Computational identification of N6-methyladenosine sites in multiple tissues of mammals

Dao, Fanny; Lv, Hao; Yang, Yuhe; Zulfiqar, Hasan; Gao, Hui; Lin, Hao

doi:10.1016/j.csbj.2020.04.015

Cited by 79 publications

(70 citation statements)

References 88 publications

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“…In our study, 10-fold cross-validation was applied to assess our prediction model. Sensitivity (Sn), specificity (Sp), accuracy (Acc), and Mathew correlation coefficient (MCC) are commonly used to measure the performance of classifiers ( Xu et al, 2018c , 2019 ; Boopathi et al, 2019 ; Huang et al, 2019 ; Liang et al, 2019 ; Stephenson et al, 2019 ; Yang H. et al, 2019 ; Basith et al, 2020 ; Dao et al, 2020a ; Hasan et al, 2020c ; Zhao et al, 2020 ), and can be defined as follows:…”

Section: Methodsmentioning

confidence: 99%

ApoPred: Identification of Apolipoproteins and Their Subfamilies With Multifarious Features

Liu

Chen

Zhang

et al. 2021

Front. Cell Dev. Biol.

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Apolipoprotein is a group of plasma proteins that are associated with a variety of diseases, such as hyperlipidemia, atherosclerosis, Alzheimer’s disease, and diabetes. In order to investigate the function of apolipoproteins and to develop effective targets for related diseases, it is necessary to accurately identify and classify apolipoproteins. Although it is possible to identify apolipoproteins accurately through biochemical experiments, they are expensive and time-consuming. This work aims to establish a high-efficiency and high-accuracy prediction model for recognition of apolipoproteins and their subfamilies. We firstly constructed a high-quality benchmark dataset including 270 apolipoproteins and 535 non-apolipoproteins. Based on the dataset, pseudo-amino acid composition (PseAAC) and composition of k-spaced amino acid pairs (CKSAAP) were used as input vectors. To improve the prediction accuracy and eliminate redundant information, analysis of variance (ANOVA) was used to rank the features. And the incremental feature selection was utilized to obtain the best feature subset. Support vector machine (SVM) was proposed to construct the classification model, which could produce the accuracy of 97.27%, sensitivity of 96.30%, and specificity of 97.76% for discriminating apolipoprotein from non-apolipoprotein in 10-fold cross-validation. In addition, the same process was repeated to generate a new model for predicting apolipoprotein subfamilies. The new model could achieve an overall accuracy of 95.93% in 10-fold cross-validation. According to our proposed model, a convenient webserver called ApoPred was established, which can be freely accessed at http://tang-biolab.com/server/ApoPred/service.html. We expect that this work will contribute to apolipoprotein function research and drug development in relevant diseases.

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Section: Methodsmentioning

confidence: 99%

ApoPred: Identification of Apolipoproteins and Their Subfamilies With Multifarious Features

Liu

Chen

Zhang

et al. 2021

Front. Cell Dev. Biol.

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“…In statistical prediction, three cross-validation methods are commonly used to examine a predictor for its effectiveness in practical applications: k-fold cross-validation, independent dataset test, and jackknife test ( Li and Li, 2008 ; Tan et al, 2019 ; Dao et al, 2020a , b ). Among the three methods, the jackknife test is deemed the most objective and rigorous.…”

Section: Methodsmentioning

confidence: 99%

Predicting Cell Wall Lytic Enzymes Using Combined Features

Jing

2021

Front. Bioeng. Biotechnol.

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Due to the overuse of antibiotics, people are worried that existing antibiotics will become ineffective against pathogens with the rapid rise of antibiotic-resistant strains. The use of cell wall lytic enzymes to destroy bacteria has become a viable alternative to avoid the crisis of antimicrobial resistance. In this paper, an improved method for cell wall lytic enzymes prediction was proposed and the amino acid composition (AAC), the dipeptide composition (DC), the position-specific score matrix auto-covariance (PSSM-AC), and the auto-covariance average chemical shift (acACS) were selected to predict the cell wall lytic enzymes with support vector machine (SVM). In order to overcome the imbalanced data classification problems and remove redundant or irrelevant features, the synthetic minority over-sampling technique (SMOTE) was used to balance the dataset. The F-score was used to select features. The Sn, Sp, MCC, and Acc were 99.35%, 99.02%, 0.98, and 99.19% with jackknife test using the optimized combination feature AAC+DC+acACS+PSSM-AC. The Sn, Sp, MCC, and Acc of cell wall lytic enzymes in our predictive model were higher than those in existing methods. This improved method may be helpful for protein function prediction.

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“…Based on these data, Dao et al. 17 built a high-quality benchmark dataset that can be used to train a computational method for identifying m 6 A sites, which contains both m 6 A site- and non-m 6 A site-containing sequences with the length of 41 nt. The CD-HIT program 25 was used to make sure that the sequence similarity of the dataset was less than 80%.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, Dao et al. 17 proposed a tool, called iRNA-m6A that can identify m 6 A modification sites in different tissues in human, mouse, and rat by using the algorithm of SVM, based on the data of Zhang et al. 18 .…”

Section: Introductionmentioning

confidence: 99%

im6A-TS-CNN: Identifying the N6-Methyladenine Site in Multiple Tissues by Using the Convolutional Neural Network

Liu

Cao

et al. 2020

Molecular Therapy - Nucleic Acids

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N 6 -methyladenosine (m 6 A) is the most abundant post-transcriptional modification and involves a series of important biological processes. Therefore, accurate detection of the m 6 A site is very important for revealing its biological functions and impacts on diseases. Although both experimental and computational methods have been proposed for identifying m 6 A sites, few of them are able to detect m 6 A sites in different tissues. With the consideration of the spatial specificity of m 6 A modification, it is necessary to develop methods able to detect the m 6 A site in different tissues. In this work, by using the convolutional neural network (CNN), we proposed a new method, called im6A-TS-CNN, that can identify m 6 A sites in brain, liver, kidney, heart, and testis of Homo sapiens , Mus musculus , and Rattus norvegicus . In im6A-TS-CNN, the samples were encoded by using the one-hot encoding scheme. The results from both a 5-fold cross-validation test and independent dataset test demonstrate that im6A-TS-CNN is better than the existing method for the same purpose. The command-line version of im6A-TS-CNN is available at https://github.com/liukeweiaway/DeepM6A_cnn .

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Computational identification of N6-methyladenosine sites in multiple tissues of mammals

Cited by 79 publications

References 88 publications

ApoPred: Identification of Apolipoproteins and Their Subfamilies With Multifarious Features

ApoPred: Identification of Apolipoproteins and Their Subfamilies With Multifarious Features

Predicting Cell Wall Lytic Enzymes Using Combined Features

im6A-TS-CNN: Identifying the N6-Methyladenine Site in Multiple Tissues by Using the Convolutional Neural Network

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