Prediction and classification of ncRNAs using structural information
© Panwar et al.; licensee BioMed Central Ltd. 2014
Received: 25 January 2013
Accepted: 4 February 2014
Published: 13 February 2014
Evidence is accumulating that non-coding transcripts, previously thought to be functionally inert, play important roles in various cellular activities. High throughput techniques like next generation sequencing have resulted in the generation of vast amounts of sequence data. It is therefore desirable, not only to discriminate coding and non-coding transcripts, but also to assign the noncoding RNA (ncRNA) transcripts into respective classes (families). Although there are several algorithms available for this task, their classification performance remains a major concern. Acknowledging the crucial role that non-coding transcripts play in cellular processes, it is required to develop algorithms that are able to precisely classify ncRNA transcripts.
In this study, we initially develop prediction tools to discriminate coding or non-coding transcripts and thereafter classify ncRNAs into respective classes. In comparison to the existing methods that employed multiple features, our SVM-based method by using a single feature (tri-nucleotide composition), achieved MCC of 0.98. Knowing that the structure of a ncRNA transcript could provide insights into its biological function, we use graph properties of predicted ncRNA structures to classify the transcripts into 18 different non-coding RNA classes. We developed classification models using a variety of algorithms (BayeNet, NaiveBayes, MultilayerPerceptron, IBk, libSVM, SMO and RandomForest) and observed that model based on RandomForest performed better than other models. As compared to the GraPPLE study, the sensitivity (of 13 classes) and specificity (of 14 classes) was higher. Moreover, the overall sensitivity of 0.43 outperforms the sensitivity of GraPPLE (0.33) whereas the overall MCC measure of 0.40 (in contrast to MCC of 0.29 of GraPPLE) was significantly higher for our method. This clearly demonstrates that our models are more accurate than existing models.
This work conclusively demonstrates that a simple feature, tri-nucleotide composition, is sufficient to discriminate between coding and non-coding RNA sequences. Similarly, graph properties based feature set along with RandomForest algorithm are most suitable to classify different ncRNA classes. We have also developed an online and standalone tool-- RNAcon (http://crdd.osdd.net/raghava/rnacon).
The assumption, that proteins are the functional resultant of most genetic information, was derived from studies primarily done on bacteria such as Escherichia coli whose genomes are dominated by protein coding sequences (80-95%). The perception that organism (functional) complexity is correlated with the number of protein coding genes was undermined, when by means of sequencing experiments it became abundantly clear that the numbers of protein coding genes do not keep up with the functional repertoire of an organism (Eg: ~1000 cell nematode worm C. elegans has ~19,000 genes and which are nearly as many as 1018-1020 cell humans have (~20,000)). On the other hand, the non-coding region of genomes increases with the complexity of organisms. For example, ~5%, 70% and 80% of the genomic regions of bacteria, unicellular eukaryotes and invertebrates respectively are annotated to be non-coding[1, 2]. Amazingly, not only is the majority of this non-coding region transcribed, but also these non-coding RNAs (ncRNA) are proving to be biologically functional[3, 4]. Many types of ncRNAs are involved in diverse cellular activities such as replication, transcription, gene expression regulation (miRNAs:), gene silencing[8, 9] and chromosome stability, RNA modification (snoRNAs:), RNA processing (RNA subunit of RNase P:), RNA stability, protein stability, translocation (SRP RNA:) and localization. Having roles in developmental processes and being involved in maintaining homeostasis, any perturbation in the abundances and or sequence of these ncRNAs results in disorders like tumorigenesis, neurological disorders, cardiovascular, developmental, autoimmune, imprinting and other human diseases and disorders.
Unraveling the functional role of this allegedly inert transcription requires the analysis of large amounts of sequence data. Recently, the ENCODE project assigned biochemical functions for 80% of the human genome, much of which is annotated to be non-protein coding. Various other high-throughput sequencing (HTS) projects are producing huge amounts of transcriptomic data. Thus, computational methods are required to analyze these humongous datasets so as to address the goal of predicting potentially functional non-coding regions and their respective function.
While it has been possible to efficiently discriminate coding and non-coding RNA sequences, for example by employing SVM based prediction models (CONC and CPC), further classifying the non-coding transcripts into functional categories remains challenging. Although, various bioinformatics tools are available for the classification of these transcripts-- their prediction performance is not satisfactory.
Knowing that nucleotide base pairing and stacking interactions between different regions provide ncRNA sequences a well-defined structure; these interactions may also further reveal biological functions. Indeed it has been shown that RNA structure is responsible for specific biological function. Minimum free energy (MFE) based approaches and thermo-stability of multiple aligned structures have also been used for the prediction of functional RNA.
