Therefore, determining the complex structures between RNAs/DNAs is crucial to understand the molecular mechanism of related RNA/DNA–RNA/DNA interactions. Here, we have presented HNADOCK, a user-friendly web server for nucleic acid (NA)–nucleic acid docking to model the 3D complex structures between two RNAs/DNAs, where both sequence and structure inputs are accepted for RNAs, while only structure inputs are supported for DNAs. HNADOCK server was tested through both unbound structure and sequence inputs on the benchmark of 60 RNA–RNA complexes and compared with the state-of-the-art algorithm SimRNA. For structure input, HNADOCK server achieved a high success rate of 71.7% for top 10 predictions, compared to 58.3% for SimRNA. For sequence input, HNADOCK server also obtained a satisfactory performance and gave a success rate of 83.3% when the bound RNA templates are included or 53.3% when excluding those bound RNA templates. It was also found that inclusion of the inter-RNA base-pairing information from RNA–RNA interaction prediction can significantly improve the docking accuracy, especially for the top prediction. HNADOCK is fast and can normally finish a job in about 10 minutes. The HNADOCK web server is available at  Nucleic acids (DNA/RNA) are one of two most important types of biological macromolecules in cells, which are not only transferring genetic information but also involved in various regulatory processes ,2. TheirDespite the importance of nucleic acid–nucleic acid interactions, compared to protein-protein and protein–nucleic acid interactions for which a number of docking algorithms and web servers have been developed to predict their complex structures , few appab initio method 3dRNA for fast RNA 3D structure prediction .RNA sequence in FASTA format.Copy and paste your The required inputs by HNADOCK server are two nucleic acid molecules. Currently, the server only accepts structures as input for DNAs, though it supports both sequences and structures as input for RNAs. For each molecule, the server accepts three types of inputs, two for structures and one for sequences, as followsOnly one type of input is needed for each molecule. For structure input, users can upload their own pdb files or provide the PDB: chain ID(s). Since our RNA 3D structure prediction and comparative modeling protocol is designed to build single-chain RNA structures from sequences, users are recommended to upload their own structures if their RNA contain multiple chains.In addition, users also have an option to provide binding site information in two forms. One is the residue information of the binding site on one molecule. The other is the residue distance constraint at the binding interface between two molecules. The binding site information, if provided, will be used during the docking process as well as the post-docking stage as a filter. A few residues about the binding site is good enough to constrain correct binding modes. An option is also provided for whether or not to refine the top 10 models. Users may also provide an email address for email notification when their job is done, and give a name to their docking job.Compared to the prediction of RNA–RNA complex structures, the prediction of DNA–DNA or DNA–RNA structures is less challenging because DNA–DNA or DNA–RNA tends to form a double-helical duplex upon binding, which may be modeled through a purely geometric method. Therefore, we will focus on the case of RNA–RNA complexes in the present test. The test cases used to validate our HNADOCK server are from our nonredundant benchmark for RNA–RNA docking and scoring . This beThe quality of a predicted RNA/DNA–RNA/DNA binding mode is measured by its interface RMSD (IRMSD) from the native complex structure after optimal superimposition of the predicted and native structures . The intThe hardware for HNADOCK server is a Linux server of two Intel(R) Xeon E5-2690 v4 2.60GHz CPUs with 28 cores and 256GB of memory. The software for the web service includes Apache HTTP, PHP and NGL viewer for the docking pipeline and model visualization. The SLURM Workload Manager is used as the job scheduler of HNADOCK server . A maximhttp://huanglab.phys.hust.edu.cn/hnadock/data/jobid, where ‘jobid’ is a unique job ID. Users can keep the result page alive or bookmark the URL for access to the docking results at a later time. Users will also be notified by email when the job is finished if a valid email address is provided at the time of job submission.After users submit their job, the web interface will be redirected to a web page showing the job ID and running status. The web page is updated every 10 seconds, showing the job status of ‘QUEUED’, ‘RUNNING’, and ‘RESULTS’. The URL to the docking results is something like Receptor and ligand RNA/DNA PDB files uploaded by users or constructed by the server from the FASTA sequence provided by users.The server pre-generates the top 100 binding models for each job. Users can download any of the top 20 binding models individually, or choose to download all the top 10 predictions or the top 100 predictions as a package.When a job is finished, the status web page will automatically show the docking results for download and visualization, as shown in Figure Users may also download all the results in a single package that includes the Receptor PDB file, Ligand PDB file, and the top 100 predictions.As the top 10 binding models are normally deemed as the most important models in macromolecular docking , the resThe result page also gives a summary of the rankings and docking scores for the top 10 complex models, where the score is based on our scoring function for RNA–RNA interactions, DITScoreRR. However, it should be noted that the docking scores here do not reflect the true binding affinities, but a relative ranking of the complex models, as DITScoreRR was not calibrated with experimental binding data . It is rAs we are developing a web service of our nucleic acid docking for predicting RNA/DNA–RNA/DNA 3D complex structures, we need to ensure that our web server is able to obtain a comparable docking performance to our local docking package. To examine this, we have done two types of validation by providing inputs through the web interface of HNADOCK server. One is to submit the unbound structures to HNADOCK server. The other is to submit the RNA sequences to the server so as to test the ability of HNADOCK server in identifying homologous RNA templates. We then collected the docking results and evaluated the qualities of predicted complex structures. As mentioned in the Benchmark section, we will focus on RNA–RNA docking and thus present the results of HNADOCK server for RNA–RNA complexes only in the present study, though the server also worked well for predicting DNA–DNA and DNA–RNA complex structures per our own test.Figure For comparison, we have also modeled the complex structures of 60 RNA–RNA test cases in our benchmark by running SimRNA (version 3.20) locally, where the SimRNA package was downloaded from the Bujnicki lab and the recommended parameters by the SimRNAweb server were used. Specifically, for each prediction, we carried out eight independent runs of the Replica Exchange Monte Carlo (MC) simulation, each employing 10 replicas. Each run comprised 500 simulation intervals (16 000 steps each) and the lowest energy frame from each interval was recorded. The resulting eight trajectories were combined to yield 40 000 conformations per target (500 conformations from each of the 10 replicas in each of the 8 simulation runs) and the top 1% scored conformations from the set were retrieved and clustered. The clustering threshold was set to 0.1 Å times the sequence length; i.e., 5.0 Å for a sequence of 50 residues. For each cluster, the medoid of the decoys was considered as its representative.ab initio rigid docking. Namely, the MC simulation is able to accommodate the RNA conformational changes upon binding, and the inter-RNA base-pairing information from RNA–RNA interaction prediction can dramatically reduce the sampling space during the simulation, thus enhancing the modeling accuracy. The benefits from RNA–RNA constraints can also be demonstrated by including the similar information into HNADOCK server. As shown in Figure ab initio docking. It should also be noted that the present SimRNA results are for the cases where the secondary structure or 3D restraints are not supplied by us, but computed by the SimRNA itself. In addition, the secondary structure and 3D restraints used for HNADOCK were predicted by the third-party programs, RNAfold and RNAup. Therefore, the results for both SimRNA and HNADOCK may be further improved with more accurate RNA secondary structure or interaction predictions. The HNADOCK models may also be further optimized by using NA refinement tools like QRNAS , where an FFT-based global docking algorithm is used to sample putative binding modes and an intrinsic scoring function for RNA/DNA–RNA/DNA interactions is integrated to calculate the binding energy scores. The server accepts structures for DNAs and both sequences and structures for RNAs, and is also able to include the optional binding site information provided by users. HNADOCK server was extensively tested on the RNA–RNA docking benchmark of 60 diverse complexes and compared with SimRNA. For structure input, HNADOCK server obtained a success rate of 53.3% and 71.7% for top 1 and 10 predictions, compared to 56.7% and 58.3% for SimRNA. For sequence input, HNADOCK server also obtained a satisfactory performance and gave a success rate of 70% and 83.3% when all the RNA templates were used or 40% and 53.3% when excluding those bound RNAs from the templates. It was also demonstrated that including the inter-RNA base-pairing information from RNA–RNA interaction predictions can significantly improve the performances of HNADOCK. On average, HNADOCK consumes about 3.5 min for an RNA–RNA docking job, compared to 7366 min for modeling an RNA–RNA complex structure by SimRNA. These results validated our HNADOCK server as an efficient and reliable docking web server for RNA/DNA–RNA/DNA structural modeling.


