Terc), the RNA component of mitochondrial RNA processing endoribonuclease (Rmrp), and Cajal body-specific RNAs. In addition, a number of novel telomere-binding non-coding RNAs were also identified. Binding of two candidate non-coding RNAs to telomeres was confirmed by immunofluorescence microscopy and RNA fluorescence in situ hybridization (RNA-FISH) analyses. The novel telomere-binding non-coding RNAs identified here may play important roles in telomere functions. To our knowledge, this study is the first non-biased identification of RNAs associated with specific genomic regions. The results presented here suggest that enChIP-RNA-Seq analyses are useful for the identification of RNAs interacting with specific genomic regions, and may help to contribute to current understanding of the regulation of genome functions.Accumulating evidence suggests that RNAs interacting with genomic regions play important roles in the regulation of genome functions, including X chromosome inactivation and gene expression. However, to our knowledge, no non-biased methods of identifying RNAs that interact with a specific genomic region have been reported. Here, we used enChIP-RNA-Seq, a combination of engineered DNA-binding molecule-mediated chromatin immunoprecipitation (enChIP) and RNA sequencing (RNA-Seq), to perform a non-biased search for RNAs interacting with telomeres. In enChIP-RNA-Seq, the target genomic regions are captured using an engineered DNA-binding molecule such as a transcription activator-like protein. Subsequently, RNAs that interact with the target genomic regions are purified and sequenced. The RNAs detected by enChIP-RNA-Seq contained known telomere-binding RNAs, including the telomerase RNA component ( The regn (FISH)  and olign (FISH) –9. ThesePax5 locus [Recently, we developed two locus-specific chromatin immunoprecipitation (locus-specific ChIP) technologies, namely insertional ChIP (iChIP) –15 and ex5 locus .2AG3 sequences (telomeric repeats) and interacting RNAs and proteins, which form a large DNA-RNA-protein complex. Telomeric repeats are maintained by the action of telomerase, which comprises a protein component named telomerase reverse transcriptase (TERT), and an RNA component named Terc [Terc, other RNAs also interact with the telomerase complex via a direct interaction with TERT or an indirect interaction with other telomerase-associated proteins [Telomeres are specialized chromatin structures that protect the ends of chromosomes from being recognized as broken DNA . Telomermed Terc , 21. In proteins , 23. AltHere, We Combined Enchip With Rna Sequencing (Rna-Seq), Hereafter Referred To As Enchip-Rna-Seq, To Perform A Non-Biased Investigation Of Non-Coding Rnas That Interact With Telomeres. Using This Method, Both Known And Novel Telomere-Interacting Rnas Were Identified. We Propose That Enchip-Rna-Seq Is A Useful Tool For Analyzing The Effects Of Specific Rnas On Genome Functions.Terc were identified by mass spectrometry and RT-PCR analyses, respectively [We recently reported the isolation of telomeres by enChIP using a transcription activator-like (TAL) protein that recognizes telomeres (Tel-TAL) . In thisectively .Terc [Rmrp) [4 or (CCCTAA)4 motifs [Here, we used enChIP-RNA-Seq to identify telomere-associated RNAs in a non-biased manner . The RNATerc , 21, thec [Rmrp) , and smac [Rmrp) . In addi4 motifs , we dete4 motifs , 28 in tTerc, which serves as a template for the telomere repeats [Terc in telomeres purified by enChIP [Rmrp is a non-coding RNA that is found in the nucleolus and mitochondria [Rmrp form a ribonucleoprotein complex that retains RNA-dependent RNA polymerase activity [As mentioned above, the telomerase holoenzyme consists of TERT, a protein with reverse transcriptase activity, and  repeats  and is a repeats , 30. A py enChIP . Rmrp ischondria , and is chondria . TERT anactivity . The scaactivity , 30, areactivity . TERRAs activity  and may activity –28.Several members of the H/ACA family of small nucleolar RNAs (snoRNAs) were more abundant in isolates from enChIP from Ba/F3 expressing 3xFN-Tel-TAL than those from Ba/F3 expressing 3xFNLDD . The funin vivo. In addition to known telomere-binding RNAs, a number of novel potential telomere-binding RNAs were also identified using enChIP-RNA-Seq, including long non-coding RNAs (lncRNAs) and members of the snoRNA family containing C and D motifs (C/D snoRNAs) (The fact that we detected known telomere-binding RNAs using enChIP-RNA-Seq suggests that it is feasible to use this technology to perform non-biased identification of RNAs interacting with genomic regions of interest snoRNAs) . To idensnoRNAs) . OverallSNORD17  and NEAT1 (nuclear-enriched abundant transcript 1), with TRF2, a marker protein of telomeres . This approach would be a method of choice for identifying RNAs that are associated with a specific genomic locus Terc, Rmrp, Terras, And Scarnas 4 repeats were extracted from each fastq file using the grep command in the UNIX system, as described previously [To estimate the number of putative TERRAs, reads containing (TTAGGG)eviously .http://www.genomics.agilent.com/article.jsp?crumbAction=push&pageId=1520) were re-annotated according to the NONCODE v3.0 database (http://www.noncode.org/) . A total of 3,540 lncRNAs were extracted using Agilent lncRNA probes . The lncRNA probes  . The RNAde.org/) .SNORD17 probe (synthesized by Life Technologies Inc.) was as follows: 5'-GTGAAATGATGATTCAGTTTATCCATTCGCTGAGTGCGCTGCACTGACCTTCTTCCAAGCCTCAGTTCCTGTTCTAGGAACTTGAGGCTATGTAGCCTGAAAATGCCCTGCAGTCTGCAGTGTTCTACTGTGAACTGCTTGTGTGTTGGCAGGCTACCGGTAAGAATGGTTGGTGTCAGCAGGGACGGGGCCCTCTGAGACCCATCTCACAAAGATGAGTGGTGAAAATCTGATCAC-3'. The human NEAT1 probe was generated by PCR amplification using 293T genomic DNA as template and the primers (hNEAT1_shortprobe_F and hNEAT1_shortprobe_R) described previously [The sequence of the human eviously . The RNAImmnofluorescence combined with RNA-FISH was performed as described previously . BrieflyS1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.


This immunomodulatory potential of RNA can be used to design RNA-based immunotherapy and vaccine adjuvants. In case of siRNA-based therapy, the immunomodulatory effect of an RNA sequence is unwanted as it may cause immunotoxicity. Thus, we developed a method for designing a single-stranded RNA (ssRNA) sequence with desired immunomodulatory potentials, for designing RNA-based therapeutics, immunotherapy and vaccine adjuvants. The dataset used for training and testing our models consists of 602 experimentally verified immunomodulatory oligoribonucleotides (IMORNs) that are ssRNA sequences of length 17 to 27 nucleotides and 520 circulating miRNAs as non-immunomodulatory sequences. We developed prediction models using various features that include composition-based features, binary profile, selected features, and hybrid features. All models were evaluated using five-fold cross-validation and external validation techniques; achieving a maximum mean Matthews Correlation Coefficient (MCC) of 0.86 with 93% accuracy. We identified motifs using MERCI software and observed the abundance of adenine (A) in motifs. Based on the above study, we developed a web server, imRNA, comprising of various modules important for designing RNA-based therapeutics ( Mammalian innate immunity has dedicated sensor and effector proteins to combat viral invasion. Interferons were the first class of molecules to be discovered, acting as armor for extracellular defense against virusesIn case of a few viral infections, cellular defense system recognizes the pathogen from its RNA sequence. In other words, RNA sequences also serve as PAMP for viral infections. It was shown that a firm innate defense against bacterial RNA involved cytosolic detection of bacterial mRNANumerous receptors for RNA in the cytosol of human cells have been discovered. In addition to the diversity of types of RNA ligands of these receptors, the overlap of the Pathogen-associated molecular patterns in the ligands of these receptors is also enigmatic77siRNAs are RNA oligonucleotides that spearhead the RNA Interference (RNAi) technology. Our cellular sensors may recognize these siRNAs as foreign RNA and may activate the immune system, which is undesirable and may cause immunotoxicity1316In this study, we collected and compiled ssRNA oligoribonucleotide sequences (ORNs) whose immunomodulatory potential has been tested experimentally. Using these experimentally validated siRNAs, we developed sequence-based models for discriminating immunomodulatory oligoribonucleotides (IMORNs) and non-immunomodulatory oligoribonucleotides (non-IMORNs). The experimentally validated IMORNs have been picked up from literature, patents, and RNAimmunoIn order to understand the nucleotide bias in IMORNs and non-IMORNs, we computed and compared the nucleotide compositions of two types of sequences. Student’s t-test between mononucleotide composition of IMORNs and non-IMORNs revealed significant compositional differences in the cases of Guanine (G) and Adenine (A) . This waThe role of motifs and patterns is crucial to discriminate and classify the biological sequences. Therefore, we have also tried to identify exclusive motifs in IMORNs and non-IMORNs. The Motif—EmeRging and with Classes—Identification (MERCI) programhttp://www.twosamplelogo.org/cgi-bin/tsl/tsl.cgi. Creation of TSL requires fixed length of all the sequences. Since the sequence length in the dataset varied from 17 to 27, we created a 17mer TSL separately for 5′ and 3′ termini of the sequences equences . TSL depequences  clearly The compositional analysis revealed that the average composition of certain mononucleotides, dinucleotides, trinucleotides, tetranucleotides and pentanucleotides is significantly higher in IMORNs in comparison to non-IMORNs. This means composition can be used as a feature for discriminating these two classes of RNA sequences. Thus, we developed support vector machine (SVM)-based models using composition of RNA sequences as input feature, for predicting IMORN sequences. To evaluate the performance of these models, we used internal validation and external validation techniques. Additionally, we also used a sampling approach for calculating the average performance of models on internal and external datasets. In the case of internal validation, we computed the performance of models on training-testing datasets (IMrnaTT) using five-fold cross-validation technique. The average performance of our models on internal datasets is shown in N will be represented by a vector of length N × 4. In the present study, we have RNA sequences of varied length thus it is difficult to represent them using fixed length vector (a requirement of SVM). To overcome this limitation, we used fixed length of mononucleotides from the termini of RNA sequences for developing binary-based models. First, we used 17 nucleotide positions from 5′ terminus of RNA sequences for developing the model using binary profile of these mononucleotides; these mononucleotides were represented by a vector of length 68 (17 × 4). Similarly, we developed models using 3′ end of RNA sequences. Finally, we developed models using both the ends of the sequences; our final model used both 5′ and 3′ termini sequences, achieving a mean accuracy and MCC of 90.44% and 0.81, respectively.In case of binary representation, we represented a mononucleotide with a vector of length four, it means a sequence of length We also checked a hybrid model having all features of trinucleotide composition along with the binary profile of 5′ and 3′ termini taken together for performance. The accuracy, in this case, was 91.79% while MCC was 0.84, both being mean values of those obtained in each of the 10 sampling cases.The performances of the composition, binary as well as hybrid models were evaluated on the independent datasets (ImrnaID) also called external validation datasets. It is important to validate any model on independent datasets as these have not been used in training or testing of the models. As shown in WEKAPrevious studies have implied the role of RNA secondary structure in immunostimulatory activity16http://crdd.osdd.net/raghava/imrna/. It has a number of modules; here we provide a brief description of two major modules of this server.In order to provide service to the scientific community, we developed a web server that implements the above prediction models. This web server is available for free use at This module allows the users to design RNA-based vaccine adjuvants with desired immunomodulatory potentials. It has three sub-modules developed for performing various predictions on ssRNA. First sub-module