The structure of an ncRNA molecule can be represented as a graph. Being a representative of relationships between different nucleotides, a ncRNA graph uses ‘nodes’ to represent the nucleotides and ‘edges’ to represent the interactions (relationships) between the nucleotides. Such a graph based representation leads to defining of different properties that represent the different characteristics of ncRNA molecules. Two levels of relationships can be defined using graph theory based properties: relationships defined on the level of the nucleotides (‘local properties’) and relationships that represent the graph itself (‘global properties’). Graph properties, derived from a graph representation of predicted ncRNA structure, have been previously used as a feature set to classify ncRNA molecules. Childs et al developed a web-based tool, GraPPLE that utilized graph properties to classify RNA molecules into Rfam families. When compared to existing methods, GraPPLE was demonstrated to be more robust to sequence divergence between the members of an Rfam family and also exhibited improved prediction accuracy.
The overall performance of various machine-learning algorithms is intrinsically dependent upon many factors. The performance parameters of GraPPLE could be affected by ‘external’ factors such as the accuracy of predicted RNA structure, the choice of classifier and normalization/optimization procedures selected. In order to explore the potential of different classes of machine learning algorithms to learn distinctive features of ncRNA classes, we employed graph properties as input parameters to a variety of machine learning methods. Additional information regarding structures containing pseudoknot interactions was also incorporated into the modeling framework. So as to facilitate comparative analysis with GraPPLE, we used the same training and testing datasets as GraPPLE. To incorporate RNA pseudoknot information IPknot software was used to predict RNA structures, as it was demonstrated to be more accurate when compared with other methods. We implemented this approach into the form of a web-server (as well as a standalone application) called ‘RNAcon’.
In order to discriminate the non-coding sequences from the coding transcripts, we initially developed a methodology for discriminating non-coding RNA from coding RNAs. Secondly, we developed models to classify ncRNAs into different classes.
Prediction of non-coding RNAs
SVM based highest prediction performances (on the basis of MCC) of different composition approaches for the discrimination between non-coding and coding RNAs
Classification of non-coding RNAs
In the classification of different non-coding RNAs, we used same 20% non-redundant dataset that was earlier used by the GraPPLE method. This dataset incorporated both training and testing datasets for 18 different classes of non-coding RNAs (for detail see Methods section).
Composition based approaches
Overall sensitivity (Q D ) of different classifiers for the classification of 18 ncRNA classes
Name of classifier
Graph properties based approach
As graph properties can represent nucleotide level (local) and structure level (global) parameters, the scale and the range of graph properties metrics vary extensively. So as to provide uniformity in the scale and range of these metrics, we normalized graph properties values into the range of -1.0 to +1.0 before applying 6 different classifiers. The NaiveBayes, MultilayerPerceptron, IBk, libSVM, SMO and RandomForest achieved sensitivity (QD) values of 0.315, 0.314, 0.314, 0.214, 0.283 and 0.400 respectively (Table 2). As the BayesNet classifier failed to run on the normalized values, we tried all these classifiers on the raw, non-normalized, value of graph properties: where BayesNet, NaiveBayes, MultilayerPerceptron, IBk, libSVM, SMO and RandomForest achieved sensitivity values of 0.397, 0.398, 0.429, 0.407, 0.056, 0.422 and 0.433 respectively (Table 2). It must be pointed out that RandomForest (100 tree and 10 seed values) based approach achieved the highest sensitivity of 0.433 and 0.40 MCC (Additional file1: Figure S5). The MultilayerPerceptron is second highest performing classifier and achieved sensitivity of 0.429 and 0.395 MCC (Additional file1: Figure S6). SMO classifier based prediction achieved sensitivity of 0.422 and 0.388 MCC (Additional file1: Figure S7).
Since we used the dataset from the previous study of GraPPLE, where they have already removed the biasness of sequence similarity, length and GC content in order to test the predictive power of graph-properties only. However, we calculated the correlation between the average length of particular ncRNA class and their prediction performance [sensitivity (QD)] from RandomForest model and achieved correlation coefficient values (R) of -0.343 between the average length and sensitivity (Additional file1: Table S7). It means, length affects the prediction performance and has a negative correlation with the sensitivity.
Comparisons of RNAcon with different existing methods
In order to evaluate the prediction performance of RNAcon (both prediction and classification of noncoding RNAs), we compared RNAcon with different gene-calling programs, CONC, CPC, GraPPLE and Rfam-based covariance models.