Arabidopsis, PARP proteins contribute to repair of DNA lesions and modulate plant responses to various abiotic and biotic stressors. Arabidopsis PARP1 and PARP2 are nuclear proteins and given that their molecular weights exceed the diffusion limit of nuclear pore complexes, an active import mechanism into the nucleus is likely. Here we use confocal microscopy of fluorescent protein-tagged Arabidopsis PARP2 and PARP2 deletion constructs in combination with site-directed mutagenesis to identify a nuclear localization sequence in PARP2 that is required for nuclear import. We report that in co-immunoprecipitation assays PARP2 interacts with several isoforms of the importin-α group of nuclear transport adapters and that PARP2 binding to IMPORTIN-α2 is mediated by the identified nuclear localization sequence. Our results demonstrate that PARP2 is a cargo protein of the canonical importin-α/β nuclear import pathway.Proteins of the Poly(ADP-Ribose) Polymerase (PARP) family modify target proteins by covalent attachment of ADP-ribose moieties onto amino acid side chains. In  The post-translational modification of proteins is an important component of plant responses to changes in their environment. Post-translational modifications (PTMs) are often rapid and reversible processes that allow plants to fine-tune the speed and duration of stress-induced signaling. Numerous PTMs have been described in plants including phosphorylation, ubiquitination, SUMOylation, acetylation, and methylation . The N-t+ as a co-substrate to transfer the ADP-ribose moiety from NAD+ onto an amino acid side chain. Similar to protein kinases, several ADP-ribosyl transferases not only ADP-ribosylate other proteins but also undergo auto-modification  with 27–47% sequence identity to HsPARP-1 and HsPARP-2. Arabidopsis PARP1 and PARP2 are active enzymes while direct evidence for PARP3 ADP-ribosyl transferase activity is lacking  located N-terminal to the catalytic domain. Based on the analysis of human PARP-1, sensing of DNA double strand breaks by the N-terminal domains might result in a conformational change of the PRD thereby relieving auto-inhibition of the catalytic domain  domains followed by a WGR domain have been predicted suggesting that the mechanism of PARP2 activation by DNA damage differs from PARP1  supplemental light (200–230 μmol m2 s-1) from tungsten lamps.SAP-WGR, pENTR4-PARP2PRD-CAT, pENTR4-PARP2SAP, pENTR4-PARP2WGR, pENTR4-PARP248-51AAAA, pENTR4-PARP248-51QMQL, pENTR4-PARP2SAP48-51AAAA, pENTR4-PARP2SAP48-51QMQL, and pENTR4-PARP2SAP92/93AA. All pENTR4 constructs lack a stop codon for translational fusion to xFP reporters. PARP2 mutant constructs were generated either by the QuikChange method (pENTR4-PARP248-51AAAA and pENTR4-PARP2SAP92/93AA) or by splice-by-overlap-extension (SOE) PCR (pENTR4-PARP248-51QMQL). To generate translational fusions between PARP2 variants and xFP tags, Gateway LR reactions between PARP2 (or PARP2 deletion) constructs and pK7FWG2 (enhanced GFP tag), or pH7RWG2 (RFP tag)   were perRFP tag)  and pK7FRFP tag) .E. coli expression construct for ΔIBB IMPORTIN-α2 [lacking the auto-inhibitory importin-β-binding (IBB) domain], a cDNA fragment coding for amino acids 75–535 was amplified and cloned into KpnI/HindIII-linearized pOPINF (E. coli PARP2 SAP (amino acids 1–105) expression construct was generated following the same strategy but using pOPINS3C (To generate the d pOPINF  via GibspOPINS3C  as exprepOPINS3C  was fusepOPINS3C  into pOPpOPINS3C  via GibsN. benthamiana leaves binary vectors were transformed into Agrobacterium tumefaciens strain GV3101::pMP90 (or GV3101::pMP90RK for YFP:HaRxL106). Agrobacteria were plated on YEB medium with appropriate antibiotics and incubated for 3 days at 28°C. On the day of infiltration, the cells were resuspended in infiltration medium  and the OD600 was adjusted to 0.8. To suppress transgene silencing Agrobacteria expressing the tomato bushy stunt virus 19K silencing suppressor were co-infiltrated. The culture of the 19K strain was adjusted to an OD600 of 6.0. After adding Acetosyringone to a final concentration of 100 μM the bacteria were incubated for 2 h at RT and then mixed in a ratio of xFP[20]:19K[3] for localization experiments or G/YFP[10]:RFP[10]:19K[3] for co-immunoprecipitation and co-localization experiments. Agrobacterium mixtures were infiltrated with a needleless syringe into leaves of 4–5 week-old N. benthamiana plants and leaf material was harvested for microscopy or protein extraction 72 h later.For transient expression in N. benthamiana were mounted on microscope slides in water and the subcellular localization of xFP-tagged proteins was analyzed using a Leica TCS SP5 confocal unit attached to a Leica DMI6000 CS microscope. GFP and YFP were excited at 488 nm and collected at 500–525 nm and 525–540 nm, respectively. RFP was excited at 561 nm and collected at 580–610 nm. The gain setting of the confocal unit was adjusted to just below the threshold for saturation of the signal. Images were acquired and analyzed using LAS AF software (Leica).Leaf discs excised from N. benthamiana leaf material in liquid nitrogen to a fine powder followed by resuspension in extraction buffer  at a ratio of 2 ml buffer per 1 g leaf material. The extracts were centrifuged at 20000 x g/4°C/20 min and the supernatant was either boiled in Sodium dodecyl sulfate (SDS) sample buffer for western blots or used for co-immunoprecipitation experiments. For western blots protein samples were separated by SDS-PAGE and electro-blotted onto nitrocellulose membrane. Antibodies used were α-GFP 210-PS-1GP (Amsbio)2 and α-mCherry ab125096 (Abcam)3. For co-immunoprecipitation a fraction of the supernatant was saved as ‘input’ sample and 15 μl of α-GFP-nanobody:Halo:His6 magnetic beads (see below) were added to 1.4 ml of the remaining supernatant. The samples were incubated on a rotating wheel at 4°C for 2 h followed by collection of the beads using a magnetic sample tube rack. The beads were washed 3 times with 1 ml extraction buffer and then boiled in 40 μl SDS sample buffer to elute protein from the beads.Protein extracts were prepared by grinding E. coli, pOPINS3C carrying His6:SUMO:SAP and pOPINE carrying the α-GFP-nanobody:Halo:His6 construct (Addgene plasmid #111090) were transformed into SHuffle® T7 Competent E. coli cells (New England Biolabs). The His6:ΔIBB-IMPORTIN-α2 protein was expressed from pOPINF in SoluBL21 cells (Genlantis). All cultures were grown to an OD600 of 1 to 1.2 at 37°C, then shifted to 18°C and expression was induced by addition of 0.5 mM IPTG. After 16-18 h the bacteria were harvested by centrifugation . His6-tagged proteins were purified using a combination of immobilized metal ion affinity chromatography and size exclusion chromatography. To this end, the cell pellets were resuspended in 50 mM Tris, 300 mM NaCl, 50 mM Glycine, 20 mM Imidazole, 5% Glycerol, pH 8.0 at a ratio of 20 ml per initial liter of culture volume. Bacterial lysis was achieved by incubation with Lysozyme  followed by sonication (2 times 2 min. at level 4 on a Branson Sonifier 150). The cell extract was cleared from debris and insoluble proteins by centrifugation  and the supernatant was loaded onto pre-equilibrated Ni2+-immobilized metal ion affinity chromatography columns . After washing out unbound proteins, His6-tagged proteins were eluted with 50 mM Tris, 300 mM NaCl, 50 mM Glycine, 250 mM Imidazole, 5% Glycerol, pH 8.0 and directly injected onto Hi-Load 26/60 Superdex 75  or Superdex 200 (His6:ΔIBB-IMPORTIN-α2) size exclusion columns  using buffer A4  as the elution buffer. Proteins were concentrated using ultrafiltration columns (Sartorius), frozen in liquid nitrogen and stored at -80°C.For protein expression in Purified His6:ΔIBB-IMPORTIN-α2 or His6:SUMO:SAP was injected onto a Superdex 200 10/300 GL size exclusion column  in a volume of 1 ml. Proteins were eluted from the column with buffer A4 at a flow rate of 0.4 ml/min and 1 ml fractions were collected. These fractions were subsequently analyzed by SDS-PAGE and Instant Blue (Expedeon) staining.® HaloTag® Beads (Promega) were washed in 1 ml A4 buffer and the beads were collected using a magnetic sample tube rack. 1.5 mg of the α-GFP nanobody:Halo:His6 protein was mixed with the washed beads in 2 ml buffer A4 and incubated on a rotating wheel for 2 h at 4°C and 15 rpm. The magnetic beads were collected and washed three times with 1 ml A4 buffer. The affinity beads were stored in 250 μl of storage buffer  at 4°C until use.To couple the α-GFP nanobody:Halo:His6 fusion protein to magnetic beads, 250 ml of MagneA. thaliana PARP2 (AT4G02390), IMPORTIN-α1 (AT3G06720), IMPORTIN-α2 (AT4G16143), IMPORTIN-α3/MOS6 (AT4G02150), IMPORTIN-α4 (AT1G09270), IMPORTIN-α6 (AT1G02690), IMPORTIN-α9 (AT5G03070); Hyaloperonospora arabidopsidis effector protein HaRxL106 (GenBank HE574762.1).N. benthamiana and confirmed that the fusion protein localized to the nucleus . We purified both proteins by immobilized metal ion affinity and subsequent size exclusion chromatography, mixed the proteins in a 1:2 (IMPORTIN-α2:SAP) molar ratio and assessed complex formation by analytical size exclusion chromatography. As shown in Figure Several NLS bind to importin-α with affinities in or below the low micro-molar range . This ofArabidopsis PARP2. This model is supported by (i) the identification of an NLS in PARP2 that is sufficient and required for nuclear import and (ii) binding studies showing that this NLS mediates interaction with IMPORTIN-α2 in plant cells , the flanking amino acids P1, P6 and P7 can influence the affinity for the receptor (47 and P6 is Asn52. Acidic residues are favored at P7, which corresponds to Ser53 in PARP2. Therefore, the divergence of the NLS flanking residues from the consensus sequence provides a possible explanation for the comparably low affinity of the PARP2 NLS to importin-α. We also note that the two Ser residues in the NLS flanking sequence could indicate regulation of nuclear import rates by phosphorylation. Post-translational modification of amino acids within or adjacent to NLS can alter the affinity for importin-α (E. coli expression system.We noticed that in direct comparison to the interaction between IMPORTIN-α2 and the oomycete effector protein HaRxL106, PARP2 showed a comparably weak association with IMPORTIN-α2 as well as other importin-α isoforms Figure . This isreceptor . Positioportin-α . Regardiin vitro, the affinity between PARP2 and importin-α is sufficient for nuclear import in plant cells (Figure The reported dissociation constants between NLS and importin-α proteins span a surprisingly large range, which might in part be explained by the different methods employed for their analyses . Althougs Figure . In nucls Figure . TherefoArabidopsis PARP2 is actively transported into the nucleus via importin-α-mediated transport. An NLS comprising - but possibly not limited to - amino acids 48–51 contributes to importin-α binding and is essential for PARP2 nuclear import.In summary, our data suggest that LW designed the research and wrote the manuscript. CC, RDM, RL, and LW performed the research. CC, RDM, and LW contributed to data analysis, collection, or interpretation. All authors have approved the submitted version of the manuscript.The 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.


