Primer IDs (pIDs) are random oligonucleotide tags used in next-generation sequencing to identify sequences that originate from the same template. These tags are produced by degenerate primers during the reverse transcription of RNA molecules into cDNA. The use of pIDs helps to track the number of RNA molecules carried through amplification and sequencing, and allows resolution of inconsistencies between reads sharing a pID. Three potential issues complicate the above applications. First, multiple cDNAs may share a pID by chance; we found that while preventing any cDNAs from sharing a pID may be unfeasible, it is still practical to limit the number of these collisions. Secondly, a pID must be observed in at least three sequences to allow error correction; as such, pIDs observed only one or two times must be rejected. If the sequencing product contains copies from a high number of RT templates but produces few reads, our findings indicate that rejecting such pIDs will discard a great deal of data. Thirdly, the use of pIDs could influence amplification and sequencing. We examined the effects of several intrinsic and extrinsic factors on sequencing reads at both the individual and ensemble level. In population sequencing applications, Sanger sequencing by the detection of chain-terminating dideoxynucleotides can only reproducibly detect minority variants that comprise a minimum of about 20% of the template population . Next-ge6 × 2 = 8192 possible pIDs. In this study, we use analytical and experimental methods to evaluate the practical consequences of pID design in the context of characterizing a population of RNA molecules by NGS-based resequencing or ‘deep sequencing’. We consider three distinct but interrelated consequences of pID design.Primer IDs (pIDs) are ‘tags’ used in NGS to address these problems ,7. pIDs collision), the probability of which is determined by the number of pIDs and the number of RNA molecules going into the reverse transcription and amplification reactions. If this happens, the two RNA molecules can no longer be distinguished using the pID. Assuming that all possible pIDs are equally likely to attach to any given RNA molecule, this situation is known in probability theory and statistics as the birthday problem  coming from a given RNA molecule, is another effect of pID design. As discussed previously, another benefit to using pIDs is the ability to turn resampling or oversampling to our advantage by using it to correct for sequencing error. If the amplicon pool has been perfectly labelled, or well-enough labelled that any collisions in labelling are infrequent enough to not significantly affect the data, then one can exactly trace which reads correspond to the same original RNA molecule. In order to construct a consensus sequence for an RNA molecule, there must be at least three reads for that molecule, as ties between two reads cannot be resolved.  Because of this, it is suggested that RNA molecules with read depth below three be rejected from analysis as a more trustworthy consensus sequence cannot be formed  errors when sequencing homopolymers, and since pIDs can introduce homopolymers to the sequenced DNA molecules, we also examine the effects of pIDs on indel error probabilities at the microscopic level.Overall, we find that the pID technique is accompanied with several intrinsically unavoidable challenges. However, our models and experimental results indicate that under an optimal range of experimental conditions, some of these challenges, such as imperfect labelling, may have a minimal impact on the utility of this technique. We also find that the other challenges can be overcome with careful consideration of experimental design and by more recent advances in NGS technologies that can yield much higher numbers of reads. The principles behind the pID method and theoretical analysis performed here apply generally to sequencing of an RNA population . Our primary experiment consists of HIV viral population sequencing on the 454 platform; the technique was originally developed in this context , and it sample; from this sample, an extract containing RNA molecules is produced; from this extract, a portion of the RNA molecules is reverse transcribed into complementary DNA (cDNA) and amplified via PCR to produce an amplicon pool consisting of DNA copies (or amplicons) of the cDNA. The HCV experiment followed the same steps except that the starting samples were serum samples rather than plasma samples. The same principles hold for any other RNA sequencing procedure which follows these steps.To avoid confusion between the different types of products and molecules occurring at different stages of the process of reverse transcription, amplification and sequencing, we will introduce the following terminology.  In HIV RNA sequencing, one starts with a plasma RNA molecule count of an amplicon pool to be the total number of HIV RNA molecules that are represented by DNA copies in the pool after extraction, reverse transcription and amplification. Neither extraction nor reverse transcription is completely efficient, and if an RNA molecule is lost during extraction or fails to reverse-transcribe, then this RNA molecule will never be amplified and, therefore, never sequenced. These inefficiencies must be considered when estimating the RNA molecule count of an amplicon pool.We define the m consecutive N's as Nm. For example, the design NNNNNNNN is N8. For a given design length m, Nm is the design that admits the most possible pIDs.Another piece of convenient notation: we will refer to a pID design consisting of Labelling: Let us briefly describe the birthday problem and its effect. Assuming that all possible pIDs are equally likely to bind to any RNA molecule during the application of sequencing primers to the RNA molecules, the probability that n RNA molecules all receive unique pIDs from a pool of N possible choices is:perfect labelling. This ignores the fact that not all RNA molecules in the sample population have been extracted and reverse transcribed, meaning that there are ‘lost’ RNA molecules that are never tagged or amplified, and, therefore, never represented in the eventual sequencing product. However, assuming further that all RNA molecules are independently equally likely to be lost, the same formula holds if n is taken to be the number of RNA molecules that successfully reverse transcribe and N is the same pool of possible pIDs.Computing the number of pIDs required to achieve a high probability of ‘good enough’ coverage  is a combinatorial problem: the formulae are reasonably straightforward to derive, but are computationally difficult. For example, suppose that we would like to compute the probability that all but 5 RNA molecules receive unique pIDs. The problem is similar in spirit to the classic problem of counting the number of ways to assemble a poker hand, such as two pair or three of a kind. We need to enumerate all the possible ways that we could end up with five RNA molecules sharing pIDs, compute the probabilities of each and sum them. Is there one pair of RNA molecules sharing one pID and another three sharing a second pID, for example, or do five RNA molecules all share one pID? We use the resulting formulae to compute the number of possible pIDs required to limit the number of collisions in a manner we make precise below.k-tag if it is shared by k RNA molecules. For example, if perfect labelling is achieved for an amplicon pool representing n RNA molecules, then there are exactly n 1-tags. If there are two pIDs that get assigned two RNA molecules each, one that gets assigned three RNA molecules, and then the rest of the RNA molecules get unique pIDs, then this amplicon pool has one 3-tag, two 2-tags and (n − 7) 1-tags.Let us introduce some notation to assist in the counting of certain assignments of pIDs to RNA molecules. We say that a pID is a k-tag configurations need to be considered. Secondly, to get results for 95% good labelling of larger RNA molecule counts, we compute approximations as described in Section S2.3 of the Supplementary Materials.We define a probabilistic model to describe the labelling of RNA molecules with pIDs and use combinatorial probability calculations  to compute the probabilities for ‘95% good’ labelling, which we define as consisting of outcomes where the number of distinct pIDs represented in the amplicon pool is at least 95% the number of RNA molecules.  This accepts that there will likely be pIDs shared by several RNA molecules represented in our amplicon pool, but in small quantities. When this becomes too computationally expensive to perform (for large RNA molecule counts), we continue on with two methods. First, we compute these probabilities for labellings better than 95% good, which reduces the amount of computation necessary because, in the language of Section S2.2 of the Supplementary Materials, fewer k-RNA to be an RNA molecule with read depth k. We will call 1- and 2-RNAs singletons and doubletons, respectively.In the following, we define a 5 copies/ml; and P2, rVL 1.78 × 105 copies/ml.  For each, 60 μl of eluent is produced from 0.5ml of plasma. Each plasma extract is serially diluted so that extracts at full concentration, 10-fold dilution, and 100-fold dilution were considered.Our primary HIV data are drawn from a serial dilution experiment using plasma samples coming from two HIV-positive subjects: P1, reported viral load (rVL) 3.12 × 109 = 262144 possible pIDs); NNDNNHNNV (46 × 33 = 110592 possible pIDs) and NBDHVBDHV (4 × 38 = 26 244 possible pIDs).Each of the 2 × 3 = 6 resulting subject-dilution combinations is amplified starting with 5μl of extract, targeting codons 96–194 of the HIV reverse transcriptase , using different degenerate primers representing different pID designs. The pID designs considered are: N9 . In total, 56 batches are so produced, and of these 54 successfully produce amplicon pools, with two failing to amplify. We will also refer to these amplicon pools simply as batches. Details of the wet bench methodology, including primers and thermal cycler settings, are included in the Supplemental Materials, Section S1.Three replicates are performed for each of the 2 × 3 × 3 = 18 subject-dilution-design combinations; we refer to a subject-dilution-design-replicate combination as a barcode is attached to the cDNA molecules produced at the reverse transcription step for each batch, allowing reads to be traced back to their source batches. There are 12 such barcodes (labelled A through L), allowing 12 batches to be run on the same sequencing region. After sequencing, the data are processed using the standard 454 quality filters (Every batch that successfully produces an amplicon pool is sequenced on a Roche/454 GS-FLX pyrosequencing platform in the ‘reverse’ direction (from the 3′ end to the 5′ end). The sequencing plate is divided into eight sequencing regions, and batches are distributed among the first seven of them. To distinguish between batches run on the same region, a different sequencing  filters  and PythWe supplement this experiment with HCV viral sequencing data examining the NS5B gene taken by the 454 platform. These data are produced from serum samples serially obtained from three HCV-positive subjects, which we refer to as HCV1 (7 time points), HCV2 (14 time points) and HCV3 (11 time points). After extraction, reverse transcription and PCR amplification is performed on an amount of extract intended to contain 10 000 RNA molecules. The pID design N9 is used in all cases. The resulting data is then processed through a different processing pipeline, developed in-house. The pipeline used for our HIV experiment is built solely for that experiment, and would require modification to work for the purposes of the larger HCV experiment our data are drawn from. The pipeline used for the HCV data is newer and more general, but its raw results appear similar to those of the original pipeline . See Sections S1 and S3.1.2 of the Supplementary Materials for further details on the experiment and the pipeline. For this experiment, we use the term ‘batch’ to refer to a specific subject and time point, and the amplicon pool coming from the corresponding sample; there are 32 batches in total.To evaluate the relationship between the number of RNA molecules observed and the dilution of the batches, we turn to a statistical model , validating the model by comparing its predictions with both our HIV data and our HCV data. Note that our model only produces loose confidence intervals, and as such we do not claim our validations against experimental data to be particularly strong evidence in favour of our model. Nonetheless they provide a simple guide to suggest how our model performs.intrinsic factors  and extrinsic factors, described in Section S4.1 of the Supplemental Materials. Using these explanatory variables, we fit a generalized linear model using a gamma distribution with inverse link function to our HIV data. We also perform this regression with our HCV data excluding the extrinsic factors, which are not applicable due to the differences in experimental conditions and processing pipelines.We define the read depth ‘success’ of a pID in a batch to be its read depth divided by the total number of reads produced from the batch it belonged to. We examine each pID sequenced to see if its success depends significantly on several Additionally, we analyse the susceptibility of different pIDs to be affected by indel error. An observed pID is considered to have an indel error if it is of length 8 or 10 and can be ‘corrected’ by a single insertion or deletion, respectively, to another observed pID of length 9. We perform a logistic regression on the probability that a pID is sequenced with an indel error using the same intrinsic and extrinsic explanatory factors as our regression on pID success. We restrict our data to pIDs that were observed more than once, because our definition of an indel error does not include pIDs exhibiting indel error that only appear once; they must be observed at least one time in their proper length 9 form, and at least one other time in an erroneous length 8 or 10 form. This analysis is only applicable to our HIV data; we cannot directly perform this analysis on our HCV data as the pipeline used does not perform this correction.We evaluated the impact of macroscopic factors on the HIV experiments where these factors were varied. Restricting to one of P1 and P2 at a time, we consider all of the sequenced batches, rejecting those that produced fewer than 500 reads (assuming such low-depth batches are indicative of a systematic problem at some point in the amplification and/or sequencing). Our data collection scheme uses the pIDs to correct for oversampling, taking the consensus