Comparison with different gene-calling programs
The gene-calling programs detect protein-coding part in the transcripts/cDNAs; therefore, they can also be used to discriminate between coding and noncoding genes. In this study, we used 2670 noncoding RNAs as positive and 5601 coding RNAs as negative datasets-- collectively called CONC dataset. RNAcon achieved 86.25% sensitivity, 90.52% specificity, 89.14% accuracy and 0.76 MCC on this CONC dataset. We evaluate performance of three commonly used gene-calling programs (AUGUSTUS, GeneMark.hmm & Glimmer.HMM) on this CONC dataset.
First, we used the standalone version 2.7 of AUGUSTUS on the CONC dataset and found that it predicted genes (protein-coding region) in the 11 (False negatives; 0.41%) noncoding RNAs out of 2670 noncoding RNAs. This means that AUGUSTUS correctly predicts 2659 (True positives) noncoding RNAs as non-coding (99.59% sensitivity). Similarly, it predicted genes in the 1111 (True negatives) coding RNAs out of total 5601 coding RNAs and it failed to predict any gene in the remaining 4490 (False positives) coding RNAs (19.84% specificity). Overall AUGUSTUS achieved 89.14% accuracy and 0.76 MCC. Likewise, GeneMark.hmm (version 2.2b) achieved 90.22% sensitivity, 71.17% specificity, 77.32% accuracy and 0.57 MCC. Comparatively, Glimmer.HMM (version 3.0.3) performed better than other two gene-calling programs and achieved 95.73% sensitivity, 71.68% specificity, 79.45% accuracy and 0.63 MCC. These results showed that RNAcon performed better (0.76 MCC) than other three gene-calling programs (Additional file1: Table S8).
The prediction of the three selected gene-calling programs is based on the prediction of protein coding genes only. These three algorithms were designed to specifically predict the protein coding genes and ignore the rest of the sequences, treating them as background noise. These gene-calling algorithms perform satisfactorily while predicting non-coding RNAs. In reality these methods are actually ignoring the non-coding "background" by just selecting for the protein coding sequences whereas RNAcon is actually discriminating coding the non-coding genes and not selectively identifying one class from the datasets.
Comparison with CONC and CPC
In the discrimination between noncoding and coding RNA, CONC has used various input features (Total 180) such as peptide length, amino acid compositions, secondary structure content, percentage of residues exposed to solvent, sequence compositional entropy, number of homologs in a protein database and alignment entropy. The CONC method was further improved by CPC method using the following six input features: LOG-ODDS score, coverage of the predicted ORF, integrity of predicted ORF, number of hits against protein database, HIT-SCORE and FRAME-SCORE. Using all these complex input features CPC reported 95.77% accuracy. For comparison purposes, we used CPC standalone software to calculate these input features from the same CONC dataset. By developing SVM-based models we achieved maximum accuracy of 94.14%, which is almost similar to five-input features (predicted SVM scores of different compositional features) based hybrid approach of RNAcon (93.97% accuracy) (Table 1). Varied factors such as learning parameters, optimization procedure and SVM version can affect this marginal performance difference. Importantly, RNAcon uses computationally simpler methodology to achieve comparable results. Considering the humongous amounts of sequence data, simple, fast and straightforward methods are requirement of the current times. For example, the RNAcon_predict (standalone version) takes less than 1 minute (0 m58.830 s) to process 30770 sequences (Rfam) using a Mac OS X (version 10.7.5) of 2.5 GHz (Intel Core i5) and 4GB RAM (1333 MHz DDR3) system. In contrast, CPC reported that their method took 3513 minutes of CPU time (Intel Xeon 3.0G and 4GB RAM) for same number (30770) of Rfam sequences. The huge amount of available transcriptomic/NGS data requires RNAcon type of method because it can easily process millions of sequences within reasonably CPU time. The RNAcon_predict processed 100000 sequences each of coding and noncoding RNA in the 6 (5 m51.616 s) and 3 (2 m52.918 s) minutes respectively. Moreover, given the importance of ncRNAs in biology, our primary emphasis was to develop a method for the ncRNA classification.
Comparison with GraPPLE
Comparison with Rfam-based covariance models (Rfam-CM)
Although, GraPPLE already compared the graph-properties based and covariance based models, the study employed MUSCLE based alignments, that may artificially handicap the performance of covariance models. Therefore, we used the original Rfam-based covariance models (Rfam-CM) and compared with our RNAcon method. All the sequences of different ncRNA classes used for the development of RNAcon were retrieved from Rfam (release 9.0 only). We evaluate Rfam-CM version and RNAcon on novel sequences those that were not included in release 9.0. In order to remove biasness in prediction, we have only taken new sequences that have no similarity (BLAST e-value 0.001) with sequences in Rfam (release 9.0). In order to extract non-redundant sequences, we search sequences of different classes/families in Rfam (release 11.0) against the same families in Rfam (release 9.0). Our final dataset contained sequences of different classes in new Rfam (release 11.0) that shows no similarity at BLAST e-value 0.001 with sequences in Rfam (release 9.0).