In vitro selection and evolution strategies have been extremely useful in the analysis of functional RNA and DNA molecules, helping to expand our knowledge of their functional repertoire and to identify and optimize DNA and RNA molecules with potential therapeutic and diagnostic applications. The great progress made in this field has prompted the development of ex vivo methods for selecting functional nucleic acids in the cellular environment. This review summarizes the most important and most recent applications of in vitro and ex vivo selection strategies aimed at exploring the therapeutic potential of nucleic acids.It was only relatively recently discovered that nucleic acids participate in a variety of biological functions, besides the storage and transmission of genetic information. Quite apart from the nucleotide sequence, it is now clear that the structure of a nucleic acid plays an essential role in its functionality, enabling catalysis and specific binding reactions.  Nucleic acids, particularly RNA, are extremely versatile molecules. Apart from their role as carriers of genetic information they can also express a phenotype, e.g., they may show a catalytic activity, have a specific binding function, or have the capacity to recruit specific molecules. in vitro . T. TEx vivell line . The impThe delivery of pharmacological compounds by aptamers has also been studied by different approaches. Doxorubicin (Dox) is a chemotherapeutic intercalating agent used against cancer. Dox intercalates between the aromatic rings of the GC pairs of the A10 aptamer, enhancing its cytotoxicity against PSMA-expressing cells and reducing the same in non-expressing cells . The samrevtat region [Aptamer B40, specific for HIV-1 gp120 , has alst region . The chit region . Finally, aptamers can also be used to colocalize RNA inhibitors with their specific target molecules at the subcellular level. Aptamers can target RNA domains, e.g., in viral RNA genomes; they can therefore improve trans-cleaving ribozymes by anchoring them to their target. Chimeric molecules composed of a hammerhead ribozyme and an aptamer both targeting the HCV IRES have efficiently inhibited IRES activity in human hepatocyte cell cultures ,157.The procedures used to identify DNA and RNA molecules of interest in large populations of variant nucleic acid molecules have contributed significantly to the development of nucleic acid-based therapeutic drugs. Aptamers show high specificity for their targets and have low toxicity and immunogenicity profiles. Since the 1990s, the design and isolation of specific aptamers using selection and evolution techniques has been optimized and even automated. This has lead to great advances in our knowledge of aptamers as therapeutic agents and has expanded our bank of inhibitory nucleic acids and their possible targets, which now include cytokines, cell receptors, viruses and even whole cells. An aptamer drug is now on the market, and several mid and late clinical trials in progress that appears to confirm the great potential of these tools. With the improvement and optimization of selection strategies, and the ongoing discoveries made in this field, success in the development of nucleic acid-based therapeutics protocols might be predicted.


Quantifying the binding affinity of protein-RNA complexes is helpful to the understanding of protein-RNA recognition mechanisms and identification of strong binding partners. Due to experimentally measured protein-RNA binding affinity data available is still limited to date, there is a pressing demand for accurate and reliable computational approaches. In this paper, we propose a computational approach, PredPRBA, which can effectively predict protein-RNA binding affinity using gradient boosted regression trees. We build a dataset of protein-RNA binding affinity that includes 103 protein-RNA complex structures manually collected from related literature. Then, we generate 37 kinds of sequence and structural features and explore the relationship between the features and protein-RNA binding affinity. We find that the binding affinity mainly depends on the structure of RNA molecules. According to the type of RNA associated with proteins composed of the protein-RNA complex, we split the 103 protein-RNA complexes into six categories. For each category, we build a gradient boosted regression tree (GBRT) model based on the generated features. We perform a comprehensive evaluation for the proposed method on the binding affinity dataset using leave-one-out cross-validation. We show that PredPRBA achieves correlations ranging from 0.723 to 0.897 among six categories, which is significantly better than other typical regression methods and the pioneer protein-RNA binding affinity predictor SPOT-Seq-RNA. In addition, a user-friendly web server has been developed to predict the binding affinity of protein-RNA complexes. The PredPRBA webserver is freely available at  Protein-RNA interactions play a crucial role in many biological processes, such as gene expression and its regulation . To undevia experimental technique , and T is the temperature. It can be seen that the binding affinity is a real-valued quantity.Where It is worth noting that previous findings have demonstrated that the structure of RNA molecules greatly influences the binding affinity between proteins and RNAs , namely We extract a total of 37 kinds of features to predict the binding affinity of the protein-RNA complexes. These features can be mainly separated into four categories, including features based on protein sequences and protein structures, features based on RNA sequences and RNA structures.We extract the protein sequences from the PDB files and then calculate the total molecular mass of the protein fraction based on the molecular weight of each amino acid. Also, the total number of hydrogen bonds  containeα-helix and β-sheet, the molecular weight of α-helix   .We use the RNA sequences in the protein-RNA complexes to obtain the molecular mass of the RNA molecules. The computational formula is as below.A, G, C, U represent the numbers of four types of bases in the RNA sequence, respectively.in which A number of features based on the RNA structure are derived to predict protein-RNA binding affinities. We use the RNA fold in ViennaRNA  to prediEnsemble learning algorithms are a family of powerful machine-learning techniques that have shown considerable success many applications . We chosn-dimension vector. Let us denote the features by x =  where ix ∈ R and the corresponding binding affinity by y. The goal of predicting binding affinity real value of the protein-RNA complexes is to find a function F*(x) that maps x to y, such that over the joint distribution of all -values, the expected value of some specified loss function Ψ) is minimized as follows:Without loss of generality, the features and the real-valued binding affinities can be described as an N is the number of samples in the training set. The GBRT algorithm iteratively constructs M different weak learners h, …, h which consist of regression trees of fixed size from training set and constructs the following additive function F(x):Let mβ and mΘ are a weight and vector of parameters for the m-th weak regression tree h, respectively, and β0 is an initial constant. Both the weight mβ and the parameters mΘ are iteratively determined from weak learner 1 to M so that the loss function Ψ) is minimized. Formally, mβ and mΘ for the m-th regression tree are determined as follows:where mF-1(x) is the (m-1)th additive function combined from the first to the (m-1)th weak regression tree.where mβ, mΘ) in a simple two-step fashion  in the sense of least-square error as follows:However, it is not straightforward to solve Eq. (5). Therefore, GBRT separately and approximately estimates  is updated as follows:Then, a new additive function v < 1 is a shrinkage parameter, also called the learning rate, to scale the step length the gradient descent procedure. Finally, the resulting binding affinity value y corresponding to the features x is given by: y = MF(x).where 0 < r is defined as the linear correlation between two random variables X and Y:The performance is evaluated using the Pearson correlation coefficient  between n is the sample size, ix, iy are the single samples indexed with i, and in which In addition, the average absolute error(MAE)  is the aWe independently conduct iterative feature selection for each class of protein-RNA complexes, as the binding affinity of the different class of complexes is influenced by the structure of RNAs and proteins. In particular, we build the protein-RNA binding affinity prediction models iteratively using each feature and compute the performance measure Pearson correlation coefficient. Next, we sort the features in descending order according to the correlation coefficient and select the top 10 features for each class complex. Finally, we adopt the greedy algorithm to add one feature to the optimal feature set at each step until the performance stops to increase. The selected features are shown in the We first conduct an experiment to check the significance of the classification of protein-RNA complexes based on RNA types. For each class of complexes, we use the top 1 and 2 features to train GBRT prediction models and compute the performance measures, respectively. As a contrast, we take all the complexes as a whole to train the prediction model using the top 1 and top 2 features. The results are shown in vs predicted ΔG in For each class of protein-RNA complexes, we train the GBRT model using the selected features to predict binding affinities. The correlation coefficients, together with MAE and R2 measures, are shown in Next, we further evaluate the performance of the method for predicting the binding affinity in different classes and reveal the features that dominate the prediction of binding affinity of protein-RNA complexes. The predicted and actual values of binding affinities for each complex in six classes of complexes are shown in in vivo. There are 21 protein-RNA complexes in this class, and the binding affinity has the variation of 10 kcal mol-1, with the lowest value being 5.86 kcal mol-1 and the highest value of 15.2 kcal mol-1. Our model built on four types of features has achieved the correlation coefficient of 0.818 by leave-one-out cross-validations. As shown in In this class of complex, proteins interact with single-stranded RNA molecules that are very common -1. Three selected features are used to build the prediction model that obtain the correlation coefficient 0.731. The physicochemical properties of the protein fraction play most important role in the prediction of the binding affinity of this class of complexes. In particular, the number of hydrophobic residues in the protein and the number of the polar residues in proteins are also features of importance, which demonstrate that the physicochemical properties of the interacting proteins have a major impact on the interaction between proteins and double-stranded RNA.The interacting partners in this class of protein-RNA complexes are protein and double-stranded RNA. The binding affinities follow the range of 6–14 kcal molThis class of complexes is composed of proteins and tRNA molecules, and four features enable our model to achieve a correlation coefficient of 0.872. From RNA loop structure includes many types, such as hairpin loops, internal loops, etc. . Our pre-1. As shown in β-sheet have an essential function in the interaction between proteins and small RNAs.One interacting partner of this class of protein-RNA complexes is the small RNA fragment. There are 11 complexes in our dataset, and the average binding affinity is 9.78 kcal mol-1. The set of four features are included in our model to predict the binding affinity, and the correlation coefficient is 0.76 on leave-one-out cross-validations. The molecular weight of α-helix and the number of the aromatic and positively charged residues in the protein are identified as important factors influencing the binding affinity. Moreover, among the protein sequence-based features, the percentage of hydrophilic and hydrophobic residues in the protein also play a vital role.The complexes that do not fall into the above five categories are assigned to miscellaneous. The reason is that the structure of RNA in this class of complexes is uncertain and software available cannot determine their specific structures, we thereby assumed that the features influencing the binding affinity of this class of complexes might be different from other classes. This class consists of 20 complexes, and the binding affinities range from 6 to 15 kcal molTo verify that the utilization of both protein-derived features and RNA-derived features improve the performance of our prediction models, we build other two GBRT prediction models, referred to as protein-based and RNA-based prediction models, using only protein-derived features or RNA-derived features alone. Next, we compare their performance to that of PredPRBA that takes advantage of both protein-derived features and RNA-derived features. Inspired by the study of protein-RNA interactions by Liu et al. , we compWe evaluate PredPRBA by conducting performance comparison with several other typical regression methods, such as Linear Regression (LR) , K-neareThe SPOT-Seq-RNA  is anothIn this paper, we propose a method for predicting the binding affinities of protein-RNA complexes using the sequence-based and structure-based features. As far as our knowledge, the data set of binding affinities of 103 protein-RNA complexes we built is the largest dataset to date. For each class of protein-RNA complexes, we have conducted systematic analysis on the importance of features in predicting the binding affinity and found that the structural features play a vital role in governing protein-RNA binding affinity. We also compared our method with several typical regression methods and the existing binding affinity predictive method, and the performance comparison has verified that our method achieved the best performance. In addition, we have also developed a web server for predicting the binding affinity of protein-RNA complexes, which is free and open to the academic community.http://PredPRBA.denglab.org/.The datasets for this study can be found in the LD, WY, and HL designed the study and conducted experiments. LD and WY performed statistical analyses. LD and HL drafted the manuscript. WY prepared the experimental materials and benchmarks. All authors have read and approved the final manuscript.This work was supported by the National Natural Science Foundation of China under grant no. 61672541 and no. 61672113, and Natural Science Foundation of Hunan Province under grant no. 2017JJ3412.The 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.


