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1.
Background
Upwards of 1200 miRNA loci have hitherto been annotated in the human genome. The specific features defining a miRNA precursor and deciding its recognition and subsequent processing are not yet exhaustively described and miRNA loci can thus not be computationally identified with sufficient confidence.Results
We rendered pre-miRNA and non-pre-miRNA hairpins as strings of integrated sequence-structure information, and used the software Teiresias to identify sequence-structure motifs (ss-motifs) of variable length in these data sets. Using only ss-motifs as features in a Support Vector Machine (SVM) algorithm for pre-miRNA identification achieved 99.2% specificity and 97.6% sensitivity on a human test data set, which is comparable to previously published algorithms employing combinations of sequence-structure and additional features. Further analysis of the ss-motif information contents revealed strongly significant deviations from those of the respective training sets, revealing important potential clues as to how the sequence and structural information of RNA hairpins are utilized by the miRNA processing apparatus.Conclusion
Integrated sequence-structure motifs of variable length apparently capture nearly all information required to distinguish miRNA precursors from other stem-loop structures. 相似文献2.
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Background
Complex networks can often be decomposed into less complex sub-networks whose structures can give hints about the functional organization of the network as a whole. However, these structural motifs can only tell one part of the functional story because in this analysis each node and edge is treated on an equal footing. In real networks, two motifs that are topologically identical but whose nodes perform very different functions will play very different roles in the network.Methodology/Principal Findings
Here, we combine structural information derived from the topology of the neuronal network of the nematode C. elegans with information about the biological function of these nodes, thus coloring nodes by function. We discover that particular colorations of motifs are significantly more abundant in the worm brain than expected by chance, and have particular computational functions that emphasize the feed-forward structure of information processing in the network, while evading feedback loops. Interneurons are strongly over-represented among the common motifs, supporting the notion that these motifs process and transduce the information from the sensor neurons towards the muscles. Some of the most common motifs identified in the search for significant colored motifs play a crucial role in the system of neurons controlling the worm''s locomotion.Conclusions/Significance
The analysis of complex networks in terms of colored motifs combines two independent data sets to generate insight about these networks that cannot be obtained with either data set alone. The method is general and should allow a decomposition of any complex networks into its functional (rather than topological) motifs as long as both wiring and functional information is available. 相似文献4.
Background
The physical interactions between proteins constitute the basis of protein quaternary structures. They dominate many biological processes in living cells. Deciphering the structural features of interacting proteins is essential to understand their cellular functions. Similar to the space of protein tertiary structures in which discrete patterns are clearly observed on fold or sub-fold motif levels, it has been found that the space of protein quaternary structures is highly degenerate due to the packing of compact secondary structure elements at interfaces. Therefore, it is necessary to further decompose the protein quaternary structural space into a more local representation.Results
Here we constructed an interface fragment pair library from the current structure database of protein complexes. After structural-based clustering, we found that more than 90% of these interface fragment pairs can be represented by a limited number of highly abundant motifs. These motifs were further used to guide complex assembly. A large-scale benchmark test shows that the native-like binding is highly likely in the structural ensemble of modeled protein complexes that were built through the library.Conclusions
Our study therefore presents supportive evidences that the space of protein quaternary structures can be represented by the combination of a small set of secondary-structure-based packing at binding interfaces. Finally, after future improvements such as adding sequence profiles, we expect this new library will be useful to predict structures of unknown protein-protein interactions.Electronic supplementary material
The online version of this article (doi:10.1186/s12859-014-0437-4) contains supplementary material, which is available to authorized users. 相似文献5.
Background
miRNAs are key players in gene expression regulation. To fully understand the complex nature of cellular differentiation or initiation and progression of disease, it is important to assess the expression patterns of as many miRNAs as possible. Thereby, identifying novel miRNAs is an essential prerequisite to make possible a comprehensive and coherent understanding of cellular biology.Methodology/Principal Findings
Based on two extensive, but previously published, small RNA sequence datasets from human embryonic stem cells and human embroid bodies, respectively [1], we identified 112 novel miRNA-like structures and were able to validate miRNA processing in 12 out of 17 investigated cases. Several miRNA candidates were furthermore substantiated by including additional available small RNA datasets, thereby demonstrating the power of combining datasets to identify miRNAs that otherwise may be assigned as experimental noise.Conclusions/Significance
Our analysis highlights that existing datasets are not yet exhaustedly studied and continuous re-analysis of the available data is important to uncover all features of small RNA sequencing. 相似文献6.
