Chlorurus microrhinos  were tagged and their internal temperatures were monitored for a 24 h period using active acoustic telemetry. At night, on the reef, C. microrhinos were found to maintain a consistent average peritoneal cavity temperature 0.16±0.005°C (SE) warmer than ambient. Diurnal internal temperatures were highly variable for individuals monitored on the reef, while in tank-based trials, peritoneal cavity temperatures tracked environmental temperatures. The mechanisms responsible for a departure of the peritoneal cavity temperature from environmental temperature occurred in C. microrhinos are not yet understood. However, the diet and behavior of the species suggests that heat in the peritoneal cavity may result primarily from endogenous thermogenesis coupled with physiological heat retention mechanisms. The presence of limited endothermy in C. microrhinos indicates that a degree of uncertainty may exist in the manner that reef fish respond to their thermal environment. At the very least, they do not always appear to respond to environmental temperatures as neutral thermal vessels and do display limited, but significant, visceral warming.Although a few pelagic species exhibit regional endothermy, most fish are regarded as ectotherms. However, we document significant regional endothermy in a benthic reef fish. Individual steephead parrotfish,  Marine fishes are largely regarded as ectotherms, incapable of metabolic thermoregulation, and therefore have body temperatures similar to the ambient water temperature Zebrasoma flavescensBalistes fuscus, B. vidua, Canthigaster jactator, Cromileptes altivelis, Forcipiger longirostris and Naso lituratusIn terrestrial environments, lizards and other ectotherms have been observed to behaviorally regulate internal temperatures and maintain their bodies within a narrow thermal range In contrast, a few fish species exhibit endothermy. In teleost endotherms, using physiological mechanisms, such as intricate counter-current blood flow systems, they are able to retain locally produced metabolic heat, thereby increasing internal temperatures Chlorurus microrhinos, the steephead parrotfish. Abundant and large, this parrotfish species is particularly important, contributing heavily to the ecosystem processes of bioerosion and algal removal C. microrhinos is a model example of a functionally important reef fish and one in which little is know about their thermal biology.Parrotfishes  are found throughout tropical and temperate reef habitats where they play a critical role in ecosystem processes on coral reef systems C. microrhinos by monitoring individuals' internal temperature relative to their ambient temperatures. More specifically, the internal temperature of C. microrhinos relative to that of its environment was evaluated over a full 24 h period to determine the nature and extent of thermoregulation on the reef and in aquaria. The basic question was: are parrotfish capable of regional endothermy in the wild?In this study, our aim was to assess the potential for regional endothermy in C. microrhinos were captured from the reef crest using monofilament barrier nets . Description of the study area is provided in Fox and Bellwood This study was conducted between April and June 2010 on Orpheus Island, Queensland, Australia , on the fringing reef of Pioneer Bay on the leeward side of the island. Adult C. microrhinos were anaesthetized in a tricane methanesulfonate (MS-222) seawater solution (0.13 gL−1) for approximately 60 s, until loss of equilibrium. At this time, the fork length  was recorded and an ultrasonic transmitter  was inserted into the peritoneal cavity of each individual through a small incision made at the mid-point between the pectoral fin base and the anus. Once tagged, incisions were treated with an antiseptic and closed using nylon sutures (Ethelon). All the methods utilized in the present study were approved by James Cook University Animal Ethics Committee (A 1321) and under the permit requirements for the Great Barrier Reef Marine Park Authority, permit number: G08/28894.1.Individual Tagged individuals were given a 24 h recovery period in a large 3,300 L (2460 mm diameter×700 mm height) flow-through tank, to minimize tagging effects and to give the incision time to heal. After 24 hrs, fish were inspected for any signs of infection. No infection was observed and all individuals displayed normal behavior.C. microrhinos in captivity and on the reef was obtained at 15 min intervals. Sampling occurred over a 24 h period to encapsulate a full diurnal environmental thermal regime. Data were obtained from the internal tag, using a directional hydrophone  and receiver unit . Environmental thermal data were collected from within the tank and on the reef using thermal data loggers (HOBO Water Temp Pro v2).The temperature of the peritoneal cavity of each Before thermal data from the environmental sensors and tags were used, all were calibrated against each other. Because of the small temperature ranges anticipated, extreme care was taken with cross calibration. Tags  and thermal data loggers (HOBO Water Temp Pro v2) were placed in a thermally homogeneous aquarium with mild water flow, uniform light exposure, and no bubbles on or around sensors. Each tag and sensor was then left to record the water temperature for at least 5 h. Pilot studies found no variation as a result of different calibration temperatures, however, short calibration periods (<15 min) were found to lack the required precision. After 5 h, results were compared and a calibration coefficient was generated for each tag or sensor, which was used to calibrate all subsequent thermal readings. This calibration was applied to the data and accounted for the small thermal variation (<0.09°C), which may arise due to differences in two different sensors used to record thermal readings.C. microrhinos were placed separately in large 3,300 L (2460 mm diameter×700 mm height) flow-through tanks. After the 24 h recovery period, temperatures were recorded over a full 24 h period for each individual.In order to obtain thermal profiles from fishes in a relatively stable thermal environment, three individual C. microrhinos were released into Pioneer Bay at the site of capture. Fish were then located using a 3.1 m kayak fitted with a directional hydrophone  during daylight hours and from a 4.6 m aluminum dinghy during the night. Individuals were tracked for a 24 h cycle, commencing at 06:00 hrs when they were located in their respective sleeping sites on the reef base, approximately one hour before the fish moved onto the reef. Thermal and positional data was recorded for each individual as they followed normal movement patterns throughout their home ranges To obtain thermal profiles from fish on the reef, a total of 5 tagged individual C. microrhinos. Environmental readings were taken at 15 min intervals, which correspond to the timing at which thermal data was collected from the fish. Location data from each individual was plotted in ArcGIS 9.0 to determine which of the thermal data loggers corresponded to the position of each C. microrhinos at the time when the temperature readings were taken. The thermal loggers were therefore in the same reef zone and within 15 m of the fish at each data recording. The majority of records were within 5 to 10 m. At night, however, thermal loggers were placed within 1 to 3 m of the known resting sites to ensure accurate environmental readings without disturbing the fish.Environmental temperatures were recorded on the outer reef flat , the reef crest (0.5 m), the subtidal reef crest (1.5 m), the reef slope (4.8 m), and the reef base (6.4 m) using thermal data loggers. Thermal data loggers were placed within the home range of the tagged individuals and were distributed according to the habitat utilization of C. microrhinos sleeping sites . Sleeping sites were confirmed by the presence of an intact mucous cocoon or a visual sighting of a sleeping C. microrhinos. Thermal variation was examined at 6 sleeping sites. At each site, data were recorded for a full 24 h period.The extent of within-habitat thermal variation also had to be evaluated to ensure that the 3 m proximity to fishes sleeping sites was sufficient to accurately represent the temperature within the fish's sleeping sites. To do this, following the main experiment, data loggers were placed in C. microrhinos and its environment, the peritoneal cavity temperature readings were subtracted from the immediate environmental temperature readings to yield a thermal difference value, to be used in subsequent analysis:To evaluate thermal differences between the peritoneal cavity of diffT values for each sampling period  in tanks and on the reef. To evaluate whether the average internal temperature difference (diffT) of individuals on the reef (n = 5) and in aquaria (n = 3) during diurnal and nocturnal samples differed from zero, a t-test for single means was used. The test was justified as the t-test is appropriate for small samples. A log10 transformation was applied to the nocturnal sample from the reef to meet the assumption of normality in the data.Diurnal sampling periods were defined as being between 06:30 hrs when fish became active and 17:45 hrs when fish returned to sleeping sites. Nocturnal sampling periods were defined as the sampling time between 18:00 hrs and 06:15 hrs when fish remained stationary in their sleeping sites. The amount of variability in reef-based diurnal and nocturnal samples was assessed using a coefficient of variance  calculated on the average C. microrhinos held in tanks remained near ambient temperatures  and was significantly different from zero . The fact that a significant difference was detected despite the potential for tag error emphasizes the fact that there is a significant discrepancy between the internal temperature of fish on the reef at night and their environment, and that this pattern is not an artifact of tag error.C. microrhinos sleeping sites was low. The temperature within sleep sites was consistently cooler (−0.07±0.004°C) than the sites immediately outside and within 5 m of sleeping holes Click here for additional data file.Figure S2Thermal profile 3 individual Chlorurus microrhinos held in tanks and the corresponding tank temperatures at 15 minute sampling intervals.(TIF)Click here for additional data file.Figure S3Assessment of the thermal microhabitat variability on the reef base between six parrotfish sleep sites, the area immediately outside sleeping sites and 5 m away from sleep sites over a 24 hour period.(TIF)Click here for additional data file.Table S1Mean relative liver weights of coral reef fishes (from Bellwood 1985).(DOC)Click here for additional data file.


The unspliced HIV-1 RNA is used as mRNA for translation of the Gag and Pol proteins and packaged as genomic RNA in the virion. In addition, HIV-1 produces more than 40 differentially spliced transcripts that encode the other viral proteins. Regulation of splicing is essential for the production of all unspliced and spliced viral RNAs. The 5’ leader region of the HIV-1 RNA contains the major 5’ splice site (5’ss) that is used in the production of all spliced RNAs. This splice donor (SD) region can fold a stem-loop structure and the stability of this hairpin may influence splicing by restricting the accessibility of the 5’ss for the splicing machinery. To test this hypothesis, the thermodynamic stability of the SD hairpin was varied through mutation and the effect on RNA splicing was analyzed.The SD hairpin in the HIV-1 leader RNA region was stabilized or destabilized through mutation in the context of the HIV-1 molecular clone LAI. The effect on virus replication was measured upon infection of the SupT1 T cell line. The mutations were also introduced into an LTR-luciferase plasmid. Upon transfection of these reporter constructs into 293T cells, the efficiency of splicing at the major 5’ss was analyzed by luciferase activity assays and RNA analysis.Both stabilization and destabilization of the SD hairpin reduced HIV-1 replication. The LTR-luciferase experiments demonstrated that stabilization of the SD hairpin reduced 5’ss usage, whereas destabilization increased splicing at this position.The stability of the SD hairpin controls the level of splicing at the major 5’ss. The wild-type SD hairpin structure inhibits 5’ss usage to a level that results in an optimal balance between unspliced and spliced RNAs for efficient virus replication.