of each pID's associated reads as the sequence of its corresponding RNA molecule, but we do not discard the singleton and doubleton reads. However, we do discard data coming from pIDs whose consensus sequence contained a mixture.Focusing on a 100 bp portion of RT, for each subject we identify all variants observed in any batch coming from that subject, across all experimental conditions. For the variants occurring at higher than 2% prevalence in total, we track their prevalence within each batch to see if the pID design used or dilution of the batch have any biasing effects. There were six such variants in subject P1 and three in subject P2.Since a visual inspection of these prevalences may not reveal any associations to the naked eye, we obtain a quantitative interpretation of these associations by performing a multinomial logistic regression on the probabilities of any given read coming from a P1 batch belonging to one of these six top P1 variants (and implicitly the probability of it belonging to any other variant outside these six), and similarly for the three top P2 variants. The explanatory factors are the pID design used and the dilution of the batch.keep scheme); and two, how mixtures in the resulting consensus sequence are handled . The keep schemes we consider are: not rejecting any pIDs; rejecting singletons and rejecting singletons and doubletons. The mixture resolution schemes we consider are: rejecting pIDs whose consensus sequence contain any mixtures ; and counting any such pIDs fractionally towards each of the consensus sequence's possible resolutions equally . For example, under this scheme the sequence AARY counts as 1/4 of a read towards all of AAAC, AAAT, AAGC and AAGT.Using the same data, we also vary the error correction method used to examine whether sequence diversity was affected. We consider two facets of the error correction method: one, which pIDs are retained and which are rejected based on read depth  but using an equal weighting mixture resolution scheme instead. We perform two multinomial regressions on the probabilities of a read belonging to any of the aforementioned top six P1 variants using first the keep scheme, and then the mixture resolution scheme, as the explanatory variable, and similarly for the top three P2 variants.The HCV data do not lend itself to this analysis. All samples come from different virus populations—even the samples coming from the same subject come from different times, so that the virus population will have evolved between sampling times—so variants should not necessarily be expected to appear across samples in similar prevalences.Recall that using pIDs to label RNA molecules in an NGS experiment should enable one to enumerate the number of RNA molecules represented within the resulting reads; however, if different RNA molecules receive the same labels, then this ability is reduced.n RNA molecules is desired, the number of pIDs required to achieve it isn.Using Equation  to compumentclass2pt{minimEquation  as\docum3; lower counts would diminish the need for NGS. At such numbers, one would require about 4.74 × 106 pIDs to obtain a 90% chance of perfect labelling. A pID design would require at least 12 fully degenerate nucleotides to yield an adequate number of pIDs . In contrast, a much lower number of pIDs (about 2.48 × 104) would be required to have a 90% chance of attaining ‘97.5%-good’ (better than 95%-good) labelling of 103 RNA molecules . Its performance on undiluted batches suggests that this model captures at least some of the true behaviour, so we proceed to use this model to make predictions on the number of RNA molecules that will be lost during error correction.Figure k-RNAs coming from an amplicon pool producing 20 000 total reads, and the same for an amplicon pool producing 3000 total reads. The lines represent different RNA molecule counts m for the amplicon pool. As the RNA molecule count increases, reads tend to belong to pIDs with fewer representatives, eventually getting close to 0 for k > 1 while the number of singletons increases. From this we see that many more RNA molecules are expected to be retained after discarding singletons and doubletons when there are an ample number of sampled reads.Figure Table S11 of the Supplementary Materials shows the percentage of reads belonging to singleton and doubleton pIDs across all sequenced batches, and demonstrates how many reads may be rejected due to error correction. Note that these numbers are totalled across all sequenced batches, so they should not be directly compared to the results in Figure Amplification and sequencing success: Results from our generalized linear model analysis of amplification and sequencing success for the HIV experiment are summarized in Table S13 of the Supplementary Materials. It appears that, of the intrinsic factors we considered, none had significant effects. Success in amplifying and producing reads was much more affected by the extrinsic factors considered, indicating that bigger discrepancies come from differences in the RT, amplification and sequencing steps. This is not surprising in view of our analyses above; pID read depth is expected to increase as the number of reads from the batch increases and as the RNA molecule count decreases, and whether this happens or not is decided mostly at the level of the extrinsic factors (i.e. at the level of the batch and how it is processed).P < 0.0001; G-content, P < 0.0001; T-content, P = 0.0061). See Table S16 of the Supplementary Materials for details.In the HCV data we found that the base content of the pID had significant effects, with each of the C-, G- and T-content having significant associations with decreased success P < 0.0001): reads with higher homopolymer scores were more likely to produce indel errors, consistent with the 454 platform's known issues with homopolymer sequencing. The number of G's appearing in the pID also appeared to have a small but borderline significant effect, with error probability increasing as the number of G's increases (P = 0.052).First, we consider the intrinsic factors. We found that the homopolymer score  had a small but significant association with a higher rate of indel error (P = 0.043). The pID design used also appeared to be significant, with the N9 and NBDHVBDHV designs being more indel-prone than the others (P < 0.0001). Only one sequencing barcode had a significant effect (P = 0.0013), with barcode F (sequence CGTGTCTCTA) associated with a slightly higher error rate over the baseline . Certain plate regions also appeared to have significant effects. Plate region 2 was associated (P = 0.0034) with a higher probability of indel error than the baseline (plate region 1), while regions 3, 4 and 5 were all associated (P < 0.0001) with lower probabilities of indel errors than the baseline. Table S15 in the Supplemental Materials shows that region 2, in particular, exhibited a lower average read quality score than the other regions save for region 7, perhaps commensurate with its effect on error probability. Note, though, that these extrinsic factors may be confounded by uneven distribution of the other intrinsic factors.We next consider the extrinsic factors. The batch rVL had a significant but small effect, with higher batch rVLs significantly associated with a slightly higher error rate (P = 0.029) increase in relative log odds from 0.072 when design NBDHVBDHV is used to 0.22 when design N9 was used. Dilution had a small but significant effect on the prevalences of two variants: for the second-most prevalent, a 10-fold decrease in concentration decreased the relative log odds by 0.095 (P = 0.041); and for the fifth-most prevalent, a 10-fold decrease in concentration decreased the relative log odds by 0.22 (P = 0.017). All coefficients of the regression may be found in Table S17 of the Supplementary Materials.Figure Using design N9 did disrupt the ordering of most to least probable, causing the fourth- and fifth-most prevalent variants to swap (except when the dilution was 100-fold). Note, though, that the effects of using design N9 on the fourth- and fifth-most prevalent variants were borderline significant at best. In all other settings, the ordering of variants by their probability did not change.P = 0.37; variant 2, P = 0.39; variant 3, P = 0.15), while dilution had a significant or borderline significant but small effect . None of these factors disrupted the ordering of the variants. See Table S18 of the Supplementary Materials for the regression coefficients.For P2, we performed the same fitting on the three most prevalent variants, noting that after removing batches producing too few reads the only batches that remained used either pID design NBDHVBDHV or NNNNNNNNN. The design used had no significant effect on any of the three variants , while changing it to removing singletons and doubletons had less significant effects, but still had a significant effect on variants 1 and 3 . Changing the keep scheme while holding fixed a censoring mixture resolution scheme had a large effect on the total number of RNA molecules observed: retaining all pIDs, 10 555 RNA molecules were identified across all P1 batches; rejecting singletons, the number was reduced to 1864; rejecting both singletons and doubletons further reduced the number to 950. Meanwhile, no significant effect was observed in changing the mixture resolution scheme from censoring to equal weighting . The mixture resolution scheme had a much smaller effect on the total number of RNA molecules observed: from a baseline of 10 555 when censoring to 11 084 when equally weighting mixtures . See Supplementary Tables S19 and S20 for the regression coefficients. Also, see Section S5 of the Supplementary Materials along with the associated figures and tables for the analogous results for the top three variants of P2.Next we consider the effect of error correction methods. Figure having insufficiently many possible pIDs can lead to collisions in labelling that detract from the usefulness of the method; anda low read count relative to the RNA molecule count interferes with the ability to use pID-based consensus error correction.Our sections on labelling and shallow read depths illustrate two issues that must be considered when using pIDs in NGS:6 HIV RNA copies/ml; the procedure followed in this study might yield amplicon pools with a RNA molecule count of upwards of 30 000. At that point, the length of the pID design required to admit a sufficiently high number of pIDs to avoid collisions may become prohibitive. One possible workaround with such samples is to dilute them down so that the RNA molecule count does not exceed the design's ‘capacity’  Nucleotide content did not have a significant association with sequencing success in our HIV data, but did in our HCV data. We did find that the occurrence of homopolymers in pIDs had a small but significant effect on indel probability, which is likely at least partly due to the known problems of the 454 platform in handling homopolymers; we also found the G content of the pID to have a small but significant effect. This indel probability was also affected by several extrinsic factors. Note that our method of detecting indel errors did not attempt to identify pIDs with more than one indel error, or with indel errors of apparent length longer than one. Such pIDs would be more likely to resolve to multiple proper pIDs, leading us to have less confidence in our resolution. Indeed, these corrections already make an arbitrary decision about which proper length pID to resolve to when there are multiple possibilities, which can cause the wrong pID to be recorded as having or not having an indel error. Moreover, our method removed pIDs which might otherwise be resolvable to a ‘proper’ length 9 pID but for substitution errors. It also ignores the possibility of errors being introduced before the sequencing stage, for example, in reverse transcription or in amplification. Another limitation is that pIDs of length 8 or 10 that result from an indel error in sequencing a length 9 pID cannot be identified if the proper length 9 pID is not sequenced. Particularly in the cases with high RNA molecule counts, this could easily be a factor because there is a good chance that the proper pID does not produce any other reads. Also, it is worth noting that using pIDs does not address indel errors that are systematically mishandled by the data processing pipeline. In the future it may be revealing to consider the effects of other intrinsic factors, such as secondary structure and the presence of hairpins, or primer dimers, as well as other extrinsic factors.We also find in ‘Effects of pIDs on Amplification and Sequencing: Macroscopic’ that the choice of pID design did not appear to greatly bias to the data, nor did the dilution of the batch. Our visual interpretation was supported by a multinomial logistic regression on these factors. The error correction scheme did, however, introduce slight but significant bias to the data, with different schemes for retaining pIDs based on read depth having significant effects on the prevalences of top variants observed for both HIV subjects. Changing the keep scheme also greatly reduced the number of RNA molecules observed, which may partially explain the effects on sequence diversity. Meanwhile, changing the mixture resolution scheme appeared to have only insignificant effects for subject P1, and had a small but significant effect for the top variant observed in subject P2. Note that the variants we considered were identified after trimming our reads to a 100 bp region using automated pairwise alignment. This was necessary to avoid regions of RT which are highly susceptible to sequencing error, but may have misidentified the desired portion of the sequence in some reads.We believe that using pIDs can be of great value in using NGS. The problems they solve—error correction of noisy NGS data and quantification of the RNA molecules sequenced—are both important technical factors that must be addressed if we want to collect the highest quality data possible. Our findings are reminders that, as with any new technology, care must be taken in the implementation of pIDs. We have focused primarily on purely theoretical limitations of the method in our sections on labelling and shallow read depths; these are hard limitations imposed by the randomness in the procedure that cannot be worked around, and so we must be aware of them. Other practical problems and deviations from the assumptions we made are bound to occur as sequencing technology moves forward, and those issues must be found in turn; we are only scratching the surface in our examination of the effects of pIDs on sequencing and analysis.Supplementary Data are available at NAR Online.


