Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC

Sabooh, M. Fazli; Iqbal, Nadeem; Khan, Muhammad Shahid; Khan, Muslim; Maqbool, Hafiz Farhan

doi:10.1016/j.jtbi.2018.04.037

Cited by 97 publications

(41 citation statements)

References 75 publications

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“…Encouraged by the successes of using PseAAC to deal with protein/peptide sequences, the concept of PseKNC (Pseudo K-tuple Nucleotide Composition) [279] was developed for generating various feature vectors for DNA/RNA sequences that have proved very useful as well [268,[279][280][281][282][283][284][285][286][287][288][289][290][291][292][293][294][295]. Particularly, in 2015 a very powerful web-server called "Pse-in-One" [296] and its updated version "Pse-in-One2.0" [297] have been established that can be used to generate any desired feature vectors for protein/peptide and DNA/RNA sequences according to the need of users' studies.…”

Section: Extension Of Pseaac To Psekncmentioning

confidence: 99%

Gordon Life Science Institute and Its Impacts on Computational Biology and Drug Development

Chou¹

2020

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Gordon Life Science Institute is the first Internet Research Institute ever established in the world. It is a non-profit institute. Those scientists who really dedicate themselves to science and loving science more than anything else can become its member. In the friendly door-opened Institute, they can maximize their time and energy to engage in their scientific creativity. They have also believed that science would be more truthful and wonderful if scientists do not have to spend a lot of time on funding application, and that great scientific findings and creations in history were often made by those who were least supported or funded but driven by interesting imagination and curiosity. Recollected in this review article is its establishing and developing processes, as well as its philosophy and accomplishments. Particularly, its productive and by-productive outcomes have covered the following five very hot topics in bioinformatics and drug development: 1) PseAAC and PseKNC; 2) Disported key theory; 3) Wenxiang diagram; 4) Multi-label system prediction; 5) 5-steps rule. Their impacts on the proteomics and genomics as well as drug development are substantially and awesome.

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Section: Extension Of Pseaac To Psekncmentioning

confidence: 99%

Gordon Life Science Institute and Its Impacts on Computational Biology and Drug Development

Chou¹

2020

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“…As an alternative to costly and labor-intensive laboratory experiments, robust, swift, and inexpensive computational methods for RNA chemical modification prediction have emerged recently, owing to the increasing amount of data generated in this post-genomics era (Libbrecht and Noble, 2015). A large number of m6A (Chen et al, 2015(Chen et al, , 2018a(Chen et al, ,b, 2019aZhou et al, 2016;Zhao et al, 2019;Zou et al, 2019) and m5C (Feng et al, 2016;Qiu et al, 2017;Li et al, 2018;Sabooh et al, 2018;Zhang et al, 2018;Yin et al, 2019) site predictors based on traditional machine learning and emerging deep learning algorithms have been proposed. However, few computational tools have been developed to predict pseudouridine sites.…”

Section: Introductionmentioning

confidence: 99%

RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites

Zhang

Ding

et al. 2020

Front. Bioeng. Biotechnol.

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One of the ubiquitous chemical modifications in RNA, pseudouridine modification is crucial for various cellular biological and physiological processes. To gain more insight into the functional mechanisms involved, it is of fundamental importance to precisely identify pseudouridine sites in RNA. Several useful machine learning approaches have become available recently, with the increasing progress of next-generation sequencing technology; however, existing methods cannot predict sites with high accuracy. Thus, a more accurate predictor is required. In this study, a random forest-based predictor named RF-PseU is proposed for prediction of pseudouridylation sites. To optimize feature representation and obtain a better model, the light gradient boosting machine algorithm and incremental feature selection strategy were used to select the optimum feature space vector for training the random forest model RF-PseU. Compared with previous state-of-the-art predictors, the results on the same benchmark data sets of three species demonstrate that RF-PseU performs better overall. The integrated average leave-one-out cross-validation and independent testing accuracy scores were 71.4% and 74.7%, respectively, representing increments of 3.63% and 4.77% versus the best existing predictor. Moreover, the final RF-PseU model for prediction was built on leave-one-out cross-validation and provides a reliable and robust tool for identifying pseudouridine sites. A web server with a user-friendly interface is accessible at http://148.70.81.170:10228/rfpseu.

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“…The m5C-HPCR was benchmarked on both Feng et al’s dataset and Qiu et al’s dataset. Sabooh et al [ 22 ] have developed a model by fusing composite encoding features including Di-Nucleotide Composition (DNC), Tri-Nucleotide Composition (TNC) and Tetra- Nucleotide Composition (TetraNC). The same dataset as that of Feng et al [ 19 ] and Zhang et al [ 21 ] was again used to build this model by using SVM.…”

Section: Introductionmentioning

confidence: 99%

m5CPred-SVM: a novel method for predicting m5C sites of RNA

et al. 2020

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Background As one of the most common post-transcriptional modifications (PTCM) in RNA, 5-cytosine-methylation plays important roles in many biological functions such as RNA metabolism and cell fate decision. Through accurate identification of 5-methylcytosine (m5C) sites on RNA, researchers can better understand the exact role of 5-cytosine-methylation in these biological functions. In recent years, computational methods of predicting m5C sites have attracted lots of interests because of its efficiency and low-cost. However, both the accuracy and efficiency of these methods are not satisfactory yet and need further improvement. Results In this work, we have developed a new computational method, m5CPred-SVM, to identify m5C sites in three species, H. sapiens, M. musculus and A. thaliana. To build this model, we first collected benchmark datasets following three recently published methods. Then, six types of sequence-based features were generated based on RNA segments and the sequential forward feature selection strategy was used to obtain the optimal feature subset. After that, the performance of models based on different learning algorithms were compared, and the model based on the support vector machine provided the highest prediction accuracy. Finally, our proposed method, m5CPred-SVM was compared with several existing methods, and the result showed that m5CPred-SVM offered substantially higher prediction accuracy than previously published methods. It is expected that our method, m5CPred-SVM, can become a useful tool for accurate identification of m5C sites. Conclusion In this study, by introducing position-specific propensity related features, we built a new model, m5CPred-SVM, to predict RNA m5C sites of three different species. The result shows that our model outperformed the existing state-of-art models. Our model is available for users through a web server at https://zhulab.ahu.edu.cn/m5CPred-SVM.

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Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC

Cited by 97 publications

References 75 publications

Gordon Life Science Institute and Its Impacts on Computational Biology and Drug Development

Gordon Life Science Institute and Its Impacts on Computational Biology and Drug Development

RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites

m5CPred-SVM: a novel method for predicting m5C sites of RNA

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