The CLASH  technique captures RNA–RNA interactions by physically joining two RNA molecules associated with a protein complex into a single chimeric RNA molecule. These events are relatively rare and considerable effort is needed to detect a small number of chimeric sequences amongst millions of non-chimeric cDNA reads resulting from a CLASH experiment. We present the “hyb” bioinformatics pipeline, which we developed to analyse high-throughput cDNA sequencing data from CLASH experiments. Although primarily designed for use with AGO CLASH data, hyb can also be used for the detection and annotation of chimeric reads in other high-throughput sequencing datasets. We examined the sensitivity and specificity of chimera detection in a test dataset using the BLAST, BLAST+, BLAT, pBLAT and Bowtie2 read alignment programs. We obtained the most reliable results in the shortest time using a combination of preprocessing with Flexbar and subsequent read-mapping using Bowtie2. The “hyb” software is distributed under the GNU GPL  and can be downloaded from  Protein–RNA binding is essential for the biological function of most RNAs, and proteins accompany RNAs throughout their life cycle, from synthesis and processing to transport and degradation. Likewise, inter- or intra-molecular RNA–RNA interactions are fundamental to many processes including splicing, translation, and gene regulation. In particular, post-transcriptional regulation of gene expression mediated by miRNA molecules that base-pair with their RNA targets has been the focus of intense research efforts in recent years The development of CLIP (Crosslinking and Immunoprecipitation) We have recently described CLASH , a method for transcriptome-wide analysis of RNA–RNA interactions Whilst primarily designed for the analysis of AGO CLASH data, our programs can also be used to identify chimeric reads in other applications of CLASH, as well as for the analysis of CRAC, CLIP, and RNA-Seq data. The precise interpretation of the chimeras will depend on the dataset under consideration. For example, analysis of chimeric reads in RNA-Seq experiments has recently been used to identify a novel class of RNAs with regulatory potential, the circRNAs 2In a CLASH experiment, interacting RNA molecules are partially digested, ligated to each other at one end, ligated to 5′ and 3′ linkers, respectively at their other end, and then subjected to single-end high-throughput sequencing. The resulting reads contain, in the following order: part of the 5′ linker, the cDNA insert, and part or all of the 3′ linker a. Our biAfter preprocessing, we use local alignment tools to map the reads to a custom database of transcript sequences or to the genomic sequence of the organism concerned. Reads with two non-contiguous matches to the database are identified as chimeras, whereas reads mapped contiguously are discarded. Mapping reads to transcript sequences, rather than to the genome, makes it easier to reject chimeric cDNAs generated by natural splicing events, because spliced cDNAs from mature mRNAs will generally be mapped contiguously to known mRNA transcripts.After the chimeras are called, we fold them computationally to predict the exact pattern of basepairing between the interacting RNA molecules. Although folding algorithms cannot accurately predict the structures of long RNA molecules, they are nevertheless reliable in predicting interactions between short fragments of RNA, such as those identified by CLASH. We then use the folding predictions to annotate and classify the RNA–RNA interactions recovered.33.13.23.2.1hyb demultiplex in=data.fastq code=barcodes.txtwhere: in=file (or in=file.gz) contains FASTQ format reads; code=file contains barcodes used to multiplex samples. "hyb" commands are normally entered on one line, but long commands can be continued onto the next line using a '\' escape character at the end of a line. The escape characters are omitted from the printed examples.Depending on the experimental design, 5′ linkers may contain a variable-length barcode for sample multiplexing and a random nucleotide prefix for monitoring of PCR amplification artefacts . We trim3.2.2bona fide genomic sequences. We have tested two linker trimming programs, flexbar and fastx-clipper, and we adopted flexbar as default. Following 3′ linker trimming, we filter the reads by insert length and by nucleotide qualities. The “preprocess” task runs this part of the analysis:hyb preprocess qc=flexbar trim=30 len=17in=data.fastqwhere: qc=flexbar selects Flexbar preprocessing; trim=30 trims reads from 3’ end to Phred Qual=30; len=17 specifies minimum read-length 17.In a typical CLASH library, many of the recovered RNA fragments are shorter than the read length, resulting in part or all of the 3′ linker sequence to be included in the reads. Accurate 3′ linker removal is important to prevent mapping errors, but overly permissive criteria for linker recognition may result in the loss of 3.2.3hyb check in=data.fastqThe complexity of CLASH libraries typically ranges from a few hundred thousand to a few million cDNA molecules 3.33.3.1make_hyb_db genome.fastaIn the previously published analyses, we used the blastall program to find high-scoring local matches between the reads and a database of transcript sequences hyb detect align=bowtie2 in=data.fastq db=hOH7where: align=bowtie2 sets the read aligner to bowtie2; db=hOH7 selects the hOH7 Bowtie2 database.The default location of the reference database is specified by the HYB_DB environment variable, as described in Section 3.3.2To call chimeras, we examine local matches between each read and the reference database, starting with the highest-scoring hits. We record the top-scoring match, all matches with the same mapping score as the top match, and all matches for which the gap or overlap with the previously matched area of the read is at most 4 nucleotides (default). We then identify as candidate chimeras all pairs of recorded matches with at most 4 nucleotides gap or overlap. This procedure rejects any reads with a contiguous full-length match to the database, since the overlap criterion would not be met for such reads. Short gaps or overlaps are allowed: gaps can represent post-transcriptional modification of one of the interaction partners , whereas overlaps frequently occur if one or more nucleotides in the middle of the read can be mapped to either chimeric fragment. We found that the small ambiguity in ligation sites identified, which results from such overlaps, is well tolerated and does not influence downstream analysis.3.3.3hyb detect type=all pref=none hval=0.1 hmax=10gmax=4 in=data.fastq db=hOH7where: type=all sets the search for chimeras in all classes of transcripts; pref=none do not prioritize miRNA–mRNA (mim) hybrids; hval=0.1 sets threshold e-value for hybrid fragments; hmax=10 sets maximum number of mapped locations of hybrid bits; gmax=4 sets maximum length of gap/overlap between hybrid bits.The procedure above yields a list of all chimera calls compatible with the read sequence. To uniquely call each chimera, we apply a series of filters and selection criteria to this list. We select the best chimera call in the list, by considering, in turn, (1) the sum of the mapping scores of both fragments of the chimera; (2) optionally, the classes of transcripts to which the read was mapped , and (3) the total number of reads mapped to each of the transcripts that form the chimera. Mapping reads to a transcript database sometimes results in ambiguous assignment to several transcripts, usually originating from the same locus. To resolve the ambiguity, we rank transcripts by the total number of mapped reads, to create an alignment reference file. We then use this reference to assign ambiguous reads to the transcript with the largest number of mapped reads. To generate consistent chimera calls across a set of related experiments, we rank transcripts according to the total number of reads across all experiments, using a common alignment reference file. The following command line illustrates the available options for identifying chimera candidates and calling chimeras:For a test input file with 31 million reads (9 million unique reads), including >400,000 chimeras, the chimera-calling part of the analysis takes approximately 12 min on a single CPU core, and it uses 45 MB of memory, when run with default parameters.3.3.4hyb detect type=mim pref=mim in=data.fastq db=hOH7where: type=mim sets the search for miRNA–mRNA (mim) hybrids and bioinformatically extend miRNA and mRNA fragments; pref=mim prioritize miRNA-mRNA (mim) hybrids.At the last stage of chimera calling, for miRNA-target chimeras, we optionally extend the miRNA fragment to cover the entire miRNA sequence retrieved from our custom database, and we extend target coordinates by 25 nucleotides to make sure that the entire interaction site is included within the called region. This type of adjustment improves chimera calls for sequencing reads that do not cover the entire length of the chimeric cDNA. The same adjustment was used in the original AGO1 CLASH study Finally, all chimeras are written to a “.hyb” file, our gff-related format that contains sequence identifiers, read sequences, 1-based mapping coordinates, and annotation information for each chimera .3.4We use the mapped coordinates of chimeras to extract the corresponding transcript or genomic sequences, and we use these sequences for RNA folding analysis. We fold transcript sequences extracted from a database, rather than read sequences, because reads may contain crosslinking-induced substitutions and deletions We use UNAFold hybrid-min with default parameters to fold RNA, and we convert the hybrid-min outputs to the commonly used dot-bracket notation, as defined in the Vienna RNA package. Optionally, the RNAup program from the Vienna package can be used for chimera folding. The dot-bracket file is then annotated with RNA sequences and coordinates to facilitate subsequent analysis.hyb analyse fold=UNAfold in=data.fastq db=hOH7where: fold=UNAfold sets hybrid-min as the RNA folding program.Overlapping chimeras are then merged, similar to the bedtools merge operation 3.5hyb preprocess qc=flexbar trim=30 len=17 min=4 checkdetect align=bowtie2 word=11 analyse fold=UNAfoldin=data.fastq db=hOH7 id=bow_flex_UNAEach of the example “hyb” commands above can be executed separately or as one command-line for a single sample. For example the following command runs the entire analysis for a non-multiplexed dataset:hyb analyse in=data.fastq db=hOH7Hyb evaluates at runtime which tasks need to be performed for a given analysis, and it uses default parameter values where needed. The same analysis as above can be run using the minimal command line below:hyb demultiplex code=barcodes.txt preprocessqc=flexbar trim=30 len=17 min=4 check detectalign=bowtie2 word=11 analyse fold=UNAfoldin=data.fastq db=hOH7It is possible to run the entire “hyb” pipeline on a multiplexed sample file using the barcode file to identify each sample, as follows:hyb analyse in=data_comp_hOH7.blast format=blastalign=none id=newIt is also possible to reuse the mapping data from a previous run of hyb, and run only the chimera calling part of the pipeline:3.6The main outputs of hyb are: a file with coordinates of all the chimeras (“.hyb”), a file with the folding analysis of chimeras (“.viennad”), and a number of intermediate files that can be used when re-running parts of the pipeline with varying parameters, and for troubleshooting. Output file formats are described in the hyb documentation. The output files can be used to further analyse the patterns of RNA–RNA interactions. For example, in a previous study, we have used information from the “viennad” files for clustering of miRNA–mRNA interactions, and the information from the “hyb” files to analyse the sequence motifs found in the targets of particular miRNAs To facilitate downstream analyses, we provide several standalone scripts: hyb_merge for clustering chimeras, hyb2gff for converting a “hyb” file to the commonly used “gff” format (splitting each chimera into two entries), and enst2genome for converting from transcript to genomic coordinates. We refer the reader to the hyb documentation for additional information about these scripts.3.7HYB_DB=~/my_db_dir hyb in=reads.txt db=my_dbid=my_analysis analyseThe execution of hyb can be configured using ‘environment’ variables