Sequence-based alignment files can be converted into VCF files using a Python script and uploaded to the RCARE server for further analysis. RCARE is available for free at http://www.snubi.org/software/rcare/.The post-transcriptional sequence modification of transcripts through RNA editing is an important mechanism for regulating protein function and is associated with human disease phenotypes. The identification of RNA editing or RNA-DNA difference (RDD) sites is a fundamental step in the study of RNA editing. However, a substantial number of false-positive RDD sites have been identified recently. A major challenge in identifying RDD sites is to distinguish between the true RNA editing sites and the false positives. Furthermore, determining the location of condition-specific RDD sites and elucidating their functional roles will help toward understanding various biological phenomena that are mediated by RNA editing. The present study developed RNA-sequence comparison and annotation for RNA editing (RCARE) for searching, annotating, and visualizing RDD sites using thousands of previously known editing sites, which can be used for comparative analyses between multiple samples. RCARE also provides evidence for improving the reliability of identified RDD sites. RCARE is a web-based comparison, annotation, and visualization tool, which provides rich biological annotations and useful summary plots. The developers of previous tools that identify or annotate RNA-editing sites seldom mention the reliability of their respective tools. In order to address the issue, RCARE utilizes a number of scientific publications and databases to find specific documentations respective to a particular RNA-editing site, which generates  RNA editing is the post-transcriptional alteration of a single nucleotidesequence in primary messenger RNA (mRNA) or non-coding RNA (ncRNA) including micro-RNA (miRNA) transcripts . The moshttp://www.ncbi.nlm.nih.gov/geo/) and ENCODE (http://genome.ucsc.edu/ENCODE/). In response, many tools have been developed for detecting or annotating RNA editing and RDD sites. Prediction tools for novel RNA editing sitessuch as rddChecker (http://genomics.jhu.edu/software/rddChecker/) perform RNA-DNA sequence comparisons with RNA-Seq and genomic DNA sequence data. The rddChecker searches for RDDs, filters known single-nucleotide polymorphisms, and determines novel RNA editing sites by comparison with known RNA editing sites. However, the rddChecker is known to suffer from false-positive results. And many of the RNA editing sites reported by Li et al. [In the last few years, huge amounts of DNA and RNA-Seq data have been generated and stored in public repositories such as GEO  and RADAevidence levelof each RNA editing site. It identifies novel RNA editing sites in the same/individual DNA sequence and RNA-Seq variant call format (VCF) data, and delivers 'executive summary' plots for the annotation and comparison of multiple samples with RNA-Seq data through a user-friendly web interface. It also provides a Python script that users can implement to preprocess raw RNA-Seq data and convert FASTQ and BAM files into RNA VCF files within a practical time frame on a desktop computer. RCARE is freely available at http://www.snubi.org/software/rcare/.This study presented a new tool for RNA-Seq comparison and annotation for RNA editing (RCARE), which determines condition-dependent RNA editing sites, provides rich systematic annotations and the evidence level of each RNA editing site was generated by downloading 1,379,404 and 10,115 human RNA editing sites (hg19) from RADAR [http://ensembl.org) and UCSC (http://genome.ucsc.edu/buildGRCCh37/hg19) to annotate the RDD site into one of the following categories: ncRNA, Ensembl Gene (ENSG) ID, Transcript (ENST) ID, Exon ID (ENSE), and repetitive element . ANNOVAR (http://www.openbioinformatics.org/annovar/) [RCARE integrates 314,880, 6,830, and 13,018 human mRNA editing sites downloaded from DARNED NCBI37/hg19) . The ref . The reKnown-RNA-editing-site-compare functions. When both the DNA sequencing and RNA-Seq data are available, the RCARE RDD-compare function can be used to compare the paired VCF files created from those data. It is based on a python script and performs a comparison between RNA and DNA sequence variants according to chromosome, position, and reference/alternative sequences. This compare function can detect novel RDD or RNA editing candidate sites from DNA and RNA-VCF files. More precise detection of RNA editing sites requires both DNA and RNA VCF files obtained from the same/individual sample.RCARE provides a Python script for the automated conversion of a FASTQ or BAM file obtained from an RNA-Seq experiment into a VCF file with the hg19 reference genome Figure . TopHat Known-RNA-editing-site-compare functions are functions which identifies RNA sites that are differentially edited between samples according to chromosome, position, and reference/alternative sequence. It also identifies RNA editing sites intersecting between input RNA-Seq samples. A detailed method for compare functions was described in Fig S3.In RCARE, http://www.highcharts.com/). It also provides four functionalities: RNA-Seq data preprocessing, comparison, annotation, and visualization.RCARE is a web-based application that was created using HTML5 (Hypertext Markup Language 5), CSS3 (Cascading Style Sheets 3), jQuery, and Highcharts API   and humaEvidence levels according to the number of resources reporting corresponding RNA editing. First, we collected five RNA editing site-related resources . Second, we integrated aforementioned resources according to chromosome, position, and reference/alternative sequences and generated evidence levels according to the number of resources, which addressed corresponding RNA editing sites. Evidence levels consist of five levels (A-E), where A is the highest level  and Aluel Table . For exaevidence level of each RNA editing site from the VCF format of the RNA-Seq data. The compare part provides the RNA editing sites of each sample and the intersection/difference between all pairwise samples with 16 useful biological annotations and evidence levels. The results page provides summary plots based on annotation categories including the distributions of genomic features, genes, ncRNAs, synonymous vs. nonsynonymous changes, types of RNA editing, and the distributions of detected RNA editing sites in each chromosome or sample  yielded 2437, 646 and 1190 RNA editing sites, respectively; we obtained 205, 605 and 334 RNA editing sites that had evidence levels of A-C from HUVEC, MCF-7 and HeLa-S3; however, these represent only 31.7%, 24.8% and 28.07% of the total RNA editing sites. This finding shows that a good evidence level is essential for RNA editing detection and annotation analysis. We also accomplish the Known-RNA-editing-site-compare functions between MCF-7 and HUVEC. As a result, we identified 2,080 breast-cancer-specific RNA editing site were detected from MCF-7. These results demonstrate the importance of detecting condition-specific RNA editing sites.The web interface consists of three sections: user manual, download, and analysis. The user manual section provides instructions regarding the conversion utilities, annotation, comparison, and results description Figure . All mane Figure . All imaWe measured execution times of Expedit and RCARE using a 37.3 Mb sample bam file. Conversion processing of RCARE was performed in 192 seconds on a 3.0 GHz CPU, 2048 MB RAM desktop environment for converting BAM to VCF. RCARE RNA editing site annotations took 7 seconds under 14.19 MBps network environments.evidence levels to indicate the reliability of each RNA editing site. Furthermore, it provides a tool for converting sequence-based alignment files into VCF files using a Python script; converted VCF files can be uploaded to the RCARE server for further analysis. The RCARE web interface can be used to easily annotate and visualize VCF-formatted RNA-Seq data, and to download results into CSV and JPG files.Efficient and user-friendly web-based system for trustworthy RNA-editing-site annotation, comparison and graph visualization. RCARE contains 321,008 human RNA editing sites with rich biological annotations and useful summary plots, as well as evidence level will greatly augment the field of RNA-editing site research. RCARE will help toward the identification of trustworthy RNA editing sites.Identification of novel RNA-editing sites became the focus of RNA-editing research about 2~3 years ago ,5,11. InThe authors declare that they have no competing interests.S. Lee conceived and designed the system, and also implemented the database and web interface (C. Park assisted the implementation of the database). J. Joung supported the project by providing biological information on RNA editing. S. Lee wrote the paper, with J. Park facilitating the process. J. Kim supervised the project. All authors read and approved the final manuscriptevidence level. A) Number of RNA editing sites at each evidence level. B) Evidence level annotations in relation to genomic features. The enrichments for genic features at each evidence level from RCARE are also shown.Number of RNA editing sites for each genomic feature within each Click here for fileevidence level within three cell line RNA-seq data. A) The ratio of evidence levels A-C versus D-E in detected RNA-editing sites within three cell lines including MCF-7 (a breast cancer cell line), HUVEC  and HeLa-S3 . B) Number of RNA editing sites at each evidence level within three cell llines.The ratio of each Click here for fileRCARE webpage user manuals.Click here for fileRCARE convert utilities manuals.Click here for file


RNA extraction is a crucial step for monitoring gene expression. Poor RNA quality (including degradation and remaining impurities) can result in misleading results. Isolation of RNA from animal tissues with high lipid content can be challenging. Especially, it is not trivial to isolate high quality RNA with a reasonable yield from adipose tissue. The aim of this study was to provide an optimized protocol for isolating total RNA from adipose tissue. This was achieved by combining the advantages of the two routinely used methods, TRI Reagent® and miRNeasy.The miRNeasy method results in cleaner samples but more prone to degradation while the TRI Reagent® method results in samples contaminated with salts and solvents but more intact. The new protocol combines the best of both methods resulting in RNA of high quality and suitable for downstream experiments like RT-qPCR, microarrays and high-throughput sequencing.The current protocol for total RNA isolation from adipose tissue yields sufficient amount of high quality total RNA free of contaminants. Adipose tissue has traditionally been viewed as a passive storage of energy in the body but has recently been recognized as a complex and highly active metabolic and endocrine organ . As suchDue to its high content in fatty acids and in some instances its low cell number, adipose tissue poses some challenges when high quality RNA needs to be isolated for downstream gene expression applications . Many routinely used protocols for isolation of RNA from adipose tissue result in RNA that is partially degraded and/or poor yield of RNA or the small RNAs are lost in the process. All these issues can cause misleading results and they are a big challenge specially when working with limited amount of tissue (i.e. human biopsies). Therefore there is a need for a