiency and predictive power of both models, as well as a sensible wrapping algorithm that optimizes the interplay of DM and CM.In this contribution we are concerned with the detection of RNase P RNA genes in genomic data from all domains of life. In , a patteth rnabob and the Rfam and from two recent publications [ushuffle[The construction of effective models of RNase P RNA genes is a non-trivial task because of the lack of strong family-specific conservation. Our strategy was to first classify the training sequences into the seven sub-families identified in the literature: arcA, arcM, bacA, bacB, nucA, fugA and fugB. The training set consists of sequences from the RNase P RNA Database  with intications ,36. A se[ushuffle in orderInfernal CMs and the manual construction of the DMs. We adopted a multi-step strategy: The RNase P RNA sequences were first divided into structural elements, then base-pairing regions were structure-aligned manually, and loop regions were sequence-aligned by means of MUSCLE[RNAfold [The training of both DM and CM requires structural alignments, whose quality is crucial for both the automatic learning procedures of the of MUSCLE. Local a[RNAfold ,40 to chSince efficiency was the major focus of DM training, we focused on selected features of local regions. To gain consensus sequence information, each alignment column was summarized and assigned with standard, "ambiguous" IUPAC nucleotides (taking into account every nucleotide appearing in the column) or the gap character (whenever the column contained at least one gap). The sequence was edited to take established structural knowledge into account. The resulting consensus sequence was then annotated in the alignment.rnabob as search engine for our DMs because of its convenient syntax and its speed.The RALEE mode  in the eInfernal package.For the subfamilies arcA, bacA, and nuc with strong variation, we constructed two variants, "DM selective" and "DM general", with different parameter settings. The selective DMs miss a few aberrant RNase P RNA genes, while the "DM general" models have a larger false positive rate. For each subfamily, only one CM is needed and automatically generated based on global structural alignment using the tools of the Bcheck wrapping algorithm takes the strategy of local to global and selective to general, Figure E-value threshold of E ≤ 10-10 must be reached.The bcheck results for which the presence of a promoter signal provides additional confirmation.To distinguish functional copy and pseudogene of eukaryotes, we analyzed their promoter regions. For this purpose we aligned 100 nt upstream of Polymerase III transcripts of the same organism and compared the RNase P RNA gene predictions. While the absence of a recognizable promoter signal does not prove that the RNase P RNA gene is not functional, we have adopted a conservative policy and include only Bcheck to scan the genomic sequences of 956 bacteria and 68 archaea organisms from GenBank. The entire computation, which surveyed 3.1G of input sequence, took approximately 4 hours to complete with a single core of 2.4 GHz Intel(R) Core(TM)2 CPU. Bcheck produced one hit per organism for 98% (1005) of organisms, see Table Bcheck predictions for three members of the phylum Chloroflex  are only partial rnpB regions including partial-P11, P12 and junctions between two stems. After removing duplicate sequences from closely related strains, we obtained 777 unique rnpB genes of which 45 belong to arcA, 10 to arcM, 621 to bacA, and 101 to bacB, see Table We used GenBank files contained annotated rnpB genes for 365 bacteria and 22 archaea, all of which were among the Bcheck predictions. We then compared start-end positions of Bcheck predictions and GenBank annotations. Only 25% of the annotations agree within a discrepancy of 5 nt or less at both ends.The GenBank annotations, furthermore, have promoter or terminator sequences included. Bcheck thus provides a substantial improvement also of the existing annotations in most cases.Inspection of sequences and predicted secondary structures shows that the published sequences are in general incomplete, lacking nucleotides at the 5' end of P4 and 3' end of P4: At the 5' end, 66% known annotations miss flanking regions of P4, ranging from 30 to 90 nucleotides. At the 3' end, 56% known annotations miss flanking regions of P4', ranging from 10 to 20 nucleotides. A few of the Bcheck prediction only in a single case: Roseiflexus RS 1. In five cases, the published annotations and the Bcheck results differ dramatically. In order to evaluate these cases further, we employed the CM model of the Rfam, which supported the authenticity of the Bcheck predictions, Table The published annotation is more accurate than the The GOS metagenomic sequences were obtained from the CAMERA project . Due to Bcheck predicted 4675 rnpB genes with median E-values of 10-78. In 211 cases two models overlap. In these cases there is a clear difference in E-values, so that the assignment to domains is unambiguous in all positive cases. After duplication removal, 2909 rnpB sequences are unique, 2857 of which belong to bacA, 49 to arcA, 3 to bacB, but none for arcM, see Table In total, GenBank and GOS data set. In the marine samples, the number of bacA rnpB genes exceeds 95%. We compared rnpB sequences from two datasets with respect to their GC content, Figure GenBank rnpB is high, with ≈ 0.6. The GC content of RNaseP RNA genes seems to be influenced by the environment, which agrees to the findings of Foerstner et al. [The ancestral types arcA and bacA are clearly predominant in both r et al. , showingrnpB genes as a marker to infer the taxonomic distribution of GOS samples. We used blast to find the closest orthologs of 2909 unique GOS rnpB genes among the 777 GenBank sequences using an E-value threshold of E < 10-50. High scoring orthologs are found for 1003 GOS rnpB genes. 914 of which have only one ortholog per gene making species assignments possible, while 39 have multiple orthologs, all coming from the same genus, making genus assignments possbile. These species assignments and genus assignments are shown in Figure Nitrosopumilus maritimus, is recognized. Among bacteria, most sequences belong to Pelagibacter ubique (75%) and Prochlorococcus marinus (13%). For 1906 GOS rnpBs (66%), no close homologs are known, suggesting that they derive from unknown species. Of these, 1859 (97.5%) belong to the bacA subfamily, 44 (2.3%) to the arcA subfamily, and 3 (0.1%) to the bacB subfamily.Obtaining a view of the taxonomic distribution within a microbial community is one central objective of metagenomic analysis. We used the detected Otolemur garnetti sequence because of a 3 nt insertion within the highly conserved P4, which is used as a block in most descriptors. For the two related fungi Coprinus cinereus and Laccaria bicolor hypothetical RNase P RNA genes have been reported [Bcheck CM is mediocre, and the insertions of several 'U's within the 'GAA' motif of the P4 5' region as well as point mutations cause failure of the Bcheck DM. On the other hand, compared to [Bcheck made novel predictions for 32 fungi and 4 metazoans , as well as the choanoflagellate Monosiga brevicollis.We investigated 237 eukaryotic genomes, Table pared to Bcheck maMeloidogyne hapla and Monosiga brevicollis, supporting that these candidates are functional copies. For 36 metazoan genomes, Bcheck made multiple predictions. In at least 16 cases, additional predictions are highly similar to the functional copy. In genome sequence assembly, the reads originating from different copies of a repeat appear identical and cause assembly errors.Strong promoter signals were identified for Homo sapiens and Pan troglodytes have functional copies on chromosome 14 and a pseudogene on chromosome 4. Among teleosts, both Danio rerio and Gasterosteus aculeatus have their functional copies and pseudogenes on the chromosome 2. In rodent family, Rattus norvegicus and Mus musculus have the pseudogenes spreading on at least 4 different chromosomes.Therefore these predictions seem to be due to assembly errors rather than constituting true paralogs. In the other 20 cases differences in the flanking regions and the RNase P RNA gene itself indicate that we see the results of gene duplications. Even though the promoter might be specific for an organism, it may differ from other polymerase III transcripts within one species. In each of these cases, a presumably functional RNase P RNA like promoter structure was found for only one of the copies. Similar duplication patterns are observed in closely related primate, fish and rodent. For instance, both Bcheck in many fungi, which had not been analyzed in much detail so far. Only eight sequences which were reported before were not recognized by Bcheck. In 119 sequences Bcheck found 37 novel RNase P RNA genes. The remaining 82 genomes are either unfinished drafts, so that the RNase P RNA is not contained in the data, or they belong to clades where RNase P RNA may be absent. In plants, red algae, and heterokonts RNase MRP RNA, an ancient paralog of RNase P RNA, is well described [Loxodonta africanus), which shows a canonical sequence interrupted by a run of Ns in the latest assembly (Loxafr3.0). On the other hand, we suspect that we missed the RNase P RNA in some fungi and in some of the basal eukaryotes because of highly divergent sequence and secondary structure.Novel RNase P RNA genes were detected by escribed ,34. One escribed ,44. An iBcheck was written in Python (version 2.5.2). Input consists of DNA or RNA sequences in fasta format, predictions are output with fasta format or with secondary structure annotated. Besides the default search algorithm, Bcheck also gives the option for searching with CM only. However, CM-only search is at least 100 times slower.Bcheck webserver to facilitate online RNase P RNA gene prediction. A searchable rnpB database was developed, including genes for 1005 microbial organisms, 147 eukaryote organisms and 4756 GOS sample sequences. The predicted pseudogenes for eukaryote organisms are also included. The "rnpB database" uses a hierarchical tree structure, consisting of 5 tables, implementing preorder tree traversal algorithm to process the query efficiently. blast is also offered in the server for homology search against the database compromising 777 unique rnpB genes. The server can be accessed at http://rna.tbi.univie.ac.at/bcheck. The Bcheck-pipeline can also be downloaded from the same location for local use in a Linux environment.We set up a Bcheck, an efficient pipeline to determine RNase P RNA genes across all three domains of life. In order to deal with the high variability of the RNase P RNA sequences and structures, we employ descriptor-based models specific for sub-families instead of a single pattern to construct more efficient filters. In the second step, improved covariance models are used to validate the candidates from the DM step and to determine nearly exact gene boundaries.The rapidly increasing size of sequence databases requires efficient tools for data analysis. In particular, homology annotation of small ncRNAs, with their short and often poorly conserved sequences poses a severe problem for large-scale annotation. Here, we describe Bcheck, we were able to determine the RNase P RNA genes in 59 out of 68 archaea, 946 out of 956 bacteria, and 147 out of 237 eukaryotes. 