allows the users to perform virtual screening of RNA oligonucleotides where the server predicts immunomodulatory potential of each oligonucleotide in a given library. Secondly, the server allows the users to design analogs by generating all the possible analogs (single site substitutions) of an RNA oligonucleotide and predicting the class (IMORN or non-IMORN) of each analog. This way, the user can select the most appropriate analogs with desired immunomodulatory potentials. Thirdly, the server facilitates the users in the identification of immunomodulatory regions (RNA oligonucleotides) in a given RNA sequence. In order to provide useful information about predicted RNA oligonucleotides, the server also computes the structure of these RNAs using RNAfold program available from the ViennaRNA package 2.0For the users working in the field of siRNA-based therapeutics, we developed a module that facilitates the users in predicting toxicity of siRNA sequences. In cases where the immunomodulatory ability of siRNA proves to be a disadvantage, the user might be in search of siRNAs with minimal immunotoxicity. First sub-module allows the users to submit a library of siRNA sequence for predicting immunotoxicity or immunomodulatory capability of each siRNA. The server also computes siRNA efficacy of query siRNAs using desiRmet al.et al.in silico platform that has modules for both the purposes – enhancing immunological therapeutic ability of ORNs as well as diminishing the siRNA immunotoxicity.In the past, meticulous efforts have demonstrated the importance of understanding the immunostimulatory potential of RNA sequence for designing better therapeutics. It has been shown previously that combining gene silencing with virus-like immunostimulation of RNAs (two-pronged strategy) can be used for effective treatment of cancer and viral infectionsIn diseases like cancer and viral infections, immunostimulatory properties of siRNA could augment the therapeutic effect that relied solely on gene silencing ability. Such bifunctionality has been achieved using sequence-based modificationset al.et al. 2008In our study, we found that the positive (immunomodulatory) sequences are enriched in A-rich motifs as compared to the negative sequences where the G-rich motifs prevail. The primary reason for this result is that our negative dataset is made from the circulating miRNAs found in the body fluids. On comparison of the nucleotide composition between the positive and the negative dataset, the difference in the U composition between positive and negative sequences was found to be least significant 8We found 617 such sequences of ssRNA in 4 patents available in the literature of which 493 sequences had length from17 to 27 nucleotides. RNAimmuno39In this study, we performed both internal and external cross-validation, as well as sampling approach for computing unbiased performance of our models. First we created a training-testing dataset called IMrnaTT that constitutes ~80% sequences (482 positive and 416 negative sequences) of main dataset IMrnaDS. We used the remaining sequences for creating independent dataset IMrnaID containing ~20% (120 positive and 104 negative sequences) of the main dataset IMrnaDS .The training-testing dataset, IMrnaTT, was subjected to five-fold cross-validation in which the dataset is divided into five parts. In each of the five rounds of evaluation, the model is trained on four parts and tested on the left out fifth part. In this manner, each part has been tested once. Finally, we computed overall performance of a model on whole IMrnaTT dataset. To optimize our SVM models, we tried various combinations of SVM parameters  for getting maximum performance. We tried most of the SVM parameters or combination of SVM parameters for developing and evaluating the performance of each model on IMrnaTT dataset. Finally, we identified the SVM parameters that gave best performance of an SVM model on our training-testing dataset IMrnaTT. The best performance of the model was found from the above process called internal validation.In case of internal validation, we optimized the performance of our models by trying all possible combinations of SVM parameters and identified the best SVM parameters. We developed an SVM model on training-testing dataset IMrnaTT using these best SVM parameters. We evaluated the performance of this SVM model on the independent dataset IMrnaID, this process being called external validation of a model.Monte-Carlo Sampling). Finally, we evaluated the performance of our models using various features on training-testing and independent dataset as described in the above sections. This process gave 10 performance values using internal and 10 performance values using external validation from 10 rounds of sampling. We computed the mean and standard deviations of these performance values on IMrnaTT and IMrnaID datasets provided in the The process of creating the training-testing dataset (IMrnaTT) and the independent dataset (IMrnaID) from the main dataset (IMrnaDS) was repeated ten times. Each time, the sequences for IMrnaTT were randomly selected from IMrnaDS, and the remaining sequences were included in IMrnaID .


Membrane transport proteins (transporters) move hydrophilic substrates across hydrophobic membranes and play vital roles in most cellular functions. Transporters represent a diverse group of proteins that differ in topology, energy coupling mechanism, and substrate specificity as well as sequence similarity. Among the functional annotations of transporters, information about their transporting substrates is especially important. The experimental identification and characterization of transporters is currently costly and time-consuming. The development of robust bioinformatics-based methods for the prediction of membrane transport proteins and their substrate specificities is therefore an important and urgent task.Support vector machine (SVM)-based computational models, which comprehensively utilize integrative protein sequence features such as amino acid composition, dipeptide composition, physico-chemical composition, biochemical composition, and position-specific scoring matrices (PSSM), were developed to predict the substrate specificity of seven transporter classes: amino acid, anion, cation, electron, protein/mRNA, sugar, and other transporters. An additional model to differentiate transporters from non-transporters was also developed. Among the developed models, the biochemical composition and PSSM hybrid model outperformed other models and achieved an overall average prediction accuracy of 76.69% with a Mathews correlation coefficient (MCC) of 0.49 and a receiver operating characteristic area under the curve (AUC) of 0.833 on our main dataset. This model also achieved an overall average prediction accuracy of 78.88% and MCC of 0.41 on an independent dataset.TrSSP: The Transporter Substrate Specificity Prediction Server, a web server that implements the SVM models developed in this paper, is freely available at http://bioinfo.noble.org/TrSSP.Our analyses suggest that evolutionary information  and the AAIndex are key features for the substrate specificity prediction of transport proteins. In comparison, similarity-based methods such as BLAST, PSI-BLAST, and hidden Markov models do not provide accurate predictions for the substrate specificity of membrane transport proteins.  Membrane transport proteins, also known as transporters, transport hydrophilic substrates across hydrophobic membranes within an individual cell or between cells, and therefore play important roles in several cellular functions, including cell metabolism, ion homeostasis, signal transduction, binding with small molecules in extracellular space, the recognition process in the immune system, energy transduction, osmoregulation, and physiological and developmental processes et al. et al. et al. http://www.tcdb.org) Early bioinformatics studies classified and assigned transport proteins to a particular transporter class based on multiple sequence alignment. Recently, several methods based on machine learning techniques have been developed et al.Arabidopsis thaliana  transport proteins and developed models to predict amino acid transporters, oligopeptides transporters, phosphate transporters, and hexose transporters et al.et al. One limitation of these methods, however, is that the prediction of substrate specificities of transporters using these general classification systems is difficult. Common protein sequence similarity search-based methods fail to predict the substrate specificities of transporters because very low similarity exists both within the same substrate transport protein classes and between different substrate transport protein classes. Recently, Schaadt TrSSP: the Transporter Substrate Specificity Prediction Server, which is a web server that implements and demonstrates these SVM models. The TrSSP web server is freely available at http://bioinfo.noble.org/TrSSP.In these previous studies, substrate-specific protein classes with insufficient data have been merged into one general class labeled “other transporters”. In this study, our main goal was to classify transport proteins into the maximum possible number of classes according to their transported substrates. To achieve this goal, we first constructed a substrate-specific transport protein dataset that consisted of seven classes of transporters exclusive to a particular substrate, i.e., amino acid transporters/oligopeptides, anion transporters, cation transporters, electron transporters, protein/mRNA transporters, sugar transporters, and other transporters. We also compiled a set of non-transporters as an extra class for background controls. For each substrate class, proteins of that class are considered as positive dataset while proteins of other classes are consider as negative dataset. We systemically analyzed the amino acid composition and physico-chemical composition of each protein and found compositional differences among different classes of proteins. We then developed support vector machine (SVM) models that utilized the different properties of transporter protein sequences. We found that our SVM model based on biochemical composition and evolutionary information  could accurately predict substrate specificity. We adopted a five-fold cross-validation evaluation schema to assess the performance of the developed models. Our best SVM models achieved accuracies of 84.08%, 69.19%, 76.59%, 81.43%, 77.96%, 78.57%, 66.73%, and 78.99% for amino acid transporters, anion transporters, cation transporters, electron transporters, protein/mRNA transporters, sugar transporters, other transporters, and non-transporters, respectively. We further evaluated the performance of these models on 180 independent proteins, and the best model achieved accuracies of 83.33%, 69.44%, 74.44%, 91.11%, 83.33%, 77.78%, 71.67%, and 80.00% for amino acid transporters, anion transporters, cation transporters, electron transporters, protein/mRNA transporters, sugar transporters, other transporters, and non-transporters, respectively. Finally, we developed Figure S1 for details about the data compilation and curation processes). The 900 remaining transporter sequences were then divided into seven major classes of transporters based on their substrate specificity: 85 amino acid/oligopeptide transporters, 72 anion transporters, 296 cation transporters, 70 electron transporters, 85 protein/mRNA transporters, 72 sugar transporters, and 220 other transporters. We also compiled 660 non-transporters as an extra class of control proteins in our model development process by randomly sampling all the proteins in UniProt release 2013_03 excluding the 10,780 transporters.We collected from the SwissProt UniProt database (release 2013_03) 10,780 transporter, carrier, and channel proteins that were well characterized at the protein level and had clear substrate annotations Table S1 for a detailed dataset partition; all the sequences are available on our TrSSP web server at http://bioinfo.noble.org/TrSSP/). We applied a five-fold cross-validation schema on the 1,380 proteins in the main dataset to develop our SVM models. The performance of these SVM models was further tested and validated on the independent dataset of 180 proteins. To evaluate the prediction accuracy of the models for each class of proteins, proteins within the same class were considered a positive predictor and proteins from the remaining classes were considered a negative predictor.We further divided the 1,560 compiled proteins into two datasets: 1) the main dataset, which consisted of 70 amino acid