in the Unix/Linux interactive command-line ‘shell’  used to invoke the program. By default, hyb sets the environment variables internally, relative to the directory “HYB_HOME” where hyb has been installed. For example, the default location for hyb data files is “HYB_DATA” and the directory containing all the alignment databases is “HYB_DATA/db”. If a user prefers to use their own database “my_db” in their own folder “my_db_dir”, they can override the defaults using e.g.:export HYB_DB=~/my_db_dirhyb in=reads.txt db=my_db id=my_analysis analyseIn this case, the “HYB_DB” variable is only set for a single hyb run as specified on the command-line. The environment variable can also be ‘exported’ to the shell environment for all hyb runs done during the same login session using e.g.:man hybAlternatively, the hyb environment variables can be set automatically by adding the ‘export’ statements to a start-up file that is read at by the shell at login . A full list of the environment variables is available in the hyb documentation and can be obtained after installing hyb using the Linux “man” command:3.8make -f hybHyb is implemented as an executable ‘Makefile’ that orchestrates the execution of a number of internal and external programs such as the read aligners and RNA folding software. Internal scripts are written in Perl, Python and Awk. Although designed for managing the compilation of computer software from large and complicated source-code contained in many different files, GNU make is well-suited to the requirements of bioinformatics analysis, which involves the manipulation of large text files and their transformation into other formats. Many bioinformaticians are familiar with software development and routinely use Makefiles, which capture the provenance of their analysis. Makefiles consist of goals (or tasks) and rules about how to create one file from another according to its dependencies or prerequisites. The hyb pipeline was developed as a Makefile and could be invoked using the GNU “make” command:#!/usr/bin/make -rRfTo create an executable Makefile, the following line was used as the first line in a plain-text file:This invokes make with no built-in rules (-r) or built-in variables (-R) on the current script file (-f). The rest of the script is a conventional Makefile, but it is not necessary to invoke make explicitly on the command-line to run it. Although a minor change from the way that Makefiles are conventionally used, this makes hyb considerably more flexible and easier to parameterise.Make has been used in other bioinformatics pipelines. For example, the “PredictProtein” server 44.1We used the published AGO1 CLASH data 4.2We constructed an in silico microRNA–mRNA fusion dataset to evaluate the performance of hyb. 10,000 30-nucleotide segments of mature mRNAs were extracted from Refseq and concatenated with mature microRNA sequences from miRBase. 84–91% of chimeras were recovered by hyb using default parameters depending on the aligner used. Blastn recovered the fewest, and pblat the most chimeras. Most reads not recovered were seen to align to more than 10 entries so were excluded by hyb; this is due to multiple transcripts for some genes being present in the database. Additionally, several mRNA fragments had sequences flanking the breakpoint which matched the end of the microRNA they were concatenated to, giving the alignments a larger overlap than allowed by hyb with default settings.Conversely, when 50-nucleotide segments of mature mRNAs from Refseq were analysed, no chimeras were called. This was expected, since the 50-nucleotide segments are mapped contiguously to mRNA sequences in the database.4.3We first aimed to establish optimal parameters for read preprocessing by comparing two read trimming programs (fastx-clipper and flexbar), with a range of 3′ adapter (linker) length and base quality thresholds. Flexbar with a 4-nucleotide linker length cutoff and base quality cutoffs between 0 and 30 gave reliable results. Fewer chimeras were recovered using fastx-clipper with low linker length cutoffs , probabl4.4The choice of the mapping program had a large effect on the time of analysis . For a tWe also tested how the choice of mapping program influenced the degree to which the targets recovered for individual miRNAs are upregulated upon miRNA inhibition. We used two miRNA inhibition datasets: one with simultaneous depletion of 25 miRNAs 4.5We tested the impact of chimera calling options on the number and reliability of interactions recovered. Reliability was estimated from average folding energy, proportion of seed interactions, proportion of chimeras in which both fragments were directly adjacent in the read, and the extent of target upregulation upon mRNA depletion. Of all the parameters examined, the chimera e-value threshold  had the largest effect . Chimera4.6Applying hyb to the data from Helwak et al. 4.7We compared hyb with the TopHat2 fusion-search algorithm, the most widely used tool for detecting fusion transcripts due to chromosomal rearrangements 5bona fide RNA–RNA interactions have previously been found in sequencing data generated by CRAC Chimeric reads corresponding to In principle, hyb can also be applied to RNA-Seq data, and our initial analyses recovered significant numbers of chimeric sequences in RNA-Seq datasets. To assess the biological significance of such chimeras, we applied hyb to RNA-Seq data from HEK293 cells, used previously to discover circular RNAs


The rationality of a fuzzy choice function is a hot research topic in the study of fuzzy choice functions. In this paper, two common fuzzy sets are studied and analyzed in the framework of the Banerjee choice function. The complete rationality and bounded rationality of fuzzy choice functions are defined based on the two fuzzy sets. An assumption is presented to study the fuzzy choice function, and especially the fuzzy choice function with bounded rationality is studied combined with some rationality conditions. Results show that the fuzzy choice function with bounded rationality also satisfies some important rationality conditions, but not vice versa. The research gives supplements to the investigation in the framework of the Banerjee choice function. The rationality of a choice function is an important issue in the research of classical decision theory. The rationality is expressed by the existence of a preference relation with respect to which the decision behavior fulfills a condition of optimum. For a choice function the problem is whether or not it is rational. The rationality has been widely studied in the context of preference modeling and choice functions. To deal with this issue, various rationality conditions have been presented by a number of authors. Among these conditions, the most popular are the Weak Axiom of Revealed Preference (WARP) by Samuelson , the Str t-norms).Along with the development of decision sciences, it was often difficult for people to obtain accurate data because the complexity of the decision making system also increased. Moreover, the participation of human is indispensable to the decision making process. The preferences between alternatives are described by crisp preference relations, which have certain limitations. The fuzzy preferences were modelled in the study of choice functions by Orlovsky in 1978. He argued that sometimes it was difficult for a decision maker to state definitely the preferences between alternatives. Therefore, the degree of preference between alternatives can be reflected by a number in . Based on fuzzy preferences, Orlovsky proposed a choice function in fuzzy environment, which is named as Orlovsky choice function . From thC(S) = G” while the main conclusions in this paper are obtained based on the condition “C(S) = M.” (3) In , T, z) = T).Associativity: ∀x ∈ ,  T = x.Boundary condition: ∀A mapping T, z) or T) is denoted as T in this paper. A t-norm T is called continuous if it is continuous as a function on the unit interval. The three most important t-norms are the minimum, T = min, the Lukasiewicz t-norm, T = max, and the  product, T = xy.Here, X denote the nonempty finite set of alternatives. Let H denote the set of all nonempty, finite, and crisp subsets of X. F denotes the set of all nonempty fuzzy subsets of X with finite supports.Let C : H → F such that ∀S ∈ H,  Supp  C(S)⊆S, where Supp  C(S) denotes the support of C(S).A fuzzy choice function is a function S ∈ H,  x ∈ S, C(S) is a fuzzy set, and C(S)(x) represents the extent to which x belongs to the fuzzy set C(S) or the extent to which x is chosen in the set S. For example, let S = {x, y, z}; if C(S)(x) = 0.8, then it implies that the extent to which x is chosen in the set S is 0.8.For all X is a function R : X × X → .A fuzzy preference relation on x, y ∈ X, R represents the degree to which x is preferred to y. Moreover, the fuzzy relation R on X is reflexive if R = 1 for any x ∈ X; R is complete if R + R ≥ 1 for any x, y ∈ X; R is strongly complete if R = 1 or R = 1 for any x, y ∈ X; R is T-transitive if T, R) ≤ R for any x, y, z ∈ X; R is acyclic if R > R, R > R,…, R > R; then R ≥ R for n ≥ 2 and any x1, x2,…, xn ∈ X.Here, for all distinct Like in the crisp case, a fuzzy choice function can induce various fuzzy preferences, among which the fuzzy revealed preference is the most important one.C be a fuzzy choice function. The fuzzy revealed preference relation R on X is defined by ∀x, y ∈ X, R = maxS∣x,y∈S}{C(S)(x).Let S ∈ H, x, y ∈ S, R ≥ C(S)(x). Moreover, the fuzzy strict preference relation P and the fuzzy indifference relation I corresponding to R are defined as follows: ∀x, y ∈ X, P = max{0, R − R}; I = min{R, R}.From Rc denotes the weak order (⪰) in the crisp case and Pc is the asymmetrical part of Rc. In the crisp case, there exist certain connections between Gc and Mc ,  i, j = 1,…, n shows the preference degree between alternatives.A city plans to build a hydroelectric plant; two experts are invited to give some opinions regarding the three technical alternativesR23 = 0.8 represents the degree to which the expert prefers alternative y to alternative z. Denote by S the subset of the set of alternatives {x, y, z}. The fuzzy choice function resulting from R is computed byThe activity of one expert is materialized in the following preference matrices:x is chosen is less than z in the set of alternatives {x, z}, and therefore z will be chosen by the expert. However, the extent to which z is chosen is less than x in the set of alternatives {x, y, z}, and therefore x or y will be chosen by the expert. This implies that the expert's choices are inconsistent in different sets of alternatives including common alternatives. In other words, the fuzzy choices of the expert are irrational or bounded rational.From the degree of the expert's inclinations, the extent to which G and M are studied and analyzed, respectively. Results show that the conclusion about the nonemptiness for the crisp set Mc can be extended to the corresponding fuzzy set, but not for the crisp set Gc.The nonemptiness for fuzzy sets In a certain assumption, a fuzzy choice function can satisfy three expansion conditions meanwhile.G and M. The bounded rationality of fuzzy choice function is studied, and results show that the fuzzy choice function with bounded rationality can satisfy some rationality conditions combined with a certain condition. Compared with complete rationality, the conclusions under bounded rationality are only necessary conditions.The complete rationality and bounded rationality of fuzzy choice functions are defined, respectively, based on Many important rationality conditions have been put forward to study the rationality of fuzzy choice functions in different frameworks, and their relationships were also discussed. In this paper, two common fuzzy sets are studied and analyzed in the framework of the Banerjee choice function. Based on this, the concepts about complete rationality and bounded rationality of fuzzy choice functions are proposed, and some conclusions of this paper are as follows.The study on the rationality of fuzzy choice functions is a wide and deep research field. At present, the research on this subject is still in early stages, and it needs to be improved and perfected further. We only made a small part of the work, and the study on the bounded rationality of fuzzy choice functions still will be our research works in future.