61% of the prokaryotic sequences and 25% of eukaryotic results were not annotated previously. The quality of the predicted rnpB genes is much better than a large fraction of the - usually blast-based - annotation available through GenBank. The size and diversity of eukaryote genomes brings with it a particular challenge in finding RNase P RNA genes, because this diversity is reflected in many aberrant features of the RNase P RNA itself. Using the fungi-specific DMs, we uncovered 32 previously unannotated sequences, which are downloadable from the Bcheck server. As in previous studies, we did not find RNase P RNA candidates in plants and Heterokonta.With Bcheck is more than 100 times faster than the direct application of Infernal (version 1.0), it is suitable in particular as a tool to screen large high-throughput sequencing data. With only a handful of false negatives (10 out of 1005 prokaryotes), Bcheck provides a highly efficient way to annotate newly sequenced genomes. A particular strength of Bcheck is its applicability to metagenomics data.Since Bcheck failed to detect a candidate, 15 have a size below 2.0 Mbp. One of them, Nanoarchaeum equitans, is among three organisms having extremely condensed genomes with length even below 0.5 Mbp. Nanoarchaeum equitans appears to have lost its RNase P RNA through genomic rearrangement at tRNA genes under the pressure of genome condensation [Among the 19 prokaryotic genomes for which ensation . In FiguBcheck models were built on the conserved sequence and secondary structure features of a large sample of RNase P RNA genes. Conceivably, the predictive power of the pipeline could be improved further by including additional information. For instance, promoter and terminator regions might be utilized. A recent survey for 7SK RNAs capitalized largely on the conserved features of the characteristic pol-III promoter signals of this ncRNA class [At present, NA class . A similILH designed the study. DY constructed descriptors, designed and implemented the pipeline. MM prepared the training sets for eukaryote models and performed eukaryote hit validation with promoter analysis. All authors collaborated on analyzing the data and writing the manuscript. All authors have read and approved the final manuscript.


Picornaviruses cause several diseases, not only in humans but also in various animal hosts. For instance, human enteroviruses can cause hand-foot-and-mouth disease, herpangina, myocarditis, acute flaccid paralysis, acute hemorrhagic conjunctivitis, severe neurological complications, including brainstem encephalitis, meningitis and poliomyelitis, and even death. The interaction between the virus and the host is important for viral replication, virulence and pathogenicity. This article reviews studies of the functions of viral and host factors that are involved in the life cycle of picornavirus. The interactions of viral capsid proteins with host cell receptors is discussed first, and the mechanisms by which the viral and host cell factors are involved in viral replication, viral translation and the switch from translation to RNA replication are then addressed. Understanding how cellular proteins interact with viral RNA or viral proteins, as well as the roles of each in viral infection, will provide insights for the design of novel antiviral agents based on these interactions. Picornaviridae consists of five genera - enteroviruses, rhinoviruses, cardioviruses, aphthoviruses, and hepatoviruses. Picornaviruses are small icosahedral particles containing a single-stranded plus sense RNA genome with approximately 7,500 nt in length. It contains a 3' poly(A) tail with a variable length from 65 to 100 nt. The viron RNA has a virus-encoded peptide, VPg, attached at its 5' terminus, but this protein is rapidly lost in the cell and most of the viral transcripts consequently lack it ..124].PV and EMCV IRES elements are similar to capped mRNA in requiring that some initiation factors bind to primed 40S subunits and to the RNA itself. These canonical factors have been identified as eIF4G and eIF4B, which bind to the viral RNA, and eIF3 and eIF2, which must prebind the 40S subunit. Furthermore, poly(A)-binding protein substantially promotes the IRES-mediated translation of EMCV and PV. This rather high requirement of picornaviral IRESs for canonical translation factors is in contrast to that of IRES elements of other viruses, such as hepatitis C virus and the cricket paralysis virus, which require few or no translation factor to bind to the ribosome .PTB  is a member of the hnRNP family and shuttles between the nucleus and the cytoplasm in a transcription-sensitive manner . PTB is Experiments on the depletion and repletion of PTB from rabbit reticulocyte lysate (RRL) have revealed that the efficient translation of FMDV mRNA and a mutant EMCV mRNA depends on PTB ,134. Intpro (and/or 3CDpro), cleaves PTB isoforms . PTBs contain three 3Cpro target sites (one major target site and two minor target sites). PTB fragments that are produced by PV infection are redistributed to the cytoplasm from the nucleus, where most of the intact PTBs are localized. Additionally, these PTB fragments inhibit poliovirus IRES-dependent translation in a cell-based assay system. The authors posit that the proteolytic cleavage of PTBs may contribute to the molecular switching from translation to the replication of polioviral RNA [Poliovirus protein, 3Ciral RNA .PV-attenuated type 3 Sabin and virulent type 3 Leon viruses translated equally well in HeLa cells, but the translation of the attenuated Sabin virus is restricted in neuroblastoma cells. The C472-to-U mutation in the IRES caused the translation defect. Comparison of IRES between Leon serotype 3 and Sabin serotype 3 PV revealed that PTB and a novel neural-cell-specific homologue of PTB (nPTB) were bound adjacent to the attenuation mutation in domain V, but binding was less efficient on the Sabin IRES. The Sabin IRES was demonstrated to have a translation defect in chicken neurons that can be rescued by increased PTB expression in the CNS .PTB has also been shown most clearly to function as an RNA chaperon, stabilizing the type II IRESs, such as EMCV and FMDV, in an active conformation ,145.pro in infected cells, and is caused by a 3Cpro-mediated cleavage event, which removes a nuclear localization signal from the C-terminus of La, but maintains the dimerization domain. The truncated La can still stimulate translation effectively, but is relocalized to the cytoplasm during viral protein synthesis [in vitro [in vivo and purified La protein in vitro [Lupus autoantigen (La) is a 52 kDa, predominantly nuclear protein. It is a target for autoimmune recognition in patients with systemic lupus erythematosus and Sjogren's syndrome. The majority of La is localized in the nucleus, and it is related to stabilization of nascent RNAs, nuclear retention of nascent transcripts, and RNA pol III transcription termination. During PV infection, La is redistributed to the cytoplasm by 3 h postinfection (p.i.) ,147. Thiin vitro . La protin vitro ,150. La in vitro .PCBPs are RNA-binding proteins that preferentially bind to single-stranded stretches of cytidines. Mammalian cells contain four isoforms, PCBP 1-4, but only PCBP1 and PCBP2 have been experimentally demonstrated to have roles in the life cycles of enteroviruses . PCBP2 iin vitro translation studies of SRp20 from HeLa cell free extracts have shown that SRp20 is required for the initiation of PV translation. Targeting SRp20 in HeLa cells with short interfering RNAs inhibited the expression of SRp20 protein and correspondingly reduced PV translation [One nucleocytoplasmic SR protein, SRp20, interacts with PCBP2 and is involved in the internal ribosome entry site-mediated translation of viral RNA. Both depletion and nslation .In vitro translation reactions were performed in HeLa cell cytoplasmic translation extracts whose cellular protein, PCBP2 was depleted [depleted . Upon dedepleted .Upstream of N-ras (Unr) is a cytoplasmic protein that contains five cold-shock domains. A depletion study of Unr from reticulocyte lysate revealed that Unr was required for the translation of rhinovirus IRES . Both HRhnRNP A1, an RNA-binding protein that shuttles between the nucleus and the cytoplasm, is a member of a large group of RNA binding proteins (hnRNPs) which are classified into several families and subfamilies based on conserved structural and functional motifs. The hnRNP A1 protein is composed of 320 amino acids; it contains two RNA-binding domains and a glycine-rich domain, which is responsible for protein-protein interaction.trans-acting factor that binds specifically to the 5' UTR of the HRV2 and regulates its translation. Furthermore, the cytoplasmic redistribution of hnRNP A1 after rhinovirus infection enhances rhinovirus IRES-mediated translation [hnRNP A1 is an internal ribosome entry site (IRES) nslation . RNA-pronslation .ITAF45 is also known as erbB3-binding protein 1 (Ebp1) or p38-2G4, which is a proliferation-dependent protein that is distributed throughout the cytoplasm from the metaphase to the telophase. ITAF45 is a proliferation-dependent protein that is undetectable in murine brain cells and so may function as a tissue-specific factor that controls the translation of particular mRNAs. The initiation on the TMEV IRES depended strongly on PTB, where the initiation on the FMDV IRES depended on both PTB and ITAF45. ITAF45 was bound specifically to a central domain of the FMDV IRES and acted synergistically with PTB to promote the binding of eIF4F to an adjacent domain .in vitro and rhinovirus IRES-mediated translation in vivo. Nucleolin/C23 mutants that contain the carboxy-terminal RNA binding domains, but lack the amino-terminal domains, act as dominant-negative mutants in in vitro translation assay. The translation inhibitory activity of these mutants