transporters, 60 anion transporters, 260 cation transporters, 60 electron transporters, 70 protein/mRNA transporters, 60 sugar transporters, 200 other transporters, and 600 non-transport proteins for a total of 1,380 proteins; and 2) an independent dataset, which consisted of 15 amino acid transporters, 12 anion transporters, 36 cation transporters, 10 electron transporters, 15 protein/mRNA transporters, 12 sugar transporters, 20 other transporters, and 60 non-transport proteins for a total of 180 proteins  features. Two consecutive amino acids are used to calculate the dipeptide composition information. This representation encompasses information about the amino acid composition as well as the local order of amino acids. The percentage of each dipeptide was calculated using the following formula:The physico-chemical composition is the composition of the physico-chemical class residues in each protein sequence. We calculated the percentage composition of charged , aliphatic , aromatic , polar , neutral , hydrophobic , positively charged , negatively charged , tiny , small , and large  residues in each protein sequence http://www.cbrc.jp/~gromiha/fold_rate/property.html; the details of each property are available at this website. We calculated the average of each biochemical property for each protein sequence using the following equation:The biochemical composition of the amino acid residues was also used as an input feature to the SVM for model development. We used a set of 49 selected physical, chemical, energetic, and conformational properties to define the biochemical composition of each protein sequence ith biochemical property in a given protein sequence, ith biochemical property, and n is the length of the protein sequence. We therefore converted the biochemical properties of each protein sequence into a vector with a fixed size of 49.Where TrSSP web server development, which significantly reduced the computational runtime. The PSSM profile of a protein sequence extracted from PSI-BLAST was used to generate a 400-dimensional input vector to the SVM by summing all the rows in the PSSM that correspond to the same amino acid in the primary sequence. Every element in this input vector was then divided by the length of the sequence and scaled to the 0–1 range using the following standard linear function:Value represents the individual final sum of the PSSM score for each amino acid PSI-BLAST  is a popular tool for the detection of distantly related proteins. PSI-BLAST calls BLAST  to construct a profile or position-specific scoring matrix (PSSM) from the multiple alignments of the highest scoring hits in an initial BLAST search . The newly generated profile is then used iteratively to perform subsequent BLAST searches, and the result of each iteration is in turn used to refine the PSSM profile Cross-validation is a practical and reliable way to test the predictive power of a newly developed model. The jack-knife or leave-one-out cross-validation (LOOCV) http://svmlight.joachims.org/. SVM-Light enables the user to define the number of parameters and choose an inbuilt kernel, such as a linear, polynomial, sigmoid, or radial basis function (RBF) kernel. In this study, we tested linear, polynomial and RBF kernels for model development and found RBF performed better than other kernels. We also optimized both cost and gamma parameters  of RBF kernel.The support vector machine (SVM) is a universal machine learning approximator based on the structural risk minimization (SRM) principle of statistical learning theory Sequence similarity remains the most popular method for the functional characterization of proteins. Therefore, we compared the performance of our SVM models for the prediction of substrate-specific transporter classes on both our main dataset and independent dataset to the following similarity search based methods: BLAST, PSI-BLAST, and hidden Markov models (HMM). In these similarity search based method development and evaluations, we used all unique transporter protein sequences without applying homology sequence filtering by using the CD-HIT tool.BLAST  is one of the most popular bioinformatics tool for functional annotation of protein and nucleotide sequences n+1 is built from a constrained multiple alignment between the query sequence and the sequences found with a sufficiently low e-value in round n. This scoring matrix produces a profile designed to identify the key positions of conserved amino acids within a motif. Subtle relationships between proteins that are distant structural or functional homologs can often be detected when this profile is used to search a database; these relationships are often not detected by a BLAST search. Therefore, we used PSI-BLAST in addition to BLAST to detect remote homologies. We conducted an iterative search in which the sequences found in one round were used to build score models for the next round of searching. Three iterations of PSI-BLAST were conducted at different cutoff e-values. This module could predict any of the seven transporter and one non-transporter classes depending on the similarity of the query protein to the proteins in the dataset. If the top hit had an e-value lower than the cut-off threshold, then the annotation of the top hit was used as the predicted annotation of the query.PSI-BLAST is a tool that produces a PSSM constructed from a multiple alignment of the top-scoring BLAST hits to a given query sequence http://hmmer.janelia.org.HMMs are statistical models of the primary structure consensus of a sequence family. HMMs were initially developed for speech recognition To implement the HMM-based method, the entire dataset was divided into 5 subsets similar to the five-fold cross-validation schema Sensitivity, specificity, accuracy, coverage, and the Matthews correlation coefficient (MCC) were calculated for each test dataset in our five-fold cross validation to test the performance of each model. Parameters computed from each subset were averaged across all five subsets to obtain a final value., which evaluates the percentage of transporters that were correctly predicted as transporters.Sensitivity was computed as , which evaluates the percentage of non-transporters that were correctly predicted as non-transport proteins.Specificity was computed as Accuracy was computed as Coverage was computed as , is a statistical parameter that assesses the quality of the binary classification for each model. The MCC accounts for both true and false positive predictions and is regarded as a balanced measure even when the two classes are different sizes. An MCC equal to 1 is regarded as a perfect prediction; an MCC close to 0 is regarded as a random prediction. In these formulas, TP (true positive) represents the number of correctly predicted transporters, TN (true negative) represents the number of correctly predicted non-transporters, FP  represents the number of non-transporters predicted as transporters, and FN  represents the number of transport proteins predicted as non-transporters.The Matthews correlation coefficient (MCC), which was computed as All the parameters described above are threshold-dependent parameters; therefore, the performance of a model depends on a threshold. An analysis of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve overcomes the threshold dependence of the above metrics. The ROC curve plots the true positive proportion  against the false positive proportion  for each model. The area under this ROC curve provides a single measure on which to evaluate the performance of each model. This well-known threshold-independent ROC analysis enables the evaluation of the performance of a binary classifier system as the discrimination threshold of that system is varied. An AUC of 1.0 indicates a perfect prediction and an AUC of 0.5 indicates that the prediction is no better than a random guess.We computed the amino acid composition of eight classes of proteins, including seven substrate-specific transporter classes and one class of non-transporters. The composition of charged amino acids, such as Asp, Glu, Arg, and Lys as well as Gly, Ile, Phe, Gln, and Val shown in Figure 1We used the amino acid composition, dipeptide composition, physico-chemical composition, biochemical composition (AAIndex), PSSM, and a combination of these properties to develop different models to discriminate amino acid/oligopeptides, anion, cation, electron, protein/mRNA, sugar, and other transporters and non-transporters. We then systematically evaluated the performance of each model using ROC analyses for (a) amino acid transporters; (b) anion transporters; (c) cation transporters; (d) electron transporters; (e) protein/mRNA transporters; (f) sugar transporters; (g) other transporters; and (h) non-transporters (see results in Table S2 and Table S4), the PSSM profile takes a long time to compute due to the size of the UniRef90 database. Therefore, we used the UniProtKB/SwissProt release 2013-03 as the reference dataset in order to reduce the PSSM computation time. We achieved a similar result when this PSSM profile was used in the SVM model, and the PSSM generation process was about 10 times faster than the generation process that used UniRef90. The hybrid model that included the biochemical composition and this PSSM profile achieved an accuracy of 83.27%, 67.14%, 76.15%, 81.43%, 74.69%, 78.57%, 66.71%, and 78.12% and an accuracy of 84.44%, 68.33%, 71.11%, 81.67%, 83.33%, 80.56%, 69.44%, and 80.00% for amino acid transporters, anion transporters, cation transporters, electron transporters, protein/mRNA transporters, sugar transporters, and other transporters, respectively, of which the performances were analyzed based on both the main dataset and the independent dataset (see Table S3 and Table S5). Confusion matrix of training data suggests that our best model working well for each class of transporters in main dataset (Table S6).Although SVM models using the PSSM profile, which was generated with UniRef90, performed well (Table S1) to further evaluate the performance of our SVM models. The best SVM model, which used both the biochemical composition and the UniRef90-generated PSSM profile, achieved an accuracy of 83.33%, 69.44%, 74.44%, 91.11%, 83.33%, 77.78%, 71.67%, and 80.00% for amino acid transporters, anion transporters, cation transporters, electron transporters, protein/mRNA transporters, sugar transporters, and other transporters, respectively .We also tested the hybrid model that used the biochemical composition and the UniProt/SwissProt-based PSSM profile on the independent dataset. This model achieved an accuracy of 84.44%, 68.33%, 71.11%, 81.67%, 83.33%, 80.56%, 69.44%, and 80.00% for amino acid transporters, anion transporters, cation transporters, electron transporters, protein/mRNA transporters, sugar transporters, and other transporters, respectively. Confusion matrix of independent data suggests that our best model working well for independent data of each class of transporters  and transporters . TTRBF web server. Our models outperformed the Chen et al. models in all cases except in the case of ion transporters by an extremely small margin , and A. thaliana proteins. To perform a proteome-level transporter analysis, we collected experimentally annotated full-length protein sequences from SwissProt release 2013-06. The details of this analysis are summarized in TrSSP web server (http://bioinfo.noble.org/TrSSP/?dowhat=Datasets). Our results suggest that E. coli has the largest percentage of transporter proteins followed by A. thaliana; humans have the lowest percentage of transporter proteins. We also observed that amino acid and sugar transporters represent the smallest percentage of transporters in all organisms tested except E. coli, and cation and electron transporters represent the highest percentage of transporters in all organisms tested. A complete list of sequences and their substrate specificities are available on the TrSSP web server.We applied our best model to predict transporters at the proteome level for Human, Drosophila, Yeast, The experimental characterization of transporters at the substrate-specific level is difficult and time consuming. Substrate-specific transporter characterization is also difficult in bioinformatics studies because transporters have remote homologies with other proteins both within and between protein classes. Advanced computational techniques that identify substrate-specific transport proteins from their primary sequences are urgently needed.et al. A. thaliana proteins, one limitation of their models is that only 61 proteins were used in the training dataset for model