simple, reliable and effective protocol to isolate high quality total RNA with sufficient yield from adipose tissues. Here an optimized and simple protocol which combines two widely used methods and allows rapid purification of high quality total RNA from adipose tissue is presented. Performance of the new protocol has been evaluated and compared with the standard methods for RNA purification.Porcine retroperitoneal adipose tissue, snap frozen in liquid nitrogen and stored at −80°C was used for the present study. Animal care, maintenance and slaughter have been conducted according to the Danish “Animal Welfare Act” (LBK 1343 of 04/12/2007).Four samples  were used for the TRI Reagent®  and miRNeasy  methods. The 4 samples were the same for these 2 methods. For the combined protocol the number of samples was increased to 10 . The phenotypic characterization of the animals as “lean” or “fat” is based on the body weight and on metabolic parameters [RNA isolation from adipose tissue was performed using TRI Reagent® method  and miRNeasy method  and a combination of both (combined protocol). Around 100 mg of tissue were used to isolate total RNA with the three mentioned methods. Using TRI Reagent® and miRNeasy the exact protocols from the manufacturers were followed. For the combined protocol, the homogenization was done in 2 ml of TRI Reagent® buffer using a gentleMACS™ Octo Dissociator system  with M tubes  and the RNA_02 program recommended by the manufacturer. After homogenization the samples were incubated at room temperature for 5 minutes. Subsequently a centrifugation step at 12000 g at 4°C for 10 min was performed and the resulting fat monolayer  using Eukaryote Total RNA StdSens kit (BioRad)  using tP < 0.05) than the other two (0.61 ± 0.25 for TRI Reagent and 1.43 ± 0.25 for miRNeasy). The RNA quality index (RQI) assessed by the Experion™ system resulted also in excellent values for the samples isolated with the new protocol, RQI = 8.7 ± 0.36 (7.62 ± 1.55 for TRI Reagent and 6.82 ± 2.98 for miRNeasy) which is quite unusual for adipose samples  and a 260/230 ratio of 1.73 ± 0.189 which was significantly higher .The TRI Reagent® method showed problems with the purity of the isolated RNA. The ratio of 260/280 was lower than 1.8-2 indicating the presence of proteins. Furthermore, the 260/230 ratio, which should ideally be 1.8 or greater, was consistently very low (under 1) revealing remains of solvents (i.e. phenol/chloroform) or chaotropic salts (guanidine isothiocyanate) in the RNA solution  we experienced that if the disruption was performed using a mortar and pestle, the fatty acids were abundant in the sample after the homogenization and the fat monolayer was very evident after the initial centrifugation step (see Figure In the step of phase separation, after addition of chloroform and 15 min. centrifugation at 12000 g and 4°C, the interphase was usually very diffuse and difficult to avoid. In order to make it more compact to avoid carrying it over when transferring the RNA upper phase, the centrifugation was extended from 15 minutes to 30 minutes.When running the RNA samples in agarose gels or in the Experion™ system the samples from the miRNeasy protocol were slightly more degraded than the ones from the TRI Reagent® and the new protocols (see Table For total RNA isolation from adipose tissue, the miRNeasy method results in cleaner samples but more prone to degradation and the TRI Reagent® method results in samples contaminated with salts and solvents but more intact. The new protocol combines the best of both methods yielding sufficient amount of high quality total RNA from adipose tissue free of contaminants and suitable for downstream experiments like RT-qPCR, microarrays and high-throughput sequencing. At present, we use this protocol routinely in our laboratory starting from frozen adipose tissue and from isolated mature adipocytes with excellent results.The author declares not to have any competing interests.


Genome replication in flavivirus requires (−) strand RNA synthesis, (+) strand RNA synthesis, and 5′-RNA capping and methylation. To carry out viral genome replication, flavivirus assembles a replication complex, consisting of both viral and host proteins, on the cytoplasmic side of the endoplasmic reticulum (ER) membrane. Two major components of the replication complex are the viral non-structural (NS) proteins NS3 and NS5. Together they possess all the enzymatic activities required for genome replication, yet how these activities are coordinated during genome replication is not clear. We provide an overview of the flaviviral genome replication process, the membrane-bound replication complex, and recent crystal structures of full-length NS5. We propose a model of how NS3 and NS5 coordinate their activities in the individual steps of (−) RNA synthesis, (+) RNA synthesis, and 5′-RNA capping and methylation. Flaviviridae, which also includes Hepacivirus and Pestivirus. The Flavivirus genus includes over 70 viruses, many of which cause arboviral diseases in humans, such as dengue (DENV), West Nile (WNV), tick-borne encephalitis (TBEV), and yellow fever virus. The 11 kb flaviviral RNA genome consists of a 5′-cap, a 5′-untranslated region (5′-UTR), a single open reading frame (ORF), and a 3′-UTR. The 5′- and 3′-UTRs contain conserved RNA secondary structures that are important for viral replication, including sequences that mediate long range 5′- and 3′-RNA interactions [Flaviviruses are positive (+) sense RNA viruses belonging to the family ractions ,5,6,7,8.ractions . Among Nractions ,13,14. Tractions ,17,18,19ractions ,21. NS5,ractions . The NS5ractions ,24. Littractions ,27,28,29ractions ,31,32,33ractions ,35. Moreractions ,37.m7GpppNm-RNA) at its 5′ end. RNA synthesis in flavivirus is semi-conservative and asymmetric. The (+) sense RNA, which is the same polarity as the viral genome, is predominantly formed over the (−) sense RNA; single-stranded (ss) RNA found in flavivirus-infected cells is (+) sense RNA, and (−) strand RNA is only detected in the dsRNA form [3H-UTP or 32P-labeled guanosine-5'-triphosphate (GTP)), three radiolabeled RNA products were identified: a double-stranded replicative form (RF) RNA, single-stranded genome length RNA, and the slowest migrating, replicative intermediate (RI) [The flavivirus replication complex carries out RNA synthesis, RNA capping and RNA methylation steps to produce the genome with a type 1 cap structure (RNA form . When DEate (RI) ,39,40. Rate (RI) ,39. BaseThe (+) strand progeny RNA is subsequently capped at its 5′ end and methylated to form a type 1 cap B. The caGenome replication in flavivirus is carried out by a membrane-bound viral replication complex consisting of viral NS proteins, viral RNA and unidentified host proteins . Membranin vitro flaviviral replication system, which can use the viral genome to synthesize the methylated, capped RNA product, is currently unavailable.Although the exact composition of the replication complex is not known, all flaviviral NS proteins have been shown to be a component of the replication complex. Using immunoprecipitation, yeast two-hybrid, and fluorescence resonance energy transfer (FRET) assays, interactions among NS proteins have been identified ,46,47,48in vivo fluorescent measurements, and yeast two-hybrid studies [At the heart of the flaviviral replication complex are NS3 and NS5, which together account for all enzymatic activities required to amplify the RNA genome and to attach a type 1 cap to its 5′ end . Other v studies ,47,57. T studies ,59,60.The physical linkage between the MTase and RdRp domains within NS5 suggests that viral genome replication and 5′-RNA capping may be coordinated between the two domains. Several genetic studies show that mutations in the MTase domain impact RNA replication by the RdRp domain, suggesting that the RdRp and MTase interact during viral genome replication ,62,63,64260Gly-Thr-Arg262 (GTR) motif, N-terminal to the inter-domain linker, is proposed to act as a hinge that allows movement of the MTase and RdRp domains relative to one another [The recently reported structures of full-length NS5 from JEV and DENV offer details of specific interactions between the MTase and RdRp domains and suggest a structural mechanism by which their activities may be coordinated within the flavivirus replication complex ,69. We h another ,69,70. AThe multifunctional flavivirus NS5 carries out several reactions. These include synthesis of (−) strand RNA, synthesis of (+) strand RNA, addition of a guanine cap to the 5′ end of (+) strand RNA, and two methylation reactions to form a type 1 cap structure at the 5′ end of the nascent RNA. Consequently, NS5 interacts with other viral proteins and several different forms of viral RNA, including both single- and double-stranded, (+) and (−) strand, capped and uncapped, and methylated and unmethylated forms of capped RNA. Thus, NS5 likely needs to adopt multiple conformations during different stages of genome replication and processing, as suggested by the conformational variation observed for the structures of full-length NS5 from JEV and DENV. Below, we summarize viral replication steps in terms of coordination of NS3 and NS5 functions.(−) strand RNA synthesis: (−) sense RNA synthesis is carried out by NS5 polymerase using the viral (+) RNA genome as a template. The function of NS3 does not seem to be required for (−) strand RNA synthesis, since NS5 (either the full-length protein or the polymerase domain) alone is capable of synthesizing RNA using viral subgenomic RNA. The (+) sense RNA genome contains cyclization sequences at the 5′- and 3′-UTR, and the cyclized genome serves as the template for (−) strand RNA synthesis ,7,8,71. (+) strand RNA synthesis: The viral replication complex uses the (−) strand RNA in the dsRNA intermediate as the template to synthesize multiple copies of (+) sense RNA. Both NS5 and NS3 activities are required for (+) RNA synthesis . NS3 hel5′-RNA capping and methylation of (+) sense RNA: The (+) sense RNA will be capped and methylated to form a type 1 cap at the 5′ end B. NS3 heCoordination of (+) RNA synthesis and 5′-RNA capping: The 5′-RNA capping and methylation steps are likely coupled with (+) sense RNA synthesis, but during which step of RNA synthesis 5′-RNA capping and methylation occur is not clear. Capping of 5′-RNA could occur on the nascent (+) strand RNA while it is being synthesized by the NS5 RdRp domain (co-transcriptional model in The identification and visualization of subcellular membrane structures used by flavivirus as replication factories have provided insight into how flavivirus may orchestrate viral replication and virion assembly in infected cells. Additionally, crystal structures of the full-length NS5 have provided details of intra- and inter-molecular interactions that stabilize different forms of NS5. These forms may interact with RNA and protein components of the replication complex differently and thus serve as a platform to promote different steps during genome replication. Future studies will be geared toward understanding how these membrane vesicles are related to the assembly and function of the viral replication complex at the molecular level. In particular, how flaviviral and host proteins establish such elaborate membrane vesicles for replication, how flaviviral NS proteins are assembled in the membrane-bound viral replication complex, and how the viral replication complex carries out individual steps of RNA replication are of great interest.