is related to their capacity to bind to the 5' UTR sequence [As a 110 kDa nucleolar protein, nucleolin/C23 protein is also an RNA binding protein which contains four RNA binding motifs . Nucleolsequence .The double-stranded RNA binding protein 76 (DRBP76) contains two dsRNA-binding motifs and is almost identical to M-phase phosphoprotein 4, NF90, translation control protein (TCP80), and NF associated with dsRNA. DRBP76 has been found to bind to the HRV2 IRES in neuronal cells and to inhibit PV-RIPO translation and propagation . The sizin vitro and in vivo. It contains four contiguous KH motifs that recognize the ARE, interact with the exosome, and the poly(A) ribonuclease (PARN), and promote the rapid decay of ARE-containing RNAs [The far upstream element binding protein 2 (FBP2) is also known as the K homology (KH)-type splicing regulatory protein (KSRP). It was originally identified as a component of a protein complex that assembles on an intronic c-src neuronal-specific splicing enhancer, and as an important adenosine-uridine element binding protein (ARE-BP) that interacts with several AREs ,166. FBPing RNAs .Biotinylated RNA-affinity chromatography and proteomic approaches were utilized to identify FBP2 as an ITAF for EV71. During EV71 infection, FBP2 was enriched in cytoplasm where viral replication occurs, whereas FBP2 was localized in the nucleus in mock-infected cells. The synthesis of viral proteins was promoted in FBP2-knockdown cells that were infected by EV71. IRES activity in FBP2-knockdown cells exceeded that in the negative control (NC) siRNA-treated cells. However, IRES activity decreased when FBP2 was over-expressed in the cells. The results of this investigation suggest that FBP2 is a novel ITAF that interacts with EV71 IRES and negatively regulates viral translation .Compelling evidence suggests that cellular RNA-binding proteins (ITAFs) are involved importantly in translation from a variety of IRES elements, suggesting potential roles for RNA-binding proteins in IRES-dependent translation. First, an RNA-binding protein recruits the translational machinery via a protein-protein or protein-RNA interaction. This putative RNA-binding protein then binds directly to the ribosomal subunit, to canonical translation factors, or to a putative mediator protein that connects other RNA-binding proteins with the basal translational machinery. Second, RNA-binding proteins may serve as 'clamping proteins,' holding various parts of IRES RNA in a particular configuration. Components of the translational machinery may bind exclusively to the RNA portion of the RNA-protein complex that is maintained by these clamping proteins.Picornaviruses use multiple RNA-protein interactions to mediate important reactions in their life cycle, including IRES-mediated translation, possible circularization of the genome, and RNA replication. The molecular mechanisms by which host factors are involved in RNA-protein/protein-protein interaction have been intensively studied; however, tissue-specific viral virulence remains unclear, and demands further investigation in the future. No information is available on whether picornaviruses can be targeted by cellular microRNAs, leading to transcriptional or translational silencing, or RNAi-mediated degradation of viral RNA. This is another field that needs to be further studied in the future.The authors declare that they have no competing interests.JYL: Contribution of the part of the induction of background, host factors and viral proteins involved in IRES-mediated translation and conclusion. SCC: Contribution of the part of host factors and capsid proteins that are involved in receptor binding. TCC: Contribution of the part of viral and host proteins involved in RNA replication. KFW: Contribution of the part of viral proteolytic activities influence cellular functions and viral and host proteins involved in RNA replication (2B/2BC). LLC: Contribution of the part of switch from translation to RNA replication. SRS: Contribution of a deep revision of the full text. All authors have read and approved the final manuscript.


With the rapid emergence of RNA databases and newly identified non-coding RNAs, an efficient compression algorithm for RNA sequence and structural information is needed for the storage and analysis of such data. Although several algorithms for compressing DNA sequences have been proposed, none of them are suitable for the compression of RNA sequences with their secondary structures simultaneously. This kind of compression not only facilitates the maintenance of RNA data, but also supplies a novel way to measure the informational complexity of RNA structural data, raising the possibility of studying the relationship between the functional activities of RNA structures and their complexities, as well as various structural properties of RNA based on compression.RNACompress employs an efficient grammar-based model to compress RNA sequences and their secondary structures. The main goals of this algorithm are two fold: (1) present a robust and effective way for RNA structural data compression; (2) design a suitable model to represent RNA secondary structure as well as derive the informational complexity of the structural data based on compression. Our extensive tests have shown that RNACompress achieves a universally better compression ratio compared with other sequence-specific or common text-specific compression algorithms, such as Gencompress, winrar and gzip. Moreover, a test of the activities of distinct GTP-binding RNAs (aptamers) compared with their structural complexity shows that our defined informational complexity can be used to describe how complexity varies with activity. These results lead to an objective means of comparing the functional properties of heteropolymers from the information perspective.RNACompress, as a useful tool for academic users. Extensive tests have shown that RNACompress is a universally efficient algorithm for the compression of RNA sequences with their secondary structures. RNACompress also serves as a good measurement of the informational complexity of RNA secondary structure, which can be used to study the functional activities of RNA molecules.A universal algorithm for the compression of RNA secondary structure as well as the evaluation of its informational complexity is discussed in this paper. We have developed  Ribonucleic acid (RNA) is an important class of molecules which performs a wide range of biological and chemical functions. Traditionally, most RNA molecules were regarded as being involved in the process of translation, including transfer RNA (tRNA) and ribosomal RNA (rRNA). Since the late 1990s, it has been widely acknowledged that there exists other type of functional RNA molecules such as non-protein-coding RNAs. These RNAs are found in organisms ranging from bacteria to mammals and affect a wide variety of processes including plasmid replication, phage development, bacterial virulence, chromosome structure, DNA transcription, RNA modification . RNA hasGenCompress [DNACompress [Biocompress [Cfact [To facilitate the maintenance and analysis of such RNA data, an efficient compression algorithm of RNA sequences is needed. Algorithms for compressing DNA sequences include Compress , DNACompCompress , Biocompcompress  and Cfacs [Cfact . Howevers [Cfact . The funs [Cfact .From a strictly mathematical point of view, compression implies understanding and comprehension . BiologiRNACompress developed in Windows and Linux platforms is accessible freely at our website. We have also defined the informational complexity of RNA structural data based on compression coupled with the theory of Kolmogorov complexity [In our study, we have developed an efficient grammar-based algorithm to compress RNA sequence and its secondary structure. The software mplexity . This kietc. Extensive tests have shown that our algorithm is fast, robust, effective and obtains a universally better compression ratio than the common text-based compression tools or primary-sequence-specific compression tools in the compression of RNA sequence with its structure. These results show that our program is a useful tool for RNA data maintenance and analysis.To the best of our knowledge, this is the first study to be published about the compression of biological sequences with structural information. Additionally, we apply the results to study functional activities of RNAs. The key idea of our compression algorithm is to use dot-bracket notation  to repreG1 and G2 are defined in our study: G1 is viewed as the key grammar to represent RNA primary sequence together with its secondary structure, while G2 is only used to model the dot-bracket sequence of RNA secondary structure and serves as a complementary to G1 to guide its generation order (As will show later). We start with parsing the dot-bracket sequence in G2 using the left-most deriving [T2. At the same time, each deriving step is mapped to construct another grammar tree T1 based on G1. Finally this left-most deriving symbol stream of T1 is encoded using Huffman coding theory [T1 or T2 is regenerated during decompression, the corresponding primary sequence and secondary structure in dot-bracket notation can be regained using the post-order traversal [Generally speaking, grammar-based compression starts by inferring the context-free grammar to represent the string. The resulting grammar is encoded as a symbol stream, which is then converted into binary. Each step affects the final size of the compressed file. In our algorithm, each step will be designed specifically to facilitate the particular goal of RNA sequence and structure compression. The main schema of our grammar-based compression and decompression is shown in Figure deriving  to get ag theory , so thatraversal  of the lIn our study, RNA primary sequence is represented in FASTA format beginning with a single-line description or comment, followed by lines containing sequence data. The description line is distinguished from the sequence data by a greater-than (">") symbol in the first column. For each sequence, the corresponding secondary structure is represented in dot-bracket notation. Dot-bracket notation is the dominant RNA secondary structure format. It uses dot to represent un-paired bases and brackets to represent base pairs in RNA stems. Many useful tools use this format as input (and output) and hence it has become an unofficial standard ,22. As fG1 and G2 to model RNA primary sequence and its secondary structure information. A CFG is very similar to a finite automaton [We have defined two concise content free grammars utomaton , and has(1). Terminals – a symbol that represents a constant value(2). Non-terminals – a symbol that has the capability of being further defined in terms of terminals and/or non-terminals, usually denoted by a capital letter.