It is proposed that the HAR1 transcript is a non-coding RNA that interacts with an unknown binding partner responsible for brain development through this inherent structural motif of bulged adenosines.Non-coding RNAs have raised a lot of interest because of their capabilities to perform enzymatic reactions and regulate gene expression in various ways. Human Accelerated Region 1 (HAR1) has been identified during the search for highly conserved regions in mammalian genomes, over one hundred base pairs long, and with high rates of substitution in the human genome. Its potential for coding for a protein is very minimal. However, the HAR1 transcript has been computationally predicted to have a stable secondary structure. Previous structure-probing experiments have suggested that the majority of differences between human and chimp constructs are in helices, designated C and D. For this reason, a 47nt construct consisting of the C and D helices along with two additional C-G pairs was synthesized, purified, and crystallized, and its x-ray structure is reported in this study. The final structure is an artificial dimer, with a bulge that forms different conformations on each monomer. This bulge has been observed in predicted secondary structures, footprinting assays, enzymatic degradation assays, NMR studies,  Human Accelerated Region 1 (HAR1) was identified as a non-coding RNA that has a putative role in mammalian brain development . HAR1 isHAR1 exhibits a significant rate of substitution in humans and is thus a part of a group of molecules named human accelerated regions (HARs). HAR1 has 18 substitutions in a region that would be expected to only have had 0.27 substitutions since the last common ancestor between humans and chimpanzees. The HAR1 region in chimpanzees has only two substitutions compared to chickens. HAR1 has not been detected in frogs, fish, or any invertebrate genomes. Resequencing HAR1 in a 24-person diversity panel confirmed that all 18 substitutions are fixed in the human population [et al., using three enzymatic probes and three chemical probes, found human and chimpanzee HAR1 RNA secondary structures to differ from those previously published as well as to dramatically differ from each other [et al.’s structures and showed that human HAR1 RNA is more dynamic compared to that of chimpanzee [In silico studies have been carried out to uncover the structural evolution of HAR1 RNA and have pointed to a more stable secondary structure in the human construct [The HAR1 RNA transcript was predicted to form a stable secondary structure by a computer program that considers RNA evolution, Evofold . The strch other . NMR stuimpanzee . In silionstruct . HoweverPollard’s structure-probing experiments suggested that the majority of differences between the human and chimp constructs were in the C and D helices. For this reason, a 47nt construct consisting of the C and D helices, along with two additional C-G pairs to ensure strong annealing between the two ends to aid in the formation of the helices, was synthesized, purified, and crystallized, and its structure is reported in this study.in vitro transcribed from a 67-nucleotide DNA template containing a double-stranded T7 polymerase promoter. The DNA template was synthesized using an Expedite 8900 instrument. Two guanosines were added to the 5' end, and two cytosines were added to the 3' end, in order to ensure strong annealing between the two ends to aid in the formation of the C and D helices. A 2'-O-methyl modification was added to the second-to-last base in order to reduce the production of the n+1 product [in vitro synthesis of the DNA templates were purchased from Glen Research.Pollard observed strong modification with DMS of A70, which is present in the predicted bulge connecting the C and D helices. The desired 47nt HAR1 RNA construct for this study, consisting of the C and D helices, was in vitro transcription, which yielded the desired 47nt RNA construct. The RNA was purified using a 12% polyacrylamide gel. The RNA was resuspended in 2mL of water and desalted using a Centricon filtering device (YM-3). After 12mL of water was passed through the Centricon, the volume was reduced until a sample with a concentration of 10mg/mL was achieved. The purity of the RNA was verified on an analytical 12% acrylamide gel.This DNA template was then used to carry out a T7 2). The hanging drop was made from 2μL of the prepared RNA and 2μL of the screening solution . One milliliter of the screening solution was placed in the reservoir, and the tray was set in a 22°C incubator.Hanging drop vapor diffusion was used to crystallize the HAR1 RNA. The RNA was folded by mixing water with the RNA to reach the concentrations tested  and then heated to 70°C for three minutes, after which it was cooled to room temperature; then, 10X crystallization buffer was added . Parameters are summarized in Synchrotron data were collected at the Stanford Synchrotron Radiation Lightsource (SSRL) on beamline 9–2. Parameters for data collection were as follows: the wavelength was 0.85565 Å, the detector distance was 300mm, the beam stop was set to 40.005 mm, an image was collected every one degree for 20 seconds, and 360° of data were collected. CNS [Coot [Phaser [REFMAC [Since this construct is a novel RNA crystal structure without a heavy atom derivative or a known homologous, high-resolution, three-dimensional structure as a model for molecular replacement, the Robertson-Scott method  was usedCNS . These pNS [Coot , process [Phaser , and ref [REFMAC . This cy1, and the mosaicity was averaged to 1.28. The Robertson-Scott method [The 47nt construct crystallized into long needle-like crystals. The space group was determined to be P2t method  was usedπ-π stacking interaction with A70 Click here for additional data file.