Histone epigenome data determined by chromatin immunoprecipitation sequencing (ChIP-seq) is used in identifying transcript regions and estimating expression levels. However, this estimation does not always correlate with eventual RNA expression levels measured by RNA sequencing (RNA-seq). Part of the inconsistency may arise from the variance in RNA stability, where the transcripts that are more or less abundant than predicted RNA expression from histone epigenome data are inferred to be more or less stable. However, there is little systematic analysis to validate this assumption. Here, we used stability data of whole transcriptome measured by 5′-bromouridine immunoprecipitation chase sequencing (BRIC-seq), which enabled us to determine the half-lives of whole transcripts including lincRNAs, and we integrated BRIC-seq with ChIP-seq to achieve better estimation of the eventual transcript levels and to understand the importance of post-transcriptional regulation that determine the eventual transcript levels.We identified discrepancies between the RNA abundance estimated by ChIP-seq and measured RNA expression from RNA-seq; for number of genes and estimated that the expression level of 865 genes was controlled at the level of RNA stability in HeLa cells. ENCODE data analysis supported the idea that RNA stability control aids to determine transcript levels in multiple cell types. We identified UPF1, EXOSC5 and STAU1, well-studied RNA degradation factors, as controlling factors for 8% of cases. Computational simulations reasonably explained the changes of eventual mRNA levels attributable to the changes in the rates of mRNA half-lives. In addition, we propose a feedback circuit that includes the regulated degradation of mRNAs encoding transcription factors to maintain the steady state level of RNA abundance. Intriguingly, these regulatory mechanisms were distinct between mRNAs and lincRNAs.Integrative analysis of ChIP-seq, RNA-seq and our BRIC-seq showed that transcriptional regulation and RNA degradation are independently regulated. In addition, RNA stability is an important determinant of eventual transcript levels. RNA binding proteins, such as UPF1, STAU1 and EXOSC5 may play active roles in such controls.The online version of this article (doi:10.1186/s12864-015-1358-y) contains supplementary material, which is available to authorized users. The eventual RNA transcript level of a gene is determined by regulation at multiple levels, including transcriptional initiation, elongation, splicing, export and degradation. Transcription initiation is regulated by complex interactions of sequence features, many of which involve chromatin modifications . Althougbona fide mRNA with full coding potential  and [DDBJ:DRA002961] and URL links to the sequencing data are available from


Cryphonectria parasitica. MyRV1 genome rearrangements are frequently generated by a multifunctional protein, p29, encoded by a positive-strand RNA virus, Cryphonectria hypovirus 1. One of its functional roles is RNA silencing suppression. Here, we explored a possible link between MyRV1 genome rearrangements and the host RNA silencing pathway using wild-type (WT) and mutant strains of both MyRV1 and the host fungus. Host strains included deletion mutants of RNA silencing components such as dicer-like (dcl) and argonaute-like (agl) genes, while virus strains included an S4 internal deletion mutant MyRV1/S4ss. Consequently, intragenic rearrangements with nearly complete duplication of the three largest segments, i.e. S1, S2 and S3, were observed even more frequently in the RNA silencing-deficient strains Δdcl2 and Δagl2 infected with MyRV1/S4ss, but not with any other viral/host strain combinations. An interesting difference was noted between genome rearrangement events in the two host strains, i.e. generation of the rearrangement required prolonged culture for Δagl2 in comparison with Δdcl2. These results suggest a role for RNA silencing that suppresses genome rearrangements of a dsRNA virus.Mycoreovirus 1 (MyRV1) has 11 double-stranded RNA genome segments (S1 to S11) and confers hypovirulence to the chestnut blight fungus,  Reoviridae, where reverse genetics is mostly unavailable (Reoviridae is one of the largest families of double-stranded (ds) RNA viruses and includes members infecting a wide range of eukaryotic organisms . Rearrangement events have been reported for all the major genera, characterized by large deletions and/or extensions that are frequently accompanied by substitutions  is an RNA-mediated post-transcriptional gene regulation mechanism conserved in eukaryotic organisms ,21. In gCryphonectria parasitica (chestnut blight fungus) (dcl2 (Δdcl2) or agl2 (Δagl2) CHV1 DI-RNA does not occur, whereas it is present spontaneously and frequently appear in RNA silencing-competent strains. While C. parasitica has four RDR gene homologues, no phenotypic effects result from the disruption of any these genes (rdr1–4)  strain (The RNA mycoviruses/filamentous fungi system provides a unique opportunity to investigate RNA recombination associated with RNA silencing . Nuss an fungus) ,24. That(rdr1–4) . FurtherMycoreovirus, family Reoviridae) undergo distinct genome rearrangements as well as the usual ones similar to those found in other Reoviridae members . Most of them are stably maintained in p29 transformant strains. Functional roles assigned to CHV1 p29 include activities as an RNA silencing suppressor, an essential N-terminal cis-element for translation and/or RNA synthesis, a papain-like protease, a symptom determinant and a replication enhancer of homologous and heterologous viruses , four AGL (agl1 to agl4) and four RDR (rdr1 to rdr4) genes (C. parasitica (EP155) or its mutants lacking the genes for dcl1 (Δdcl1), dcl2 (Δdcl2) and agl2 (Δagl2) were generous gifts from Dr. Donald L. Nuss (rdr1 disruptant was prepared in this study as described below.The genes encoding possible key enzymes for RNA silencing in the fungus are two DCL (4) genes ,25,37,38 L. Nuss ,37. An rTransgenic EP155 containing the coding domain of CHV1-EP713 p29 (Twtp29) has been reported earlier .Hyphal anastomosis was carried out between fungal strains which were vegetatively compatible, aiming to horizontally transmit MyRV1. Prior to anastomosis, the fungal partners were refreshed in potato dextrose agar (PDA) for at least 5–7 days. Each anastomosing fungal pair was inoculated in close proximity (10 mm) to each other in a 10-cm plate containing 25 ml of PDA (co-culturing) and three replications were performed. Mycelial blocks were taken randomly from the fungal pair, i.e. the donor (virus source) side and the recipient side (virus-free strain) to check the movement of the virus and eventual gene rearrangement. The plates were incubated at room temperature for approximately 4–5 weeks for the standard procedure , or as specified.dcl2 and/or Δagl2 hosts under the same conditions simultaneously and maintained for the same period. Newly MyRV1-acquired recipient fungi in the first transmission experiment were used as the virus donor for the second co-culture, as well as for the third co-culture.For stability and rearranged segment biogenesis assays , after four weeks of incubation, each virus strain was transferred to EP155, ΔQuick and small-scale dsRNA preparation in 2.0 ml and 1.5 ml Eppendorf tubes was carried out. Briefly, mycelial colonies grown for 3–5 days were peeled off from a PDA plate lined with cellophane, extracted with STE buffer  plus 1.2% SDS, and clarified with one round each of phenol-chloroform and chloroform. The resulting aqueous layer was mixed with a 15% volume of ethanol (EtOH) and a small amount (0.1 g) of CC41 cellulose (Whatman) for column chromatography of dsRNAs. After 1–2 h of continuous agitation at room temperature, each tube was washed with STE-ethanol  three times with brief vortexing and centrifugation between washes. The bound dsRNA was eluted once or twice from dried CC41 powder by the addition of STE buffer and precipitated with ethanol. Each sample was subjected to 1% or 1.4% agarose gel electrophoresis and assessed for the production of rearranged genomic dsRNA segments.C. parasitica strains were inoculated in 50 ml PDB and extracted in two rounds of phenol-chloroform followed by one round of chloroform. After precipitation of the resulting nucleic acid in EtOH, RNA was treated with RQ1 RNase-free DNase I (Promega) and an RNase inhibitor. The single-stranded RNA preparation was enriched by precipitation with 2 M LiCl. Washing, pre-hybridization and hybridization were carried out as described previously  conditions set out by Sun and Suzuki . Clean pSemi-quantitative RT-PCR was similarly employed to confirm the presence and type of the rearranged segments generated during the transmission experiments.The primers used in this study are listed in Supplementary Table S1.rdr1 gene (Δrdr1) was generated by homology-dependent gene replacement technology by N. Suzuki in the Nuss laboratory. One of the RDR homologs, rdr1, is an orthologous gene to Neurospora crassa qde1, a major player in quelling (RNA silencing in N. crassa) . After screening of approximately 50 transformants of the WT fungal strain (EP155) with the construct, a disruptant strain (Δrdr1) was obtained. Disruption of the rdr1 gene was confirmed by Southern blot and RT-PCR analyses . Namely, the rdr1 gene was replaced with a gene cassette encoding hygromycin B phosphotransferase (hph), which confers resistance to hygromycin; neither of the rdr1-specific DNA bands was detected by Southern blotting or RT-PCR analysis (see Supplementary Table S1 for primer sequences). Positive reactions were found with the WT fungus (EP155). Note that Δrdr1 was prepared independently of the rdr1 disruptants reported recently by (A knockout mutant for the  crassa) . We madeently by .C. parasitica strains , a potent inducer of rearrangements, was also included in the assay (Table Since RNA silencing is anticipated to be one of the most important factors affecting MyRV1-genome rearrangement (see the Introduction), we examined this possibility using WT and mutant ns Table    showed that duplicated large segments (DLSs) of different sizes and intensities with slower migration than S1 4127 bp) in agarose gel electrophoresis were observed approximately 40 days after co-culturing (a.c.) at a frequency of 70–100% , all subcultures carried DLSs in Δagl2. At this time point, there was a difference between Δagl2 and Δdcl2; while intact corresponding segments were completely replaced by DLSs in Δdcl2-infected MyRV1/S4ss , they were still visible in Δagl2 with lower band intensity . It was also noteworthy that two DLSs found in a single isolate at day 44  converged into one at 66 days a.c. Even at 120 days a.c. the gel profiles remained the same in the two fungal strains (data not shown). It should be noted that S10ss occasionally appeared below the S4ss band in some DLS-carrying strains after prolonged