The innate immune system faces the difficult task of keeping a fine balance between sensitive detection of microbial presence and avoidance of autoimmunity. To this aim, key mechanisms of innate responses rely on isolation of pathogens in specialized subcellular compartments, or sensing of specific microbial patterns absent from the host. Efficient detection of foreign RNA in the cytosol requires an additional layer of complexity from the immune system. In this particular case, innate sensors should be able to distinguish self and non-self molecules that share several similar properties. In this review, we discuss this interplay between cytosolic pattern recognition receptors and the microbial RNA they detect. We describe how microbial RNAs gain access to the cytosol, which receptors they activate and counter-strategies developed by microorganisms to avoid this response. When Janeway formulated the theory of pattern recognition in 1989, he proposed that host cells could sense microbial infection owing to receptors able to recognize invariant molecular structures defined as pathogen-associated molecular patterns (PAMPs). These patterns would be present in groups of pathogens, but absent in the host . TLRs coInitially thought to detect exclusively microbial derived ligands, PRRs were later shown to recognize host derived danger signals, which are released in response to stress conditions such as cellular damage or tissue injury . Under nIn contrast to cytosolic DNA, RNA sensing in the cytoplasm raises many questions on the mechanisms used by the innate system to specifically distinguish non-self-RNA from self-RNA. During infection, microbial RNAs share the cytosolic cellular compartment with several host RNA species, including messenger RNA (mRNA), transfer RNA (tRNA), ribosomal RNA (rRNA), microRNA, and other small regulatory RNAs. As a consequence, cytosolic sensors must display a high affinity for specific microbial features to avoid activation by host molecules that would otherwise elicit autoimmune responses. Despite this apparent challenge, efficient detection of foreign RNA in the cytosol is essential for innate immunity. During certain viral infections, RNA may be the only microbial PAMP produced throughout most of the replication cycle. Additionally, our laboratory previously showed that recognition of bacterial mRNA in the cytosol was critical to elicit a robust innate response against bacterial infection . FinallyIn this review, we summarize the current understanding of cytosolic RNA sensing. We describe instances in which microbial RNAs gain access to the cytosol, the PRRs they activate, their corresponding ligands and strategies developed by microorganisms to conceal their RNAs.RNA entry into host cells generally takes place during the first steps of a microbial infection. We distinguish four processes leading to the presence of microbial RNA in the cytosol of eukaryotic cells, where it can engage host PRRs Figure .A first process, observed during RNA virus infection, consists of viral genome release into the cytosol during the cell entry step of the replication cycle. Viruses can directly release their genome at the plasma membrane after binding to a receptor. Alternatively, they can be first internalized through endocytosis or macropinocytosis. Endocytosed virus particles will typically traffic through endosomal vesicles by actin-dependent and/or microtubule-dependent transport . SpecifiOther viral RNA PAMPs can be produced during viral replication. David Baltimore has defined a classification of viruses based on the mechanism of mRNA production . VirusesEscherichia coli could activate receptors in the cytosol after phagocytosis by macrophages  RNA, subsequently recognized by cytosolic RNA receptors , 19. Thiumophila , 19. AltThe best-studied cytosolic RNA sensors are the three members of RIG-I-like Receptors (RLRs), a subfamily of the DExD/H-box family of helicases. They consist of retinoic acid-inducible gene I (RIG-I), melanoma differentiation factor 5 (MDA5), and laboratory of genetics and physiology 2 (LGP2). They share a similar organization with three distinct domains: (i) a C-terminal repressor domain (RD) embedded within the C-terminal domain (CTD); (ii) a central ATPase containing DExD/H-box helicase domain able to bind RNA; and (iii) a N-terminal tandem CARD domain that mediates downstream signaling, and which is present in RIG-I and MDA5 but absent in LGP2. Upon activation by RNA ligands, RIG-I and MDA5 are subsequently recruited to the adaptor protein Mitochondrial Antiviral Signaling (MAVS) via a CARD–CARD interaction and lead to activation of NF-κB and IRFs –23. In cThe RIG-I ligand has been characterized as an RNA molecule with two distinct features: (i) a 5′-ppp moiety , 27 and Mononegavirales produce defective interfering (DI) viral genomes containing panhandle structures that activate RIG-I in infected cells ssRNA viruses , 39 or sed cells .E. coli have a 5′-ppp  as a synthetic dsRNA mimic, studies have shown that MDA5 binds long, but not short dsRNA , 40, 48.Melanoma differentiation factor 5 detects infection by viral families known to produce long dsRNA structures during their replication cycle, including (+)ssRNA viruses like picornaviruses, dsRNA viruses like reoviruses, or DNA viruses like poxviruses , 50–53. Prior to the structural study mentioned above, multiple observations raised the possibility that there may exist additional MDA5 ligands, different from the consensus long dsRNA. Thus, a study has shown that MDA5 cooperates with the ribonuclease RNase L to induce IFN-I in response to a viral mRNA from parainfluenza 5 virus . InteresAfter binding to their specific ligands, both RIG-I and MDA5 activate MAVS to trigger a common signaling pathway. The majority of MAVS is localized on the mitochondrial membrane and its engagement by RLRs causes a conformational change that propagates to adjacent un-activated MAVS in a prion-like behavior . The forFinally, MAVS has been recently shown to interact with NOD-like receptor family, pyrin domain containing 3 (NLRP3) and promote its recruitment to the mitochondria. The authors emphasize the central role of MAVS in innate immune signaling events by showing its importance in the functioning of NLRP3 inflammasome and the production of IL-1β . Of noteThe third member of RLRs, LGP2, is able to bind dsRNA , 64, howApart from RLR, several recent studies have highlighted the importance of other DExD/H-box helicases in microbial RNA sensing. RNA helicases of the DEAD box family are involved in various different steps of RNA metabolism [reviewed in Ref. ]. They sDDX3 (DDX3X) can bind poly(I:C) or vesicular stomatitis virus (VSV) RNA and was shown to enhance the IFN-I response to VSV infection by interaction with the RLR-MAVS complex. Overexpression assays suggest that DDX3 precipitates with RIG-I and MDA5 . Since DUsing overexpression and knock-down experiments, DHX9 was shown to be required for the production of IFN-I and pro-inflammatory cytokines in response to poly(I:C), influenza virus, and reovirus by a murine splenic DC line and bone-marrow derived DCs. DHX9 can bind dsRNA via its dsRNA-binding motif and interact with MAVS through both its helicase C-terminal domain and HA2-DUF .Myeloid DCs have also been shown to express a complex composed of DDX1, DDX21, and DHX36 that triggers an antiviral program in response to poly(I:C), in a pathway dependent of the adapter molecule TIR-domain containing adapter-inducing interferon-β (TRIF). DDX1 binds to poly(I:C) via its helicase A domain, while DHX36 and DDX21 bind the TIR domain of TRIF via their HA2-DUF and PRK domains, respectively. This complex seems to be required for the innate response against influenza or reovirus infection . NotablyAnother recent study by Yong-Jun Liu’s group identified another helicase, DHX33, as a cytosolic RNA receptor able to activate the NLRP3 inflammasome . DHX33 iin vitro, independently of the 5′-ppp , which is important for antiviral activity. PKR is activated by dsRNA from viruses and is a component of MAPK and NF-κB signaling pathways [reviewed in Ref. ]. Activain vitro transcribed oligonucleotides, crystal structure studies have demonstrated that IFIT proteins contain a positively charged cavity designed to engage, without any particular sequence specificity, ssRNA with a 5′-ppp end. Contrary to RIG-I, IFIT proteins cannot bind blunt-ended 5′-ppp dsRNA, and owing to the limitations imposed by their RNA-binding pockets, IFIT1 and IFIT5 require 5’-overhangs of at least 5 or 3 nt, respectively (Proteins from the Interferon-induced protein with tetratricopeptide repeats (IFITs) family, such as IFIT1 and 5, bind 5′-ppp of viral RNA . Using sectively .O-methyltransferase mutant of Japanese encephalitis virus, another study showed that IFIT1 preferentially binds to 5′ capped 2′-O-unmethylated mRNA  is a member of the NOD1/Apaf-1 family and encodes a protein with two CARD domains and six leucine-rich repeats (LRRs). NOD2 is primarily known for its ability to recognize bacterial peptidoglycan, but it also plays a role in the antiviral response. NOD2 has been shown to activate MAVS after stimulation with viral ssRNA or human respiratory syncytial virus infection . NLRP3 iL. monocytogenes in macrophages (Ifnb1 promoter.Leucine-rich repeat flightless-interacting protein 1 (LRRFIP1) contributes to the production of IFN-β induced by VSV and rophages . Mostly rophages . LRRFIP1in vitro transcribed RNA sequences into the cytosol of a reporter cell line, suggesting a potential role for a cytoplasmic RNA sensor ssRNA viruses edit the 5′-ppp moieties in their genomes as well as replication intermediates into 5′ mono-phosphates to avoid recognition by RLRs . Arenavind IFITs , 85. Connd IFITs , which pnd IFITs , 86. Simnd IFITs , 105.Flaviviruses and other viruses are also known to induce cellular membrane reorganization that allows them to replicate in subcellular compartments, creating new replication-dependent organelles . Thus, tThe NS1 protein from influenza virus can prevent RNA sensing through the formation of a chain of NS1 molecules along the influenza dsRNA backbone . PicornaThe multiplicity of PRR pathways is an essential determinant of the immune system’s ability to sense with precision the level of microbial threat and to respond accordingly . HoweverThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