(3). Production rules – rules by which non-terminals can be replaced.In our study, two grammars are defined as:G1:S: LS | eL: aSu | uSa | cSg | gSc | uSg | gSu | a| u| c| gG2:S: LS | eL: (S) | •S and L are non-terminals, e is empty string, and the symbols a, u, c, g,  and • are terminals representing the 4 different bases, left bracket, right bracket and dot, respectively.For both grammars, G1 is a combination grammar to analyze RNA primary sequence and secondary structure simultaneously. It can model Watson-Crick base pairs A-U, G-C and Wobble base pair G-U in RNA secondary structure. G2 is aimed at modeling the dot-bracket sequence of RNA secondary structure. With these two grammars, two kinds of grammar trees can be generated for the RNA primary sequence and secondary structure, respectively, as shown in Figure G1is ambiguous, meaning that the same primary sequence can be generated with more than one grammar tree, while G2 is unambiguous which means that one dot-bracket string corresponding to only one grammar tree [G2 to guide G1during the grammar parsing to identify the grammar tree of G1.Here mar tree . Thus weBased on the two grammars we have defined, we are able to perform the compression as shown in Figure G2, and the LL(1) parser is used to derive the left-most derivation of the input sequence. A LL parser is a top-down parser for a subset of the context-free grammars [left-most derivation of the sentence. Practically, there are two common ways to describe how a given string can be derived from the start symbol of a given grammar. The simplest way is to list the consecutive strings of symbols, beginning with the start symbol and ending with the string, and the rules that have been applied. If we introduce a strategy such as "always replace the left-most non-terminal first" then for context-free grammars the list of applied grammar rules is by itself sufficient. This is defined as the left-most derivation of a string [We start from parsing the dot-bracket sequence of RNA secondary structure using grammars . It parsa string .LL(1) parser, it uses one token of look-ahead when parsing a sentence. The parser consists of:As for (1) an input buffer, a string from the grammar(2) a stack on which to store the terminals and non-terminals from the grammar yet to be parsed.(3) a parsing table which tells it what (if any) grammar rule to apply given the symbols on top of its stack and the next input token.left-most derivation are followed depending on whether the top of the stack is a non-terminal, a terminal or the special symbol #:In our study, the parser applies the rule in the parser table Table  which we(1) If the top of the stack is a non-terminal symbol, the non-terminal symbol and the symbol on the input stream is looked up in the parsing table to determine which rule of the grammar to use. The number of the rule is written to the output stream. If the parsing table indicates that there is no such rule then it reports an error and stops.(2) If the top of the stack is a terminal symbol, then it is compared to the symbol on the input stream. If they are equal they are both removed. If they are not equal, the parser reports an error and stops.(3) If the top is # and on the input stream there is also a # then the parser reports that it has successfully parsed the input, otherwise it reports an error. In both cases the parser will stop.left-most derivation to the output stream or it will have reported an error.These steps are repeated until the parser stops, and then it will have either completely parsed the input or written a G2 is used to guide the left-most derivation of G1 since it is ambiguous. The mapping of the left-most derivation of G2 to G1 is straightforward: '' will be mapped to the corresponding base pairs of the RNA secondary structure and '•' will be mapped to the corresponding un-paired bases. After this mapping, a left-most derivation of G1 is obtained and the Huffman coding is performed on the symbol stream of this left-most derivation to encode them into a bit stream, as discussed follow.As mentioned above, Huffman coding is an entropy encoding algorithm used for lossless data compression. The term refers to the use of a variable-length code table for encoding a source symbol where the variable-length code table has been derived in a particular way based on the estimated probability of occurrence for each possible value of the source symbol . Huffmanleft-most derivation of G1 based on the probability associated for each production rules. G1 can be viewed as a stochastic context free grammar (SCFG) [RNABase [etc. are examined and the final statistical probabilities are listed in Table We use variable-length code table to encode the symbol stream of r (SCFG) . We have[RNABase . Nearly It should be noted that for different types of RNA or RNA in different species, the frequency distribution of their base pairs or un-paired bases are different, thus the production probabilities of the rules are different. However, from a statistical perspective, we aim at designing a universal compression algorithm for all types of RNA, thus we make use of these general probabilities here. For more specific RNA types, more specific probabilities can be used.The Huffman tree based on our statistical probabilities is shown in Figure LL(1) parser and the corresponding operation is also listed.We took the RNA sequence in Figure The final bit stream of this left-most derivation is 0 01111 0 1110 0 101 0 00 0 010 0 1111 1 1 1 0 010 1, for a total of 35 bits.In our work, the compression ratio of the compression algorithm can be computed in two ways:R1 = uncompressed_file_bytesize/compressed_file_bytesize, or R2 = (n × (H1 + H2))/o, where n is the number of the bases in input RNA sequence. H1 and H2 are the information entropy of the RNA primary sequence and secondary structure, respectively. o is the number of bits in compressed file.R1 is a straightforward way to compute the compression ratio based on the byte size of the input file and the compressed file, R2 is more specific based on entropy theory. In the definition of R2, the uncompressed input file is divided into two parts: RNA primary sequence and RNA secondary structure in dot-bracket notation. From an information entropy perspective [H1) and secondary structure (H2) can be computed as:While spective , since tPi and P'i are the occurrence probabilities of each bases and characters in dot-bracket notation. If we consider a RNA sequence with infinite length, then Pi = 1/4 and P'i = 1/3, assuming an independent probability distribution of 4 base pairs and 3 characters, thus H1 = 2 and H2 ≈ 1.585. This means that 2 bits is enough for encode the RNA primary sequence and 1.585 bit can be used to encoding RNA secondary structure in dot-bracket notation. Note that in our implementation, the occurrence probabilities of 4 bases and 3 characters will be computed according to the particular RNA.Where K(•) of an object o is defined by the length of the shortest program P for a Universal Turing Machine U that is needed to output o [K(x) represents the minimal amount of information required to generate × by an algorithm.The definition of informational complexity of RNA structural data underlies the concept of Kolmogorov complexity. The Kolmogorov complexity output o . Intuitix is asymptotically close to the entropy of the information source emitting x. However, Kolmogorov complexity is non-computable in the Turing sense [It is well known that there is a relationship between Kolmogorov complexity of sequences and Shannon information theory : the expng sense  and in png sense .RNACompress. This definition is straightforward, yet with rigorously theoretical support. Later experiment will prove that our informational complexity can reveal the relationship between structural complexity and functional activity of RNA aptamers, which could be useful in predicting the functional utility of novel heteropolymers.In summary, the informational complexity of a given RNA sequence with its secondary structure is approximated by the compressed bit string using RNACompress is tested, and secondly the results are applied to reveal the relationship between binding activities and structural complexity of RNA aptamers.Our experiments are performed in two parts: first the compression ability of RNACompress on 7 benchmark files that are access freely at our website. These 7 data files are generated from different databases or curated from literatures, covering diverse types of RNA molecules with their secondary structures , including rRNA, tRNA and small non-coding RNAs. Note that the compression of one RNA sequence makes no sense from the statistical perspective, since RNA sequence is generally much shorter than the DNA sequences or the whole genomes. In our test, the input file contains a set of RNAs. Also the sequence identities in each input file are different. The intention to use these extensive data files is two folds: to test the behavior of our algorithm in compression of files with different sequence identities and to demonstrate that our algorithm is universally efficient for different type of RNAs. Detail descriptions of these 7 test files are listed in Table We have tested the compression ability of R1 and R2) of RNACompress with three other algorithms: Gencompress, winrar and gzip. Gencompress (DNACompress is the newest version of Gencompress) has reported to be the top algorithm among all the other sequence-specific compression algorithms, such as Biocompress and Cfact. The other two algorithms, winrar  and gzip (based on Lempel-Ziv coding/LZ77), are two classical text compression algorithm widely used in Windows and Linux, respectively. Detail comparisons between RNACompress and these three algorithms are listed in Table We have compared the running time and compression ratios  of the aptamer informational complexity onto the binding activities, as done by Carothers et al.. Our results are consistent with their study, which proves that the informational complexity defined here is reasonable when studying the relationship between functional activities and structural complexity of RNA molecules  currently we are focused on modeling two dominant types of base pairs in RNA secondary structure: Watson-Crick pairs and Wobble pairs. There also exists other minor variations of base-pairing in nucleic acids, such as Hoogsteen base pair (A-T) . One chaRNACompress is to consider the repeat pattern of RNA motifs in RNA secondary structure. This is different from the repeat pattern identified in primary sequences, as used in Gencompress etc. Also it will be helpful to approximate the Kolmogorov complexity and evaluate the informational complexity more accuracy. RNA motifs are basic building blocks used repeatedly, and in various combinations, to form different RNA types and define their unique structural and functional properties. Currently many algorithms for RNA motif identifications have been proposed [(2) one promising way to improve the compression ability of proposed ,32,33. Het al., RNA secondary structure comparison based on compression: a methodological study, manuscript in preparation).(3) another application of compression RNA secondary structure is that it is a great alignment-free tool for RNA secondary structure comparison. A universal (dis)similarity measure (USM) can be defined to measure the pair-wise distance of RNA secondary structures based on the compression, as we will demonstrate elsewhere , sorted by their affinity binding activities.Click here for file