culture (data not shown). These results suggest that Δagl2 and Δdcl2 differ at least in the rate at which DLSs develop and in the robustness of replacement of normal unaltered segments.While DLSs were detected in both MyRV1/S4ss-infected Δdcl2 and Δagl2 via hyphal anastomosis, and a total of nine samples (three samples each from three independent co-cultures) per host were assayed by RT-PCR at six different time points . It is also noteworthy that only one of the DLSs  was observed in certain single fungal subcultures. Consistent with the results of agarose gel electrophoresis from previous experiments (data not shown), no DLS-carrying isolate was detected in WT EP155 even by this sensitive method (Supplementary Table S2).To further confirm this difference in the rate of the appearance of DLSs in different host fungal strains, we utilized a highly sensitive method originally developed by  for detedcl2 and Δagl2 supported the occurrence of DLS, the two strains differed in both their generation rate and possession of intact segments.These combined results indicate that while both Δdcl2 appeared stable when sub-cultured continuously for several weeks. We tested whether these DLSs are transferred and persist in the absence or presence of the dcl2 gene. For this purpose, DLS-carrying MyRV1/S4ss strains were transferred in parallel to EP155 and Δdcl2 host backgrounds via hyphal anastomosis three times at four-week intervals, and examined for the presence of rearranged segments . In contrast, the cognate segments of DLSs started to reappear or became dominant in some sub-isolates in the WT fungus . For example, S1Da (the rearranged segment) and S1  co-existed in most of the EP155 sub-isolates infected with MyRV1/S4ss+S1Da and some sub-isolates lost S1D (lanes 3 and 6), while no S1 band was seen in any sub-isolates of Δdcl2 (see top panel). The ratio of S1Da to S1 varied among EP155 subcultures when examined based on visual inspection of the band intensity. A similar trend was observed for MyRV1/S4ss+S3Da (bottom panel). All sub-cultured derivatives of Δdcl2 contained S3Da but lacked S3 (invisible in the agarose gel), while the intact S3 appeared in WT EP155 with ratios different to that of S3Da. MyRV1/S4ss+S2Db showed a different maintenance profile at the first co-culture. In all subcultures, both S2D and S2 co-existed stably regardless of the host fungal strains at this time point (middle panel). Interestingly, some EP155 sub-isolates lost S2Db completely in the second and third co-cultures (apparent reversion) . Similar fates were observed for S1Da and S3Da, but these were more frequently lost during further subcultures (data not shown).Supplementary Figure S6 shows representative dsRNA gel profiles for WT MyRV1, DLS-carrying and non-carrying MyRV1/S4ss obtained after the first co-culture. All DLSs were transferred along with MyRV1/S4ss and maintained in Δdcl2 gene. However, upon reintroduction (first co-culture) of DLS-carrying isolates into WT host strains and during successive subcultures (subcultures 2 and 3), DLSs tended to become a minor population or are lost, and cognate segments were restored in reverse proportions. These findings clearly indicate that absence of DCL2 is required for stable maintenance of DLSs associated with MyRV1/S4ss.The above results show that DLSs are stably maintained in the absence of the dcl2 and Δagl2 were chosen as host backgrounds for this assay because they maintain DLSs stably. It was found that S1Da transcripts much larger than intact S1 transcripts were detectable in MyRV1/S4ss+S1Da by an S1-specific probe   , deletioilencing . We inves Figure ,24 reporf DI-RNA ,24. Bujaf DI-RNA ,48. The dcl2 and Δagl2 infected with MyRV1/S4ss. No significant difference was observed regardless of three different temperature settings, i.e. 20°C, 25°C or 30°C. MyRV1/S4ss+DLSs appeared only in Δdcl2 and Δagl2 within the range of frequencies shown in Table In plants, RNA silencing operates in a temperature-dependent manner and plants grown at higher temperatures show higher RNA silencing activities . This tein vitro by RdRps from positive strand RNA viruses (The rearranged segments observed here are distinct from the rearrangements reported previously for reoviruses. The MyRV1/S4ss rearrangements found in RNA silencing-defective mutants have certain characteristics: (1) they all occur in the three largest segments, S1–S3, that encode structural proteins, i.e. RdRp (VP1), the major capsid protein VP2) and the capping enzyme (VP3) ,50 and  and the  for a re viruses . It has  viruses . The DLS viruses  contain cis- or trans-elements for packaging, assortment, replication, transcription and/or translation (PARTT)  . Inspect (PARTT) ,57. MyRV (PARTT) . Conside (PARTT) ,58. Rece (PARTT) ,19. It wT Figure .dcl2 and Δagl2) or suppressed (Twtp29) fungal strains, i.e. MyRV1 DLSs are stably maintained in those host strains .Extended segments of rotaviruses are known to be packaged preferentially over the corresponding parental segments such as segments 5, 7 and 11 ,59,60. OReovirus RNA synthesis is believed to occur within core particles or core-like particulates which engage the same number of RNA polymerase complexes as the genome segment number. It is anticipated that gene duplication events occur at the transcription step (multiple rounds of positive strand synthesis), not during replication (single round of negative strand synthesis) . A four-It remains unknown as to how RNA silencing deficiency or suppression is related to MyRV1 rearrangements. Our previous studies showed the frequent occurrence of MyRV1 rearrangements in transformants with the CHV1 p29 ,31. The in vivo interactions of CHV1 p29 and a MyRV1 non-structural protein VP9, as well as co-fractionation of p29 and the genomic dsRNAs (dcl2 or Δagl2? What is the molecular mechanism by which RNA silencing contributes to the suppression of MyRV1/S4ss genome rearrangement? What is the molecular basis of differences in the effectiveness of MyRV1 rearrangement generation and/or maintenance between Δdcl2 or Δagl2, and how does it fit into the model recently proposed for RNA synthesis of a reovirus based on the structure of RdRp 3D (We previously offered two hypotheses to account for the CHV1 p29-mediated generation of MyRV1 rearrangements. One is that p29 is directly associated with MyRV1 replication based on physical c dsRNAs . The othc dsRNAs  confirmsc dsRNAs . Another RdRp 3D ?Supplementary Data are available at NAR Online.


In eukaryotes, mRNAs are synthesized in the nucleus and then exported to the cytoplasm where they are translated into proteins. We have mapped an element, which when present in the 3’terminal exon or in an unspliced mRNA, inhibits mRNA nuclear export. This element has the same sequence as the consensus 5’splice site motif that is used to define the start of introns. Previously it was shown that when this motif is retained in the mRNA, it causes defects in 3’cleavage and polyadenylation and promotes mRNA decay. Our new data indicates that this motif also inhibits nuclear export and promotes the targeting of transcripts to nuclear speckles, foci within the nucleus which have been linked to splicing. The motif, however, does not disrupt splicing or the recruitment of UAP56 or TAP/Nxf1 to the RNA, which are normally required for nuclear export. Genome wide analysis of human mRNAs, lncRNA and eRNAs indicates that this motif is depleted from naturally intronless mRNAs and eRNAs, but less so in lncRNAs. This motif is also depleted from the beginning and ends of the 3’terminal exons of spliced mRNAs, but less so for lncRNAs. Our data suggests that the presence of the 5’splice site motif in mature RNAs promotes their nuclear retention and may help to distinguish mRNAs from misprocessed transcripts and transcriptional noise. In mammalian cells, intergenic transcription accounts for a large fraction of the total nascent RNA output, approximately equal to the amount of protein-coding RNA ,2. The vin vitro dATP (Perkin Elmer) and the Prime-a-gene labeling system (Promega). The probes were purified using a 1 ml syringe column lined with glass wool and Sephadex-G beads, denatured at 95 °C for 5 minutes, added to the membrane and probed overnight at 65 °C. The membrane was washed for at least four times in wash buffer , exposed on a phosphoimager cassette for 4–72 hrs and imaged with the Typhoon phosphoimager system.For Northern blotting, total RNA was isolated from transfected U2OS cells as for the RT-PCR experiment. The RNA was separated on a 1–1.5% agarose gel in 1X MOPS buffer  with 3% formaldehyde. Capillary action was used to transfer the RNA onto a nitrocellulose member using 20X SSC solution. The blots were pre-hybridized with Church buffer  for 4 hrs. Radiolabelled probes oligonucleotides specific for For all α-amanitin chase experiments, 1 mg/ml α-amanitin (Sigma) was added after 18–24 hrs following transfection and total RNA was isolated at various time points post drug treatment as indicated in the The UAP56 RNA-IP experiments were performed exactly as previously described , using a6 to 1 x 107 U2OS cells were co-transfected with 4.5 μg each of c-ftz-Δi +/- V5-His and FLAG-TAP plasmids  as previously described [cOmplete mini-protease inhibitor (Roche)) and gently mixed for 25 minutes at 4 °C to ensure complete lysis. The cell lysis was cleared by centrifugation at 16,100g for 10 min and 0.45 ml supernatant was mixed with 50μl ANTI-FLAG M2 Affinity Gel beads  pre-washed 3X times with IP wash buffer  and incubated for 2 to 2.5 hours at 4 °C. As a control experiment, the same amount of cell lysate was added to the same volume of unconjugated Protein A-Sepharose 4B beads (Invitrogen). Following incubation, the beads were washes 4 – 5X times with IP wash buffer and RNA was isolated using the Trizol precipitation as described before. To synthesize the first strand cDNA, a random hexamer primer (ThermoScientific) was used following the SuperscriptIII RT (Invitrogen) manufacturer’s protocol. qRT-PCR was performed as previously described in [For the FLAG-TAP RNA-IP experiment, ~ 6.75 x 10escribed . 18 to 2ribed in , using tSequences for protein-coding genes, 3’UTRs and intergenic regions were downloaded from the UCSD genome browser using Biomart. 3’exons for protein coding genes, and lists of genes with 3’UTR introns were obtained from Ben Blencowe and Ulrich Braunschweig (University of Toronto). Intronless protein-coding genes were downloaded from the intronless gene database . LncRNAsS1 Figβ-globin-Δi or β-globin-Δi-V5-His constructs. 18–24 hrs later cells were fixed and stained for β-globin mRNA, SC35 and DNA (by DAPI stain) as in β-globin mRNA in red and SC35 in green. Examples of nuclear speckles enriched in β-globin-V5-His mRNA are denoted by blue arrowheads. Scale bar = 10μm.U2OS cells were transfected with plasmids containing either (TIF)Click here for additional data file.S1 File(TXT)Click here for additional data file.


