Correction: genetic algorithm learning as a robust approach to RNA editing site site prediction.
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ABSTRACT:
SUBMITTER: Thompson J
PROVIDER: S-EPMC1569880 | biostudies-literature | 2006
REPOSITORIES: biostudies-literature
Publications
Correction: genetic algorithm learning as a robust approach to RNA editing site site prediction.
Thompson James J Gopal Shuba S
BMC bioinformatics 20060906
After the publication of [1], we were alerted to an error in our data. The error was an one-off miscalculation in the extraction of position information for our set of true negatives. Our data set should have used randomly selected non-edited cytosines (C) as true negatives, but the data generation phase resulted in a set of nucleotides that were each one nucleotide downstream of known, unedited cytosines. The consequences of this error are reflected in changes to our results, although the gener ...[more]
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Project description:Background: Pseudouridine (Ψ) is a common ribonucleotide modification that plays a significant role in many biological processes. The identification of Ψ modification sites is of great significance for disease mechanism and biological processes research in which machine learning algorithms are desirable as the lab exploratory techniques are expensive and time-consuming. Results: In this work, we propose a deep learning framework, called PseUdeep, to identify Ψ sites of three species: H. sapiens, S. cerevisiae, and M. musculus. In this method, three encoding methods are used to extract the features of RNA sequences, that is, one-hot encoding, K-tuple nucleotide frequency pattern, and position-specific nucleotide composition. The three feature matrices are convoluted twice and fed into the capsule neural network and bidirectional gated recurrent unit network with a self-attention mechanism for classification. Conclusion: Compared with other state-of-the-art methods, our model gets the highest accuracy of the prediction on the independent testing data set S-200; the accuracy improves 12.38%, and on the independent testing data set H-200, the accuracy improves 0.68%. Moreover, the dimensions of the features we derive from the RNA sequences are only 109,109, and 119 in H. sapiens, M. musculus, and S. cerevisiae, which is much smaller than those used in the traditional algorithms. On evaluation via tenfold cross-validation and two independent testing data sets, PseUdeep outperforms the best traditional machine learning model available. PseUdeep source code and data sets are available at https://github.com/dan111262/PseUdeep.
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Project description: Not available
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Project description:Pentatricopeptide repeat (PPR) proteins with an E domain have been identified as specific factors for C to U RNA editing in plant organelles. These PPR proteins bind to a unique sequence motif 5' of their target editing sites. Recently, involvement of a combinatorial amino acid code in the P (normal length) and S type (short) PPR domains in sequence specific RNA binding was reported. PPR proteins involved in RNA editing, however, contain not only P and S motifs but also their long variants L (long) and L2 (long2) and the S2 (short2) motifs. We now find that inclusion of these motifs improves the prediction of RNA editing target sites. Previously overlooked RNA editing target sites are suggested from the PPR motif structures of known E-class PPR proteins and are experimentally verified. RNA editing target sites are assigned for the novel PPR protein MEF32 (mitochondrial editing factor 32) and are confirmed in the cDNA.
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Project description:The important roles of machine learning and ferroptosis in bladder cancer (BCa) are still poorly understood. In this study, a comprehensive analysis of 19 ferroptosis-related genes (FRGs) was performed in 1322 patients with BCa from four independent patient cohorts and a pan-cancer cohort of 9824 patients. Twelve FRGs were selected through machine learning algorithm to construct the prognosis model. Significantly differential survival outcomes (hazard ratio (HR) = 2.09, 95% confidence interval (CI): 1.55−2.82, p < 0.0001) were observed between patients with high and low ferroptosis scores in the TCGA cohort, which was also verified in the E-MTAB-4321 cohort (HR = 4.71, 95% CI: 1.58−14.03, p < 0.0001), the GSE31684 cohort (HR = 1.76, 95% CI: 1.08−2.87, p = 0.02), and the pan-cancer cohort (HR = 1.15, 95% CI: 1.07−1.24, p < 0.0001). Tumor immunity-related pathways, including the IL-17 signaling pathway and JAK-STAT signaling pathway, were found to be associated with the ferroptosis score in BCa through a functional enrichment analysis. Further verification in the IMvigor210 cohort revealed the BCa patients with high ferroptosis scores tended to have worse survival outcome after receiving tumor immunotherapy. Significantly different ferroptosis scores could also be found between BCa patients with different reactions to treatment with immune checkpoint inhibitors.