Background
State-of-the-art protein-ligand docking methods are generally limited by the traditionally low accuracy of their scoring functions, which are used to predict binding affinity and thus vital for discriminating between active and inactive compounds. Despite intensive research over the years, classical scoring functions have reached a plateau in their predictive performance. These assume a predetermined additive functional form for some sophisticated numerical features, and use standard multivariate linear regression (MLR) on experimental data to derive the coefficients.Results
In this study we show that such a simple functional form is detrimental for the prediction performance of a scoring function, and replacing linear regression by machine learning techniques like random forest (RF) can improve prediction performance. We investigate the conditions of applying RF under various contexts and find that given sufficient training samples RF manages to comprehensively capture the non-linearity between structural features and measured binding affinities. Incorporating more structural features and training with more samples can both boost RF performance. In addition, we analyze the importance of structural features to binding affinity prediction using the RF variable importance tool. Lastly, we use Cyscore, a top performing empirical scoring function, as a baseline for comparison study.Conclusions
Machine-learning scoring functions are fundamentally different from classical scoring functions because the former circumvents the fixed functional form relating structural features with binding affinities. RF, but not MLR, can effectively exploit more structural features and more training samples, leading to higher prediction performance. The future availability of more X-ray crystal structures will further widen the performance gap between RF-based and MLR-based scoring functions. This further stresses the importance of substituting RF for MLR in scoring function development.Electronic supplementary material
The online version of this article (doi:10.1186/1471-2105-15-291) contains supplementary material, which is available to authorized users. 相似文献7.
Motivation
The conformational B-cell epitopes are the specific sites on the antigens that have immune functions. The identification of conformational B-cell epitopes is of great importance to immunologists for facilitating the design of peptide-based vaccines. As an attempt to narrow the search for experimental validation, various computational models have been developed for the epitope prediction by using antigen structures. However, the application of these models is undermined by the limited number of available antigen structures. In contrast to the most of available structure-based methods, we here attempt to accurately predict conformational B-cell epitopes from antigen sequences.Methods
In this paper, we explore various sequence-derived features, which have been observed to be associated with the location of epitopes or ever used in the similar tasks. These features are evaluated and ranked by their discriminative performance on the benchmark datasets. From the perspective of information science, the combination of various features can usually lead to better results than the individual features. In order to build the robust model, we adopt the ensemble learning approach to incorporate various features, and develop the ensemble model to predict conformational epitopes from antigen sequences.Results
Evaluated by the leave-one-out cross validation, the proposed method gives out the mean AUC scores of 0.687 and 0.651 on two datasets respectively compiled from the bound structures and unbound structures. When compared with publicly available servers by using the independent dataset, our method yields better or comparable performance. The results demonstrate the proposed method is useful for the sequence-based conformational epitope prediction.Availability
The web server and datasets are freely available at http://bcell.whu.edu.cn. 相似文献8.
Background
Early and accurate identification of adverse drug reactions (ADRs) is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features. There is therefore an urgent need to review and analyze previous computation models to obtain general conclusions that can provide useful guidance to construct more effective computational models to predict ADRs.Principal Findings
In the current study, the main work is to compare and analyze the performance of existing computational methods to predict ADRs, by implementing and evaluating additional algorithms that have been earlier used for predicting drug targets. Our results indicated that topological and intrinsic features were complementary to an extent and the Jaccard coefficient had an important and general effect on the prediction of drug-ADR associations. By comparing the structure of each algorithm, final formulas of these algorithms were all converted to linear model in form, based on this finding we propose a new algorithm called the general weighted profile method and it yielded the best overall performance among the algorithms investigated in this paper.Conclusion
Several meaningful conclusions and useful findings regarding the prediction of ADRs are provided for selecting optimal features and algorithms. 相似文献9.
Jesper R. G?din Ferdinand M. van’t Hooft Per Eriksson Lasse Folkersen 《BMC bioinformatics》2015,16(1)
Background
One aspect in which RNA sequencing is more valuable than microarray-based methods is the ability to examine the allelic imbalance of the expression of a gene. This process is often a complex task that entails quality control, alignment, and the counting of reads over heterozygous single-nucleotide polymorphisms. Allelic imbalance analysis is subject to technical biases, due to differences in the sequences of the measured alleles. Flexible bioinformatics tools are needed to ease the workflow while retaining as much RNA sequencing information as possible throughout the analysis to detect and address the possible biases.Results
We present AllelicImblance, a software program that is designed to detect, manage, and visualize allelic imbalances comprehensively. The purpose of this software is to allow users to pose genetic questions in any RNA sequencing experiment quickly, enhancing the general utility of RNA sequencing. The visualization features can reveal notable, non-trivial allelic imbalance behavior over specific regions, such as exons.Conclusions
The software provides a complete framework to perform allelic imbalance analyses of aligned RNA sequencing data, from detection to visualization, within the robust and versatile management class, ASEset.Electronic supplementary material
The online version of this article (doi:10.1186/s12859-015-0620-2) contains supplementary material, which is available to authorized users. 相似文献10.