Salmonella Typhimurium and validate the bacterial proteins ClpX, DnaJ and UbiG to associate with RNA in vivo.In recent years, hundreds of novel RNA-binding proteins (RBPs) have been identified, leading to the discovery of novel RNA-binding domains. Furthermore, unstructured or disordered low-complexity regions of RBPs have been identified to play an important role in interactions with nucleic acids. However, these advances in understanding RBPs are limited mainly to eukaryotic species and we only have limited tools to faithfully predict RNA-binders in bacteria. Here, we describe a support vector machine-based method, called TriPepSVM, for the prediction of RNA-binding proteins. TriPepSVM applies string kernels to directly handle protein sequences using tri-peptide frequencies. Testing the method in human and bacteria, we find that several RBP-enriched tri-peptides occur more often in structurally disordered regions of RBPs. TriPepSVM outperforms existing applications, which consider classical structural features of RNA-binding or homology, in the task of RBP prediction in both human and bacteria. Finally, we predict 66 novel RBPs in  Gene regulation in eukaryotes occurs at several levels and involves the action of transcription factors, chromatin, RNA-binding proteins (RBPs) and other RNAs. RBPs and messenger RNAs (mRNAs) form ribonucleoprotein complexes (RNPs) by dynamic, transient interactions, which control different steps in RNA metabolism, such as RNA stability, degradation, splicing and polyadenylation. Furthermore, numerous diseases like neuropathies, cancer and metabolic disorders have also been linked to defects in RBPs expression and function .Interactome Capture (RIC) has enabled proteome-wide identifications of RBPs . RIC utiIntrinsically disordered regions (IDRs) are widespread in the proteome and have been shown to be involved in regulatory functions, including direct RNA binding . RBPs idThe occurrence of IDRs within RBPs appears to be conserved from yeast to humans. In earlier work, we defined a core set of RBPs conserved from yeast to human and identified [K]- and [R]-rich tripeptide repeat motifs as conserved across evolution, making IDRs plastic components of RBP co-evolution . ImportaResearch over the past two decades has revealed extensive post-transcriptional control in bacteria as well, with regulatory networks comprising RBPs and small non-coding RNAs (sRNAs). Bacterial RBPs are relatively simple, often possessing only a few or even a single RBD per protein, which recognizes a short RNA sequence . This isin silico methods have been developed to predict RBPs from primary sequence and/or protein structure (Since interactome capture relies on the use of oligo(dT) to isolate proteins bound to mRNA, the method is not applicable to bacterial species which generally lack polyadenylation (except for degradation purposes). Although experimental approaches have been developed recently to extend RBP catalogs to include RBPs not identified by RIC techniques ,16, bacttructure . Approactructure . Furthertructure ,21. Suchin silico methods that identify RBPs based on known domains might have some limitations, as one third of RBPs from recent experimental studies do not have prior RNA-binding related homology or domain annotation , as well as the observation that IDRs are subject to strong sequence constraints under the form of conserved amino acid triplets conserved in the RBPs, prompted us to explore the possibility that RBPs might be confidently predicted based purely on the occurrence of short amino acid et al. in the context of protein family classification (We set up to predict whether a protein is likely to be an RBP or not based on primary sequence using a string kernel (spectrum kernel) support vector machine (SVM). The spectrum kernel in combination with SVMs was first successfully introduced by Leslie fication . Supportfication  and in Rfication .Escherichia coli and Salmonella Typhimurium. In addition, it also reliably predicts RBPs across related species. Our method recovers RBPs characterized previously in RIC studies with high sensitivity and finds both, RBPs adopting classical RNA-binding architectures as well as RBPs lacking known RBDs.In this paper, we describe our newly developed RBP prediction method TriPepSVM. It applies for the first time string kernel support vector machines for RBP prediction. The model uses exclusively k-mer counts to classify RBPs versus non-RBPs in potentially any species. It does not depend on any prior biological information such as RBD annotation or structure, and therefore allows RBP prediction in an unbiased manner. We show that TriPepSVM performs better than other methods in RBP prediction in human and the bacteria E. coli and Salmonella. It is based on the UniprotKB database , also including all associated sub-terms, i.e. tRNA binding, snRNA binding and poly(A) RNA binding. The number of annotated RBPs in the QuickGO database is limited for some organisms, therefore our pipeline also supports a recursive mode to collect positive data from all members of a specified taxon or branch. For example, taxon 590 will retrieve annotation for all Salmonella strains. In order to avoid duplicate annotations or paralogs in UniprotKB, the software CD-Hit  sequences, also called k-mers, that it contains , is then a vector where each entry counts the number of times each k-mer, from a pool of |Σ|k possible k-mers from an alphabet of size Σ, occurs in the given sequence. The k-spectrum kernel is denoted as the dot product between two sequence feature vectors:TriPepSVM is a discriminative machine learning model based on Support Vector Machines (SVMs) which is trained to classify RBPs versus non-RBPs based on sequence content alone. SVMs are a class of supervised learning algorithms which, given a set of labelled training vectors (positive and negative input examples) learn a linear decision boundary by finding the optimal hyperplane to discriminate between the two classes . The resWe randomly split the collected data into training (90%) and testing 10%) data sets, and stratified the data such that training and test data contain both roughly the same ratio of positives to negatives  for known RBDs from the Pfam database (PFAM 27.0). We obtain 219 different HMMs annotated as ‘RNA binding/recognition’ in the Pfam description, quickGO or Protein Data Bank . We thenZ-score). Statistically significant matching templates with low free energy of the RBP-RNA complex are then used in order to assign a putative RNA-binding function to the query protein. We used a local version of the tool, with E-value < 0.001 from PSI-BLAST, a minimum template matching Z-score of at least 8.04 and maximum binding free energy of –0.565, as proposed by the authors.SPOT-Seq-RNA is a template-based technique to predict RNA-binding potential of a query protein . It emplRNApred is an SVM-based approach to predict RBPs  and its RBPPred is also based on Support Vector Machines for the classification task of RBPs versus non-RBPs . CompareThe performance of all methods was evaluated using two metrics: the area under the receiving operating characteristics curve (AUROC) and the area under the precision-recall curve (AUPR). Both metrics require a set of proteins which have been scored according to their likelihood of being an RBP or not, as well as the known protein class. However, PR curves give a better idea of the false discovery rate of each method as they can better account for class imbalance  comparedWe also computed sensitivity, specificity, precision, balanced accuracy and Matthews correlation coefficient  MCC, se for the We analysed the relative abundance of all tripeptides in structurally disordered versus ordered regions of RBPs with the IUPred tool , which aSalmonella enterica subsp. enterica serovar Typhimurium strain SL1344 was cultivated in standard LB medium if not stated otherwise. We generated chromosomal insertions of FLAG-encoding sequences downstream of candidate genes using the Lambda Red technique  and proteins transferred onto nitrocellulose membranes (BioRad). Membranes were blocked during 30 min with PBST-M , 5% milk) and incubated with dilutions 1:1000 of anti-FLAG  overnight at 4°C (or 2 h room temperature). Antibody binding was detected using the respective anti-mouseHRP secondary antibody (Proteintech) and Clarity ECL Western Blotting Substrate for chemiluminescence in a ChemiDocMP imaging system (BioRad).600 of 2.0. Cultures were either directly irradiated in LB (no centrifugation step before irradiation) by placing on Petri dishes which were kept on ice, exposure to UV light (λ = 254 nm) at 5 J/cm2 in a CL-1000 ultraviolet crosslinker device (Ultra-Violet Products Ltd) and centrifuged at 4°C for 10 min or centrifuged at room temperature for 10 min at 15 000 g and the pellets resuspended in 0.1 vol. of the original volume with water irradiated, and then centrifuged again .For each strain, 100 ml bacterial cultures were grown to an ODImmunoprecipitation and PNK assay of bacterial FLAG-tagged proteins and radioactive labeling of RNA by PNK was performed as described . For detSalmonella and E. coli.We propose TriPepSVM, a SVM-based model to discriminate RNA-binding proteins from non-RNA binders based on the amino acid sequence of the protein of interest. We apply it to the proteomes of human, Data Sets), we compute the best combination of hyper-parameters k (k-mer length), C (model cost) and W+ and W− (class weights) during model training by conducting a grid search in a 10-fold cross-validation setting (see Paragraph Model training). We select C = 1 and k = 3 for all three taxa, as this combination always yielded the best balanced accuracy during the hyper-parameter tuning. We further identify the optimal class weights for human and Salmonella of W+ = 1.8 and W− = 0.2. For E. coli, we identify W+ = 1.0 and W− = 0.1.After collection of the data (see Paragraph Model training). We show that TriPepSVM is capable of recovering most known RBPs in the test set while still maintaining a good specificity , yielding an area under the ROC curve (AUROC) of 0.83 and an Area under the Precision Recall Curve (AUPR) of 0.53 in human .E. coli and Salmonella. TriPepSVM outperformed all other methods in terms of MCC on all three species and had a slightly higher balanced accuracy compared to the best and most recent tool RBPPred. In addition, TriPepSVM reaches a good compromise between sensitivity and specificity at the optimal cutoff . Equally to RNApred, TriPepSVM exhibited the best performance, with a constant runtime in the order of seconds on a single CPU. On the other hand, the runtime of RBPPred and SPOT-seq-RNA increased linearly with the size of the data sets, up to 83 hours and 12 days for 250 sequences, respectively . This highlights that structure template-based methods do not scale sufficiently well when applied in a proteome-wide fashion.We found 2944 proteins with a predicted RNA-binding capability in the human proteome see . To asseIt should be taken into account that the vast majority of RIC-RBPs are mRNA-binders since they were identified using poly-A RNA selection and eukaryotic mRNA only constitutes for a small subset of cellular RNA (around 5%). TriPepSVM, however, was trained on a superset of all known RBPs. Consistent with this, we also identify 14 cytosolic and 6 mitochondrial tRNA ligases, 6 tRNA/rRNA methyltransferases (NSUN2-6) and the majority of ribosomal proteins .The recent consensus for human RBPs ranges from hundreds  to >2000Among the 990 predicted RNA-binders are >200 proteins with ATP-binding capacity, from these are 13 proteins of the AAA ATPase family. The overlap between RNA- and single nucleotide binding is generally very high among the established RBPs as well . Also enE. coli and Salmonella, using Equation Feature importance scores). The highest ranked k-mers (top 50 ranking) are listed in the Salmonella and E. coli, while in k-mers with their corresponding weights are reported. In all three organisms, k-mers containing lysine (K), arginine (R) and glycine (G) are found to have the largest SVM weight. Finding K and R enriched among the triplets was expected since positively charged residues are known to be involved in direct RNA interaction; e.g. the RGG box is a known RNA-binding motif in unstructured regions of proteins (in vitro as well as liquid-liquid phase separations (LLPS) in vivo (k-mers with a negative weight (contributing to classification as non-RBP) contain leucine (L) and/or glutamic acid (E). Consistent with this, E and L are the residues most absent from RNA-binding sites in human cells  than between human and Salmonella (Pearson correlation of 0.11) or human and E. coli (Pearson correlation of 0.12)  Figure –C.k-mers from human, E. coli and Salmonella to our previously identified triplets conserved in eukaryotic evolution (k-mers found to be conserved in eukaryotic RBPs (mainly KR- and RG-containing triplets) seem to be important not only for human, but also for bacterial RBP classification.Next, we compared the weights of all volution   to obtain a larger positive training set, we searched the complete Salmonella Typhimurium proteome for RBPs. We correctly identify 108 of the Uniprot-annotated 115 RBPs, resulting in a 94% recovery rate. We correctly predict Hfq . CspC, on the other hand, is present in SwissProt but was not yet marked as RNA-binding and we fail to predict it for Salmonella. In E. coli, however, we correctly predict CspC as RNA-binder.We next used TriPepSVM to predict potential RBPs in a prokaryotic organism. As for most bacteria, RBPs are poorly annotated in bind RNA . After tdict Hfq , and Prodict Hfq  which ardict Hfq  and Salmlmonella . UnfortuSalmonella , DnaJ (l levels ), UbiG (l levels ) and Cysl levels ) as pred 73% see . Importa 73% see ,55. To e 73% see , we alsoSalmonella RBPs are well predicted by TriPepSVM trained on E. coli and viceversa (BACC of 80% in both cases), in line with the observation that the sequence k-mers required to identify RBPs are very similar in both organisms  out of the four could be confirmed to bind RNA in vivo  many recently-found RBPs lack known RNA-binding domains  and ii) Salmonella and E. coli sharing many more common sequence k-mers in their RNA-binding proteome than human and Salmonella or human and E. coli.Additionally, we have shown that RBP predictions made by training a classifier in one species and predicting in another can be very accurate when performed between related species, while cross-species prediction is more difficult between human and bacteria. This can be explained by the different rules learned by classifiers trained on different species, with E. coli and Salmonella (see Figure The fact that TriPepSVM does not classify all human proteins with a Pfam-domain (see Figure All in all, we show that the propensity of a protein to bind RNA is mostly encoded in its primary sequence and can be confidently predicted based solely on combinations of short amino acid triplets. TriPepSVM outperforms previous approaches which make use of more complex protein features in discriminating RBPs from non-RBPs. It can in principle be applied to any species, from eukaryotes to bacteria where limited experimental data are available. Besides being a valuable RBP prediction method from sequence alone, our approach can pinpoint the important sequence patterns which distinguish RBPs from non-RBPs and points to disordered regions as main determinants of RBP-RNA interactions, in line with the latest studies.https://github.com/marsicoLab/TriPepSVM.The collection pipeline as well as the source code for TriPepSVM are available on Github, Supplementary DataClick here for additional data file.