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Project description:The bubble point pressure (P b) is a crucial pressure-volume-temperature (PVT) property and a primary input needed for performing many petroleum engineering calculations, such as reservoir simulation. The industrial practice of determining P b is by direct measurement from PVT tests or prediction using empirical correlations. The main problems encountered with the published empirical correlations are their lack of accuracy and the noncomprehensive data set used to develop the model. In addition, most of the published correlations have not proven the relationships between the inputs and outputs as part of the validation process (i.e., no trend analysis was conducted). Nowadays, deep learning techniques such as long short-term memory (LSTM) networks have begun to replace the empirical correlations as they generate high accuracy. This study, therefore, presents a robust LSTM-based model for predicting P b using a global data set of 760 collected data points from different fields worldwide to build the model. The developed model was then validated by applying trend analysis to ensure that the model follows the correct relationships between the inputs and outputs and performing statistical analysis after comparing the most published correlations. The robustness and accuracy of the model have been verified by performing various statistical analyses and using additional data that was not part of the data set used to develop the model. The trend analysis results have proven that the proposed LSTM-based model follows the correct relationships, indicating the model's reliability. Furthermore, the statistical analysis results have shown that the lowest average absolute percent relative error (AAPRE) is 8.422% and the highest correlation coefficient is 0.99. These values are much better than those given by the most accurate models in the literature.
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Project description:Adenosine deaminases that act on RNA are a conserved family of enzymes that catalyze a natural process of site-directed mutagenesis. Biochemically, they convert adenosine to inosine, a nucleotide that is read as guanosine during translation; thus when editing occurs in mRNAs, codons can be recoded and the changes can alter protein function. By removing the endogenous targeting domains from human adenosine deaminase that acts on RNA 2 and replacing them with an antisense RNA oligonucleotide, we have engineered a recombinant enzyme that can be directed to edit anywhere along the RNA registry. Here we demonstrate that this enzyme can efficiently and selectively edit a single adenosine. As proof of principle in vitro, we correct a premature termination codon in mRNAs encoding the cystic fibrosis transmembrane conductance regulator anion channel. In Xenopus oocytes, we show that a genetically encoded version of our editase can correct cystic fibrosis transmembrane conductance regulator mRNA, restore full-length protein, and reestablish functional chloride currents across the plasma membrane. Finally, in a human cell line, we show that a genetically encoded version of our editase and guide RNA can correct a nonfunctional version of enhanced green fluorescent protein, which contains a premature termination codon. This technology should spearhead powerful approaches to correcting a wide variety of genetic mutations and fine-tuning protein function through targeted nucleotide deamination.
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Project description:In this study we apply a genetic algorithm to a set of RNA sequences to find common RNA secondary structures. Our method is a three-step procedure. At the first stage of the procedure for each sequence, a genetic algorithm is used to optimize the structures in a population to a certain degree of stability. In this step, the free energy of a structure is the fitness criterion for the algorithm. Next, for each structure, we define a measure of structural conservation with respect to those in other sequences. We use this measure in a genetic algorithm to improve the structural similarity among sequences for the structures in the population of a sequence. Finally, we select those structures satisfying certain conditions of structural stability and similarity as predicted common structures for a set of RNA sequences. We have obtained satisfactory results from a set of tRNA, 5S rRNA, rev response elements (RRE) of HIV-1 and RRE of HIV-2/SIV, respectively.
| S-EPMC102574 | biostudies-literature