Surprisingly, Rfam-CM (release 9.0) performed unsatisfactorily on these (novel as well as non-homologous) sequences and classified only 5.35% ncRNAs correctly. When we employed RNAcon for predicting the classes of these sequences, the prediction accuracy was 25.8% (Additional file1: Table S9). It is noteworthy that RNAcon was able to accurately predict two non-coding RNA families (HACA-BOX and miRNA), whose sequences were novel in the comparative analysis. Above analysis indicates that RNAcon can also classify non-redundant non-coding sequences, where Rfam fails to classify the same. Overall RNAcon is a useful tool, which can classify even sequences which have low sequence similarity; it will complement existing tools like Rfam-CM.
For the purpose of discriminating between non-coding and coding RNA sequences, SVM based simple tri-nucleotide compositions (TNC) approach performed well. Although, nucleotide composition based approaches have been used previously by CONC, the study also involved the use of various other features such as peptide length, amino acid compositions, secondary structure content, percentage of residues exposed to solvent, sequence compositional entropy, number of homologs in a protein database and alignment entropy. Although biologically relevant, all these features incorporate un-necessary complexity to the problem of discriminating coding from non-coding RNAs. An advantage of using the TNC approach is that when developed into a web-based/standalone application, it efficiently discriminates coding and non-coding RNA, before we further classify them into different ncRNA classes. WEKA software was used to select 14 most contributing tri-nucleotides and observed that CUA, GGG, GUA, UAA, UAC and UAG are preferred in non-coding RNAs whereas ACG, CCG, CGA, CGC, CGG, CGU, GCG and UCG are preferred in coding RNAs (Figure 1). Obviously, TNC of the stop codon UAG and UAA are more abundant in ncRNAs whereas CG containing tri-nucleotides (ACG, CCG, CGA, CGC, CGG, CGU, GCG and UCG) are more preferred in the coding RNAs.
To classify different ncRNAs, 20 different graph properties of IPknot-based predicted structures were calculated using the igraph R package. Although biological interpretation of various graph properties is not as yet established, properties related to local or global features of any ncRNA structure could reveal interesting insights of different ncRNA classes. For examples measures like betweenness and centrality could reveal the most "central" nucleotides—depicting important roles these core nodes may play in the flow of information. Global properties like degree, diameter, girth and density provide us with a holistic view of the ncRNA structures, revealing the overall "compactness" of the different classes. A thorough analysis of the biological significance if these properties could indeed prove to be beneficial. WEKA package that contains various classifiers such as BayesNet, NaiveBayes, MultilayerPerceptron, IBk, libSVM, SMO and RandomForest was used to develop and test different classification models. By applying 6 different classifiers, we found that RandomForest was the best performing classifier and achieved highest MCC of 0.40 and outperformed the MCC (0.29) of GraPPLE method. The graph properties based approach performed well (QD > 0.60) for HACA-BOX, MIRNA, 5.8S-rRNA, tRNA, 6S-RNA, tmRNA and Intron-gp-1 ncRNAs while its performance was average (QD = 0.30 to 0.60) for the 5S-rRNA, SRP, T-box and RIBOZYME ncRNAs. The approach failed (QD < 0.30) to classify CD-BOX, IRES, LEADER, Intron-gp-1, SSU-rRNA5 and RIBOSWITCH (Figure 2). The reason was because most of the CD-BOX, LEADER, IRES, Intron-gp-1, SSU-rRNA5 and RIBOSWITCH sequences were wrongly predicted as 5.8S-rRNA, HACA-BOX, T-box, RIBOZYME, Intron-gp-1 and 5.8S-rRNA respectively. Many factors, such as accuracy of predicted structures and conversion of structures into the informative graph properties influence the prediction performance.