Circular RNAs (circRNAs) are transcript isoforms generated by back-splicing of exons and circularisation of the transcript. Recent genome-wide maps created for circular RNAs in humans and other model organisms have motivated us to explore the repertoire of circular RNAs in zebrafish, a popular model organism. We generated RNA-seq data for five major zebrafish tissues - Blood, Brain, Heart, Gills and Muscle. The repertoire RNA sequence reads left over after reference mapping to linear transcripts were used to identify unique back-spliced exons utilizing a split-mapping algorithm. Our analysis revealed 3,428 novel circRNAs in zebrafish. Further in-depth analysis suggested that majority of the circRNAs were derived from previously well-annotated protein-coding and long noncoding RNA gene loci. In addition, many of the circular RNAs showed extensive tissue specificity. We independently validated a subset of circRNAs using polymerase chain reaction (PCR) and divergent set of primers. Expression analysis using quantitative real time PCR recapitulate selected tissue specificity in the candidates studied. This study provides a comprehensive genome-wide map of circular RNAs in zebrafish tissues. Circular RNAs encompass one such transcript isoform that has been recently identified in Humans2 and model organisms including mouse2, worms2, fruitfly3 and viruses4. In contrast to the linear transcript isoforms, circular RNAs are formed by a unique mechanism of back-splicing between the 3′ and 5′ splice sites of a gene, resulting in circularised transcript isoform5. Existence of circular RNAs have been previously studied in viruses, but their biogenesis and molecular function in higher eukaryotic systems has remained elusive, though a number of recent evidence suggests their role in regulating important biological functions7. Though a few candidates have been catalogued in humans, as in the case of DCC , a candidate tumour suppressor gene8, these RNAs were largely considered as arising from sequencing artefacts, sequencing errors or products of tandem duplication or reverse transcription-induced template switching. In addition, these transcripts remained elusive because standard transcriptome profiling approaches largely enriched the poly-adenylated fraction of the transcriptome. The large-scale discovery of circular RNAs have been made possible by deep sequencing approaches and mapping algorithms, which enable the accurate annotation and quantitation of circular RNAs9. The methodologies rely on the fact that in contrast to linear isoforms of the transcript that are amenable to exonuclease activity, circular RNAs are resistant to exonuclease activity due to their specific conformation5. In addition, the back-splicing forms a new sequence conformation which could be accurately identified by split-read mapping of deep sequencing reads. Recent genome-scale annotation of circular RNAs in humans10 and mouse11 have suggested a prevalent expression of circular RNAs in various tissues and cell types. Many of these orthologous loci were shown to encode for circular RNAs. Evidence suggests that significant number of circular RNAs could function as microRNA sponges12. The human circular RNA, CIRS7 (previously known as CDR1AS) has been shown to be associated with midbrain development in zebrafish by virtue of being a microRNA sponge for miR-72. Additional evidence on the role of circular RNAs in modulating the pathophysiology of diseases are emerging14.The recent years have seen an in-depth annotation of transcriptome using next-generation sequencing technologies, significantly enhancing the repertoire of transcripts and transcript isoforms identified for protein-coding as well as noncoding RNA genesDanio rerio, zebrafish, has been a popular model system to understand human diseases16. It has been estimated that over 70% of human genes have an orthologue in zebrafish17. Apart from the advantages of being small and embryos being transparent in early development, availability of efficient tools to manipulate the genome and the availability of genome sequences have been well documented19. Recent reports employing RNA sequencing approach has characterized the transcriptome of zebrafish, apart from uncovering a large repertoire of long noncoding RNAs expressed in developmental stages20 as well as adult tissues21. Nevertheless, the earlier RNA-Seq approaches were limited to identifying poly-adenylated transcripts. The availability of approaches to sequence whole RNA has provided a unique opportunity to identify novel transcript isoforms including circular RNAs in zebrafish.et al. reported the identification of circRNAs in zebrafish22 but the expression of these circular RNAs in developmental stages and their tissue specificities remain unknown.Recently Shen In the present study, we use an RNA-seq approach along with computational algorithms to discover 3,428 novel circular RNAs in adult zebrafish. A significant number of the circular RNAs showed tissue specificity. A subset of newly discovered circular RNAs were validated using experimental approaches, and their expression analysis revealed strong concordance with the tissue specificity observed. Our analysis also a subset of circRNAs are derived from orthologous genes in zebrafish and human indicating an evolutionarily conserved molecular mechanism between two species. This report describes a comprehensive genome-wide map of tissue-wise circular RNAs in zebrafish and uncovers a hitherto unknown repertoire of circular RNAs in the organism.Experiments using Zebrafish were performed as per the recommendations and guidelines prescribed by the CSIR Institute of Genomics and Integrative Biology (CSIR- IGIB), India and approved by the Institutional Animal Ethics Committee (IAEC) of CSIR-IGIB which included proactive measures to minimise animal suffering.16.The adult Assam wild type zebrafish (hereafter abbreviated as ASWT) maintained at the CSIR-Institute of Genomics and Integrative Biology were used in this studyTissues were isolated from adult zebrafish anaesthetized using 0.004% Tricaine . Extreme care was taken to avoid contamination to obtain pure homogenous tissue samples. Blood, brain, muscle, gills and heart tissues were extracted for this study. The tissues were repeatedly washed in PBS to remove contaminating debris. The tissue samples were homogenized in Trizol for cell lysis . RNA was isolated from the homogenized tissue samples using RNeasy kit . Sample preparation for sequencing was carried out using Truseq stranded RNA sample preparation kit  as per supplier’s instructions. In order to remove the ribosomal RNA (rRNA), one microgram of total RNA was hybridised with Ribo-zero gold rRNA removal probe. Upon removing rRNA, the samples were processed for fragmentation in the presence of ionic cations at 37 degree Celcius. First stranded complementary DNA (cDNA) was prepared by random hexamers and superscript II reverse transcriptase  in presence of Actinomycin D to facilitate RNA dependent synthesis for improving strand specificity. The second strand was synthesised with second strand cDNA mix containing dUTP instead of dTTP and subjected to A base addition followed by adapter ligation. Final libraries were prepared by amplifying adapter ligated double strand cDNA. Clusters were generated on Hiseq flow-cell v3 (Illumina) in cBot according to standard protocol .23. We used a Phred quality filter cut-off 30. We have submitted sequencing reads for each tissue on SRA archive with submission ID SUB3157917. To identify circular RNAs, we used the circular RNA pipeline findcirc, described by Memczak and co-workers2. Findcirc pipeline was designed to identify circular RNA back splice junctions and identifies reads overlapping the back-splice site. The pipelineuses customized scripts to filter out potential false positives based on the read alignment scores. The pipeline is very user friendly and requires modest computational hardware. Anchor sequence of 20 nucleotides from head and tail of the reads are mapped and extended until full reads were mapped. Contiguous reads with maximum of 2 mismatch and a splice site of GU/AG were applied as filters to enhance the specificity of circular RNA identification. We used the Zv9 reference genome for zebrafish, downloaded from UCSC genome bioinformatics24. Reads were aligned to the indexed Zv9 zebrafish genome using a read aligner tool Bowtie226. We used the options–very-sensitive for end-to-end alignment and minimum score alignment C,-15,0 as used in previous studies2. Reads that aligned to the full length genome were discarded. We extracted 20 bps anchor sequences from both the ends of the remaining unmapped reads. These anchors were aligned back to probe for splice site junctions. From the splice site junction, 20 bps were extended in the reverse direction keeping their paired order under consideration. Anchors aligned in reverse direction (5′ head to 3′ tail junction) were considered potential circular RNA sequences as shown in the Fig. We generated on average 10–12 million reads for each tissue with 101 bp fragment length using Illumina HiSeq. 2500  as per standard protocols provided by the manufacturer. Adapter trimming, quality filters and trimming was implemented using Trimmomatic PEWe have used publically available polyA RNA-seq datasets to identify false-positive candidate circular RNA. The unique backsplice junctions is a specific characteristics of circular RNAs. By the premise, any candidates the computational pipeline would detect from RNAseq of samples prepared by the standard polyA based primers would be artefacts and therefore would enable estimate of the false positives. The list of accession number for datasets used for RNA-seq is mentioned in Supplementary Data Table 27. This tool is not dependent on annotation or circular RNA enrichment, rather uses unique paired chiastic clipping model to identify circular RNAs from the read alignment file. This tool uses BWA as aligner to generate alignment file in SAM format. In the present analysis, BWA-MEM algorithm was used to generate SAM file for each tissue RNA-seq reads and the files were further processed through the CIRI pipeline to identify circRNA junctions.We have used another circRNA identification pipeline, CIRI. CIRI is a popular circular RNA identification as well as annotation tool using RNA-sequencing reads28. The dataset encompassed a total of 2,267 lncRNAs.The genomic coordinates corresponding to RefSeq genes and their untranslated regions (UTRs) and coding sequences (CDS) were retrieved from UCSC genome browser using the Table View option. Similarly the coordinates were retrieved from Ensembl using BioMart. The circular RNA splice junctions were mapped separately to the genomic contexts. We mapped the positions specifically to the whole gene, exons, introns, 5′ UTR, 3′ UTR and 1000 bases upstream and downstream of the genes. The co-ordinates of lncRNAs were downloaded from zflncRNApedia, a comprehensive resource for zebrafish lncRNAs maintained by our group29. In order to validate the origin of the back-spliced transcripts from the transcriptome, and not from the genome, PCR amplification was performed on cDNAs of corresponding tissues and gDNA of ASWT zebrafish, simultaneously, using divergent primers. Convergent primers of beta- actin were used as control for all combinations of reactions.Divergent and convergent primers were designed to selectively amplify the circular and linear transcripts respectively, using coordinates predicted through the bioinformatics pipeline. cDNA was prepared from 200 ng of total RNA from individual tissues, isolated as described before. First strand cDNA synthesis was performed using Superscript II reverse transcriptase . The genomic DNA (gDNA) was isolated from ASWT zebrafish using the standard isopropanol precipitation methodet al. 2013. 