The large archives of formalin-fixed paraffin-embedded (FFPE) tissue specimens that exist are a highly valuable source of sample material for molecular biological analysis, including gene expression profiling. However, current data on adverse effects of standard pathological practice on the usefulness of biomolecular analytes obtained from such archived specimens is largely anecdotal. Here, we present a systematic examination of the most relevant parameters for integrity and useability of RNA obtained from FFPE samples, including storage time and conditions, fixation time, and specimen size. The results are particularly relevant for any application relying on cDNA synthesis as an initial step of the procedure, such as RT-PCR, and microarray analysis. For preservation of tissue samples, formalin fixation followed by embedding in paraffin has been the method of choice for decades, mostly because this treatment maintains morphological features of the original tissue particularly well. With the development of molecular biology techniques for research and, increasingly, diagnostic purposes, there is a growing interest to use the vast archives of formalin-fixed paraffin-embedded (FFPE) tissue samples in these applications as well On a molecular basis, the relevant issues are nucleic acid fragmentation, and modification by chemical reaction between formaldehyde and nucleic acids, including crosslinking with proteins and other biomolecules In this study, we discern the influences that fixation and embedding, storage time and conditions have on the integrity of RNA isolated from FFPE samples, and demonstrate how these parameters affect RNA performance in cDNA synthesis and RT-PCR applications.260 measurement and tested for the extent of RNA fragmentation using capillary electrophoresis .To assess the effects of storage time and conditions on the quality of RNA from FFPE samples, freshly prepared paraffin blocks containing formalin-fixed specimens from different rat tissues  were stored at different temperatures (room temperature (20–25°C), 4°C and 37°C). Blocks were also stored at room temperature either open, or protected from air (under nitrogen), and/or sunlight. From each sample, 10 µm sections were cut for subsequent RNA isolation after 0, 1, 3, 6, and 12 months storage time, and RNA was purified at each time point from 1–4 sections using the RNeasy FFPE kit. The RNA obtained was quantified by ODT values in real-time RT-PCR by as much as 5–6 cycles compared to samples isolated without this step (data not shown). However, previous work has shown that a significant part of the crosslinks formed by reaction of formaldehyde with amino groups in nucleic acid bases and proteins are irreversible To determine in how far storage time and conditions affect usefulness of RNA isolated from FFPE material, one-step RT-PCR amplification was performed with primer sets directed against the rat HPRT gene, producing amplicons of different lengths from about 100 to 1100 nucleotides. Notably, even with RNA isolated from FFPE sections within three days after embedding, amplification of fragments up to 700 nt was successful , whereasExposure of individual FFPE sections to light and air had a negative influence on RNA quality, but storage of entire FFPE blocks either in the open or protected from air and light did not influence the effect of storage time on RNA quality, as long as the uppermost section was discarded at each time point, and sections not directly exposed to air were used for RNA preparation (data not shown).In real-time PCR applications, amplicons are usually shorter than 200 nt. However, in cases where oligo-dT priming is used for cDNA synthesis, the distance between the 3′ end of the mRNA and the 5′ end of the PCR forward primer becomes the limiting factor, because shorter cDNA products may not contain the entire region to be amplified. Consequently, RT-PCR amplicons should be within ∼500 nt or less from the mRNA 3′ end when oligo-dT priming is used for cDNA synthesis. This is not a concern when random or gene-specific primers are used for cDNA synthesis. Similar considerations regarding cDNA length and distance from the 3′ prime end apply to other workflows that rely on cDNA synthesis, such as microarray hybridizations, in cases where oligo-dT priming is used.Another critical factor for nucleic acid quality from FFPE samples are the conditions used for formalin fixation. It is crucial to keep the time between sample acquisition and fixation as short as possible in order to avoid tissue autolysis and nucleic acid degradation by endogenous nucleases. In addition, overfixation can become an issue, particularly if fixation proceeds for considerably longer than 24 hours, resulting in more irreversible crosslinks To assess the influence of overfixation on RNA quality, we compared tissue samples fixed either overnight or for 72 hours in formalin, compared to RNAlater-stabilised tissue samples from the same animal. BioAnalyser data show that even after 72 h fixation in formalin, the RNA is only marginally fragmented, and ribosomal RNA bands are still largely intact , thus clWhile overfixation did not directly result in increased RNA fragmentation, our preliminary data indicate that overfixation may result in faster RNA fragmentation during storage of FFPE blocks (data not shown).With the exception of small biopsies, penetration of the tissue by the fixative is the rate-limiting step in fixation, with initial penetration rates for formalin around 1 mm per hour. Penetration rates decrease with depth. Thus, around 8 hours are required for penetration of 5 mm tissue We examined the effect of specimen size and thickness by comparing rat kidney and brain samples that were either fixed whole (about 1 cm thick), or cut into smaller pieces, around 3–4 mm thick . AnalysiThe degradation effect observed here is surprisingly strong, considering that the thickness of the intact rat organs used was around 1 cm only. It is possible that the intact organs are more slowly penetrated compared to equally sized pieces cut from larger tissues.To compare our results from FFPE specimens obtained from animal tissues under strictly controlled conditions with real-life pathology samples, FFPE specimens from surgically removed lung carcinoma were obtained see . The FFPT) values of 29–35 . For cDNA synthesis, the QuantiTect RT kit (QIAGEN) was used, either with a primer mix composed of random and oligo-dT primers (as contained in the kit), or, for comparison, with oligo-dT primer. Using the primer mix, all of the 1- and 2-year old samples and one of the 4-year samples produced threshold cycle  show similar degradation kinetics, integrity of RNA can be assessed using electrophoretic methods, and depends mainly on the quality of the fixed tissue sample (time between resection and effective fixation), storage time and conditions of the paraffin blocks , and preThe RNA isolation procedure we used involves a short proteinase K digestion, followed by incubation at 70°C under conditions that promote breakage of methylene crosslinks which result from formalin fixation. Proteinase K digestion is necessary to release RNA from the meshwork of crosslinked protein and nucleic acids by digesting the protein portion of the crosslinked molecules, potentially down to the level of tetrapeptides Even though integrity of RNA isolated from FFPE specimens clearly deteriorates with storage time, performance in RT-PCR assays is relatively poor even for seemingly intact RNA, and changes relatively little during the first 12 months of storage at room temperature–indicating that the limiting factor is chemical modification by formaldehyde. Only after storage for more than 12 months or at elevated temperatures, RNA integrity becomes the limiting factor for RT-PCR performance–roughly in agreement with the fragment length observed in electrophoresis of RNA from such aged samples.Fixation with formaldehyde efficiently and irreversibly inactivates endogenous RNases. Thus, while RNA integrity is a poor predictor of performance for RNA from relatively fresh FFPE samples, it provides valuable information about RNA damage and tissue autolysis that occurred prior to embedding, i.e. in the time between resection and the onset of fixation. For FFPE specimens stored over a longer time period (months or years), the fragmentation state of the isolated RNA reflects both the storage time and conditions (temperature), and also the treatment of the samples prior to embedding.The cancer specimens we tested demonstrate that RT-PCR results can be obtained even from FFPE samples that yield strongly degraded RNA, although the required RNA input may be higher than for intact RNA, and protocols may have to be adapted. The lack of correlation between RNA integrity and storage time, even for those FFPE blocks less than 2 years old, indicates that RNase activity was not destroyed quickly after surgical removal of the tumor tissue, presumably because large size of the fixed specimens prevented fast enough penetration of formaldehyde throughout the tissue.For clinical practice, where FFPE specimens are collected for potential use in gene expression analysis, or any molecular biology assays in addition to histopathology, the following recommendations should be considered where possible: The time between surgical removal of the specimen and fixation in formalin should be as short as possible, and the tissue should not be thicker than approximately 5 mm to allow quick and efficient inactivation