The prediction performance based comparison of RNAcon with three gene-calling programs indicates that RNAcon performs better in discriminating non-coding and coding sequences. Similarly, Rfam-based covariance models performed poor to classify the novel/non-similar sequences whereas comparatively structural information based graph-properties of RNAcon method performed well because graph-properties based features provide both local as well as global structural features of a particular class. The performance of Rfam-based covariance models was poor because we evaluated performance on the Rfam 11.0 sequences, which have very low similarity (Cutoff threshold 0.001 E-value) with the Rfam 9.0 database. If we evaluate all the sequences in the Rfam 11.0, which are not included in the Rfam 9.0 (non-intersecting and only present in Rfam 11.0), then performance will be much higher. Additionally, a simple algorithm for discriminating coding and noncoding RNAs is efficient enough to process thousands of RNAs in few minutes. Currently, RNAcon method was not developed for some newly emerging noncoding classes such as lncRNAs and CRISPR. In the future, we hope that prediction performance will be improved with more accurate and efficient structure prediction algorithms, more biologically relevant graph properties and classifiers and will also integrate the new ncRNA classes.
In this study, a systematic attempt has been made to predict and classify ncRNAs. SVM based TNC approach discriminated noncoding and coding RNAs efficiently. Furthermore, graph properties based approach classifies different ncRNA classes using RandomForest classifier. Analysis showed that length of RNAs has a negative correlation with the prediction sensitivity for classifying noncoding RNAs. Comparatively, RNAcon performed well than other gene-calling programs and Rfam-based covariance models. All these prediction models have been implemented in the form of a freely available ‘RNAcon’ web-server/standalone.
In this study, we used two different datasets for the prediction and classification of ncRNAs.
Dataset for the prediction of ncRNAs
We used three datasets for the development of noncoding RNA predictions- (i) main dataset, (ii) independent dataset and (iii) CONC datasets. We utilized a total of 444417 non-coding RNA sequences (all the available sequences) from Rfam release 10.0 database and 97836 coding RNA sequences from RefSeq database. In order to retrieve RefSeq sequences using Entrez query, we used nucleotide section of NCBI browser (http://www.ncbi.nlm.nih.gov/nuccore) with a command (srcdb_refseq_reviewed [prop] & mRNA). It retrieved all the reviewed mRNA sequences present in the RefSeq database. Non-redundant datasets- 25% using BLASTCLUST software were created thereafter. This dataset of 40906 non-coding and 62473 coding RNA sequences was used as the main datasets. Randomly 20453 non-coding (50% of total non-coding) and 31237 coding (50% of total coding) RNA sequences were used as an independent dataset. The SVM based model training was done on the remaining 50% of both noncoding and coding RNA and performances were tested on the independent datasets. The training datasets are 25% non-redundant than testing or independent dataset. All the sequences of training dataset are less than 25% similar than any sequence of independent dataset. We also used the noncoding and coding RNA sequences from the CONC dataset. This dataset was also used by the CPC method. In all the prediction methods, non-coding and coding RNA sequences were used as positive and negative sets respectively.
Dataset for the classification of different ncRNAs
In the classification of different non-coding RNA classes, we used the previously developed dataset of GraPPLE method, which was originally obtained from Rfam release 9.0. This dataset contained 20% non-redundant sequences of 18 different non-coding RNA classes (CD-BOX, HACA-BOX, IRES, LEADER, MIRNA, 5S-rRNA, 5.8S-rRNA, tRNA, 6S-RNA, SRP, tmRNA, Intron-gp-1, Intron-gp-2, SECIS, SSU-rRNA5, T-box, RIBOSWITCH and RIBOZYME). Different datasets for the training and testing of each ncRNA class were used and sequence similarity between training and testing datasets was ≤ 20%.
Previously, it has been shown that the composition-based approaches are useful to develop machine learning based prediction of biological sequences. Most of machine learning algorithm requires fixed length of input features. Thus, we calculated mono-, di-, tri-, tetra- and penta-nucleotde compositions of 4, 16, 64, 256, and 1024 input features respectively. The major challenge is to develop efficient prediction tool with less possible input features so it is not advisable to use tetra and penta-nucleotide compositions for the predictions.
We applied a hybrid approach for the discrimination between noncoding and coding RNAs. In this approach we used five predicted SVM scores of all approaches (MNC, DNC, TNC, TTNC and PNC) as input features and developed a separate SVM-based prediction model.
We predicted the secondary structures of non-coding RNA using IPknot software. It predicts pseudoknots based on the maximizing expected accuracy and the output is generated in the dot-parenthesis format of five secondary structures: open small bracket, close small bracket, open square bracket, close square bracket and dot. The small brackets, square brackets and dots denote the allowed base pair, pseudoknots and unpaired bases respectively.
igraph R package and graph properties
The predicted ncRNA structures were used for the calculation of graph properties using igraph R package. A total of 20 different graph properties: Articulation points, Average path length, Average node betweenness, Variance of node betweenness, Average edge betweenness, Variance of edge betweenness, Average co-citation coupling, Average bibliographic coupling, Average closeness centrality, Variance of closeness centrality, Average Burt's constraint, Variance of Burt's constraint, Average degree, Diameter, Girth, Average coreness, Variance of coreness, Maximum coreness, Graph density and Transitivity were calculated. These are the same graph properties, which were used by GraPPLE method and details of all graph properties given in the Additional file1: Table S10. The numerical values of these graph properties were used as input features for machine learning algorithms and further prediction tool development for classification of different ncRNA classes.