development. These models were also not made available through software or a web server for users to analyze their own sequences. Chen et al. et al. model and the Ou et al. model, is limited in that the proposed threshold of 0.65 is not reliable for the prediction of transporters. Barghash et al. Although Schaadt TrSSP web server would take approximately 6–15 minutes per sequence to run when the UniRef90 database was used for PSSM generation. To significantly reduce the PSSM computational time, we implemented parallel computing for PSSM generation and used the UniProt/SwissProt database as the reference database, which reduced the runtime of our TrSSP server to approximately 10 minutes for approximately 200 sequences with no impact on model performance.The models developed in the present study can simultaneously predict whether a query protein is a transporter or non-transporter protein and its substrate specificity for seven transporter protein classes. One advantage of our model is that it can differentiate cation and anion transporters. Our PSSM-based model demonstrated superior performance with respect to substrate specificity prediction. However, this model was computationally demanding when the PSSM profile was generated from the UniRef90 database. We observed that our We observed that sequence-similarity based methods such as BLAST, PSI-BLAST, and HMM were unable to accurately predict substrate-specific transporter classes. These results were expected because transporter proteins are diverse and have a remote homology both within and between transporter classes. Our current study suggests that we can predict the substrate specificity of transport proteins using SVM models that incorporate the biochemical composition, amino acid composition, and PSSM profile of transporter proteins. Our five-fold cross-validation method on the main dataset revealed that the best model, which included the AAIndex and the PSSM profile, achieved a prediction accuracy of 84.08%, 69.19%, 76.59%, 81.43%, 77.96%, 78.57%, 66.73%, and 78.99% for amino acid transporters, anion transporters, cation transporters, electron transporters, protein/mRNA transporters, sugar transporters, and other transporters, respectively. This model also achieved similar prediction accuracy on the independent dataset. Therefore, the models developed in the present study not only outperform the current available classifiers but also predict substrate specificity for more transporter classes than previous methods.http://bioinfo.noble.org/TrSSP. Users can upload or paste protein sequences in Fasta format for transporter and substrate prediction. We have provided six prediction modules on this web server: an amino acid composition based SVM, an AAIndex based SVM, a PSSM (SwissProt) based SVM, an AAIndex/PSSM (SwissProt) hybrid SVM, a PSSM (UniRef90) based SVM, and an AAIndex/PSSM (UniRef90) hybrid SVM. The TrSSP web server uses the amino acid composition module as the default. For the amino acid composition and AAIndex based modules, users can upload/paste a maximum of 2,000 sequences for batch predictions. Due to the high computational demand, we provide 1) PSSM (SwissProt) or AAIndex/PSSM (SwissProt) hybrid modules where users can upload/paste a maximum of 1,000 sequences and 2) PSSM (UniRef90) or AAIndex/PSSM (UniRef90) hybrid modules where users can upload/paste a maximum of 280 sequences for batch predictions. Although we have implemented a parallel PSSM generation, the PSSM-based modules have a long runtime; therefore, we provide users with the option to enter their email address to retrieve their prediction at a later time (within 120 days).We have developed a web server based on this work, which is freely available at Figure S1Data compilation and curation. A flowchart of the data compilation and curation processes. The values to the left of the text indicate the number of proteins that were available to the analysis at each step.(TIF)Click here for additional data file.Table S1The numbers of samples in the main dataset and independent dataset for different transporter classes.(DOCX)Click here for additional data file.Table S2The performances of the SwissProt-based PSSM models on the main dataset.(DOCX)Click here for additional data file.Table S3The performances of the hybrid AAIndex and SwissProt-based PSSM models on the main dataset.(DOCX)Click here for additional data file.Table S4The performances of the SwissProt-based PSSM models on the independent dataset.(DOCX)Click here for additional data file.Table S5The performances of the hybrid AAIndex and SwissProt-based PSSM models on the independent dataset.(DOCX)Click here for additional data file.Table S6Confusion matrix of best SVM models (AAindex+PSSM) on main dataset.(DOCX)Click here for additional data file.Table S7Confusion matrix of best SVM models (AAindex+PSSM) on independent dataset.(DOCX)Click here for additional data file.


Cell-free RNA (cfRNA) transcripts known to be expressed by the fetal brain are detectable by quantitative reverse transcription PCR (RT-qPCR) in amniotic fluid and represent potential biomarkers of neurodevelopment. The aim of this study was to compare the cfRNA yields from amniotic fluid (AF) collected in a commercial RNA stabilization product with the traditional method of freezing alone.GAPDH, and three genes associated with neurodevelopment . The yield from samples collected in Streck RNA BCT and plain centrifuge tubes were compared with the paired t test. GAPDH, NRXN3 and ZBTB18 amplified successfully in all samples, but NTRK3 did not. The RNA yield was significantly lower in samples collected in the Streck RNA BCT compared with the traditional storage method of freezing alone for all three successfully amplified genes (p < 0.0001).Thirteen women undergoing elective Cesarean birth at term without labor had whole AF collected at the time of uterine incision, prior to membrane rupture. Patient samples were split between Streck RNA blood collection tubes (BCT) and plain sterile polypropylene centrifuge tubes. Cell-free RNA from the AF supernatant was extracted according to a previously published protocol. RT qPCR was performed for the reference gene Selected cfRNA neurodevelopment transcripts are consistently detectable in third trimester AF. There appears to be no benefit in collecting AF in Streck RNA BCT for quantitative studies of AF cell-free RNA. For most couples facing a diagnosis of a fetal abnormality, the projected neurodevelopmental outcome for their baby is of primary importance. To date, our methods of antenatal prediction of long term neurodevelopmental outcome has been confined to only a few modalities—imaging with ultrasound and MRI, and maternal blood analysis for markers of aneuploidy or neural tube defects. A prenatal predictor of neurodevelopmental outcome that is not dependent on the presence of structural brain malformations would be of great value in perinatal medicine.A novel approach would be to examine the genetic transcripts found in the amniotic fluid that encode genes responsible for neurodevelopment. Amniotic fluid (AF) cell-free RNA (cfRNA) has previously been shown to be a novel source of information about multiple organ systems including the fetal brain –3. The mFunctional analysis of cfRNA suggests that is more promising than amniocyte RNA as a source of potential biomarkers of fetal neurodevelopment . HoweverCommercial RNA stabilization reagents have been developed for use with whole blood  and saliva , but not yet for AF. A collection tube for the stabilization of cfRNA in blood (Streck RNA BCT™) is also available. This cfRNA stabilization tube is compatible with the Qiagen Circulating Nucleic Acid kit used for AF RNA extraction, according to the manufacturer.The aims of this study were (1) to determine if candidate neurodevelopmental transcripts are consistently detectable in AF using RT-qPCR and (2) to compare RNA yields from AF collected in commercial RNA stabilization products with the standard method of freezing alone. The Mercy Health Human Research Ethics Committee approved this study (Ref. #14/20).g and 10 min 1600×g) and the supernatant frozen at −80 °C. The whole AF aliquots collected in Streck RNA BCT were kept at room temperature for up to 7 days (as per manufacturer’s instructions) before being centrifuged as above and the supernatant processed for cell-free RNA extraction in the same batch as the paired standard sample.Consenting participants undergoing undergoing elective Cesarean section without labour were prospectively recruited and had whole AF collected at the time of uterine incision prior to membrane rupture, as previously described . AF sampRNA was extracted with the Qiagen Circulating Nucleic Acid kit according to the previously published method . The volglyceraldehyde 3-phosphate dehydrogenase (GAPDH), and three genes associated with neurodevelopment: (1) neurexin 3(NRXN3), a neuronal cell surface protein-encoding gene involved in cell recognition and adhesion; (2) neurotrophic tyrosine kinase receptor type 3 (NTRK3), cell-surface receptor involved in neurotrophin signaling of cell differentiation; (3) and zinc finger and BTB domain containing 18 , a transcriptional repressor of genes involved in neurodevelopment. These genes were selected on the basis of prior detection in microarray studies of AF [Real-time quantitative PCR (qPCR) was performed after reverse transcription using Applied Biosystems High Capacity cDNA Reverse Transcription Kit according to the manufacturer’s instructions. Quantitative gene expression analyses were performed with inventoried Taqman Gene Expression Assays  for the reference gene dhesion;  neurotroes of AF , documenes of AF  and fetaGAPDH successfully amplified in all samples. Of the candidate neurodevelopmental genes, ZBTB18 and NRXN3 amplified in all samples. However, NTRK3 was present in very low abundance and failed to amplify in 50 % of total aliquots, precluding any statistical analysis.Thirteen women consented to participate and had successful amniotic fluid collection at the time of Cesarean section. All AF samples were processed within 7 days. The reference gene GAPDH, NRXN and ZBTB18 respectively   to determine if there was a statistically significant difference in expression according to collection method. The mean Ct results did not demonstrate any improvement in RNA yield with Streck RNA BCT compared with the conventional collection and storage method. In fact, the RNA collected and stored with the conventional method resulted in amplification of all genes at a significantly lower Ct (i.e. higher abundance), with the mean Ct difference of −3.12, −2.39 and −2.47 for NRXN3 and ZBTB18 are consistently detectable in term amniotic fluid supernatant. There appears to be no benefit in collecting whole AF in Streck RNA BCT over plain centrifuge tubes for studies of AF cell-free RNA.This study confirmed that cell-free transcripts GAPDH using RT-qPCR correlated well with downstream amplification and microarray hybridization success [GAPDH yield was lower with the Streck samples compared with the standard methods, indicating no benefit to using Streck BCT for whole AF sample collection.One of the major challenges of working with cell-free RNA is assessing the quality of the extracted product. Conventional methods of assessing RNA quality such as the RNA Integrity Number (RIN) produced by the Agilent 2100 Bioanalyzer system uses ribosomal RNA peaks to quantify the quality of RNA. Cell-free RNA does not contain these ribosomal RNA peaks and therefore does not produce a meaningful RIN. In prior work, we found that quantification of  success . In thisThis lack of improvement with the use of an RNA stabilizing product may be due to the already degraded nature of AF cfRNA at the time of sampling, and implies that there is minimum room for optimization of AF RNA yield with stabilization reagents. Furthermore, the Streck RNA BCT is designed for stabilizing cfRNA in whole blood, and it is not optimized for AF, which may contain additional substances that interfere with its performance.NTRK3 in all samples is consistent with prior whole transcriptome analysis demonstrating that neurological system gene expression is relatively downregulated in term samples compared with midgestation [The failure to consistently detect estation , 10.Another potential reason for the low yield was the omission of carrier RNA from the RNA extraction step . This waIn summary, this study has confirmed the presence of several potential mRNA markers of neurodevelopment with RT qPCR. Further research is required to optimize sample collection and storage for future studies of AF cfRNA.