After a reconstruction of Hockett’s views on design features, we raise two criticisms: focus on the means at the expense of content and focus on the code itself rather than the cognitive abilities of its users. Finally, referring to empirical data, we illustrate some of the problems resulting from Hockett’s approach by addressing three specific points—namely arbitrariness and semanticity, cultural transmission, and displacement—and show how the change of perspective allows to overcome those difficulties.The set of design features developed by Charles Hockett in the 1950s and 1960s remains probably the most influential means of juxtaposing animal communication with human language. However, the general theoretical perspective of Hockett is largely incompatible with that of modern language evolution research. Consequently, we argue that his classificatory system—while useful for some descriptive purposes—is of very limited use as a theoretical framework for evolutionary linguistics. We see this incompatibility as related to the ontology of language, i.e. deriving from Hockett’s interest in language as a product rather than a suite of sensorimotor, cognitive and social abilities that enable the use but also acquisition of language by biological creatures (the  We see this incompatibility as deriving from Hockett’s interest in language as a product, leading to a “phenetic” classificatory system tracing superficial similarities. Specifically, we point to two underlying problems: language evolution and the reasons behind the sudden surge of interest it has generated. Next we discuss two fundamental reasons why Hockett’s lists of design features cannot be integrated with this perspective. Those reasons are illustrated with three examples of this incommensurability: the features of arbitrariness/semanticity, cultural transmission and displacement, whose discussion we ground in recent empirical data. We conclude by proposing an alternative approach to (what we take to have been) Hockett’s primary goal, i.e. capturing the difference between the communication of humans and non-human animals.We begin by introducing Hockett’s system, and then contemporary research on language origins, with emphasis on the field of Hockett’s reflection on the design features of language can be divided into three phases: the initial statement  and 1959A Course in Modern Linguistics, a linguistics textbook for college students. (Hockett [Hockett first discussed the design features of language in (Hockett  1967). Acode model of communication . In this particular respect, Hockett’s view aligns with the accounts of communication found in biological sciences, e.g. sociobiology  and consequences (behaviours caused by messages): “A message is displaced to the extent that the key features in its antecedents and consequences are removed from the time and place of transmission” , and “Logical considerations in the study of animal communication”  .The semantic : 142. FiThe introduction of the six new properties doesn’t alter Hockett’s conception of language and the way he defines it. In fact, all of them can be deduced from the old set of features. In the previous format comprised of the seven features, Hockett insists that linguistic behaviour prototypically manifests itself in the vocal auditory channel and—as already indicated—broadcast transmission/directional reception, rapid fading and to an important extent total feedback describe selected characteristics of this channel; whereas the features of semanticity and discreteness were previously subsumed under the discussion of duality. Even in “Logical considerations” , which cprevarication, reflexiveness and learnability—giving in total a list of sixteen features. In the 1966 account, Hockett uses the concept of design features as a platform to discuss language universals that pertain to extremely versatile properties of language, ranging from very general ones, such as the existence of a language in every human culture or the primacy of spoken language over its written form, to specific aspects of grammatical description, such as the presence of proper nouns in every language or the universality of distinctions in vowel quality.The format of 13 properties is—in the tertiary literature on language and linguistics—treated as the standard presentation of Hockett’s design features  is best described as a research area unified by a common goal: to explain the emergence and subsequent development of the species-specific ability of human beings to acquire and use language. It should be distinguished from both historical linguistics and a narrower notion of the evolution of languages , the latter being a quasi-evolutionary, long term historical change in modern-day linguistic systems  major issues: firstly, Hockett’s classificatory system It has been observed  is unsubstantiated in three related ways. Firstly, as is now well known, sign languages are fully equivalent to spoken ones in practically every relevant respect, including morphosyntax, dialectisation, historical change, rough cerebral localisation, and acquisition by children , far from remaining neutral, has ramifications for other aspects of how we define language. In the evolutionary context, it gives rise to two misconceptions. Firstly, it introduces an unfounded bias against looking for the origins of language in gestural communication. The gestural approach to language evolution inaugurated by Gordon Hewes , while nThus, focus on the physical characteristics of the medium of signalling is misplaced. Hockett’s features mentioned above may be useful descriptively in capturing interesting facts about speech  creates puzzling continuities, such as between humans and bees (see footnote 13), monkeys or gibbons (cf. Hockett 1960a: 10–11). Below, we exemplify how many of those problems disappear and the expected (human—great ape) continuities reappear when we turn our attention away from communication towards general cognition; and even within communication—away from the modality towards the content.A note is in order. In line with the majority view in language evolution, our text explicitly prioritises the “individualistic-internalistic” perspective. As we stated in section “functional reference  of numerous authors after Hockett. As is well known, the calls of vervet monkeys demonstrate a kind of referential specificity, termed unlike each other in most relevant respects. Finally, alarm calls are present in a number of nonprimate or even non-mammalian species  productivity  of intersubjective conceptual contents, i.e. of semantic information. This mechanism enables the so-called “ratchet effect” ”). But once again, when we shift the perspective away from communication, we find clear patterns of group-specific, culturally transmitted behaviours in chimpanzees: “chimpanzee cultures” have been widely recognised as real at least since the influential paper by Whiten et al. , a chronology of stepping stones (further development of those skills), and a plausible but falsifiable scenario of selection pressures that would have led to the achievement of those stepping stones. Crucial to the explanation are the social, cognitive and anatomical preadaptations (stepping stones) that are not directly visible in communication but are the necessary prerequisites. As mentioned above, the main areas whose investigation shows most promise for a better understanding of the sources and character of this uniqueness of language are: cooperation with non-relatives, shared intentionality, metarepresentation and Theory of Mind, mimesis and intentional imitation, enhanced memory and executive function, symbolic representation, open-endedness, and recursion—all of which are better understood as cognitive skills rather than features of language in the E-sense.In contrast, language evolution needs a primarily “internalistic” perspective, directly informed by modern evolutionary theory and social and ecological perspectives. An idealised “complete” evolutionary explanation for language would require an “LCA baseline” (socio-cognitive and anatomical skills of the last common ancestor of This paper should not be treated as a comprehensive evaluation of Hockett’s design features. For example, we do not question the descriptive value of his system, nor do we deny the productive applicability of individual features, such as “rapid fading”, to specific problems in language evolution studies (Galantucci et al.


The averaged minimum light intensity in coral reefs is 450 μmol photons m-2 s-1. The global area of potential reef habitats calculated by the model is 330.5 × 103 km2. Compared with previous studies, the tolerance limits for temperature, salinity, and nutrients have not changed much, whereas the minimum value of aragonite saturation in coral reef waters has decreased from 3.28 to 2.82. The potential reef habitat area calculated with ReefHab is about 121×103 km2 larger than the area estimated from the charted reefs, suggesting that the growth potential of coral reefs is higher than currently observed.Coral reefs are found within a limited range of environmental conditions or tolerance limits. Estimating these limits is a critical prerequisite for understanding the impacts of climate change on the biogeography of coral reefs. Here we used the diagnostic model ReefHab to determine the current environmental tolerance limits for coral reefs and the global distribution of potential coral reef habitats as a function of six factors: temperature, salinity, nitrate, phosphate, aragonite saturation state, and light. To determine these tolerance limits, we extracted maximum and minimum values of all environmental variables in corresponding locations where coral reefs are present. We found that the global, annually averaged tolerance limits for coral reefs are 21.7—29.6 °C for temperature, 28.7—40.4 psu for salinity, 4.51 μmol L They ach year . The fitach year ,8.th century, the global surface average temperature has increased by 0.74°C [In the last decades, many studies have documented the impacts of climate change on different coral reef ecosystems around the globe  . It is wy 0.74°C  and, cony 0.74°C . Besidesy 0.74°C –17. Othey 0.74°C –20. QuanOne of the first attempts to quantitatively predict the biogeography of coral reef global habitats on a global scale is represented by the works of Kleypas ,22 with Here we use the ReefHab model in combination with the latest available environmental data and high-resolution bathymetry to predict the present day potential reef habitats for coral growth at the global scale. Our predictions are then discussed in the context of the actual observations of coral reef occurrences. Finally, by using ReefHab in an inverse mode, we determine new suitable environmental limits for coral reefs.https://www.nodc.noaa.gov/OC5/WOA09/netcdf_data.html in netCDF format). Alkalinity and Dissolved Inorganic Carbon (DIC) at a 1° × 1° spatial resolution [http://cdiac.ornl.gov/ftp/oceans/GLODAP_Gridded_Data/ in netCDF format) are used to calculate the aragonite saturation state (Ωara) with the software CO2SYS [We use the diagnostic model ReefHab , which wOA) 2009 –25 at a solution   is determined using the equation:Imin is the minimum light intensity necessary for reef growth (in μmol photons m-2 s-1), PAR (in μmol photons m-2 s-1) is the average photosynthetically available radiation at sea surface, and K490 (in m-1) is the attenuation coefficient of light at wavelength 490 nm. Both PAR and K490 are from SeaWiFS Level 3 data (available at http://oceancolor.gsfc.nasa.gov/cgi/l3) and have a spatial resolution of 5’ × 5’. The model calculates Zmax in each 5’ × 5’ grid cell. This information is then used in combination with the high-resolution (30” × 30”) bottom topography data from the General Bathymetric Chart of the Oceans  to check for the light criteria. The smallest 5’ × 5’ grid cell of PAR and K490 data is therefore subdivided into 100 cells of 30” × 30” resolution to match with bathymetry.The maximum depth of reef growth  is generated  because water depth variations occur over small scales and exert a strong control over reef distribution. The spatial resolution of the environmental variables is much coarser (1° × 1°); however, these data do not vary considerably within their respective resolutions. The model results are finally presented on a 1° × 1° spatial resolution map and the