RNA-seq is a next generation sequencing method with a wide range of applications including single nucleotide polymorphism (SNP) detection, splice junction identification, and gene expression level measurement. However, the RNA-seq sequence data can be biased during library constructions resulting in incorrect data for SNP, splice junction, and gene expression studies. Here, we developed new library preparation methods to limit such biases.A whole transcriptome library prepared for the SOLiD system displayed numerous read duplications (pile-ups) and gaps in known exons. The pile-ups and gaps of the whole transcriptome library caused a loss of SNP and splice junction information and reduced the quality of gene expression results. Further, we found clear sequence biases for both 5' and 3' end reads in the whole transcriptome library. To remove this bias, RNaseIII fragmentation was replaced with heat fragmentation. For adaptor ligation, T4 Polynucleotide Kinase (T4PNK) was used following heat fragmentation. However, its kinase and phosphatase activities introduced additional sequence biases. To minimize them, we used OptiKinase before T4PNK. Our study further revealed the specific target sequences of RNaseIII and T4PNK.Our results suggest that the heat fragmentation removed the RNaseIII sequence bias and significantly reduced the pile-ups and gaps. OptiKinase minimized the T4PNK sequence biases and removed most of the remaining pile-ups and gaps, thus maximizing the quality of RNA-seq data.This article was reviewed by Dr. A. Kolodziejczyk (nominated by Dr. Sarah Teichmann), Dr. Eugene Koonin, and Dr. Christoph Adami. For the full reviews, see the Reviewers' Comments section. Next generation sequencing is revolutionizing biological data acquisition. It can be used instead of many existing specialized measurement approaches. For example, one of the next generation sequencing techniques, RNA-seq, has replaced microarrays in nearly all application areas. Microarrays detect gene expression levels based on known probes. By contrast, RNA-seq does not have this limitation. It can measure expression levels in any genomic region. In addition, it also provides information about SNPs and the location of splicing junctions -3.However, one disadvantage of RNA-seq is that its sequence data can be biased by library construction. For example, mapped read counts can over or underestimate true RNA abundances based on a variety of library construction steps, such as reverse transcription, adaptor ligation, or amplification -7. In a RNA-seq library construction methods vary among sequencing instruments. ABI SOLiD, Illumina HiSeq, and Roche 454 are three major RNA-seq sequencers. They have their own library preparation methods, and other companies also developed library construction methods for the sequencers. Furthermore, customized methods have been designed based on researchers’ interests, for example, RNA-editing or poly(A) tail studies ,10. In pIn this study, we compared different whole transcriptome libraries to each other as well as to a library specific to the human GABAB1 gene. There was substantial difference between the two library preparations. For the gene specific library, complementary DNA (cDNA) was fragmented after gene specific reverse transcription and amplification, and adaptors were ligated to the fragmented cDNA  and transfer RNAs (tRNAs). After filtering, the sequence data of the gene specific and whole transcriptome libraries were mapped against human reference genome, hg18, and visualized at the GABAB1 gene Figure .For each step, the remaining read numbers were calculated  fragmentation was performed with sonication, and adaptors were ligated to the fragmented dscDNA using DNA ligase Figure a. SonicaBy contrast, the whole transcriptome library construction method used RNaseIII fragmentation at the very beginning of the protocol Figure b. PerhapWe used RNA ligase for adaptor ligation. RNA ligase prefers to bind to certain phosphate donors, but preference patterns have been studied only for short oligomers . In our The bias for reverse transcription has been previously studied for the Illumina RNA-seq system . The ranFinally, amplification is known to have GC bias . This biTherefore, we excluded the three steps adaptor ligation, reverse transcription, and amplification as major sources of 5′ end sequence bias of the whole transcriptome library. We next focused our efforts on RNaseIII. RNaseIII can digest ssRNAs of preribosomal RNA and bacteriophage T4 ,14. HoweBecause RNaseIII specifically recognizes RNA secondary structure and leaves a unique sequence in the short fragments, it is not likely to be a perfectly random cutter. Therefore, the long fragments could have an RNaseIII specific sequence pattern. On the basis of this reason, we replaced RNaseIII fragmentation by an alternative method using heat.We fragmented RNA at 95°C when it was denatured. In denatured RNA the hydrogen bonds are broken and only covalent bonds remain. Denatured RNA has no secondary structure. Therefore, each base can be attacked equally by heat, and we do not expect that heat fragmentation can introduce 5′ end bias.However, heat fragmented RNA could not be used directly for adaptor ligation. The 5' and 3' ends of the heat fragmented RNA needed to be modified. We used T4PNK for the modification  and in the measurement of expression levels. Even though overall gene expression levels were probably not significantly affected by the pile-ups and gaps, a more precise expression analysis, e.g. a comparison of expression patterns among highly expressed short genes and exons, could be significantly affected by these biases.RNA-seq based specific cutting sequence identification may provide useful applications in the prediction of miRNA genes. The proteins Drosha and Dicer belong to the RNaseIII family and produce miRNAs . SimilarWe have found that the standard RNA-seq protocol, SOLiD™ Total RNA-Seq Kit, has substantial sequence biases and pile-ups in mapped reads. The biases could be tracked to RNaseIII activity which preferentially cuts at specific locations. Other steps in library preparation, specifically RNA ligation, reverse transcription, and amplification, seemed to be free of bias in the RNA-seq preparation method. By changing fragmentation from RNaseIII to heat the sequence bias was largely eliminated, and the vast majority of the sequence pile-ups disappeared. Therefore, heat fragmentation was superior to commercial RNaseIII methods when preparing RNA-seq.Among the various methods of library preparation we studied, Heat + OptiK was overall the least biased. However, while vastly improved over the manufacturer's protocol, it is not a completely unbiased method. Entropy calculations and sequence logos showed small amounts of bias near the beginning of both 5′ and 3′ reads. Some of the remaining biases at fragment ends seem to be caused by T4PNK. The origins of any other minor deviations from complete randomness are not clear. They may be due to any combination of the other steps in library preparation, i.e. RNA ligation, reverse transcription, PCR amplification, and so on. Unfortunately, it is impossible to exclude all of the possibly biased steps from RNA-seq library preparation due to current technical limitations. Therefore, the individual steps should be optimized or substituted for less biased steps.Total RNA from human prefrontal cortex was obtained from Ambion . RNA quality was evaluated using the Agilent 2100 Bioanalyzer RNA Nano Chip . After treating samples with DNase , rRNA was depleted from total RNA using the RiboMinus kit . rRNA depletion was verified by the Agilent 2100 Bioanalyzer RNA Nano Chip. Whole transcriptome libraries were prepared using the SOLiD™ Total RNA-Seq Kit  according to the manufacturer’s protocol for SOLiD™ 4 System.To prepare a gene specific library, gene specific primers for reverse transcription and amplification were designed at the 3′ UTR of human GABAB1. The primers were designed using Primer3  and testGUSB) were used for endogenous controls. For each amplification cycle, gene specific amplification levels were calculated from both endogenous controls. Among amplification cycles, a cycle was selected as soon as both gene specific amplification levels reached plateau. The selected cycle was the same as the optimal amplification cycle chosen from the gel electrophoresis data.From the gel electrophoresis data, we optimized the number of amplification cycle based on band intensity. As soon as the band intensity reached maximum, the optimal amplification cycle was reached, and we chose that cycle number for further analysis. The chosen cycle had the lowest level of non-specific PCR products among the amplification cycles containing maximum levels of GABAB1 gene specific PCR products. The optimal amplification cycle was verified with quantitative real-time PCR experiments using TaqMan® Gene Expression Assays . Based on a previous GABAB1 study , the priGel extraction removed small amplification products and the primers for reverse transcription, template switching, and amplification. According to the SOLiD™ 3 System Library Preparation Guide , the remaining dscDNA was sonicated to about 200 bp length, and the gene specific library was prepared for SOLiD sequencing.The whole transcriptome and gene specific libraries were sequenced using the SOLiD system. For each library, TopHat was used for sequence mapping against hg18 . To visuA fraction of the reads we obtained mapped uniquely to a single location in the genome while another fraction mapped to multiple locations. To include the reads that mapped to multiple locations in our analysis without artificially inflating expression counts, we assigned each multiple mapped read to exactly one mapped location, chosen at random. Whether we included or excluded multiple mapped reads did not qualitatively alter the conclusions we could draw from our experiments (data not shown).PA, PT, PG, and PC represent the relative abundances of each of the 4 bases . Among them, a random sample of 10,000 reads was used as input for sequence logo  [From all the mapped reads, we calculated entropy (pattern) ,25. For As computational controls, for each library we computationally generated random exome sequences. Based on mapped read locations and splicing junction data, we predicted a possibly existing 200 bp cDNA sequence in the RNA-seq library for each mapped read. We randomly took continuous 50 bp or 35 bp sequences from the individual 200 bp cDNA sequence. For entropy calculations, we generated these control reads 1000 times. However, WebLogo cannot take multiple sequence data inputs to generate a single sequence logo. Therefore, we generated random sequences without iteration for the sequence logo study.We evaluated two alternative heat fragmentation methods. Under the first method, 300 ng rRNA depleted total RNA was added into nuclease free water  up to 4.5 ul. Using a PCR machine, the RNA containing 0.2 ml tube was incubated at 95°C for 80 minutes. Fragmented RNAs were verified using the Agilent 2100 Bioanalyzer RNA Nano Chip. The size of RNA fragments was around 200bp. For the second method, we performed heat fragmentation in buffers containing divalent ions following