C. elegans the CELF (CUGBP and Etr-3 Like Factor) family RBP UNC-75 is required for axon regeneration. Using crosslinking immunoprecipitation coupled with deep sequencing (CLIP-seq) we identify a set of genes involved in synaptic transmission as mRNA targets of UNC-75. In particular, we show that UNC-75 regulates alternative splicing of two mRNA isoforms of the SNARE Syntaxin/unc-64. In C. elegans mutants lacking unc-75 or its targets, regenerating axons form growth cones, yet are deficient in extension. Extending these findings to mammalian axon regeneration, we show that mouse Celf2 expression is upregulated after peripheral nerve injury and that Celf2 mutant mice are defective in axon regeneration. Further, mRNAs for several Syntaxins show CELF2 dependent regulation. Our data delineate a post-transcriptional regulatory pathway with a conserved role in regenerative axon extension.Axon injury triggers dramatic changes in gene expression. While transcriptional regulation of injury-induced gene expression is widely studied, less is known about the roles of RNA binding proteins (RBPs) in post-transcriptional regulation during axon regeneration. In DOI:http://dx.doi.org/10.7554/eLife.16072.001 Nerve cells or neurons carry information around the body along projections known as axons. An injury or trauma, such as a stroke, can damage the axons and lead to permanent disability because the damaged axons fail to regenerate over long distances.Axon damage triggers large changes in the activity of many genes that promote regeneration. When a gene is active, its DNA is copied to make molecules of messenger RNA (mRNA), which are then used as templates to make proteins. Many mRNAs undergo a process called alternative splicing, in which different combinations of mRNA sections may be removed from the final molecule. This enables a single gene to produce more than one type of protein.Caenorhabditis elegans has previously been shown to be involved in axon regeneration, but it was not clear how UNC-75 acts on neurons. Here, Chen et al. combined a technique called CLIP-seq (Cross-linking ImmunoPrecipitation-deep sequencing) with genetic testing to identify the mRNAs that UNC-75 regulates during axon regeneration.Recent studies point to an important role for so-called RNA binding proteins in regulating the alternative splicing process. An RNA binding protein called UNC-75 in a worm known as C. elegans genes required for information to pass between neurons whose mRNAs are also targeted by UNC-75. Many of these genes are also required for axon regeneration. Chen et al. studied one of the mRNA targets – which encodes a protein called syntaxin – in more detail and found that the syntaxin mRNA is required for regenerating axons over long distances. UNC-75 alternatively splices this mRNA to produce a particular form of syntaxin that is mainly found in neurons. Mutant worms that lack either UNC-75 or syntaxin are unable to properly regenerate axons over long distances.The experiments found a set of Further experiments show that a mouse protein known as CELF2 that is equivalent to worm UNC-75 plays a similar role in regenerating axons. Moreover, mouse CELF2 restores the ability of worm neurons that lack UNC-75 to regenerate. Like worm UNC-75, the mouse protein is also involved in alternative splicing of syntaxin. The next step is to examine the other mRNA targets of UNC-75 to find out what role they play in axon regeneration and other processes in neurons.DOI:http://dx.doi.org/10.7554/eLife.16072.002 C. elegans, Drosophila and mouse, acting through RNA repair and splicing  family RNA binding proteins are highly conserved throughout animals . All sixbehavior . Celf4 ddisorder , whereaserotonin . HoweverC. elegans UNC-75 and mouse CELF2, both of which are localized to the nucleus , a small deletion affecting the last exon encoding part of RRM3 and a nuclear localization signal (Pmec-4-GFP::UNC-75ΔNLS(aa 1–472) showed diffuse fluorescence throughout the cell, as compared to full-length GFP::UNC-75, which localizes to neuronal nuclei  fully rescued PLM regrowth, indicating that UNC-75 acts cell-autonomously ; dlk-1(0) did not further reduce PLM axon regrowth compared to dlk-1(0), and the regrowing axons had no regenerative growth cones, resembling dlk-1(0) (unc-75(0) partially improved regrowth compared to unc-75(0) single mutants  partially rescued unc-75(0) PLM regrowth defects  ,3B. Over mutants , suggestregrowth ; 2015. e defects , but not defects , suggest mutants . Thus, tunc-75(0) mutants, in which the locomotion defect was rescued by the FLAG::UNC-75 transgene (crosslinking immunoprecipitation coupled with deep sequencing (CLIP-seq)   , located in an intron of  C27H6.5 . AlthougP = 3.9E-20, Fisher’s exact test), nucleotide-binding  and transmembrane , consistent with previously reported functions of UNC-75 in alternative splicing, and validating the quality of our CLIP-seq. The most significantly enriched signaling network is MAPK pathway .We performed Gene Ontology (GO) analysis on the 533 potential protein coding targets of UNC-75 using DAVID . The thrP = 1.6E-6, 4 genes including unc-32, unc-41, unc-64, unc-75 and unc-104). Mutants of these genes have normal PLM axon outgrowth in development and display significantly reduced PLM regrowth  of unc-64  in the UNC-64A transmembrane domain causes complete loss of function, indicating the importance of this domain (unc-64(md130) (plf) allele affecting both isoforms (unc-64(plf)  , a partiisoforms . We furt-64(plf) . Thus UNunc-64 (md130 or e246) results in a partial block in PLM axon regeneration (unc-64(js115) null mutants arrest in the first larval stage (unc-64 in regeneration. We first tested unc-64(js115) null mutants in which lethality, but not movement, was rescued by expression of UNC-64 under the combined control of acr-2, unc-17, and glr-1 promoters (unc-64(plf) (unc-64A(cDNA) driven by the pan-neural rgef-1 promoter, which fully rescued unc-64(0) lethality and locomotor defects (unc-64A(+) in touch neurons using Pmec-7-nCre (unc-64(0) PLM axons developed normally (unc-64(plf)  mutants had dynamic filopodia but did not effectively elongate, resembling those of unc-75(0) (unc-64(md130) were rescued by pan-neuronal expression of either UNC-64A or UNC-64B  . To addr defects . We thenc-7-nCre . The uncnormally  and disp-64(plf) . We concnc-75(0) . Moreove UNC-64B , suggestunc-75 mutants, we overexpressed UNC-64 in unc-75 mutants and examined axon regrowth. Strikingly, transgenes containing unc-64 genomic DNA, which produce both UNC-64A and UNC-64B, significantly suppressed the PLM regeneration defects of unc-75 mutants  neurons using RT-qPCR. Celf4 transcripts increased during postnatal development  to generate a -Celf2 allele, and verified the genomic deletion of exon 3. These constitutive -/-Celf2mice died neonatally, with 5% escapers surviving up to 3 weeks. No obvious morphological defects were detected in newborn -/-Celf2 animals by histological analysis (not shown). To test the role of Celf2 in axon growth, we assayed cultured primary neurons. Explants of DRG neurons derived from E13.5 -/-Celf2embryos displayed significantly reduced axon extension  allele to a Nestin-Cre line  that stably expresses BLRP (biotin ligase recognition peptide) tagged CELF2 , although the reporter lacked intron 7b  is pervasive in neurons and has been implicated in axon guidance, synaptogenesis and synaptic transmission . The funCelf2 transcripts . We find that transgenic expression of UNC-64A (cDNA bypassing splicing) also significantly suppressed unc-75 locomotor and regeneration phenotypes whereas overexpression of UNC-64B was unable to rescue. The simplest interpretation of these data is that UNC-64B is unable to fully substitute for reduced levels of UNC-64A. Possibly, UNC-64B but not UNC-64A requires some additional cofactor lacking in unc-75.UNC-75 dependent splicing is critical for UNC-64A expression in neurons, at the expense of UNC-64B. The roles of the two UNC-64 isoforms and the mechanistic basis for their functional difference have remained a topic of debate. As truncation of UNC-64A causes apparent complete loss of function , UNC-64Ay unc-75 . HoweverThe C-terminal membrane anchors of UNC-64A and B are both 25 aa residues long and differ only in the last 14 aa, suggesting that the precise composition of the anchor is critical for Syntaxin function. Both have the same number of hydrophobic residues in their C-termini, with the only discernible difference being that these residues are more clustered in the B isoform. Nevertheless seemingly small differences in hydrophobicity can cause Syntaxins to sort into distinct membrane domains , and mayunc-64, Syntaxin2 encodes two alternatively spliced isoforms that differ in the membrane anchor. Syntaxin2  was first identified as an extracellular morphogen ; Prgef-1-UNC-75S(juIs369)] worms were crosslinked in a 150 mm plate (with no food) with an energy output of 6 kJ/m2. Worms were lysed by sonication in Homogenization Buffer . Lysates were centrifuged at 16,000g for 15 min at 4°C and supernatants collected and incubated with M2 magnetic beads (Sigma) overnight on a rotator.CLIP-seq was performed as previously described . CZ146622 and 0.5% NP-40). Beads were incubated with 500 μl of Micrococcal Nuclease Reaction Buffer  containing 1 ng of Micrococcal Nuclease (NEB) for a total of 10 min at 4°C with intermittent shaking on a Thermomixer (Eppendorf) (1200 rpm for 1 min and then 1200 rpm for 15 sec every 3 min). Beads were then washed twice with PNK+EGTA Buffer , twice with Wash Buffer and twice with PNK Buffer. The beads were incubated for 10 min at 37°C in the Thermomixer with intermittent shaking (1200 rpm for 15 sec every 3 min) in 80 μl NEB Buffer 3 containing 30 units of Calf Intestinal Phosphatase (NEB). The beads were then washed twice with PNK+EGTA Buffer, twice with PNK Buffer and twice with 0.1 mg/ml BSA. An RNA linker  with a puromycin modification at the 3′ end to avoid self-circularization was linked to the mRNA present in the complexes by T4 RNA Ligase and incubated overnight at 16°C with gentle shaking (1300 rpm every 5 min for 15 s in the Thermomixer). The beads were washed three times with PNK Buffer and incubated in 80 μl PNK Buffer (NEB) with 40 units of T4 PNK enzyme (NEB) in the presence of 32P-γ-ATP (1 mCi). Samples were incubated for 10 min at 37°C with intermittent shaking (1000 rpm every 4 min for 15 s). Cold ATP was added to the reaction at a final concentration of 1.25 mM and incubated for 5 min. The reaction was terminated with three washes of PNK+EGTA Buffer. Complexes were eluted from the beads by incubation for 10 min at 70°C in Nupage LDS Buffer. Samples were loaded onto a native 10% Bis-Tris gel with MOPS SDS Running Buffer. Next, the samples were transferred to a nitrocellulose membrane. The band corresponding to UNC-75/RNA complexes (65–100 kDa) was cut out of the membrane and proteins degraded by Proteinase K. The samples were then subjected to phenol/chloroform extraction followed by ethanol precipitation. RNA was resuspended and ligated to 5′ RNA Linker  with T4 RNA ligase at 16°C overnight. The RNA samples were then treated with DNase I followed by phenol/chloroform extraction and ethanol precipitation. The ligated