percentage of potential reef habitat is calculated based on the percentage of positively evaluated 30” × 30” grid cells falling within a 1° × 1° grid cell.The model checks every 1° × 1° grid cell of the 360 × 80 matrix if temperature, salinity, nitrate, phosphate, and Ωenerated . Ωara is490 [Our results are compared with the works of Kleypas ,28, whic490  with spahttp://data.unep-wcmc.org/datasets/13 as DBF data, which we transformed in HDF). These observations have been compiled from a variety of sources. Deep and cold water corals are not included in this study. The majority of the data, 85%, originates from the Millennuim Coral Reef Mapping Project and are mapped at a 30 m resolution. Of this large data fraction, only 35% has been validated [The model results  are qualitatively compared against charted reef observations of the Global Distribution of Coral Reefs 2010 ,35,36  where observations showed the presence of reefs. We then considered the global maximum and minimum values of each environmental variable. These values represent the average environmental ranges for observed coral reefs and are later used with the model to predict the potential reef habitats, i.e. all those locations of the oceans, besides those already known from the observation, that can potentially host coral reefs.Imin) required for coral growth .The overlay of the observed reef locations with the GEBCO_08 bathymetry revealed inconsistencies between ocean depth and reef occurrence by showing the presence of coral reefs in waters deeper than 2000 m and up to 6000 m see . Such inl growth , therefoIn order to analyse how model performance changes when using different tolerance limits, we run the ReefHab model with the most recent environmental datasets (WOA 2009) but in combination with the tolerance limits of Kleypas, hereafter K97 tolerance limits. In addition, we compare and discuss our results against the tolerance limits later suggested by Kleypas et al. , hereaftFinally, our newly derived tolerance limits are determined on the basis of annual climatologies and do not take into account short-term  extremes. Although short-term disturbances can have lethal consequences for corals, it is the long-term  environmental condition that determines the presence/absence of coral reefs and that is relevant to our study. However, for comparability, we also calculated the tolerance limits on the same temporal scales (i.e. weekly and monthly) considered for deriving the K97 and K99 limits.The evaluation of the model performance consists of two major aspects: 1) the spatial pattern of the predicted potential reef habitats is compared with the observed coral-reef distribution on a 1° × 1° spatial resolution, and 2) the area of predicted potential reef habitats is compared with the area determined from observed reefs.In order to compare the distribution pattern of predicted potential reef habitats with the observed coral reefs, we produced a 360 × 80 matrix of ones and zeros for, respectively, the presence (when the percentage of reef habitat is above 0) and absence of coral reefs . A similar matrix was produced for the observed coral-reef distribution. By subtracting the matrix of predicted potential reef habitats from the matrix of observed coral reefs, we generated a spatial distribution matrix with -1, 0, and +1. The value -1 represents a false positive (FP), i.e. the model predicts a suitable reef habitat in a grid cell where coral reefs are not observed. The value +1 represents a false negative (FN), i.e. the model does not predict a suitable reef habitat in a grid cell where reefs are actually observed. The value 0 reflects a match between model results and observed reefs and represents both a true positive (TP) and a true negative (TN).Imin, we used the Receiver Operating Characteristics (ROC) graph [To evaluate the response of the model to changes in C) graph , by plot-1 (K97) to 4.51 μmol L-1 (this study) and the phosphate threshold has increased from 0.2 μmol L-1 (K97) to 0.63 μmol L-1 (this study). Note, however, that the K97 tolerance limits were initially based on values quoted in the literature and subsequently refined visually by comparing predictions of ReefHab with reef locations known at that time [ara threshold below which coral reefs disappear is 2.82, which contrasts with the value of 3.28 suggested earlier by Kleypas et al. [hat time . Later, hat time  determinImin was determined with a standard optimization technique. When Imin is increased from 50 to 450 μmol photons m-2 s-1, false positives decrease steadily from 742 to 413, whereas false negatives increase from 14 to 51  in the ROC graph, is obtained with Imin = 450 μmol photons m-2 s-1.The model response at different OC graph . Due to When ReefHab is run with the K97 tolerance limits, the model produces 473 false negatives. With our newly derived tolerance limits, false negatives are decreased to 51. Note that false negative model decisions could not be totally avoided due to the problems with bathymetry, as described in the . The strara) in combination with the K97 tolerance limits, and the observed coral-reef distribution. The model reproduces a reasonable general pattern of potential reef habitats in the tropical and subtropical ocean although with some exceptions. For example, the model overestimates the occurrence of coral reefs in the Mediterranean Sea and it underestimates the occurrence of reefs in the Red Sea and in the Persian Gulf. The model does not capture the coral reefs of the Indian Ocean, Seychelles, Chagos Archipelago, and Maldives. In Southeast Asia, the model underestimates the occurrence of some coral reefs in the Java Sea and the Flores Sea and over-predicts reefs in the central and western coasts of northern Australia. In the Pacific, the model does not capture some small reefs such as Johnston Atoll, Palmyra Atoll, Tuvalu, Howland Island, and Galápagos Island. The model, however, performs well in the Atlantic/Caribbean region although the occurrence of reefs is overestimated along the Brazilian coasts.Fig The predicted potential reef habitats obtained with the tolerance limits derived in this study  and 5C. 3 km2. The actual area where coral reefs are observed is about 209.5 × 103 km2 . For comparison purposes, we computed the new environmental tolerance ranges on the same time scales although, as explained in the Methods section, our focus lies on the annually averaged conditions that sustain coral reefs. The newly derived limits for temperature are similar to the K97 and K99 limits when using weekly data . On an aIn contrast to the temperature, the tolerance range for salinity that we obtained on a monthly basis is similar to the K99 limits, whereas it is wider than the K97 limits. When calculated on an annual basis, the range becomes narrower than when using the monthly data, because again the values are smoothed by the longer-term average. The upper limit is determined by the Red Sea, which has the highest salinities (up to 41.1 psu) of all ocean waters. Whereas the lower limit is determined by the Gulf of Thailand, which experiences salinity values as low as 25.4 psu during the rainy season. Such broad tolerance limits for salinity are consistent with evidence suggesting that corals’ metabolic performance is only weakly sensitive to changes in this variable ,42,49,50The annual thresholds for nitrate and phosphate that we obtained are up to three times higher than the K97 limits, but similar to the K99 limits . These hImin) in the range of 250–300 μmol photons m-2 s-1 by comparing the total reef area predicted by ReefHab with the estimate of Smith in 1978 [Imin = 450 μmol photons m-2 s-1 is a more plausible minimum light threshold for coral-reef habitat in today's ocean waters. This higher Imin value we found with respect to earlier works produces, consistently, a smaller total coral-reef area than the previous studies [Imin. Specifically, when Imin increases from 50 to 450 μmol photons m-2 s-1, the model predictions in terms of both coral-reef distribution patterns and habitat area become more accurate . With Imin = 600 μmol photons m-2 day-1, the potential habitat area is closest to the observations (209.49 × 103 km2 predicted vs. 209.68 × 103 km2 observed), but such a good match is obtained at the cost of a less accurate prediction of reef distribution patterns . The minimum light intensity necessary for coral reefs that we found here reflects, therefore, the best balance between distribution patterns and reef area or, in other words, between (1) false predictions, i.e. false positives and false negatives .Kleypas  estimate in 1978 . Newer e in 1978 ,52, whic studies ,51. We aegatives , and (2)egatives . Light per Imin  than Pav Islands . And eve Islands . Our stu Islands ,54, whicara of 3.28. This limit has been adopted in the literature as “the standard value” below which no reefs occur [ara ranges between 2.82 and 3.28. The total reef area in these waters is 8.82 × 103 km2, which accounts for about 4.2% of the global coral-reef coverage. Declining seawater pH due to the absorption of increasing atmospheric CO2, the process of ocean acidification, reduces carbonate ion concentrations and thus Ωara [ara [Oculina patagonica) can survive acidified conditions (minimum pH = 7.3) for up to one year, although without accreting calcium carbonate [Stylophora pistillata) can even calcify at Ωara values as low as 0.68 in the laboratory, albeit at rates lower than when subject to higher Ωara [ara = 2.9 [ara of 2.82 is found at the northern Great Barrier Reef, which is obviously covered by the GLODAP dataset.Kleypas et al.  suggestefs occur ,55. Our hus Ωara . Severalara [ara ,56–58. Iarbonate . Other sher Ωara . These era = 2.9 . Unfortuara as potential causes. Our results lend weight to their suggestion by showing that waters of the northern Great Barrier Reef are characterized by Ωara values close to the minimum threshold.De’ath et al. (2009)  showed tThe optimization of the environmental boundary limits for coral-reef habitats helped us to substantially reduce the number of false negatives  to only 51 occurrences . Therefo3 km2 [3 km2 [3 km2. Whether this lower value with respect to earlier estimates is an indication of a global decline in coral-reef cover or an improvement with respect to rather optimistic estimates is not easy to judge.A precise estimate of the global coral reef habitat area is important for understanding the potential impact of changing environmental conditions on coral-reef biogeography. Different estimates of global coral-reef coverage are found in the literature ,51,52,633 km2  to 1500 3 km2. The model, however, predicts the “potential” reef habitat, which is by definition an overestimation of the “real” coral-reef area. In addition, the ReefHab model predicts potential reef habitats only as a function of six physical and chemical environmental factors: 1) temperature, 2) salinity, 3) nitrate, 4) phosphate, 5) aragonite saturation state, and 6) light. Besides these environmental factors, the world’s coral reefs also face threats from a wide range of human activities, including coastal development, runoff of fertilizer from agricultural activities, physical damages from anchors and ship groundings, overfishing, and tourism. Omitting these difficult to quantify factors may also lead to an overestimation of potential reef habitats with respect to the actual observations. Uncertainties can also affect the actual reef observations. For example, although in some regions the presence of reefs is well known .(TIF)Click here for additional data file.S2 FigThe maps are on a 1° × 1° spatial resolution. Only considering temperature, given the definition of false positive, the red grid cell in false positive for temperature represents the area within the tolerance limits we set for potential reef habitats, but no observed reef found in that area. False positives contribute to overestimation of reef areas, for example, in Mediterranean Sea where non-reef corals there. The same way of interpreting the information for false negative, red grid cells contribute underestimation of observed reef, for example, due to unsuitable phosphate tolerance, lots of reefs are not captured by ReefHab in Indo-Pacific region.(TIF)Click here for additional data file.S3 FigThe same way of interpreting the information as in (TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.