5ug of RNA was treated with 15 units of RNAse R for 15 minutes to degrade linear RNA and enrich circular RNAs. RNAse R depleted (RNAseR+) and untreated RNA (RNAseR−) was further purified using LiCl method and RNA conc was determined. 1ug of the untreated RNA and same volume of RNAse R treated RNA was converted to cDNA using Superscript II reverse transcriptase . PCR amplification was performed for selected circular RNA candidates and two linear mRNA as a control in the reaction. These two liner mRNA controls included one tissue specific positive control and second universal control as beta-actin. Primer sequences for PCR are summarised in Supplementary Data Table RNA isolated from each of the 5 tissues was treated with RNAse R as described in Memczak tal1, mdka, tnnt2c, vmhc and cx44.2 were used for the corresponding tissues blood, brain, muscle, heart and gills respectively. Primer sequences for qRT- PCR are summarised in Supplementary Data Table Expression analysis was attempted for selected candidate circular RNAs, which showed tissue-specific expression patterns. RNA was isolated from blood, brain, muscle, heart and gills using RNeasy kit  and cDNA was synthesized, as previously described. Linear and circular transcript levels were assayed by quantitative Real Time Polymerase Chain Reaction (qRT-PCR), carried out using Sybr Green mix  and detected by Lightcycler LC 480 (Roche). Respective gene markers for individual tissues were used as positive controls in all experimental sets. The markers 30. Disease genes were mapped from the Online Mendelian Inheritance in Man (OMIM) and zebrafish mutant information was retrieved from ZFIN using the Ensembl gene ID.We retrieved the human orthologues for zebrafish genes, which had at least one circular RNA originating from the gene boundaries. The circular RNA for the human orthologues were retrieved from circBase, a comprehensive online resource for circular RNAsA schematic representation summarising the approach used for identification and validation of circular RNAs in zebrafish is summarised in Fig. 2. The algorithm uses unmapped reads as templates, aligns and extends 20 nucleotides from either end of the reads, called ‘anchor sequences. These anchor sequences were further extended. The reads with flanking splice site GU/AG were included as potential circular RNA reads. We allowed maximum of 2 mismatches and unique alignment with best matching score in the extension of anchor sequences. The algorithm was applied on unmapped reads from each dataset independently. We used an empirical cut-off of at least 2 reads mapping the back-splice junction to prioritise high quality circular RNAs. Our analysis revealed that ~40% of unmapped reads spanned the back-splice junctions in each dataset  of circular RNAs, with approximately 176 circular RNAs arising from the chromosome Fig. .Figure 3We also used an additional pipeline, CIRI, to identify circular RNA junctions to be able to compare and cross-validate the candidate circular RNAs identified FindCirc. The number of circRNAs identified from this pipeline are listed in the Supplementary Table 30. We overlapped the circular RNA splice sites with the ensemble annotation file and found ~27% of novel splice sites FindCirc circRNA and ~22% of novel splice sites from CIRI circRNAs. The venn diagram is shown in Supplementary Figure We analysed the genomic context of the circular RNAs vis-a-vis annotated Ensembl and Refseq genes. Our analysis suggests that a significant proportion of the circular RNAs in zebrafish arise from genic loci. Out of all, 1,833 circular RNAs (54.47%) map to RefSeq gene loci, whereas 3,015 (87.95%) map to Ensembl gene loci Fig. . SummaryFor the circular RNAs mapping to protein coding gene loci, we further examined whether they have specificity/preponderance to specific gene features. To this end, we examined the splice junctions with respect to the various gene features: CDS, Introns, 3′ and 5′UTRs and 1000bases upstream and downstream of the genes. Since the genomic regions encompassing the features differed in their overall size, a normalised measure adjusting for the bases in each feature was also computed. Our analysis revealed circular RNAs have a preponderance to the 5′UTR of genes. This was followed by the 3′UTR and CDS. Introns had the least preponderance, as expected by the fact that they do not form part of the messenger RNA. This preponderance suggests a potential regulatory nature of circRNAs. These observations were consistent across the RefSeq and Ensembl gene annotations. Incidentally, the 1000 bp upstream and downstream of the gene loci also had a significant number of splice junction mappings  analysis of genes from exonic circRNA clearly showed that most of the genes are either part of chromatin coiling and nuclear processing  circular RNAs in zebrafish mapped to long noncoding RNA loci  showed a tissue restricted expression. The largest number of circular RNAs specific for a tissue was in gills, which had a total of 910, followed by blood (727), heart (523), muscle (376) and brain (159). A total of 733 (21.38%) circular RNAs were shared between any two of the five tissues analysed, out of which only 45 (1.31%) of the circular RNAs were shared between all the five tissues. Functional analysis of the protein-coding genes harbouring these circular RNAs revealed most annotated genes are involved in regulating signalling pathways. The overlaps of circular RNAs discovered in the different tissues is summarised in Fig. We selected candidate circular RNAs for independent validation using PCR followed by quantitation using qRT-PCR using a set of divergent primers for the circular RNA junction and convergent primers for the linear transcript isoforms.hbaa, ank, ikzf, map2k5 and epb4.1 from blood dataset; dab1, fam107a, clstn1, sox9b and taf6 from brain dataset; rmb41, znf609a, eef1al and furina from heart dataset; srek1, nexn, tnnt2a, ttnb1 and ttnb2 from muscle dataset and zp3, atp1a1a, sd:key241l7.3, sd:key194m7.4 and sd:key146l10.7 from gill dataset. This list also included a few candidates which were tissue restricted taf6 (brain), epb4.1 (blood), ttnb1 (muscle). The selection includes circular RNAs arising from genes associated with a variety of functions. For example taf6 (transcription initiation factor subunit 6) is required for transcription activity31, while ttnb1 codes for a muscle protein32. furina, has a role in functional in processing proteins, their regulation and trafficking33 while epb4.1 is an erythrocyte membrane protein34.The genes for validation were randomly selected and include actb1) was used as the positive control, for which the convergent primers were able to amplify the product in both cDNA and as well as gDNA as the template. We could successfully amplify the circular isoform for all the candidates tested. The splice junctions were further confirmed for a few selected candidates by capillary sequencing . Results for the experimental validation is summarised in Fig. Towards validating the predicted circular RNAs, we used a PCR based approach to identify the back-splice junctions. The experiment was designed based on the logic that the convergent  primers would amplify the linear isoform of RNA, whereas the divergent primers would amplify only the circular RNA isoform. The divergent primers of length ~20 nucleotides were designed facing the back-splice junctions to obtain an amplicon of approximately 200 bp. cDNA prepared from individual tissues and genomic DNA isolated from ASWT zebrafish were used for the validation. We selected four candidates, arising from the exonic and intronic loci in the zebrafish genome. The amplicons spanning the junction of the circular RNAs were amplified from the cDNA, whereas no product was obtained in case of the gDNA as template. For all reactions, beta-actin (actb and linear counterpart of one of circular RNA of a given tissue) was only observed in the RNAse R - sample, which explain the activity of the RNAse R treatment  and untreated (RNAse R−) samples in all the 5 tissues confirmed the existence of circular RNAs. Whereas product for linear mRNA . As a case example, we focussed on one candidate circular RNA arising out of a gene conserved and harbour circular RNAs in human and zebrafish. The human clstn1 gene can potentially help us understand the role of the circular RNAs.Conservation and orthologue between both species in case of In the present analysis, we provide a genome-scale map of circular RNAs in major tissues in adult zebrafish. Our analysis uncovers a total of 3,428 circular RNAs from 5 tissues which include blood, brain, heart, gills and muscle for zebrafish. Independent validation of the circular RNA junctions were attempted using divergent primer sets and polymerase chain reaction, which suggests high concordance. Further analysis revealed a large majority of the circular RNAs were derived from protein-coding gene loci. A small minority did arise from long noncoding RNA gene loci. Expression analysis revealed many of the circular RNAs showed tissue-specific patterns of expression. The expression levels of a few candidates were independently validated using RT-PCR, which suggested high concordance. Analysis also revealed that a number of zebrafish lncRNAs arise from human-orthologous genes in zebrafish, suggesting the utility and opportunity to study the biological functions and regulatory networks of human circular RNAs in zebrafish. Our analysis, to the best of our knowledge reports the comprehensive map of circular RNAs in zebrafish. We envision this report could serve as the starting point to understand circular RNA biology and its regulatory networks in key biological processes including development and disease in zebrafish.supplementary_info