RNA was reverse transcribed with P3 primer (5’-CAAGCAGAAGACGGCATACGAG-3’) followed by PCR. PCR primers 5’-CAAGCAGAAGACGGCATACGAG and 5’-GCTGATGCTACGACCACAGG-3’ were used. PCR product was analyzed on PAGE gel, bands corresponding to 75–150 nt were isolated and the DNA was extracted for sequencing.Beads were collected and washed twice with Wash Buffer , twice with High Salt Wash Buffer  and twice with Polynucleotide Kinase Buffer (PNK Buffer) . After removing PCR duplicates and non-unique reads, 688,276 unique reads were obtained. From these unique reads, overlapping CLIP tags were grouped to define CLIP peaks. We identified 1219 peaks distributed over 820 protein-coding genes, among which 410 genes are annotated with gene names (e.g. scpl-1), the remaining 410 genes being listed by sequence names (e.g. Y106G6D.8)  . Crossli06G6D.8) . Therefo06G6D.8)  when def06G6D.8) . As expe06G6D.8) .http://www.atcc.org/products/all/CCL-131.aspx#generalinformation) and cultured in DMEM (high glucose) containing 10% FBS with 5% CO2. To generate an inducible Neuro2A stable cell line expressing biotin-tagged Celf2 (cDNA) that can be controlled to express at endogenous levels, we modified the original in vivo biotin-tagging system using a Tet-On retroviral inducible system from Clontech. Briefly, Celf2 was fused in-frame to the C terminus of the peptide MAGGLNDIFEAQKIEWHEDTGGGGSGGGGSGENLYFQSDYKDDDDK in the BLRP expression construct. Amino acids 1–20 represent a biotin ligase recognition peptide (BLRP), in which the lysine residue at position 13 is a substrate for the bacterial biotin ligase (BirA) upon co-expression in mammalian cells  by pipetting up and down several times, followed by incubation on ice for 15 min. 25 μl detergent (10% NP40) was added per 500 μl 1x Hypotonic Buffer followed by vortexing for 10 s at highest speed. The homogenate ws centrifuged for 10 min at 3,000 rpm at 4°C to get the nuclear fraction in the pellet. The pellet was then washed twice in 1x Hypotonic Buffer and resuspended in 1 ml CLIP lysis buffer  per 100 μl pellet followed by sonication on ice. 5 ul Turbo DNase was added per 1 ml of lysates followed by incubation at 37°Cfor 3–5 min. Lysates were centrifuged at 16,000 g for 10 min at 4°C and supernatants collected and incubated with streptavidin magnetic beads (Sigma) overnight on a rotator.The Neuro2A cell line was obtained from ATCC (an cells . The glyC. elegans (see above) with some modification. Complexes were eluted from the beads and run on a native 10% Bis-Tris gel followed by transferring to a nitrocellulose membrane. The band corresponding to the CELF2/RNA complexes (65–100 kDa) was cut out of the membrane. After RNA extraction, RNA was resuspended and ligated to 5′ RNA Linker with T4 RNA ligase at 16°C overnight. The ligated RNA was reverse transcribed with P3 primer (5’-CAAGCAGAAGACGGCATACGAG-3’) followed by two rounds of PCR. PCR primers 5’-CAAGCAGAAGACGGCATACGAG and 5’-GCTGATGCTACGACCACAGG-3’ were used for the first round of PCR for 12 cycles. Primer 5’-CAAGCAGAAGACGGCATACGAG-3’ and barcoded PCR primers 5’- AATGATACGGCGACCACCGAGATNNNNGCTGATGCTACGACCACAGG-3’ were used for the second round of PCR for 4 cycles. PCR product was analyzed on PAGE gel, bands corresponding to 75–150 nt were isolated and the DNA was extracted for deep sequencing. We obtained 19,563,928 reads, among which 13,505,933 were mapped to mouse genome. After filtering PCR duplicates and non-unique tags, we obtained 4,587,451 unique tags. We used an algorithm in peak calling to include peaks with and without CIMS. The peaks with top 15% significance score were distributed over 2919 protein coding genes (Beads were then treated as for the CLIP-seq in ng genes .The CLIP-seq data are available at the Gene Expression Omnibus under the accession number GSE78111.http://www.bioinformatics.babraham.ac.uk/projects/fastqc). Sequencing adapters and over-represented short sequences were trimmed using cutadapt  or M. musculus genome (GRCm38/mm10), respectively, by GSNAP as previously described , with soWe used MEME-ChIP  for de n3’-RNA-seq was performed as previously described . BrieflyCelf2 allele consisting of loxP sites flanking Celf2 exon 3 . Cells heterozygous for the targeted mutation were microinjected into blastocysts to obtain germ-line transmission following standard procedures. Mice were genotyped using PCR .Mouse husbandry and surgeries were performed under the supervision of the University of California San Diego Institutional Animal Care and Use Committee (IACUC). We used homologous recombination to create a 'floxed' 2 exon 3 . A 1.1 kZP3-Cre  then to cover slips pre-coated with poly-ornithine and 5 μg/ml laminin. The DRGs were allowed to adhere for 4 hr before flooding the wells with culture medium. 24 hr later, DRGs were fixed with 4% PFA and stained with anti-Tuj1 .Adult (8 weeks) DRGs were dissected in cold F12 medium and then digested with 0.5 mg/ml collagenase  and 1 mg/ml dispase  for 40 min at 37°C followed by 0.125% trypsin digestion for 30 min at 37°C. Tissues were triturated in culture medium (NBA with 2% of B27 and 10% of FBS) with 1 ml tips and passed through a 0.45 μm cell strainer. Cells passing through the strainer were spun down and re-suspended in culture medium and plated to 12-well plates pre-coated with poly-ornithine and 5 μg/ml laminin. For in vitro axon regrowth analysis, 24 hr after plating, cells were re-suspended and re-plated to pre-coated cover slips. 24 hr later, re-plated cells were fixed and stained with anti-Tuj1.2 and sciatic nerves obtained for analysis. Sciatic nerves were fixed in 4% paraformaldehyde for 3 hr, then washed with PBS, immersed in 30% sucrose in PBS, cryopreserved in OCT compound (TissueTek) and cryosectioned at 10 μm thickness. Samples were immunostained with anti-SCG10 (1:3000) . After staining, multiple images along the nerve were taken using a 10X objective (Zeiss LSM710) and montaged using Photoshop (Adobe). Representative images are shown in 2 month old mice were anesthetized with isoflurane and the sciatic nerve exposed by a small incision on the skin. The nerve was crushed with a pair of fine (#55) forceps for 20 s and the crush site marked using activated carbon powder . 3 days Total RNA from worms or mouse tissues  were extracted using Trizol (Invitrogen) or RNeasy kit (Qiagen) following the manufacturers’ protocols. First strand cDNA was reverse-transcribed using SuperScript III (Invitrogen). qPCR was run on Bio-Rad CFX96 Touch Real-Time PCR Detection System with iQ SYBR Supermix (Bio-Rad). Data were analyzed using CFX manager (Bio-Rad). PCR primers are listed in .A two-tailed Student’s test was used for comparisons of two groups. One-way ANOVA with Bonferroni post test was used to compare multiple groups in Prism . In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.C. elegans and mammals through alternative splicing of syntaxins" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom, Graeme W Davis, is a member of our Board of Reviewing Editors and the evaluation has been overseen by Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Aaron D Gitler (Reviewer #2).Thank you for submitting your article "CELF RNA binding proteins promote axon regeneration in The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:C. elegans unc-75/CLEF is critical for regenerative axonal extension -but not for developmental axonal growth or regenerative growth cone formation, that it acts cell-autonomously, and that nuclear localization of unc-75/CLEF is critical for its pro-regenerative function. Through CLIP-seq the authors then identify neuronal unc-75/CLEF targets and demonstrate that unc-64/syntaxin is a major target critical for axonal regeneration.In this manuscript by Chen et al., the authors investigate the role of the RBP binding protein unc-75/CLEF and one of its targets unc-64/syntaxin in axonal regeneration. The authors provide compelling evidence that in C. elegans with definitive experiments in mice using conditional CLEF2 KO animals, a major step defining conserved gene function. Overall, this is an impressive manuscript that uncovers a conserved role for the unc-75/CLEF2 RNS binding protein in promoting axonal regeneration. Of particular note, this manuscript strongly reinforces the notion that genetic pathways critical for regeneration of single axons in C. elegans also function in multi- axonal regeneration of vertebrate peripheral nerves, a notion that no longer can be ignored.The authors then complement their findings in eLife. None of the reviewers had major concerns with the work. However, each had minor comments that the authors are asked to consider addressing prior to resubmission.This manuscript was reviewed by three individuals. All considered the data, interpretation or presentation to adhere to high standards and general interest consistent with publication at Minor Comments:1) Please present non-normalized data in figure format or in table format to supplement the figures that contain normalized data.2) Please re-visit the statistical analysis in 3) Are the quantifications of GFP intensity normalized somehow in 4) In 5) C. elegans syntaxin promotes axonal regeneration. While not the major focus of this manuscript, a possible mechanism how syntaxin promotes regeneration is not provided. A discussion regarding possible mechanisms would be helpful.6) The authors demonstrate that in 7) The labeling of many figures could be improved for clarity. For example, the red asterisks indicating the cell bodies in Reviewer #1:The manuscript by Chen and colleagues places CEFL family RBPs in a new context, axonal regeneration. The topic should have a broad appeal; at a molecular level (RBP function) and phenomenologically (regeneration). The manuscript is well written and the data are nicely presented. In each instance, the effects being observed are large and convincing. The genetic analyses are thorough, including the generation of splicing reporters that are convincing and show large effect. The genetic data are appropriately interpreted. At the end of this manuscript, we are left with the finding that alternative syntaxin splicing is involved in the regenerative response, but there is no indication how syntaxin might participate. However, the paper, as written, is strong and the authors should not be required to make that next mechanistic leap in this paper. The story is solid and already makes the leap from work to mammalian systems, providing evidence of conserved signaling across distant species. I have only minor concerns.Reviewer #2:The authors have presented a thorough study that characterizes how CELF RNA binding proteins can regulate the alternative splicing of syntaxins, which may play a role in axon regeneration. The experiments had a logical flow starting from a genomics approach using CLIP-seq to identify RNA binding targets of UNC-75, followed by more functional genetic studies in nematodes and mice. Overall, the experiments were of high quality, the results are interesting, and will be of interest to the neuroscience community. I only have the following questions for the authors to consider.1) Are the quantifications of GFP intensity normalized somehow in 2) In Reviewer #3C. elegans unc-75/CLEF is critical for regenerative axonal extension -but not for developmental axonal growth or regenerative growth cone formation, that it acts cell-autonomously, and that nuclear localization of unc-75/CLEF is critical for its pro-regenerative function. Through CLIP-seq the authors then identify neuronal unc-75/CLEF targets and demonstrate that unc-64/syntaxin is a major target critical for axonal regeneration.In this manuscript by Chen et al., the authors investigate the role of the RBP binding protein unc-75/CLEF and one of its targets unc-64/syntaxin in axonal regeneration. The authors provide compelling evidence that in C. elegans with definitive experiments in mice using conditional CLEF2 KO animals! Overall, this is a very impressive manuscript that uncovers a conserved role for the unc-75/CLEF2 RNS binding protein in promoting axonal regeneration. I expect this manuscript to have a major impact on the field, in part because it delineates a novel, conserved pathway that promotes regeneration in vivo, but also because it strongly reinforces the notion that genetic pathways critical for regeneration of single axons in C. elegans also function in multi- axonal regeneration of vertebrate peripheral nerves, a notion that no longer can be ignored!What sets this manuscript apart from most is that the authors complement their findings in  1) Please present non-normalized data in figure format or in table format to supplement the figures that contain normalized data.We have provided a new Experimental variation in axon regrowth length results in part from known sensitivity of the laser to changes in temperature and humidity, and thus wild type controls show slight variation in mean regrowth between different days. To compare data sets from day to day, we routinely normalize axon regrowth to the average regrowth length of wild type controls performed on the same day as the experiment. Nevertheless in the data sets presented here such day-to-day variation was relatively low, so the normalized data display very similar pattern when compared to the non-normalized data.2) Please re-visit the statistical analysis in This point might be some kind of misunderstanding. As stated in the original MS, the analysis in 3) Are the quantifications of GFP intensity normalized somehow in The GFP intensity in 4) In juEx5488 and juEx5490) CELF2 and CELF4 were tagged with mCherry, and by visual inspection the fusion proteins were expressed at similar levels . We have added a statement to this effect in the legend to In the transgenes used here (5) The graph (assuming 7D/E is a typo for 6D/E) shows mean intensity ± SEM, with data pooled from 4 mutants and 5 controls. There was some variability from nerve to nerve in both controls and mutants. We have selected the images that are representative of the mean intensity to convey the mutant phenotypes6) The authors demonstrate that in C. elegans syntaxin promotes axonal regeneration. While not the major focus of this manuscript, a possible mechanism how syntaxin promotes regeneration is not provided. A discussion regarding possible mechanisms would be helpful.We thank the reviewer for the comment and have added a paragraph to the Discussion surveying previous relevant findings on syntaxins and axon growth:“The mechanism by which syntaxin contributes to regenerative axonal elongation remains to be elucidated. Because unc-64 mutants are defective in axon extension rather than growth cone reformation, we believe it is less likely that syntaxin is required for membrane resealing immediately after injury. […] Finally, syntaxin might contribute to membrane expansion via a non-fusogenic mechanism as shown for the SNARE Sec22b .”7) The labeling of many figures could be improved for clarity. For example, the red asterisks indicating the cell bodies in We appreciate the comments and have improved the labeling and figure legends.


Guitar R/Bioconductor package for sketching the transcriptomic view of RNA-related biological features represented by genome based coordinates. Internally, Guitar package extracts the standardized RNA coordinates with respect to the landmarks of RNA transcripts, with which hundreds of millions of RNA-related genomic features can then be efficiently analyzed within minutes. We demonstrated the usage of Guitar package in analyzing posttranscriptional RNA modifications (5-methylcytosine and N6-methyladenosine) derived from high-throughput sequencing approaches (MeRIP-Seq and RNA BS-Seq) and show that RNA 5-methylcytosine (m5C) is enriched in 5′UTR. The newly developed Guitar R/Bioconductor package achieves stable performance on the data tested and revealed novel biological insights. It will effectively facilitate the analysis of RNA methylation data and other RNA-related biological features in the future.Biological features, such as genes and transcription factor binding sites, are often denoted with genome-based coordinates as the genomic features. While genome-based representation is usually very effective in correlating various biological features, it can be tedious to examine the relationship between RNA-related genomic features and the landmarks of RNA transcripts with existing tools due to the difficulty in the conversion between genome-based coordinates and RNA-based coordinates. We developed here an open source With genome-based coordinates, the relationship between different biological features can be easily inferred. Currently, genomic features  have become the basis of many bioinformatics tools in various biological data processing pipelines, and dedicated types of operation are also available . While gne (m5C) .6A and m5C, are recently found to play various important roles in a number of biological processes, such as translation efficiency [As an emerging layer of gene expression regulation, posttranscriptional RNA modifications, including mficiency , microRNficiency , RNA-proficiency . Togetheficiency –11 for uficiency , 13 haveficiency  has beenficiency ; howeverIncreased Ambiguity due to Isoforms. The ambiguity in genome-based coordinates is often due to the repeats. Compared with genome, transcriptome has increased complexity due to transcript isoform and alternative splicing. It is possible that an RNA methylation site falls into the CDS of one transcript but on the 3′UTR of another isoform transcript. It is known that many genes have very large number of isoform transcripts, so it may be impossible to specifically assign a genomic feature aligned on this gene to a particular isoform transcript.Variation in Transcript Length. In human and mouse, transcripts vary greatly in size. It is important to notice that 1000 base pairs (bp) from start codon may be a long distance for shorter genes but not for the longer ones. For genomic coordinates, bp has been shown to be a reasonable unit; however, when measuring distance between two RNA-related features, a relative unit standardized by the entire width of the transcript may be more suitable. This idea has been used widely today in many studies. Please note that the technical resolution remains the same when measured in bp on shorter or longer transcripts. When a standardized coordinate is used, longer transcripts actually obtain a higher resolution in terms of standardized coordinate compared with the shorter transcripts. Compared with a transcript with 20 k bp, it is more difficult to tell whether the RNA methylation site on a 200 bp transcript is close to its stop codon, even though the technical resolution remains the same.Complexity in Landmarks of RNA Transcript. For histone modifications and transcription factor binding sites, landmarks of interests are often the transcription starting site (TSS) and transcription ending sites (TES). For RNA methylation, two additional landmarks are the start codon and stop codon of mRNA and the setting is further complicated by the existence of long noncoding RNA (lncRNA) and various small RNA families. Furthermore, some mRNAs may not have 3′UTR or 5′UTR. Additionally, the 5′UTR, CDS, and 3′UTR are apparently of different length for most genes, so the 3 components need to be standardized independently when summarized or compared with other transcripts. There are already tools, such as ngs.plot [ngs.plot , developFor the aforementioned reasons, a dedicated approach needs to be developed for visualization of RNA methylation data and other RNA-related biological features.Compared with genome-regulated biological features , visualization of RNA-related features (such as RNA methylation sites) represented in genomic coordinates is nontrivial due to the following reasons: Guitar for gene annotation guided transcriptomic analysis of RNA-related genomic features, such as RNA methylation sites denoted in genome-based coordinates. The approach is detailed next.We develop here an open source R package Guitar coordinates, which is essentially the genomic projection of the standardized transcriptomic coordinates. Specifically, each component of a single transcript is divided into a number of bins of equal width. For long noncoding RNA, the whole transcript is a single component; for mRNA, there are 3 components, that is, 5′UTR, CDS, and 3′UTR. Their genomic projected coordinates are then obtained with the help of GenomicFeatures R/Bioconductor package [ Guitar coordinates are essentially still genome-based coordinates but clearly associated with landmarks of transcript, for example, 0.2 standardized lncRNA length from the TSS. The procedures for generating Guitar coordinates are illustrated in To visualize the multiple RNA-related features together, transcripts of different length need to be standardized in the first place. For this purpose, we constructed the package . Please  Guitar coordinates need to be generated separately for all the 3 regions. In order to make the 3 components comparable, each component is standardized independently and contributes to 1/3 of the entire coding transcript . For lncRNA, this is not needed and the Guitar coordinates are generated for the entire lncRNA.As mentioned previously, for mRNA, of interest are usually 3 components rather than a single one, that is, 5′UTR, CDS, and 3′UTR. Consistently, the Guitar coordinates. To ensure the specificity of the generated Guitar coordinates, filtering of highly ambiguous transcripts may be needed. Two filters are implemented. Firstly, a length filter is implemented to select transcripts longer than a user-defined threshold. This is to ensure the generated Guitar coordinates have sufficient resolution from the technology perspective with the data analyzed. For techniques with single-base resolution, this cutoff can be smaller , while, for techniques with lower resolution, it should be larger . Secondly, an ambiguity filter is implemented to discard genes with too many isoforms, which can cause ambiguity in the feature assignment stage. The implementation of this filter is mainly to reduce memory usage and save computation time . In addition, the filter may be necessary for less-studied species with problematic gene annotation. The second filter can filter out a number of genes with higher isoform ambiguity, and doing so will significantly decrease the memory usage for the constructed Guitar coordinates and save computation time. For this purpose, we implemented a simple