of RNases and other enzymatic acitvities that may influence gene expression profiles. RNA should preferrably be isolated within 1 year after embedding, i.e. before fragmentation during storage of FFPE samples becomes limiting for performance of the RNA in enzymatic assays. Finally the RNA isolation procedure should be optimized to reverse effects from formaldehyde crosslinking as much as possible. For cDNA synthesis, oligo-dT priming should be avoided, unless all target sequences to be analysed are within 100–500 nt of the mRNA 3′ end, depending on RNA integrity .Although our analysis was limited to RT-PCR applications, the same considerations should apply to microarray analysis and other applications that rely on cDNA synthesis as well.Organs were prepared from adult Wistar rats, cut to appr. 3–5 mm thick pieces, and immediately submerged in 10% neutral buffered formalin for fixation and embedding in paraffin, or stabilized in RNAlater solution , for reference samples with ideal RNA preservation, respectively. Formalin fixation of tissue specimens was performed over night (20–24 h) at room temperature, unless otherwise noted. Specimens were dehydrated in 70%, 90%, and 100% ethanol, followed by xylene, for 2×1 h each solution, then embedded in Paraplast Plus  for 2–3 h at 60°C. For RNA isolation, 10 µm sections were cut on a standard microtome. In each case, the first 2 sections were discarded to exclude negative effects from exposure to air.FFPE specimens from surgically removed lung carcinoma were obtained using routine diagnostic procedures; the study was approved by the ethical committee of the Ärztekammer Hamburg.260 measurement, RNA integrity was assessed by microcapillary electrophoresis .For preparation of RNA from FFPE samples, the RNeasy FFPE kit  was used according to manufacturers recommendations. Depending on the tissue, 1 to 4 sections of 10 µm thickness were used per preparation. For RNA preparation from RNAlater stabilized samples, RNeasy plus or RNeasy Lipid Tissue kits were used. RNA yields were determined by APrimer pairs were designed to generate amplicons of different lengths from the rat HPRT  and Rpl4  mRNAs, respectively . See For human samples, real-time RT-PCR assays were designed for the TBP gene, with the amplicons at different distances from the mRNA 3′ end (128, 227 and 428 nt, counted from the 5′ end–see


Because the mechanisms of RNA decay and maturation are best characterized in Escherichia coli, the majority of this review will focus on how these factors modulate mRNA stability in this organism. However, we also address the effects of RNases, RNA binding proteins, sRNAs on mRNA turnover, and gene expression in Bacillus subtilis, which has served as a model for studying RNA processing in gram-positive organisms. We conclude by discussing emerging studies on the role modulating mRNA stability has on gene expression in the important human pathogen Staphylococcus aureus.The regulation of mRNA turnover is a recently appreciated phenomenon by which bacteria modulate gene expression. This review outlines the mechanisms by which three major classes of bacterial  RNA steady-state levels are a function of both transcript synthesis and decay. Nonetheless, studies of prokaryotic mRNA regulation have historically interpreted changes in mRNA titers on the basis of transcript synthesis alone. It has been recently recognized that this is likely an oversimplification; the modulation of mRNA decay also profoundly affects mRNA titers and, consequently, protein production. A growing body of literature suggests that the regulation of mRNA turnover may be a ubiquitous phenomenon spanning many, if not all, bacterial species. As a precursor to reviewing the factors that regulate bacterial mRNA turnover, we provide examples of how alterations in transcript degradation mediate bacterial adaptation to stress conditions, growth phase transition, and pathogenesis. This is designed to provide the reader with an appreciation of the magnitude of biological responses that are regulated at the level of mRNA turnover and introduce three classes of molecules that govern these changes: ribonucleases (RNases), RNA binding proteins, and noncoding RNAs (sRNA). Escherichia coli cold shock protein CspA, which resolves low temperature mediated mRNA secondary structures that would otherwise impede translation, reaches more than 10% of the total cellular protein concentration during cold shock conditions  is an essential endoribonuclease that organizes other components of the E. coli degradosome and initiates bulk RNA decay  mRNA, tRNA, and rRNAs in vitro [RNase R  is a processive 3′ → 5′ exoribonuclease that accounts for ~90% of all exoribonucleolytic activity of poly(A) RNA[RNase II (y(A) RNA, 104. Thy(A) RNA. It alsoy(A) RNA, 106. orn; 38 kDa) is an essential, processive 3′ → 5′ exoribonuclease which, as the name implies, cleaves short oligoribonucleotides. The enzyme copurifies with PNPase suggesting that it may catalyze digestion of oligoribonucleotide intermediates generated during PNPase-mediated mRNA decay[Oligo-RNase  mRNA is stabilized by elements in its 5′ UTR[ompA mRNA[ompA and the sRNA in vitro and presumably facilitates base pairing between these RNAs in vivo [The Host factor I protein  is an RNA binding protein that affects mRNA stability by facilitating base pairing between sRNAs (described below) and their mRNA targets. This, in turn, can increase or decrease a transcript's accessibility to ribonucleases–112. Fors 5′ UTR, 114. HompA mRNA, 14. Hfq in vivo , 14. Pai in vivo , 14. Add in vivo . In addi in vivo . E. coli[glgCAP. During exponential phase growth when nutrient sources are readily available, CsrA binds to the 5′ UTR of glgCAP mRNA which, in turn, inhibits ribosome loading and promotes transcript degradation [glgCAP transcripts. Rather, the protein becomes predominantly sequestered into a ribonucleoprotein complex comprised of 18 CsrA subunits and one small RNA, CsrB[The carbon storage regulator protein  is a negative regulator of postexponentially induced metabolic pathways; a positive regulator of glycolysis, acetate metabolism, and motility; and a repressor of biofilm formation in E. coli–18. The adation , 118. DuNA, CsrB.pgaABCD which prevents PGA  production; a cell surface polysaccharide that promotes biofilm formation [glgCAP regulation; binding may inhibit translation initiation and increase ribonuclease degradation [flhDC, presumably by binding to the transcript [Although not as extensively characterized, CsrA also seems to regulate the transcript stability and, consequently, protein production of several virulence factors, 17. Howormation . Thus, Cradation . ConversE. coli RNA binding proteins that influence protein production by altering mRNA stability. In addition, the histone-like protein H-NS regulates mRNA stability by binding to target transcripts [As described above, Hfq and CsrA are two well-characterized nscripts . Other Hnscripts . FurtherE. coli; many of these are components of stress responsive regulons[rnpB, tmRNA, and 4.5S regulate gene expression through entirely different processes. rnpB processes tRNA molecules and thus affects translation [The third class of regulatory molecules discussed here is small noncoding RNAs (sRNAs). More than 80 sRNAs have been identified in regulons, 123. sRregulons. Other snslation , 4.5S isnslation , and tmRnslation . In the nslation , 128. hns and rpoS [rpoS, transcript is destabilized by the formation of a stable hairpin in its 5′ UTR. Doing so sequesters the ribosome binding site and promotes RNase III-mediated transcript degradation [rpoS enabling efficient translation which stabilizes the message and results in increased protein production [hns inhibits ribosome entry which destabilizes the message resulting in decreased H-NS abundance [One of the best-studied sRNAs is DsrA which affects the mRNA turnover of at least two target transcripts: and rpoS . As in tadation . Howeverduction , 132. Inbundance .2+ is abundant, Fur becomes activated and inhibits expression of genes involved in various iron acquisition systems [The ferric uptake regulator, Fur, has classically been considered a repressor protein but also indirectly activates gene expression in response to iron availability via an sRNA. When Fe systems . Other g systems . In that systems . Other e systems ), OmrA/B systems ), RprA ( systems ), MicA/S systems , 14), Mi systems ), SgrS ( systems ), and Sp systems ). pcnB; 53 kDa) is responsible for adding 10–40 nt poly(A) tails to bacterial RNA species [E. coli polyadenylated mRNA molecules are rapidly degraded by the 3′ → 5′ exoribonucleases RNase II and PNPase. It is believed that poly(A) tails provide a single-stranded extension region upon which these RNases can bind and initiate decay when otherwise inhibitory secondary structures are present [pcnB in E. coli destabilizes rpoS, trxA, lpp, ompA, and total RNA [rpoS is stabilized by RNase II [rpoS poly(A) tail making the transcript less susceptible to PNPase-mediated decay [We do not intend to give the impression that the three classes of molecules discussed above, RNases, RNA binding proteins, and sRNAs, are the sole mediators of bacterial mRNA turnover. In fact, Deborah Steege published an excellent review highlighting the identification, characterization, and cellular role of polyadenylation in bacteria . Poly(A)species , 143. Alspecies , 143. Inpresent , 144. Footal RNA . Nonetheotal RNA . As mentNase II . In vivod decay .E. coli gene expression by affecting mRNA stability. As will become evident (discussed below), these factors likely modulate mRNA stability in the model gram-positive organism B. subtilis and the human pathogen S. aureus as well. Collectively, ribonucleases, RNA binding proteins, and noncoding RNA molecules dynamically regulate E. coli mRNA degradation are still in their infancy, even less is known about the factors that affect gram-positive bacterial mRNA turnover, even within the model organism B. subtilis. Here, we will overview the similarities and differences between B. subtilis and E. coli mRNA turnover factors.While studies of the mechanism(s) of E. coli and B. subtilis share several ribonuclease sequence homologues, whereas others are unique to each organism  and J2 , have recently been reported to perform as functional homologues to E. coli RNase E [B. subtilis thrS mRNA leader with behavior expected of an RNase E functional homolog [B. thuringiensis stationary phase insecticidal protein transcript cryIIIA and the trp leader sequence [organism . Particu RNase E . The con homolog . In addi homolog . Interesequence , 27. B. subtilis