a previous study  and otheAfter heat fragmentation, RNA ends had to be modified to allow adaptor ligation. We used T4PNK  to add phosphate groups to 5′ ends and leave hydroxyl groups to 3′ ends. Adaptor ligation and subsequent steps of library construction were identical to the manufacturer′s protocol of the SOLiD™ Total RNA-Seq Kit .To minimize known sequence bias of T4PNK, we introduced an additional treatment step to the heat fragmentation library construction. After heat fragmentation, we used OptiKinase  to add phosphate groups to the 5′ ends of fragmented RNAs. Subsequently, we proceeded with T4PNK as before to leave hydroxyl groups to the 3′ ends of fragmented RNAs.For all library constructions except gene specific library construction, we used the same PCR cycle for amplification to minimize amplification artifact variation. We prepared mostly two more replicate libraries to confirm the sequence bias patterns. For all whole transcriptome and gene specific libraries, we did the same sequence mapping and visualization.The existence of biases caused by preparation of sequencing libraries is well known. In previous studies, several issues were investigated, such as GC content or length bias due to PCR amplification. This paper shows that RNase III is a source of 3’ and 5’ bias, which was not known before and is complementary to previous research.In the paper the authors show a comparison of a gene-specific library constructed using primers specific for the gene’s 3’ UTR and a whole transcriptome library. One can see that the gene specific library has significantly less ‘pile ups’. The authors suggest that the reason may be read duplication under SOLID sequencing due to 5’ bias in the transcript. As the libraries were constructed using very different methods, one cannot conclude the precise causes of the differences between them.In the results section, it is mentioned that different primers were used for construction of the libraries, but the fact that authors used completely different protocols to create them is omitted. As stated in the paper, the method of library preparation is crucial for any biases that may occur, so the authors should make it clearer that substantially different methods were used to create these libraries.Author’s response: Thank you for pointing this out. We have revised the first section of Results so that it now clearly spells out the substantial difference between the preparation methods used for the gene specific library and the whole transcriptome libraries.It would also be informative to compare not only how reads map to GABAB1 using gene-specific and whole transcriptome libraries, but also libraries done using improved protocols.Author’s response: We have expanded Results and Methods sections to include this information.Subsequent experiments show that biases introduced by enzymes cause pile ups. The authors systematically test enzymes used in library preparation for 3’ and 5’ biases. They identify the cause of these biases and show alternative method to minimize them in a very straightforward and clear way.This discovery will help scientists to construct sequencing libraries for the SOLID system. It may also be used to improve methods for computational bias correction in the data. Additionally it also gives a basic answer about the sequence preference of RNase III.In the discussion it is suggested that as Dicer and Drosha are RNase III family enzymes, the knowledge of sequence preference of RNase III will help to improve miRNA prediction accuracy. Since these proteins do not belong to the same subgroup within RNase III family, it is very unlikely that they share the same cutting site. The recognition sequences of Drosha and Dicer have been studied, and it was shown that it is the 5’ end of pre-miRNA that is recognised by Dicer .Author’s response: We appreciate these comments. We have revised the Discussion accordingly, and we now emphasize that a study similar to ours could be performed to improve our understanding of the sequence specificity of Drosha and Dicer.To sum up, this paper shows a good set of experiments that in thorough way test the effect of enzymes used in library preparation on the 5’ and 3’ biases.This manuscript addresses a methodological problem of great importance, namely the sequence biases of the RNA-seq method. In the Introduction, the authors note that "For example, one of the next generation sequencing techniques, RNA-seq, may ultimately replace microarrays" which I think is an understatement. The RNA-seq method has already superseded microarrays for the study of gene expression and is extremely widely used of other purposes as well. Thus, the issue of biases looms large. The results presented here are potentially important as the authors detect inherent biases associated with RNAse III cleavage and the show that the extremely simple method of heat fragmentation (combined with T4 kinase treatment) alleviates the bias.Author’s response: We have revised the statement on microarrays to better reflect the current state of the field.I do not have any problems with the technical aspects of the article although I have to admit that I am familiar with RNA-seq only at the level of data analysis not data generation. What I am somewhat unhappy with, is the following final sentence of the RESULTS section of the abstract: "The identification of RNaseIII target sequences could improve microRNA (miRNA) gene prediction accuracy if other members of the RNaseIII family, including Drosha and Dicer, also have specific target sequences". And then, the final sentence of the CONCLUSIONS, also in the abstract: "This is the first application of RNA-seq to discover unknown enzyme target sequences." This theme does not show again until the Conclusions in the main text, and as such, these statements in the abstract, however interesting in principle, seem a sort of non-sequitur. The if in the first of the quoted sentences, regarding the specificity of Dicer and Drosha is a huge IF, and the claimed application of RNA-seq to target identification is not really developed in the article. In my view, it would be much better to either develop these ideas more carefully, at least in the Discussion, or drop them altogether. The article is useful and potentially important without this excursion into a somewhat different area.Author’s response: We agree with this concern. We have removed this topic from the Abstract, and we have modified the Discussion as explained in our response to Dr. Kolodziejczyk.This reviewer provided no comments for publication.SNP: Single nucleotide polymorphism; UTR: Untranslated region; cDNA: Complementary DNA; rRNA: Ribosomal RNA; tRNA: Transfer RNA; dscDNA: Double stranded complementary DNA; ssRNA: Single stranded RNA; bp: Base pairs; T4PNK: T4 Polynucleotide Kinase; miRNA: microRNA; ORF: Open reading frame; sscDNA: Single stranded complementary DNA.The authors declare that they have no competing interests related to this paper.CL conceived research and designed experiments; CL and JKW conducted experiments; CL and COW designed computational and analytical methods; CL analyzed data; CL drafted the manuscript; CL, COW, RAH, and RDM contributed to final manuscript writing and its revision; All authors read and approved the final manuscript for publication.Mapping statistics of RNA-seq libraries.Click here for fileRead pile-ups and gaps of the whole transcriptome library mapping. The pile-up mapping patterns and gaps were identified for several genes , ACTB, PPIA, GAPDH, and PGK1. They are shown in (a), (b), (c), and (d), respectively.Click here for fileSequence bias pattern without pile-up reads. After removing pile-up reads from mapped reads of whole transcriptome library and Ctl, their sequence bias patterns of RNaseIII were assessed. (a) Sequence logo and entropy were calculated after filtering out pile-up reads from whole transcriptome library mapped reads. (b) and (c) For Ctl we also did sequence logo and entropy analysis without pile-up reads. (b) is for 5’ reads, and (c) represents 3’ reads.Click here for file


Translation initiation of picornavirus RNA is driven by an internal ribosome entry site (IRES) element located upstream of the initiator codon. RNA structure organization as well as RNA-protein interaction plays a fundamental role in internal initiation. IRES activity has been mainly analyzed in the context of reporter genes, lacking regions of the viral genome potentially affecting translation efficiency. With the aim to understand the vulnerability of the IRES and translation start region to small molecules in the context of the viral genome, we designed a set of customized RNase-resistant 2′O-methyl antisense oligoribonucleotides (2′OMe AONs) based on RNA structure data. These AONs were then used to monitor their capacity to interfere viral RNA translation, and thus, to inhibit virus yield. Foot-and-mouth disease virus (FMDV) RNA translation can be initiated at two in-frame AUG codons. We show here that a 2′OMe AON complementary to AUG2 inhibited viral multiplication more efficiently than the one that targeted AUG1. Furthermore, the response of the viral RNA to AONs targeting the IRES region denoted important differences between tissue culture cells and cell-free systems, reinforcing the need to analyze viral RNA response in living cells. Importantly, we have identified four specific motifs within the IRES element that are targets for viral inhibitors both in tissue culture cells and in cell-free systems. The identified targets define accessible regions to small molecules, which disturb either the RNA structural organization or the RNA-protein interactions needed to initiate translation in FMDV RNA. Picornaviridae family. FMDV causes a contagious disease that affects cattle and a large number of wild cloven-hoofed ungulates 7GpppN) typically present in cellular mRNAs at its 5′ end. Instead, a viral protein (VPg) is covalently linked to the 5′end of the viral RNA cis-replication element (cre), and the internal ribosome entry site (IRES) element that mediates cap-independent translation of the viral RNA Foot-and-mouth disease virus (FMDV) is the prototype of the aphthovirus genus of the cis-acting elements that recruit the 40S ribosomal subunits to the mRNA with the help of cellular trans-acting factors cre element Picornavirus IRESes are in vitro and in vivo to dimethyl sulfate (DMS) The FMDV IRES showed significant differences in the accessibility in vitro was lacking.Antisense oligonucleotides are single-stranded sequences that form a hybrid with their complementary RNA via Watson-Crick base pairing. The resulting hybrid can block gene expression by various mechanisms, depending on the chemical composition of the oligonucleotide and location of the hybrid. The strength of the hybrid depends on factors such as