strategy by counting the number of overlapping transcripts on the genome, that is, checking the number of transcripts sharing exons. In the default setting of Guitar package, we filtered out transcripts that overlap with more than 3 other transcripts on the genome. The parameter provides reasonable good results in the data tested and can be easily customized by the user. After applying the ambiguity filter, if a genomic feature still overlaps with multiple transcripts, the ambiguity will be factored in the analysis. Specifically, if a site is located on both CDS and 3′UTR of the same transcript, then the Guitar coordinates associated with those components should have overlapped with that genomic feature, and those coordinates are labeled as associated with the genomic feature with weight 1, indicating the feature is fully associated with this transcript. However, if a genomic feature is associated with n transcripts, that is, it overlaps with n transcripts, then the overlapping coordinates are associated with that genomic feature with weight 1/n, reflecting the ambiguity in association.Due to the existence of isoform ambiguity, the same genomic location may be associated with multiple transcripts and thus related to multiple Guitar coordinates can be compared with other genomic features, and the number of overlapped features can then be counted and standardized on mRNA and lncRNA, respectively. The distribution of genomic features on RNA will then be summarized and visualized by ggplot2 package for quality graphics [GuitarPlot for fast visualization of various genomic features in different formats. The general working procedures for Guitar package are shown in To sketch the transcriptomic view of genomic features, thegraphics . We also Guitar package:Including neighborhood DNA regions for comparison. The neighborhood DNA regions (promoter region and its complementary DNA at the 3′ end) can be optionally included in analysis of Guitar package. This will be useful for analyzing genomic features that are related to both DNA and RNA, such as H3K4me3 ChIP-Seq data.Ambiguous Assignment of the Ambiguous Genomic Features. Due to ambiguity of heterogeneous transcriptome, some genomic features overlap with more than one transcript and thus with ambiguous belongings. In the Guitar package, ambiguous features are also counted in the analysis, with their weights equally divided between all the overlapping transcripts. However, we can optionally filter out genomic features overlapping with more than a predefined number of transcripts (Default 5) to completely discard the impact of these highly ambiguous features.Resizing the Components in Visualization. In practice, different components of mRNA, that is, 5′UTR, CDS, and 3′UTR, are of quite different width. 5′UTR is usually much shorter than 3′UTR and CDS in mouse and human. Although these components are treated independently when building the Guitar coordinates, we still calculated the average length of each component across different genes, so the true relative width can be optionally reflected in the plot generated from Guitar package; however, doing so may make the 5′UTR region too small for clear observation.Connection with ggplot2 for More Complex Graphics. The Guitar may optionally return intermediate results, which can be reused in the ggplot2 package for more complex graphics. This is designed for the advanced users only and not recommended.Besides the aforementioned structure, there are a few more useful features provided by Guitar R/Bioconductor package for gene annotation guided transcriptomic analysis of RNA-related genomic features. It is currently and publicly available from Bioconductor. In this section, we show the application of Guitar package to the analysis of RNA methylation sites denoted as genomic features with a few examples. More information is available from the documentation of the Guitar R/Bioconductor package.We developed the6A) [6A methylation sites are obtained with exomePeak R/Bioconductor package [In the first example, we study MeRIP-Seq data  profilin6A) , 19 site package . Compare6A sites with more than 8 times of enrichment are overly present near the stop codon but highly deficient near transcription starting site (TSS) and on 5′UTR. The different distribution patterns of the highly and weakly methylated sites may indicate function versatility and call for more specialized analysis targeting the sites with different methylation level. In contrast to distribution preference of m6A sites on mRNA, it is almost uniformly (or randomly) distributed on lncRNA regardless of the “IP/input ratio” specified.We implemented exomePeak setting different “IP/input ratio” , and we then examined the distributions of the peaks in different exonic regions at each enrichment threshold. As shown in In the aforementioned example, we implemented the default.6A methylation sites are enriched near stop codon and discovered that highly enriched m6A methylation sites are depleted on 5′UTR. Next, we study 5-methylcytosine (m5C) [5C methylation [5C on mRNA are still largely elusive [In previous study, we confirmed that RNA mne (m5C) , 21 profne (m5C) , 22. Difhylation , 24, the elusive , and its5C methylation profiles in mouse embryo fibroblast [The bisulfite sequencing data characterizing the RNA mbroblast  was direbroblast  to removbroblast  and 42465C on both lncRNA and mRNA has a very strong 5′ bias; that is, more reported C residuals from BS-Seq data are on the 5′UTR region. The distribution patterns of 3 groups of residuals with different methylation level are quite different on mRNA, with highly methylated Cs (higher than 1.65% with 0.05 confidence level) prominently enriched on 5′UTR and near start codon. A cytosine residual located on 5′UTR is around 1 times more likely to be methylated than that on CDS and 0.68 times more likely than that on 3′UTR. While previous study indicates RNA m5C is enriched on both 5′UTR and 3′UTR, our analysis with increased resolution indicates the enrichment is a lot stronger on 5′UTR than 3′UTR [6A, which is enriched near stop codon and 3′UTR [5C and m6A may work in a complementary manner. On lncRNA, however, despite the overall 5′ bias, there is no apparent difference in their distribution patterns revealed.It can be seen from an 3′UTR . We also5C methylated C residuals on RNA and DNA. The same processing pipe line as described previously is applied to mouse embryo fibroblast whole genome bisulfite sequencing data [5C methylation rather than that on RNA. A total of 3641243 cytosine residuals are reported by Bismark, and they are then divided into 3 groups  based on whether their methylation level is significantly higher or lower than the average reported DNA methylation level (65.79%) with a binomial test at 0.05 confidence level. The distributions of the 3 groups of cytosine residuals are still profiled by Guitar package with neighborhood DNA regions included, and the results are shown in We in the following compared the relative distribution of highly and lowly ming data , which p Guitar R package, which can be a useful tool for analyzing RNA-related genomic features, especially RNA methylation. Built upon the highly efficient GRangesList structure and GenomicFeatures R/Bioconductor packages, Guitar can efficiently process millions of genomic features within minutes for efficient transcriptomic analysis. It may automatically download gene information from UCSC genome browser, including neighborhood DNA regions, and allocate the weight of ambiguous features. The developed Guitar coordinates also provide low level conversion from transcript-based coordinates to genome-based coordinates, which should facilitate various customized analysis. Nevertheless, there are still a number of issues remaining to be addressed.Currently, most biological features are represented with genome-based coordinates, making it rather tedious for comparing with transcriptomic landmarks. We developed a Guitar package. Currently, all genomic features and all RNA transcripts have the same weight; this is fine when the genomic features are biological features such as RNA methylation sites, but it may cause problem when the genomic features are NGS mapped reads, such as RNAseq reads, because some genes are a lot more highly expressed than the other genes, and the reads generated from these genes may dominate the result. A more robust approach may be to use the median abundance of all RNA transcripts rather the summed density. It is also possible to develop a boxplot-like visualization scheme in the future to indicate the estimated higher and lower bounds for the distribution of tested features.Firstly, highly abundant transcripts and ambiguous genomic features may dominate the result from Guitar package only facilitates the comparison of feature distribution on mRNA transcripts or on lncRNA transcripts. A cross-comparison between mRNA and lncRNA is not supported so far. In general, it is still very difficult to rigorously contrast the distribution of genomic features on mRNA and lncRNA due to their intrinsic difference. Compared with mRNA, lncRNA usually has lower expression level and less number of exons and is less conserved in sequence.Secondly, the developed normalization scheme within Guitar package adapts two strategies to address this issue. Firstly, highly ambiguous genomic features or transcripts are excluded from the analysis. Secondly, the weight of ambiguous mapping is evenly divided among transcripts. It should be possible to develop a more rigorous formulation, for example, with fuzzy system model [Thirdly, because of the heterogeneity of transcriptome and the discrepancy between transcriptomic and genomic features, ambiguity arises and cannot be resolved perfectly. Previous study of transcriptomic distribution of RNA methylation relies on the longest transcript to eliminate ambiguity in feature assignment, which may require further justification on why the longest transcript should be the canonical one. A more reasonable solution may be to rely on the most abundant isoform transcript; however, the isoform quantification is usually a difficult problem, and such information may not be handy and sometimes may be unavailable at all. Currently,em model –33, to f Guitar package, we also realized the Guitar coordinates may potentially be used for accessing the data quality of MeRIP-Seq datasets. The reproducibility, uniformity, homogeneity, and the information entropy within RNA-related sequencing data  can be conveniently assessed with the help of Guitar coordinates; however, this application has not been explored so far and will be our future work.Fourthly, during the development process of the Guitar package is capable of sketching the approximate transcriptomic distribution of millions of genomic features within minutes in a single line of command. We believe that the newly developed Guitar coordinates and the Guitar package have achieved stable performance on the data tested and should effectively facilitate the analysis of RNA methylation data and other RNA-related biological features.Despite the aforementioned limitations, theSupplementary Material contains information of Figure S1 and Table S1, in which we Compare different settings of ambiguity filter.