ribonuclease, RNase P, has been shown to affect the mRNA stability of the adenine efflux pump transcript,pbuE [B. subtilis ribonucleases that have been identified to date. It is highly likely that as their characterization intensifies, a subset of these ribonucleases will be determined to affect mRNA stability.In addition to its role in tRNA processing, the pt,pbuE . Other rB. subtilis RNA degradation machinery, the organism's RNA binding proteins have not been fully characterized. Studies have revealed that certain RNA binding protein functions are conserved across species, whereas others are not. For instance, as in the case of E. coli, B. subtilis CsrA may affect mRNA degradation [hag (flagellin) transcript, inhibits translation initiation, and prevents cell motility. However, it remains to be seen whether CsrA binding effects hag mRNA stability [E. coli, Hfq is not required for B. subtilis sRNA:mRNA duplex formation [As with the radation . The protability . Conversrmation . B. subtilis sRNAs have not been as extensively characterized as their E. coli counterparts. Nonetheless, recent work suggests that B. subtilis sRNAs are produced and do have regulatory functions. Silvaggi et al. found a set of sRNAs that are induced in response to sporulation [B. subtilis sRNA, SR1, regulates expression of genes involved in arginine catabolism [B. subtilis sRNA production, effects, and mechanism of action, it will be exciting to determine the similarities and differences among sRNAs of various bacterial species.Likewise, the biological role(s) and mechanism of action of rulation . Furtherabolism , 152. AsB. subtilis. However, additional studies in this organism are certainly required to further characterize the components described here and identify additional factors that influence this mechanism of gene regulation. Although B. subtilis is considered the model gram-positive organism for studying cellular processes, it is in fact quite different from other gram-positive bacteria. For example, the organism is motile and undergoes sporulation, whereas other gram-positive bacteria, such as S. aureus, do not. Likewise, RNA turnover mechanisms may not be conserved across all gram-positive bacteria. As described below, mechanisms of mRNA turnover have recently been described and may play a dynamic role in virulence and adaptation to stress responses in the human pathogen S. aureus.As described above, several studies are in progress to characterize mechanisms that alter mRNA turnover in E. coli and B. subtilis have provided insight into the processes that regulate bacterial RNA stability. Until recently, this mechanism of gene regulation was largely uncharacterized within the human pathogen S. aureus. In this final section, we discuss emerging efforts to characterize factors that contribute to RNA turnover in this organism.As discussed above, studies from S. aureus mRNA turnover is a highly dynamic process. Indeed, during log phase growth the half-life of ~85% of S. aureus transcripts is ≤2.5 minutes, yet many transcripts are stabilized as cells transition to stationary phase growth; the half-life of only ~48% of mRNA species is ≤2.5 minutes . Whblished; ). CollecS. aureus global mRNA turnover have not yet been characterized, a transcript specific ribonuclease, RNase III, has been shown to affect the mRNA decay of virulencefactors [spa (protein A). This, in turn, facilitates RNase III-mediated spa mRNA decay [S. aureus [E. coli, this ribonuclease is presumably responsible for tRNA maturation but may contribute to mRNA decay as well. BLAST analyses suggest that S. aureus harbors at least 14B. subtilis RNasehomologues , affects global mRNA turnover. As in the case for E. coli, disruption of the S. aureus pnpA gene results in a mild global change in mRNA stability  [Even less is known aboutrocesses–157. We protein) . SarA maS. aureus produces a well-characterized noncoding-like RNA regulator, RNAIII, which modulates virulence factor transcript stability[S. aureus sRNA-like regulators. Indeed, Pichon and Felden identified twelve sRNA-like molecules that are differentially expressed among strains and in a growth phase-specific manner. Seven of these are encoded on S. aureus pathogenicity islands and are presumably involved in the regulation of virulence factors. Of these, one was shown, in vitro, to base pair with the 3′ UTR of an ABC transporter mRNA [S. aureus produces 139 small stable  RNA molecules [S. aureus small RNAs will affect the mRNA stability of factors involved in virulence, metabolism, and adaptation to otherwise deleterious growth conditions. As mentioned above,tability, 24. Thetability. Thus, tter mRNA . Althouglecules , 22. BasS. aureus. Certainly, further studies are needed to determine the function of each ribonuclease, identify the regulatory cues that mediate alterations to mRNA stability, and establish the biological significance of altering mRNA stability in this important human pathogen.Global and transcript specific studies indicate that the regulation of gene expression by altering mRNA stability is a dynamic and previously unappreciated means of controlling gene expression in E. coli RNA degradosome, RNase E, among gram-positive bacteria. Clearly, further studies are required to better characterize already identified members of each organism's mRNA turnover machinery and to expand identification of previously unrecognized components. The modulation of mRNA turnover is a recently appreciated regulatory phenomenon that spans most, if not all, bacterial species. Presumably, altering the mRNA degradation properties of individual or subsets of mRNA species allows the cell to quickly adapt to endogenous or exogenous cues without having to expend the energy required for de novo transcript synthesis. A survey of the three factors described here, ribonucleases, RNA binding proteins, and sRNAs among three genetically divergent organisms, suggests that mechanisms of modulating mRNA turnover are generally conserved across bacteria. As the field matures, it is likely that additional conserved RNA stabilization and destabilization processes will be identified. Despite these similarities, it is also obvious that species specific differences do occur. Perhaps this is most evident by the absence of a sequence homolog to the central component of the


Structures of the nucleoprotein of three negative strand RNA virus families, borna disease virus, rhabdovirus and influenza A virus, are now available. Structural comparisons showed that the topology of the RNA binding region from the three proteins is very similar. The RNA was shown to fit into a cavity formed by the two distinct domains of the RNA binding region in the rhabdovirus nucleoprotein. Two helices connecting the two domains characterize the center of the cavity. The nucleoproteins contain at least 5 conserved helices in the N-terminal domain and 3 conserved helices in the C-terminal domain. Since all negative strand RNA viruses are required to have the ribonucleoprotein complex as their active genomic templates, it is perceivable that the (5H+3H) structure is a common motif in the nucleoprotein of negative strand RNA viruses. Negative strand RNA viruses are different from all other viruses because their RNA genomes are always enwrapped by a virally coded nucleoprotein (N) to form a ribonucleoprotein (RNP) complex. This complex serves as the template for viral RNA synthesis  and form the structural core when packaged into virions. The RNP is formed concomitant with replication of viral genomic RNAs by the viral RNA-dependent RNA polymerase. The RNP structure is a unique feature of negative strand RNA viruses such that the polymerase complex can only copy the RNA sequence in the RNP, not naked RNA. In addition, virion assembly also requires the RNP structure to be packaged in the virion. These unique functions that are common among negative strand RNA viruses may require conserved structural motifs in the N protein. Such conservation has been observed in capsid proteins of spherical viruses which share a β-barrel motif . The eigCrystal structures of the N protein from borna disease virus (BDV) , two rha1AYM) viral protein 1 (VP1) and viral protein 2 (VP2) [4SBV([1A34) [1DZL) type 16 [The β-barrel structural motif of spherical viruses can serve as benchmarks for recognizing conserved structural folds in other viral proteins. Four spherical viruses were selected, including human rhinovirus serotype 16  , a singl2) [4SBV , a small type 16 , a doubl type 16 ,12.2GIC) and the RABV (PDB accession code 2GTT) N proteins were superimposed with that of the BDV N protein (PDB accession code 1N93), respectively, by use of the FATCAT program with either rigid or flexible alignments [The BDV N protein consists of 370 amino acids, the N protein from VSV and RABV consists of 422 and 450 amino acids, respectively, and the FLUAV N (commonly known as NP) protein consists of 489 amino acids. There is no detectable homology at the amino acid sequence level among these N proteins. The structures of the VSV  is structurally similar to the RNA binding region of the VSV N protein, but the two domains within this region are positioned differently in the FLUAV N structure (see below). This shows that the two domains in the FLUAV N structure have a large change in their relative orientations compared to those in the VSV N structure. As a result, each domain in the two proteins could only be superimposed separately  in the C-terminal domain (three 310 helices in the BDV N protein and one in the VSV N protein). Additional α-helices can be found in the BDV N protein  motif, may be a common motif responsible for encapsidating RNA by the N protein of negative strand RNA viruses Figure . The helH motif, The structural alignments of the N proteins from three negative strand RNA virus families have significant predictive values in recognizing the RNA binding site and the side-by-side interactions of the BDV N protein, which was not revealed when the BDV N structure was determined alone in the absence of bound RNA. The chemical properties of the homologous cavity in the BDV N protein and the pattern of intermolecular interactions are consistent with its functions to assemble the viral RNP. It also suggested a possible conformation of the FLUAV N protein which may be more suitable for RNA binding than the conformation observed in the recent crystal structure.The author(s) declare that they have no competing interests.ML, TJG, XZ, JT, SQ generated data used in the analysis, proposed and discussed the hypothesis. ML and TJG wrote the manuscript. All authors read and approved the final manuscript.