Support vector machines (SVM)
In this study, we used a well-known machine learning technique Support Vector Machine (SVM), which is based on the structural minimization principle of statistics theory. This is supervised learning method and can be use for both classification and regression requirements. It provides a number of parameters and kernels for the proper optimization of model training. The SVMlight Version 6.02 package of SVM was used and three different (linear, polynomial and radial basis function) kernels were applied for model building. We optimized different parameters & kernels and developed efficient prediction models for the discrimination between coding and non-coding RNAs.
WEKA is a single platform of various machine-learning algorithms. We used WEKA 3.6.4 version, which contains different classifiers such as BayeNet, NaiveBayes, MultilayerPerceptron, IBk, libSVM, SMO and RandomForest. We applied all these classifiers for the classification of different ncRNA classes.
Ten-fold cross-validation and performance evaluation
Where TP, TN, FP and FN are True Positives, True Negative, False Positives and False Negatives respectively.
Where Zij is an entry in a confusion matrix of 18 ncRNA classes, i and j are index for the actual and predicted family respectively.
RNAcon web-server and standalone
We implemented TNC features based SVM model (parameter: -z c -t 2 -g 0.01 -c 6 -j 2) for discriminating noncoding and coding RNAs and graph properties based RandomForest model (parameter: -I 100 -K 0 -S 10) for the classification of ncRNAs into a webserver called RNAcon. The RNAcon web-server and standalone (Linux-based command-line mode) both are freely available for the help of global scientific community and available fromhttp://crdd.osdd.net/raghava/rnacon/%20web-address.
The authors are thankful to the Dr. Ge Gao for providing the CONC dataset and Dr. Liam Childs for providing the datasets used in the GraPPLE method. Council of Scientific and Industrial Research (CSIR) and Department of Biotechnology (DBT), Government of India for financial assistance. This report has Institute of Microbial Technology (IMTECH) communication no. 096/2012.
- Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W, Funke R, Gage D, Harris K, Heaford A, Howland J, Kann L, Lehoczky J, LeVine R, McEwan P, McKernan K, Meldrim J, Mesirov JP, Miranda C, Morris W, Naylor J, Raymond C, Rosetti M, Santos R, Sheridan A, Sougnez C, et al: Initial sequencing and analysis of the human genome. Nature. 2001, 409: 860-921. 10.1038/35057062.PubMedView ArticleGoogle Scholar
- Dunham I, Kundaje A, Aldred SF, Collins PJ, Davis CA, Doyle F, Epstein CB, Frietze S, Harrow J, Kaul R, Khatun J, Lajoie BR, Landt SG, Lee B-K, Pauli F, Rosenbloom KR, Sabo P, Safi A, Sanyal A, Shoresh N, Simon JM, Song L, Trinklein ND, Altshuler RC, Birney E, Brown JB, Cheng C, Djebali S, Dong X, Ernst J, et al: An integrated encyclopedia of DNA elements in the human genome. Nature. 2012, 489: 57-74. 10.1038/nature11247.View ArticleGoogle Scholar
- Costa FF: Non-coding RNAs: lost in translation?. Gene. 2007, 386: 1-10. 10.1016/j.gene.2006.09.028.PubMedView ArticleGoogle Scholar
- Collins LJ, Penny D: The RNA infrastructure: dark matter of the eukaryotic cell?. Trends Genet. 2009, 25: 120-128. 10.1016/j.tig.2008.12.003.PubMedView ArticleGoogle Scholar
- Mason M, Schuller A, Skordalakes E: Telomerase structure function. Curr Opin Struct Biol. 2011, 21: 92-100. 10.1016/j.sbi.2010.11.005.PubMedView ArticleGoogle Scholar
- Yang Z, Zhu Q, Luo K, Zhou Q: The 7SK small nuclear RNA inhibits the CDK9/cyclin T1 kinase to control transcription. Nature. 2001, 414: 317-322. 10.1038/35104575.PubMedView ArticleGoogle Scholar