thermodynamic stability, the secondary structure of the target mRNA, and the proximity of the hybridization site to functional motifs on the designated transcript such as translational start site. Unmodified oligonucleotides are unstable within cells due to rapid nuclease degradation. By contrast, phosphorothioate in vitro translation of the same RNA. These differences, which are likely due to the composition of the cell cytoplasm and the cell-free system, emphasize the need to use living cells to measure the inhibitory capacity of small molecules.Here, by taking advantage of the RNA structural analysis of the IRES-AUG region FMDV RNA translation initiation is peculiar in that protein synthesis can be initiated at two start codons, AUG1 and AUG2 . While bTo fill this gap, we made use of an infectious cDNA clone P<0.05. Overall, in agreement with the P values obtained in the t-test, 60% was the threshold separating inhibitor from non-inhibitor molecules. The oligonucleotide SCR had no effect on FMDV RNA infectivity . No inhibition was observed in the presence of SCR, again showing a sequence-specific effect of AONs. Thus, we conclude that there is a contrast between cell-free systems and transfected cells in the response to AON interference depending upon the targeted initiation codon.Cell-free systems are often used to determine translation efficiency. Thus, to measure the ability of 2′OMe AONs to inhibit protein synthesis in the context of the complete viral RNA, FMDV RNA preincubated with SCR, AUG1 or AUG2 oligonucleotides under the same annealing conditions used in the RNA transfection assays was used to program rabbit reticulocyte lysates (RRL) translation. The control RNA, without any AON, was efficiently translated, as expected. Analysis of the translation efficiency of FMDV RNA annealed to AONs AUG1 or AUG2 revealed a marked difference in protein synthesis . In contcre  was noticed , the yieP<0.01). It is important to note that these AONs showed the highest potency in inhibiting the viral multiplication 24 hpt, with virus yields below 40%. The inhibition noticed at 48 hpt indicated the potential of the AONs to inhibit viral replication despite the growing number of viral RNA molecules during the viral multiplication process.The capacity of AONs to reduce viral replication was further investigated by extending the incubation of the cells up to 48 hpt. Compared to control RNA, the virus yields revealed that five oligonucleotides  remained inhibitory 48 hpt  (P<0.01)in vitro translation system. The inhibition of polyprotein translation in vitro induced by AONs annealed to the FMDV RNA varied depending on each molecule . In contrast, AONs targeting the stem were non-inhibitory . The inhibition observed by blocking the apical region of the central domain, reflected the susceptibility of the IRES conserved motifs and revealed the crucial role of the central domain in the translation process.A striking result was noted in the lack of inhibition exerted by AONs complementary to domain 4, with values above 60% . HoweverTo extend our analysis of AON inhibition to virus infection, we used BHK-21 cell monolayers treated with AONs SCR, AUG1, AUG2 and mock-treated to carry out a virus infection as described in cis-acting elements in the viral RNA that may contribute to IRES function that are absent in reporter sequences and second, to establish a comparison between the responses observed in cell-free systems and in tissue culture cells.Here we have made use of 2′OMe AONs complementary to the FMDV IRES-AUG translation start region to explore the capacity of these molecules to interfere viral RNA translation, and thus, viral multiplication. Specifically, we were interested in analyzing the response of the IRES-AUG region in the context of the viral RNA for several reasons. First, to take into account the in vitro translation and transfected cells are unknown; however they support a different accessibility of these two regions to external molecules despite being only 84 nt apart. Further, our results support the need to use in vivo assays to validate the interference of small molecules in viral gene expression.Our results reinforce the main role of AUG2 in viral RNA translation in tissue culture cells. This is supported by the strong inhibition of the virus yield measured 24 hpt with the AON complementary to AUG2, while that complementary to AUG1 only partially reduced virus multiplication . In bothThe interference observed with a collection of thirty-two customized AONs targeting the entire IRES region revealed large differences in the capacity of these molecules to inhibit viral gene expression, and therefore multiplication of the viral RNA. Specifically, oligonucleotides complementary to domain 2, the apical region of domain 3, the basal region of domain 4 and domain 5 were the most efficient inhibitors in transfected cells, which were still detected 48 hpt . This inin vitro translation indicated a differential response in some, but not all, IRES regions. Specifically, AONs 183, 207, 432 and 452 were inhibitory (as observed in transfected cells). However, several AONs complementary to the apical region of domain 3 were inhibitors in vitro but not in living cells. In agreement with this result, differences in DMS and amino-methyl psoralen accessibility to naked FMDV IRES or RNA expressed in the cell cytoplasm were already noticed in vitro suggested important conformational changes, which may depend on the interaction of the IRES with transacting factors present in different concentrations in the cell cytoplasm. In addition, interactions with other cis-acting RNA elements such as the FMDV 3′UTR Annealing of 2′OMe AONs to FMDV RNA prior to program As previously mentioned, domains 4 and 5 provide the binding site for eIFs and IRES-transacting factors (ITAFs) controlling internal initiation of translation Our results are in partial disagreement with a previous study that used morpholino oligomers targeting domain 5, AUG1 and AUG2 to inhibit FMDV multiplication In summary, we have taken advantage of the stability of 2′OMe AONs to explore the accessibility of the entire IRES-AUG region in the context of the viral RNA. Overall, we have identified critical regions of the IRES element that led to a significant decrease of virus yield when targeted with 2′OMe AONs. Identification of these regions in tissue culture cells by monitoring the reduction in viral protein translation or reduced virus multiplication emphasizes the relevance of certain viral RNA region in controlling viral gene expression. In addition, they open up new avenues to develop novel tools aimed to inhibit viral infection.in vitro using SP6 RNA polymerase, as described The plasmid pMT28, encoding a cDNA copy of FMDV C-S8c1 genome inserted into pGEM-1 under the control of the SP6 promoter was described previously http://blast.ncbi.nlm.nih.gov/) to prevent any possible match in the FMDV RNA or the host cellular RNAs. MFold (http://www.tbi.univie.ac.at/~ivo/RNA/) and oligo analyzer  were used to predict the secondary structure of each antisense oligoribonucleotide to prevent self-dimerization, formation of stable hairpins, and to optimize its hybridization to the IRES region avoiding perfect hairpin targets deduced from RNA probing data Thirty-two 2′O-Methyl antisense oligoribonucleotides designed to hybridize with the IRES, AUG1 and AUG2 of FMDV RNA , with 2′In vitro translation was performed by adding the RNA annealed with AON to a reaction mix containing 6.5 µl of nuclease-treated rabbit reticulocyte lysates (RRL) (Promega), 1 µl (1 mM) amino acid mix less methionine and 0.5 µl (6 µCi) 35S-methionine in a final volume of 10 µl. Translation reaction was carried out at 30°C for 60 min. The reaction was treated with 1 µl of RNase A, incubated for 10 min at room temperature and fractionated in polyacrylamide gel with sodium dodecyl sulfate (SDS-PAGE) The optimal inhibitory condition was determined using a concentration range  of two AONs, AUG and SCR, annealed to RNA (250 ng) for 20 min at 37°C (data not shown). in vitro was determined using increasing amounts of FMDV RNA (25 pg to 2 ng) to transfect BHK-21 cell monolayers (5×105 cells); virus yield was titrated 24 hpt in fresh cell monolayers. Typically, virus yield from 50 pg of FMDV RNA yielded about 1.6×103 plaque forming units (PFU)/ml. In the RNA infectivity assay, and prior to RNA transfection, 50 pg of in vitro synthesized FMDV RNA was annealed with each oligonucleotide (10 nM) for 20 min at 37°C. Triplicates of BHK-21 cells grown in 35 mm well dishes, washed three times with Dulbeccós modified Eagle's medium (DMEM), were transfected with a mixture of lipofectine-RNA annealed with each AON in 0.5 ml of DMEM, as described The infectivity of viral RNA synthesized Equal amounts of total protein prepared from transfected BHK-21 transfected cells were resolved in 10% SDS-PAGE, transferred to a PVDF membrane (Biorad) using a semidry electrotransfer device. VP1 protein was detected using the SD6 mouse monoclonal antibody (1∶1000) Virus yield from three independent RNA infectivity assays were titrated in fresh susceptible IBRS-2 cells to determine the capacity of each 2′OMe AON to inhibit PFU. Monolayers of IBRS-2 cells were infected with serial dilutions of the supernatant from transfected BHK-21 cells. One hour after adsorption, the viral inoculum was removed and the cells were washed three times, overlaid with DMEM medium with 0.5% agar supplemented with 2% fetal calf serum. Virus titer (PFU/ml) was scored 24 hours post-infection (hpi) by fixing the cells with 2% formaldehyde solution and stained with 0.3% crystal violet in 2% formaldehyde solution. The viral titer from the 24 and 48 hpt supernatant was determined by counting the viral plaques that developed after 24 hpi 5 cells) were incubated with SCR, AUG1 or AUG2 ASO (0.4 μM) or DMEM alone 3 h prior to FMDV infection. Then, cell monolayers were infected with C-S8c1 FMDV at a multiplicity of infection (MOI) of 0.1 PFU/cell. After 1 h of adsorption, the inoculum was removed by 3 washes with DMEM-HCl pH 6 to inactivate free virus, twice with DMEM to restore pH and then, incubated with DMEM supplemented with 2% FCS and the appropriate AON (0.4 μM) for 6 h. Supernatants were collected at 0 and 6 hpi for virus titer determination, as described BHK-21 cell monolayers  was annealed with the indicated concentrations of AONs AUG1, AUG2 or the scramble SCR, prior to transfect confluent BHK-21 cell monolayers in duplicate. Virus yield was determined using fresh cells monolayers as the number of plaque forming units (PFU)/ml in the supernatant 24 hpt as described in (TIF)Click here for additional data file.Table S1Inhibition of FMDV RNA infectivity by phosphorothionate antisense oligonucleotides.(DOCX)Click here for additional data file.


