iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences

Chen, Wei; Feng, Pengmian; Yang, Hui; Ding, Hui; Lin, Hao; Chou, Kuo‐Chen

doi:10.18632/oncotarget.13758

Cited by 211 publications

(89 citation statements)

References 107 publications

(107 reference statements)

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“…This not only represents the future direction for developing any computational methods, 138 but is also particularly important for most experimental scientists working in drug development 39 . Accordingly, as done in a series of recent studies,63, 66, 67, 107, 112, 117, 127, 139, 140, 141 the web server for 2L-piRNA has been established as well. Moreover, to maximize users’ convenience, a step-by-step guide is provided below.

Step 1.

…”

Section: Methodsmentioning

confidence: 99%

2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function

Liu

Yang

Chou

2017

Molecular Therapy - Nucleic Acids

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231

107

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Involved with important cellular or gene functions and implicated with many kinds of cancers, piRNAs, or piwi-interacting RNAs, are of small non-coding RNA with around 19–33 nt in length. Given a small non-coding RNA molecule, can we predict whether it is of piRNA according to its sequence information alone? Furthermore, there are two types of piRNA: one has the function of instructing target mRNA deadenylation, and the other does not. Can we discriminate one from the other? With the avalanche of RNA sequences emerging in the postgenomic age, it is urgent to address the two problems for both basic research and drug development. Unfortunately, to the best of our knowledge, so far no computational methods whatsoever could be used to deal with the second problem, let alone deal with the two problems together. Here, by incorporating the physicochemical properties of nucleotides into the pseudo K-tuple nucleotide composition (PseKNC), we proposed a powerful predictor called 2L-piRNA. It is a two-layer ensemble classifier, in which the first layer is for identifying whether a query RNA molecule is piRNA or non-piRNA, and the second layer for identifying whether a piRNA is with or without the function of instructing target mRNA deadenylation. Rigorous cross-validations have indicated that the success rates achieved by the proposed predictor are quite high. For the convenience of most biologists and drug development scientists, the web server for 2L-piRNA has been established at http://bioinformatics.hitsz.edu.cn/2L-piRNA/, by which users can easily get their desired results without the need to go through the mathematical details.

show abstract

Step 1.

…”

Section: Methodsmentioning

confidence: 99%

2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function

Liu

Yang

Chou

2017

Molecular Therapy - Nucleic Acids

Self Cite

231

107

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“…To develop a useful sequence-based statistical predictor for a biological system as reported in a series of recent publications [74][75][76][77][78][79][80][81][82][83], the Chou's 5-step rule should be observed [84]: (1) How to construct or select a valid dataset to train and test the predictor? (2) How to formulate the biological sequence samples with an effective mathematical expression that can truly reflect their intrinsic correlation with the target to be predicted?…”

Section: Methodsmentioning

confidence: 99%

Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate

Yu¹,

Li²,

Yang³

et al. 2017

Preprint

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The knowledge of protein function is essential for the study of biological processes, the understanding of disease mechanism and the exploration of novel therapeutic target. Apart from experimental methods, a number of in-silico approaches have been developed and extensively used for protein function prediction.Among these approaches, BLAST predicts functions based on protein sequence similarity, and machine learning predicts functional families from protein sequences irrespective of their similarity, which complements BLAST and other methods in predicting diverse classes of proteins including distantly related proteins and homologous proteins of different functions. However, their identification accuracies and the false discovery rate have not yet been assessed so far, which greatly limits the usage of these prediction algorithms. Herein, a comprehensive comparison of the performances among four popular functional prediction algorithms (BLAST, SVM, PNN and KNN) was conducted. In particular, the performance of these algorithms were systematically assessed by four metrics (sensitivity, specificity, accuracy and Matthews correlation coefficient) based on the independent test datasets generated from 93 protein families defined by UniProtKB Keywords. Moreover, the false discovery rates of these algorithms were evaluated by scanning the genomes of four representative model species (homo sapiens, arabidopsis thaliana, saccharomyces cerevisiae and mycobacterium tuberculosis). As a result, the substantially higher sensitivity and stability of BLAST and SVM were observed compared with that of PNN and KNN. But the machine learning algorithms (PNN, KNN and SVM) were found capable of significantly reducing the false discovery rate (SVM < PNN ≈ KNN). In summary, this study comprehensively assessed the performance of four popular algorithms applied to protein function prediction, which could facilitate the selection of the most appropriate method in the related biomedical research. KEYWORDSfalse discovery rate; machine learning; protein function prediction; support vector machine; BLAST Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted:

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“…The chitosan-mediated TLR3-siRNA transfection had a potential therapeutic effect on remitting uveitis [87]. In a Chinese Han population, a high copy number of TLR7 conferred risk for BD patients [88]. In the Japanese population, the homozygous genotypes and homozygous deplotype configuration of TLR9 SNPs was associated with the susceptibility to BD [89].…”

Section: Immune System Regulation Genesmentioning

confidence: 99%

“…The mean ± SD of binding energies was calculated from the dockings. AutoDock Tools and Visual Molecular Dynamics (VMD, Theoretical and Computational Biophysics group at the Beckman Institute, University of Illinois at Urbana-Champaign) [88,89] was used to visualize the docking result. Surface representation images that show the binding pocket of TcPMCA1 were generated using VMD software.…”

Section: Molecular Dockingmentioning

confidence: 99%

Special Protein Molecules Computational Identification

2018

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iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences

Cited by 211 publications

References 107 publications

2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function

2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function

Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate

Special Protein Molecules Computational Identification

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