- Lagos-Quintana M, Rauhut R, Lendeckel W, Tuschl T: Identification of novel genes coding for small expressed RNAs. Science (New York, NY). 2001, 294: 853-858. 10.1126/science.1064921.View ArticleGoogle Scholar
- Hannon GJ: RNA interference. Nature. 2002, 418: 244-251. 10.1038/418244a.PubMedView ArticleGoogle Scholar
- Wilson RC, Doudna JA: Molecular mechanisms of RNA interference. Annu Rev Biophys. 2013, 42: 217-39. 10.1146/annurev-biophys-083012-130404.PubMedView ArticleGoogle Scholar
- Moazed D: Small RNAs in transcriptional gene silencing and genome defence. Nature. 2009, 457: 413-40. 10.1038/nature07756.PubMed CentralPubMedView ArticleGoogle Scholar
- Lowe TM, Eddy SR: A computational screen for methylation guide snoRNAs in yeast. Science (New York, NY). 1999, 283: 1168-1171. 10.1126/science.283.5405.1168.View ArticleGoogle Scholar
- Brown JW: The Ribonuclease P Database. Nucleic Acids Res. 1999, 27: 314-10.1093/nar/27.1.314.PubMed CentralPubMedView ArticleGoogle Scholar
- Storz G: An expanding universe of noncoding RNAs. Science (New York, NY). 2002, 296: 1260-1263. 10.1126/science.1072249.View ArticleGoogle Scholar
- Gueneau De Novoa P, Williams KP: The tmRNA website: reductive evolution of tmRNA in plastids and other endosymbionts. Nucleic Acids Res. 2004, 32: D104-1058. 10.1093/nar/gkh102.PubMed CentralPubMedView ArticleGoogle Scholar
- Keenan RJ, Freymann DM, Stroud RM, Walter P: The signal recognition particle. Annu Rev Biochem. 2001, 70: 755-775. 10.1146/annurev.biochem.70.1.755.PubMedView ArticleGoogle Scholar
- Rosenblad MA, Gorodkin J, Knudsen B, Zwieb C, Samuelsson T: SRPDB: Signal Recognition Particle Database. Nucleic Acids Res. 2003, 31: 363-364. 10.1093/nar/gkg107.PubMed CentralPubMedView ArticleGoogle Scholar
- Croce CM: Causes and consequences of microRNA dysregulation in cancer. Nat Rev Genet. 2009, 10: 704-714. 10.1038/nrg2634.PubMed CentralPubMedView ArticleGoogle Scholar
- Schaefer A, O’Carroll D, Tan CL, Hillman D, Sugimori M, Llinas R, Greengard P: Cerebellar neurodegeneration in the absence of microRNAs. J Exp Med. 2007, 204: 1553-1558. 10.1084/jem.20070823.PubMed CentralPubMedView ArticleGoogle Scholar
- Zhao Y, Ransom JF, Li A, Vedantham V, von Drehle M, Muth AN, Tsuchihashi T, McManus MT, Schwartz RJ, Srivastava D: Dysregulation of cardiogenesis, cardiac conduction, and cell cycle in mice lacking miRNA-1-2. Cell. 2007, 129: 303-317. 10.1016/j.cell.2007.03.030.PubMedView ArticleGoogle Scholar
- He L, Hannon GJ: MicroRNAs: small RNAs with a big role in gene regulation. Nat Rev Genet. 2004, 5: 522-531. 10.1038/nrg1379.PubMedView ArticleGoogle Scholar
- Horsthemke B, Wagstaff J: Mechanisms of imprinting of the Prader-Willi/Angelman region. Am J Med Genet A. 2008, 146A: 2041-2052. 10.1002/ajmg.a.32364.PubMedView ArticleGoogle Scholar
- Esteller M: Non-coding RNAs in human disease. Nat Rev Genet. 2011, 12: 861-874. 10.1038/nrg3074.PubMedView ArticleGoogle Scholar
- Okazaki Y, Furuno M, Kasukawa T, Adachi J, Bono H, Kondo S, Nikaido I, Osato N, Saito R, Suzuki H, Yamanaka I, Kiyosawa H, Yagi K, Tomaru Y, Hasegawa Y, Nogami A, Schönbach C, Gojobori T, Baldarelli R, Hill DP, Bult C, Hume DA, Quackenbush J, Schriml LM, Kanapin A, Matsuda H, Batalov S, Beisel KW, Blake JA, Bradt D, et al: Analysis of the mouse transcriptome based on functional annotation of 60,770 full-length cDNAs. Nature. 2002, 420: 563-573. 10.1038/nature01266.PubMedView ArticleGoogle Scholar
- Liu J, Gough J, Rost B: Distinguishing protein-coding from non-coding RNAs through support vector machines. PLoS Genet. 2006, 2: e29-10.1371/journal.pgen.0020029.PubMed CentralPubMedView ArticleGoogle Scholar