Discovery that the transcriptional output of the human genome is far more complex than predicted by the current set of protein-coding annotations and that most RNAs produced do not appear to encode proteins has transformed our understanding of genome complexity and suggests new paradigms of genome regulation. However, the fraction of all cellular RNA whose function we do not understand and the fraction of the genome that is utilized to produce that RNA remain controversial. This is not simply a bookkeeping issue because the degree to which this un-annotated transcription is present has important implications with respect to its biologic function and to the general architecture of genome regulation. For example, efforts to elucidate how non-coding RNAs (ncRNAs) regulate genome function will be compromised if that class of RNAs is dismissed as simply 'transcriptional noise'.We show that the relative mass of RNA whose function and/or structure we do not understand , as a proportion of all non-ribosomal, non-mitochondrial human RNA (mt-RNA), can be greater than that of protein-encoding transcripts. This observation is obscured in studies that focus only on polyA-selected RNA, a method that enriches for protein coding RNAs and at the same time discards the vast majority of RNA prior to analysis. We further show the presence of a large number of very long, abundantly-transcribed regions (100's of kb) in intergenic space and further show that expression of these regions is associated with neoplastic transformation. These overlap some regions found previously in normal human embryonic tissues and raises an interesting hypothesis as to the function of these ncRNAs in both early development and neoplastic transformation.We conclude that 'dark matter' RNA can constitute the majority of non-ribosomal, non-mitochondrial-RNA and a significant fraction arises from numerous very long, intergenic transcribed regions that could be involved in neoplastic transformation. A variety of techniques, most notably tiling arrays -4, massiThe advent of next-generation sequencing technologies and their application to RNA analysis  providesWe provide estimates of the relative mass of the 'dark matter' RNA in cells of two organisms - human and fly. To our knowledge, while used in a number of reports, this term has not been defined and, thus, we offer our definition of this term: 'dark matter' RNA includes any RNA whose function we do not currently understand and any RNA that is currently un-annotated. The latter category may include both protein-coding and nc-RNAs. A more detailed breakdown of what is included and how these species might arise is provided in the Additional File Another parameter requiring definition is the measurement for transcriptome complexity used here. In general, the fraction of 'dark matter' RNA could be measured either as a fraction of nucleotide bases in the genome that are used to encode these transcripts or as the relative mass of these RNAs as a fraction of mass of all RNAs in the cell after removing ribosomal and mtRNAs. Most techniques, notably tiling arrays have provided the former estimate. Importantly, however, this estimator alone cannot provide a complete description of the complexity of 'dark matter' RNA as it is possible that it can be highly complex in terms of the sequences present in this population and yet comprise a minute fraction of total RNA population by mass. This has been suggested to be the case by the recent report of van Bakel et al. who estimate that 'dark matter' RNA constitutes 12% of all RNA by mass . In thatIn order to evaluate the true nature of the complete cellular RNA population, we analysed RNA from various tissue sources and different RNA preparations in two different species: human and Drosophila  samples from four different patients. These included a pair of matching cell lines derived from a single patient, one from the primary-tumour established in culture prior to chemotherapy (CHLA-9) and one from a metastatic-lesion cultured after chemotherapy (CHLA-10). A second matched set of samples was derived from primary and metastatic EFT tumours from a different patient. An additional primary and metastatic tumour sample were each obtained from different patients . The primary and metastatic tumour samples served to exclude the possibility that the observed non-exonic RNAs are an artifact of tumour cell line culture conditions. Figure et al. . Briefly, their properties were as follows:Total RNA was obtained from commercial sources for the K562 cell line  and normal liver and normal brain tissues . Total RNA was isolated from a pair of cell lines from a patient with EFT, one established at diagnosis (CHLA-9) and the second from a nodal metastasis obtained after chemotherapy (CHLA-10) . Most EFR) assay  and veri1. Primary No.2 tumour: sacral tumour; age -13 years and 9 months; EWSR1 gene rearrangement positive by FISH2. Metastasis No.2 tumour: lung metastasis; same patient as Primary No.2 tumourEWSR1 gene rearrangement positive by FISH3. Primary No.3 tumour: paraspinal tumour; age - 16 years and 9 months; EWSR1/FLI-1 genes.4. Metastasis No.4 tumour: metastasis of a primary thoraco-abdominal tumour; age -14 years and 8 months; real-time polymerase chain reaction and sequence analysis confirmed type I fusion of Before further fractionation, total RNA was treated with DNase I as follows: 50 μg of total RNA was mixed with 10 μL of 10X DNase I buffer (Roche); 2 μL of RNaseOut ; and 8 μL of recombinant DNase I  and incubated for 45 min at 37°C. The RNA was then purified using the RNeasy MinElute kit (Invitrogen).The DNase I-treated total RNA was either unfractionated  or fractionated using one of the following methods: (1) depleted of ribosomal RNA (rRNA) using the RiboMinus kit (Invitrogen); (2) polyA+ fraction was selected using a magnetic bead-based purification kit ; or, (3) polyA+ fraction was selected using the oligo-dT cellulose method (Micro Poly(A)Purist Kit, Ambion, Texas, USA).2 were added. The samples were returned to the thermocycler and allowed to incubate at 15°C for 20 min. Then, 2.5 μL of RNaseOut and 2.5 μL of SuperScript III reverse transcriptase were added and the samples incubated at 25°C for 10 min, 42°C for 40 min, 55°C for 50 min and 70°C for 10 min.Except where noted, 100-400 ng of DNase I -treated RNA was mixed with the following reagents from the SuperScript III kit (Invitrogen). First, 10 μL of 50 ng/μL Random Hexamers and 2 μL of 10 mM dNTPs were added in the total volume of 25 μL. The mixture was then placed in a thermocycler and heat denatured at 65°C for 5 min followed by rapid cooling on ice. Next, 5 μL of 10X cDNA synthesis buffer, 5 μL of 0.1 M DTT and 10 μL of 25 mM MgClAfter reverse transcription, RNA was removed by adding 1 μL of RNaseH (Invitrogen) and 1 μL of RNase If  to each sample and incubating at 37°C for 30 min. The cDNA was then purified by two rounds of purification over Performa columns  and quantified using a NanoDrop spectrophotometer.2, 5 μL of 10x terminal deoxynucleotidyl transferase (TdT) buffer (New England BioLabs), 5 μL of 50 μM dATP and 1.5 μL of TdT  was then added and the samples incubated at 42°C for 1 h and at 70°C for 10 min.Next, a 3' poly-A tail was added to the cDNA samples. cDNA (100 ng) was mixed with a control oligo to monitor tail length and water in a total volume of 33.5 μL. The mixture was denatured at 95°C for 5 min followed by rapid cooling on ice. Five microlitres of 2.5 mM CoClThe 3' ends of the poly-A-tailed cDNA were then blocked with biotin-ddATP. The sample was denatured at 95°C for 5 min followed by rapid cooling on ice. We then added 0.3 μL of 1 mM biotin-ddATP  and 1.5 μL of TdT followed by incubation at 37°C for 45 min and 70°C for 10 min.The control oligo was removed by digestion with the USER enzyme (New England BioLabs). Then 1 μL of the USER enzyme (1 U) was added to the sample and incubated at 37°C for 30 min.The sample was then purified using AMPure beads  by bringing the volume up to 60 μL with water and adding 72 μL of the AMPure beads followed by incubation at room temperature for 30 min with agitation. The beads were then captured on a magnetic stand and washed twice with 70% ethanol. The beads were allowed to air dry for 5-7 min, resuspended in 20 μL of water and left open for 30 min on the magnet. The eluate was collected and the beads were resuspended again in 20 μL of water and then left for 5 min on the magnet. The eluate was collected again and combined with the first eluate.Typically, samples were hybridized to the HeliScope flow cell in 20 μL at a loading concentration of 100-350 pM.http://open.helicosbio.com/mwiki/index.php/Releases and described at http://open.helicosbio.com/helisphere_user_guide/index.html. Alignments were conducted with indexDPgenomic software freely available on the Helicos website http://open.helicosbio.com/mwiki/index.php/Releases. The aligner maximizes the aligned yield of SMS reads due to the ability to align reads in which the predominant error is represented by deletions common in single-molecule sequencing. For the genomic alignments, reads were aligned to the NCBIv36 version of the genome supplemented with the complete ribosomal repeat unit (Gen Bank Accession U13369.1) or the DM3 version of the fly genome supplemented with the complete ribosomal repeat unit (Gen Bank Accession M21017.1) using the Helicos BASIC analysis pipeline http://open.helicosbio.com/helisphere_user_guide/index.html. The sequence reads were filtered to include reads with a minimal length of 25 bases and aligned using a stringent normalized score of 4.5 (human genome) and 4.3 (fly genome). Aligned reads were further filtered for reads having a unique best alignment score. The mapped reads were then compared to UCSC genes and FlyBase gene annotations to partition them into exonic, intronic and intergenic reads.SMS reads were trimmed for leading T homopolymers and were filtered for reads with a minimal length of 25 bases after trimming using a suite of Helicos tools available at The normalized score was defined as follows:Score = (No. matches*5 - No. mismatches*4)/length_readFor example, in the following alignment:Tag Sequence CCTCCGTGTTGTTCCAGCC-CAGTGCTCGCAGGRef Sequence C-TCCGTGTTGTTCCAGCCACAGTGCTCGCAGGLength of alignment block: 33Length of tag sequence: 32Number of matches: 31Number of errors: 2Score: (31*5) - (2*4) = 155 - 8 = 147Normalized score = 147/32 = 4.59375http://www.bioviz.org/igb/download.shtml by applying the following thresholds to the density graphs for each chromosome:Reads not overlapping the genic regions from the Ewing tumours and K562 were pooled and used to generate the densities of read coverage throughout the genome. The vlinc domains were defined in the Integrated Genome Browser Threshold = 80th percentileMaxGap = 5000MinRun = 50000ftp://fantom.gsc.riken.jp/FANTOM3/boundary_set/pair53_clusters.txt.gz. The coordinates were lifted from the mm5 version of the mouse genome to hg18 in a two step process using the UCSC liftOver tool: mm5- > mm8 and mm8- > hg18.The coordinates of the 181,047 reliable 5'/3' clusters described in the Table 3 of the FANTOM3 consortium paper [in situ hybridization; lincNAs: large intergenic non-coding; mtRNA: mitochondrial RNA; ncRNA: non-coding RNA; rDNA: recomninant DNA; vlinc: very long intergenic non-coding; SMS: single molecule sequencing; STR: short tandem repeat; TdT: terminal deoxynucleotidyl transferase.EFT: Ewing Family of Tumours; FISH: fluorescent PK, PM, JFT and TJT designed the study. GsL, CPR and TJT provided the experimental material. PK performed the analysis. PK, GsL, RJA, JFT and TJT wrote the manuscript and provided input on the analysis and the presentation of the data. PK, TR and FO performed the molecular biological experiments. PHBS and GR provided intellectual contribution to the manuscript. All authors read and approved the final manuscript.Definition of the 'dark matter' RNA. Provides a definition of what kind of RNA molecules should be included in the 'dark matter' RNA realm and argumentation for their inclusion.Click here for fileTable S1. Distribution of single-molecule sequencing reads obtained from polyA+, rRNA-depleted (Ribominus) and total RNA from different tissues among different types of annotations.Click here for fileTable S2. Distribution of single-molecule sequencing reads obtained from ribosomal RNA-depleted (Ribominus) RNA from Ewing Family of Tumours solid tumours and cell lines.Click here for fileTable S3. Coordinates of the very long intergenic regions and their normalized expression levels in each tissue.Click here for fileTable S4. Normalized expression levels of University of California Santa Cruz genes in each tissue. The total number of reads within each of the regions was normalized to 10 M non-ribosomal non-mitochondrial unique reads in each sample.Click here for fileTable S5. Spearman correlation matrix based on expression of very long intergenic non-codings and University of California Santa Cruz genes between different tumour and normal tissuesClick here for fileTable S6. Overlap between very long intergenic non-codings and FANTOM3 intergenic clusters of over 50 kb.Click here for file