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Background
The exosome complex is an essential RNA 3′-end processing and degradation machinery. In archaeal organisms, the exosome consists of a catalytic ring and an RNA-binding ring, both of which were previously reported to assume three-fold symmetry.Methodology/Principal Findings
Here we report an asymmetric 2.9 Å Sulfolobus solfataricus archaeal exosome structure in which the three-fold symmetry is broken due to combined rigid body and thermal motions mainly within the RNA-binding ring. Since increased conformational flexibility was also observed in the RNA-binding ring of the related bacterial PNPase, we speculate that this may reflect an evolutionarily conserved mechanism to accommodate diverse RNA substrates for degradation.Conclusion/Significance
This study clearly shows the dynamic structures within the RNA-binding domains, which provides additional insights on mechanism of asymmetric RNA binding and processing. 相似文献13.
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Background
The ever increasing discovery of non-coding RNAs leads to unprecedented demand for the accurate modeling of RNA folding, including the predictions of two-dimensional (base pair) and three-dimensional all-atom structures and folding stabilities. Accurate modeling of RNA structure and stability has far-reaching impact on our understanding of RNA functions in human health and our ability to design RNA-based therapeutic strategies.Results
The Vfold server offers a web interface to predict (a) RNA two-dimensional structure from the nucleotide sequence, (b) three-dimensional structure from the two-dimensional structure and the sequence, and (c) folding thermodynamics (heat capacity melting curve) from the sequence. To predict the two-dimensional structure (base pairs), the server generates an ensemble of structures, including loop structures with the different intra-loop mismatches, and evaluates the free energies using the experimental parameters for the base stacks and the loop entropy parameters given by a coarse-grained RNA folding model (the Vfold model) for the loops. To predict the three-dimensional structure, the server assembles the motif scaffolds using structure templates extracted from the known PDB structures and refines the structure using all-atom energy minimization.Conclusions
The Vfold-based web server provides a user friendly tool for the prediction of RNA structure and stability. The web server and the source codes are freely accessible for public use at “http://rna.physics.missouri.edu”. 相似文献18.
Christina Otto Mathias M?hl Steffen Heyne Mika Amit Gad M Landau Rolf Backofen Sebastian Will 《BMC bioinformatics》2014,15(1)
Background
Identifying sequence-structure motifs common to two RNAs can speed up the comparison of structural RNAs substantially. The core algorithm of the existent approach ExpaRNA solves this problem for a priori known input structures. However, such structures are rarely known; moreover, predicting them computationally is no rescue, since single sequence structure prediction is highly unreliable.Results
The novel algorithm ExpaRNA-P computes exactly matching sequence-structure motifs in entire Boltzmann-distributed structure ensembles of two RNAs; thereby we match and fold RNAs simultaneously, analogous to the well-known “simultaneous alignment and folding” of RNAs. While this implies much higher flexibility compared to ExpaRNA, ExpaRNA-P has the same very low complexity (quadratic in time and space), which is enabled by its novel structure ensemble-based sparsification. Furthermore, we devise a generalized chaining algorithm to compute compatible subsets of ExpaRNA-P’s sequence-structure motifs. Resulting in the very fast RNA alignment approach ExpLoc-P, we utilize the best chain as anchor constraints for the sequence-structure alignment tool LocARNA. ExpLoc-P is benchmarked in several variants and versus state-of-the-art approaches. In particular, we formally introduce and evaluate strict and relaxed variants of the problem; the latter makes the approach sensitive to compensatory mutations. Across a benchmark set of typical non-coding RNAs, ExpLoc-P has similar accuracy to LocARNA but is four times faster (in both variants), while it achieves a speed-up over 30-fold for the longest benchmark sequences (≈400nt). Finally, different ExpLoc-P variants enable tailoring of the method to specific application scenarios. ExpaRNA-P and ExpLoc-P are distributed as part of the LocARNA package. The source code is freely available at http://www.bioinf.uni-freiburg.de/Software/ExpaRNA-P.Conclusions
ExpaRNA-P’s novel ensemble-based sparsification reduces its complexity to quadratic time and space. Thereby, ExpaRNA-P significantly speeds up sequence-structure alignment while maintaining the alignment quality. Different ExpaRNA-P variants support a wide range of applications.Electronic supplementary material
The online version of this article (doi:10.1186/s12859-014-0404-0) contains supplementary material, which is available to authorized users. 相似文献19.
Michael J. Monument Kirsten M. Johnson Elizabeth McIlvaine Lisa Abegglen W. Scott Watkins Lynn B. Jorde Richard B. Womer Natalie Beeler Laura Monovich Elizabeth R. Lawlor Julia A. Bridge Joshua D. Schiffman Mark D. Krailo R. Lor Randall Stephen L. Lessnick 《PloS one》2014,9(8)