FUS, TARDBP, SOD1 and VCP genes) and healthy controls. This dataset allows the whole-transcriptome characterization of PBMCs content, both in terms of coding and non-coding RNAs, in order to compare the disease state to the healthy controls, both for sporadic patients and for mutated patients. Our dataset is a starting point for the omni-comprehensive analysis of coding and lncRNAs, from an easy to withdraw, manage and store tissue that shows to be a suitable model for RNA profiling in ALS.Coding and long non-coding RNA (lncRNA) metabolism is now revealing its crucial role in Amyotrophic Lateral Sclerosis (ALS) pathogenesis. In this work, we present a dataset obtained via Illumina RNA-seq analysis on Peripheral Blood Mononuclear Cells (PBMCs) from sporadic and mutated ALS patients (mutations in  RNA-seq analyses are the gold standard for in depth characterization of global changes in gene expression levels between different conditions 2. The heterogeneity of lncRNAs, both in biological type and function, is a major obstacle that makes it difficult to extract information about lncRNAs function and to enrich our knowledge3. While coding genes are widely annotated, high-quality catalogues of lncRNAs and information on the tissues in which they are expressed are recently being constructed4–6. Recent efforts are directed to characterize this largely unexplored functional component of the genome. Deep sequencing is a precious instrument to obtain information suitable to investigate both coding and lncRNAs simultaneously in the same sample, and this comprehensive RNA analysis can elucidate fundamental mechanisms in gene expression regulation at post-transcriptional level8.Such comprehensive analysis are currently bringing the light on the expression of largely unexplored RNA classes. While coding RNA involvement in a wide plethora of disorders has been subject of intense research, the more recently revealed class of lncRNAs is only starting to be characterized. LncRNAs are RNA transcripts greater than 200 nucleotides that lack an open reading frame and therefore do not encode proteins9–13. ALS is an adult-onset, progressive and fatal neurodegenerative disease, caused by the selective loss of both upper and lower motor neurons in the cerebral cortex, brainstem and spinal cord. The pathogenesis of the disease is still unknown. Moreover, the investigation of ALS mechanism is challenging due to the difficulty to obtain a sufficient amount of biological material from ALS patients: it is indeed impossible to access biological samples from the most affected areas, such as spinal cord. PBMCs have been shown to be a convincing and realistic model for ALS, since many pathways, typically located in neurons, are also activated in these cells15.There is mounting evidence that altered RNA metabolism, both involving coding and non-coding RNAs, plays an important role in ALS pathogenesis16 and low percentage of familial ALS cases (5-10%) impose the requirement of data sharing and data aggregation among different hospitals and laboratories in order to obtain a sufficient sample size. Detailed curation of these datasets is paramount for accurate interpretation, widespread dissemination, and repurposing of data.Furthermore, low ALS incidence (ranging between 1 and 2.5/100’000)SOD1, FUS, TARDBP and VCP) and 7 healthy controls. More in detail, these data are suitable for antisense lncRNAs detection and characterization because of the chemistry of the protocol properly design to maintain strandness information. Strand-specific RNA-Seq permits a more complete understanding of the transcriptome, enhancing resolution for sense/antisense profiling17.In this paper, we present a dataset suitable for the analysis for both coding RNAs and lncRNAs in PBMCs of 15 sporadic ALS (sALS) patients, 9 ALS patients with mutation in classical ALS-genes 8.To our knowledge, no dataset of RNA expression in PBMCs from ALS patients is publicly available. Our dataset is a starting point for the omni-comprehensive analysis of RNA classes, from an easy to withdraw, manage and store tissue that shows to be a suitable model for RNA profiling in ALS18. For these reasons, the re-usability of this dataset is related to the possibility to expand studies with smaller sample sizes, especially for mutated patients, to identify new sense and antisense transcripts with altered expression in both sporadic and mutated ALS patients compared to healthy controls and to provide an open-angle point of view for a broad-spectrum characterization.It was shown that in RNA-seq studies, increasing sample size is the best choice to enhance statistical power24 ALS patients and 7 age- and sex-matched healthy controls (CTRL) were recruited after obtaining written informed consent . ALS pat8.The study protocol to obtain PBMCs from patients and controls was approved by the Ethical Committee of the National Neurological Institute “C. Mondino”, IRCCS . Before being enrolled, the subjects participating in the study signed an informed consent form (Protocol n°375/04 – version 07/01/2004). All experiments were performed in accordance with relevant guidelines and regulations. These methods and following methods are expanded versions of descriptions in our related workPBMCs were prepared by centrifugation. Peripheral blood was layered (density = 1.077) and centrifuged at 950 g for 30 min. After isolation on a Ficoll-Histopaque layer , cell viability was assayed by a trypan blue exclusion test and cells were then used for RNA extraction.Samples were homogenized and total RNA was isolated by Trizol® reagent  following the manufacturer’s protocol. RNAs were quantified using a Nanodrop ND-100 Spectrophotometer  and a 2100 Bioanalyzer ; RNAs with a 260:280 ratio of ≥1.5 and an RNA integrity number of ≥8 were deep sequenced .http://support.illumina.com/downloads/bcl2fastq-conversion-software-v217.html) starting from .bcl files produced by Illumina NextSeq sequencer . The qualities of the libraries were assessed by 2100 Bioanalyzer with a DNA1000 assay. Libraries were quantified by qPCR using the KAPA Library Quantification kit for Illumina sequencing platforms (KAPA Biosystems); RNA processing has been carried out using Illumina NextSeq 500 Sequencing, using 8 samples for each run mixing samples and controls in each flowcell to avoid not manageable batch-effects. FastQ files were generated via llumina bcl2fastq2  after adapter trimming with cutadapt software. Per base sequence quality plots, showing the Phred quality score distribution among all sample reads are shown in 20, this dataset is suitable for differential expression analysis, since about 60% of samples (21/36) have at least 10 M reads. Furthermore, biological replicates are the most effective strategy to improve statistical power in such experiments. For this reason, we decided to share our dataset with the scientific community, to have the possibility of integrating our data with similar data obtained in different laboratories.Mapping results are summarized in 21.Expressed transcripts per sample were evaluated imposing a minimum threshold of 5 counts per gene to consider it as expressed. 22 (see Code Availability 4). This tool was selected because of its superior performance in identifying isoforms differential expression23.Differential expression analysis for all transcripts (coding and non coding RNAs) was performed with the R package DESeq22| ≥ 1 and a FDR ≤ 0.1. Analysis workflow is summarized in Genes were considered differentially expressed and retained for further analysis with |log24. Results are summarized in For each sample, we evaluated the number of detected transcripts for coding and lncRNAs, separately. Transcripts were considered if covered by at least 5 readsThe following software and versions were used for quality control and data analysis as described in the main text:http://www.bioinformatics.babraham.ac.uk/projects/fastqc/FastQC v0.11.6 was used for quality assessment of raw FASTQ sequencing data: https://github.com/alexdobin/STAR/releasesSTAR 2.6 was used for reads mapping to the human GRCh38 genome assembly: http://bioinf.wehi.edu.au/featureCounts/RSEM v1.3.0, was used for gene expression quantification: http://bioconductor.org/packages/DESeq2DESeq2 v1.20.0 was used for differential gene expression analysis: Raw FASTQ files for the RNA-seq libraries were deposited to the NCBI Sequence Read Archive (SRA), and have been assigned BioProject accession PRJNA416880, RNAs were quantified using a Nanodrop ND-100 Spectrophotometer  and a 2100 Bioanalyzer ; RNAs with a 260:280 ratio of ≥1.5 and an RNA integrity number of ≥8 were subjected to deep sequencing.We applied FastQC v0.11.5 software to verify that per base quality scores are suitable for downstream analysis . MappingHow to cite this article: Zucca, S. et al. RNA-Seq profiling in Peripheral Blood Mononuclear Cells of Amyotrophic Lateral Sclerosis patients and controls. Sci. Data. 6:190006 https://doi.org/10.1038/sdata.2019.6 (2019).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


A collection of recombinant rotaviruses that express the fluorescent markers UnaG, mKate, mRuby, TagBFP, CFP, or YFP as separate proteins was generated. Genes for the fluorescent proteins were inserted into genome segment 7 without compromising expression of the protein NSP3. These recombinant rotaviruses are valuable for analyzing rotavirus biology by fluorescence-based live-cell imaging. A collection of recombinant rotaviruses that express the fluorescent markers UnaG, mKate, mRuby, TagBFP, CFP, or YFP as separate proteins was generated. Genes for the fluorescent proteins were inserted into genome segment 7 without compromising expression of the protein NSP3. These recombinant rotaviruses are valuable for analyzing rotavirus biology by fluorescence-based live-cell imaging. Reoviridae family, rotaviruses have a segmented double-stranded RNA (dsRNA) genome that is replicated using viral positive-sense RNA [(+)RNA] as a template. Recently, an efficient plasmid-based reverse genetics (RG) system was developed that allows any of the 11 dsRNA segments of the rotavirus genome (strain SA11) to be genetically modified  with 11 T7 (pT7) expression plasmids, each directing synthesis of a different viral (+)RNA, and a cytomegalovirus (CMV) plasmid that mediates expression of the RNA capping enzyme of African swine fever virus (ASFV) NP868R . We modiThis collection of recombinant rotaviruses provides a foundation for establishing fluorescence-based live-cell imaging methods for determining titers of the virus and monitoring spread of the virus, characterizing neutralizing antibodies and antiviral agents, and analyzing viral gene expression during the replication cycle. Moreover, rotaviruses expressing different FPs allow experiments designed to understand parameters affecting coinfection frequencies and the formation of reassortment viruses.The recombinant rotavirus strains in this collection are available to laboratories operating with biosafety level 2 (BSL2) certification that have received institutional approval to receive and perform experiments with rotavirus. Requests for strains should be directed to the corresponding author. GenBank accession numbers of the segment 7 RNAs in the rotavirus-2A/FP strains are given in