- Kong L, Zhang Y, Ye Z-Q, Liu X-Q, Zhao S-Q, Wei L, Gao G: CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Res. 2007, 35: W345-349. 10.1093/nar/gkm391.PubMed CentralPubMedView ArticleGoogle Scholar
- Mathews DH, Turner DH: Prediction of RNA secondary structure by free energy minimization. Curr Opin Struct Biol. 2006, 16: 270-278. 10.1016/j.sbi.2006.05.010.PubMedView ArticleGoogle Scholar
- Rivas E, Eddy SR: Secondary structure alone is generally not statistically significant for the detection of noncoding RNAs. Bioinformatics. 2000, 16: 583-605. 10.1093/bioinformatics/16.7.583.PubMedView ArticleGoogle Scholar
- Washietl S, Hofacker IL, Stadler PF: Fast and reliable prediction of noncoding RNAs. Proc Natl Acad Sci U S A. 2005, 102: 2454-2459. 10.1073/pnas.0409169102.PubMed CentralPubMedView ArticleGoogle Scholar
- Karklin Y, Meraz RF, Holbrook SR: Classification of non-coding RNA using graph representations of secondary structure. Pac Symp Biocomput. 2005, 4-15. (PMID: 15759609)Google Scholar
- Childs L, Nikoloski Z, May P, Walther D: Identification and classification of ncRNA molecules using graph properties. Nucleic Acids Res. 2009, 37: e66-10.1093/nar/gkp206.PubMed CentralPubMedView ArticleGoogle Scholar
- Sato K, Kato Y, Hamada M, Akutsu T, Asai K: IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming. Bioinformatics. 2011, 27: i85-93. 10.1093/bioinformatics/btr215.PubMed CentralPubMedView ArticleGoogle Scholar
- Panwar B, Raghava GPS: Prediction and classification of aminoacyl tRNA synthetases using PROSITE domains. BMC Genomics. 2010, 11: 507-10.1186/1471-2164-11-507.PubMed CentralPubMedView ArticleGoogle Scholar
- Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH: The WEKA data mining software: an update. SIGKDD Explorations. 2009, 11: 10-18. 10.1145/1656274.1656278.View ArticleGoogle Scholar
- Csardi G, Nepusz T: The igraph software package for complex network research. Inter Journal. 2006, Complex Systems: 1695-Google Scholar
- Hoff KJ, Stanke M: WebAUGUSTUS–a web service for training AUGUSTUS and predicting genes in eukaryotes. Nucleic Acids Res. 2013, 41: W123-128. 10.1093/nar/gkt418.PubMed CentralPubMedView ArticleGoogle Scholar
- Besemer J, Borodovsky M: GeneMark: web software for gene finding in prokaryotes, eukaryotes and viruses. Nucleic Acids Res. 2005, 33: W451-454. 10.1093/nar/gki487.PubMed CentralPubMedView ArticleGoogle Scholar
- Majoros WH, Pertea M, Salzberg SL: TigrScan and GlimmerHMM: two open source ab initio eukaryotic gene-finders. Bioinformatics. 2004, 20: 2878-2879. 10.1093/bioinformatics/bth315.PubMedView ArticleGoogle Scholar
- Griffiths-Jones S, Bateman A, Marshall M, Khanna A, Eddy SR: Rfam: an RNA family database. Nucleic Acids Res. 2003, 31: 439-441. 10.1093/nar/gkg006.PubMed CentralPubMedView ArticleGoogle Scholar
- Pruitt KD, Tatusova T, Klimke W, Maglott DR: NCBI Reference Sequences: current status, policy and new initiatives. Nucleic Acids Res. 2009, 37: D32-36. 10.1093/nar/gkn721.PubMed CentralPubMedView ArticleGoogle Scholar
- Vapnik VN: An overview of statistical learning theory. IEEE Trans Neural Netw. 1999, 10: 988-999. 10.1109/72.788640.PubMedView ArticleGoogle Scholar
- Joachims T: Making large-Scale SVM Learning Practical. Adv Kernel Methods Support Learn. 1999, 169-184. (ISBN:0-262-19416-3)Google Scholar
- Panwar B, Raghava GPS: Predicting sub-cellular localization of tRNA synthetases from their primary structures. Amino Acids. 2012, 42: 1703-1713. 10.1007/s00726-011-0872-8.PubMedView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.