Interestingly, over 20% of the small RNA population in grain came from a single foldback structure, which generated eight phased 21-nt siRNAs. This is reminiscent of a newly arising miRNA derived from duplication of progenitor genes Small RNAs (∼20 to 24 nucleotides) function as naturally occurring molecules critical in developmental pathways in plants and animals  Additional endogenous siRNAs often associated with repetitive elements have also been characterized that are processed from long double stranded RNA (dsRNA), and function to silence transcription in cis through modification of the chromatin state.Rice is one of the world's most important food crops, as it is produced in over 100 countries and is a staple food for half of the world's population . In addition to the importance of rice as a food staple, its genetic synteny, ease of transformation and an assembled genome Oryza sativa spp. japonica cv. Nipponbare utilizing a deep sequencing approach. As approximately 70% of all human food consumption is derived from seeds As in-depth annotation and functional gene network relationships are developed for rice, the analysis of small RNA expression will facilitate a deeper understanding of these relationships. An extensive and diverse population of small RNAs have been identified in Arabidopsis and rice utilizing high throughput sequencing methods Small RNA libraries were constructed from three pools of mature, dormant rice grain and three-week post germination seedlings utilizing RNA-adapter mediated ligation Arabidopsis thaliana genome and 621 between the rice grain small RNA and Drosophila melanogaster genome. As previously reported To contribute to our understanding of small RNA function and conservation, we compared rice small RNA sequences to genomes of species representing important lineages throughout evolution. We found the highest conservation among plant species with a lesser percentage similarity among distantly related species. For example, there are 13,288 matches between the rice grain small RNA and The three replicates of rice grain libraries allow us to estimate expression and assess the quality and coverage of each sequencing reaction. Amongst the three replicates, we obtained 285,873 unique sequences, however little overlap was observed among these sequences in independent libraries. We found only ∼1.4% of unique sequences were shared among all three replicates, and only ∼5.9% between at least two replicates . This ilP value for each bin and defined hotspots as P<E-50  . RegionsSmall RNAs from hotspots of seedling and grain libraries were categorized into repetitive elements, miRNAs, miRNA targets, sparse hotspots, phased hotspots such as ta-siRNAs, and disperse hotspots . Phased P<E-50 for hotspots resulted in a combined total of 498 small RNA clusters in grain and seedling , rather than individual small RNA sequences. This changed the overlap of the three rice grain replicates to 67% for 1 Kb bins over 1.4% overlap that was found when unique sequences were compared . For anaseedling . The larbona fide miRNAs, we chose to only analyze the five corresponding to sparse small RNA hotspots. Expression of putative miRNAs from sparse hotspots was confirmed by Northern blot analysis , PPR repeats (miRMON13), and CACTA transposon (miRMON22) gene families . A non-cIn our small RNA libraries, miRNAs accounted for 33.0% of all seedling small RNAs, in comparison to only 2.4% from grain . DespiteWe chose to examine the locus on Chromosome 6 in detail, due to it generating siRNAs with the highest abundance, which were specific to rice grain. The phased siRNAs were distributed between two tightly clustered regions . We clonArabidopsis thaliana Col-0 ecotype, and mutants dcl1-7 and dcl4-1 diagnostic for miRNAs and ta-siRNAs, respectively. We detected P5 and P7 phased siRNAs in Col-0 and dcl1-7  to proton-dependent oligopeptide transporter (POT) genes  genes . Phased In addition to the phased siRNAs found on Chromosome 6, we inspected three abundant disperse hotspots on Chromosome 1 from the grain libraries that map to HAP5 transcripts . In contWhile our efforts in deep sequencing of rice grain and seedling tissue have revealed a number of unique elements in the regulatory small RNA family of rice, the functions of many of these remain to be elucidated. We have developed a method of hotspot determination to uniquely validate the class of small RNA in the absence of readily available mutants, which would lend themselves to characterization of small RNAs. Characterization in this manner allows for more rigorous analysis of small RNA data from sequenced genomes, despite the fact that it may produce false negatives, as indicated by the predicted 1072 pre-miRNAs based solely on foldback data. Using sparse hotspots as the defining criteria for miRNAs limited this set to five novel miRNAs, which were validated by Northern blot and predicted targets for two of the five novel miRNAs were validated by 5′ RACE analysis. Furthermore, we compared miRNAs from our large prediction set to those reported recently from wheat Target prediction for the 5 novel miRNAs, revealed that miRMON13 targets a clade of PPR genes. The PPR family has expanded considerably in plants, comprising >450 genes in Arabidopsis and >600 in rice In Arabidopsis, small RNAs principally of the 24-nt class, accumulated in pericentromeric regions A striking finding was the abundance of 21-nt siRNAs specific to rice grain that were not attributable to miRNAs. We identified hotspots expressing extraordinarily abundant 21-nt phased siRNAs (Os06g21900) or disperse ∼21-nt siRNAs  specific to dormant grain. Similar miRNA-like genes have been described in Arabidopsis In addition to abundant grain specific phased siRNAs, three HAP5 loci were found that generate an extraordinary number of 21-nt siRNAs across the length of the transcript. These transcripts could represent nat-siRNA loci, although we were unable to identify any antisense cDNAs or MPSS data indicating antisense PolII transcription. This could be due to the lack of available transcript/cDNA from mature rice grain. There is no evidence for inverted repeats from the transcribed regions at these loci, indicating that these (siRNAs) are unlikely to be DCL4 products Together with HAP2 and HAP3 proteins, HAP5 forms a ternary CCAAT transcription factor complex. This complex has known roles in controlling expression of seed storage protein genes in Arabidopsis All data will be deposited into NCBI GEO upon publication of this manuscript.Oryza sativa spp. japonica cv. Nipponbare was used for small RNA library construction and Northern blot analysis. Plant material for RNA isolation was obtained from dehulled mature grain and 23 day old seedlings planted from the same seed lot and grown in a greenhouse under non-stress conditions. RNA was isolated for Northern analysis from Zea mays var. LH244 leaves, roots (both at stage V6), and 32–39 day after pollination kernels; Glycine max var. A3525 trifoliate leaves, roots, and S3 to S5 seed; and Arabidopsis thaliana ecotype Columbia-0. Seed for dcl1-7 (CS3089) and dcl4-1 (GK_106G05) were obtained from the Arabidopsis Biological Resource Center. A. thaliana was transformed with a CaMV 35S binary expression vector using the floral dip method, and events containing a single T-DNA insertion selected. Total RNA was isolated from plant tissues using TRIzol reagent (Invitrogen).Small RNA library construction was performed as described previously See γ32P-ATP using OptiKinase (USB Corporation) Five micrograms of total RNA was separated on a 17% PAGE-urea gel and blotted as previously described Target Validation using a 5′ RACE assay was done with the GeneRacer Kit (Invitrogen). Poly(A) mRNA was isolated from 3 week old rice seedling tissue, ligated to the adaptor, converted to cDNA and PCR amplified, using gene-specific and adaptor-specific primers. PCR products were gel purified, cloned and sequenced. Successfully validated miRNA target gene-specific primer sequences are shown in Figure S1(a) The majority of small RNAs matched to miRNAs, rRNA repeats, and transposable elements. Annotated miRNAs from miRBase accounted for 33.0% of seedling small RNAs and 2.4% from grain. (b) The size distribution of small RNA populations from grain and seedling were similar, with 21-nt small RNAs as the most abundant class, followed by the 24-nt class.(0.08 MB AI)Click here for additional data file.Figure S2Small RNA expression from grain and seedling libraries, calculated as transcripts per quarter million (tpq) across the twelve rice chromosomes. Abundance was normalized by dividing small RNA tpq by the number of perfect matches in the rice genome. For rice grain, the average tpq of the three libraries was used. Centromere position is indicated by a circle and bins containing small RNA hotspots are indicated in red. Each bar represents a 100 kb bin. A ceiling of 500 tpq was used. Location of phased siRNAs are indicated by an asterisk (*). The majority of rice or monocot specific miRNAs are represented by a single locus.(0.55 MB PDF)Click here for additional data file.Figure S3Alignment of the foldback structure regions of exon 3 and the reverse complement of exon 2 from Os06g21900 with putative progenitor genes. Conserved positions are highlighted as follows: 7/7 red, 5/7 or 6/7 orange, 4/7 yellow, 3/7 green (including at least one conserved base in Os06g21900 foldback).(0.09 MB PDF)Click here for additional data file.Table S1Sources of genome and transcriptome data.(0.02 MB XLS)Click here for additional data file.Table S2Small RNA hotspots from rice.(0.10 MB XLS)Click here for additional data file.Table S3Small RNA probes for Northern blot analysis & Target Validation(0.02 MB XLS)Click here for additional data file.Table S4Predicted Targets for miRMON13 and miRMON18.(0.02 MB XLS)Click here for additional data file.Methods S1Statistical methods used in hotspot determination.(0.05 MB DOC)Click here for additional data file.Data S1Sequence of phased siRNA precursor Os06g21900(0.02 MB DOC)Click here for additional data file.