Sequence-based bacterial small RNAs prediction using ensemble learning strategies

Tang, Guifeng; Shi, Jingwen; Wu, Wenjian; Yue, Xiang; Zhang, Wen

doi:10.1186/s12859-018-2535-1

Cited by 15 publications

(21 citation statements)

References 60 publications

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“…In this paper, instead of using PSSM and its derived features, the focus was on constructing an efficient sub-Golgi protein RF classifier, namely rfGPT, based only on amino acid and dipeptide composition-based feature vectors. Related studies (Li et al, 2016; Luo et al, 2016; Tang et al, 2018; Zhang et al, 2018a,b) have demonstrated the effectiveness of composition and dipeptide and amino acid composition-based features for solving bioinformatics problems. The rfGPT with 55-dimensional features of 2-gap dipeptide composition attained better jackknife cross-validation scores (ACC = 91.1%; MCC = 0.823; Sn = 87.4%; Sp = 94.7%) and better independent testing results (ACC = 89.1%; MCC = 0.631; Sn = 53.8%; Sp = 98.0%) than those classifiers trained on the same data set (Ding et al, 2013; Jiao and Du, 2016a,b).…”

Section: Introductionmentioning

confidence: 99%

A Random Forest Sub-Golgi Protein Classifier Optimized via Dipeptide and Amino Acid Composition Features

Jin

Ding

et al. 2019

Front. Bioeng. Biotechnol.

103

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To gain insight into the malfunction of the Golgi apparatus and its relationship to various genetic and neurodegenerative diseases, the identification of sub-Golgi proteins, both cis-Golgi and trans-Golgi proteins, is of great significance. In this study, a state-of-art random forests sub-Golgi protein classifier, rfGPT, was developed. The rfGPT used 2-gap dipeptide and split amino acid composition for the feature vectors and was combined with the synthetic minority over-sampling technique (SMOTE) and an analysis of variance (ANOVA) feature selection method. The rfGPT was trained on a sub-Golgi protein sequence data set (137 sequences), with sequence identity less than 25%. For the optimal rfGPT classifier with 93 features, the accuracy (ACC) was 90.5%; the Matthews correlation coefficient (MCC) was 0.811; the sensitivity (Sn) was 92.6%; and the specificity (Sp) was 88.4%. The independent testing scores for the rfGPT were ACC = 90.6%; MCC = 0.696; Sn = 96.1%; and Sp = 69.2%. Although the independent testing accuracy was 4.4% lower than that for the best reported sub-Golgi classifier trained on a data set with 40% sequence identity (304 sequences), the rfGPT is currently the top sub-Golgi protein predictor utilizing feature vectors without any position-specific scoring matrix and its derivative features. Therefore, the rfGPT is a more practical tool, because no sequence alignment is required with tens of millions of protein sequences. To date, the rfGPT is the Golgi classifier with the best independent testing scores, optimized by training on smaller benchmark data sets. Feature importance analysis proves that the non-polar and aliphatic residues composition, the (aromatic residues) + (non-polar, aliphatic residues) dipeptide and aromatic residues composition between NH2-termial and COOH-terminal of protein sequences are the three top biological features for distinguishing the sub-Golgi proteins.

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Section: Introductionmentioning

confidence: 99%

A Random Forest Sub-Golgi Protein Classifier Optimized via Dipeptide and Amino Acid Composition Features

Jin

Ding

et al. 2019

Front. Bioeng. Biotechnol.

103

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“…Although there are several experimental methods based on the high-throughput techniques that have been developed to recognize the Ψ modifications, they are both costly and time consuming 13, 14, 15, 16, 17. In addition, researchers are facing an explosive increase of RNA data in the post-genomic age 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30. Therefore, intelligent computational approaches are highly desirable to predict Ψ sites on RNA sequences.…”

Section: Introductionmentioning

confidence: 99%

Is There Any Sequence Feature in the RNA Pseudouridine Modification Prediction Problem?

Dou

Ding

et al. 2020

Molecular Therapy - Nucleic Acids

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Pseudouridine (Ψ) is the most abundant RNA modification and has been found in many kinds of RNAs, including snRNA, rRNA, tRNA, mRNA, and snoRNA. Thus, Ψ sites play a significant role in basic research and drug development. Although some experimental techniques have been developed to identify Ψ sites, they are expensive and time consuming, especially in the post-genomic era with the explosive growth of known RNA sequences. Thus, highly accurate computational methods are urgently required to quickly detect the Ψ sites on uncharacterized RNA sequences. Several predictors have been proposed using multifarious features, but their evaluated performances are still unsatisfactory. In this study, we first identified Ψ sites for H. sapiens, S. cerevisiae, and M. musculus using the sequence features from the bi-profile Bayes (BPB) method based on the random forest (RF) and support vector machine (SVM) algorithms, where the performances were evaluated using 5-fold cross-validation and independent tests. It was found that the SVM-based accuracies were 3.55% and 5.09% lower than the iPseU-CUU predictor for the H_990 and S_628 datasets, respectively. Almost the same-level results were obtained for M_994 and an independent H_200 dataset, even showing a 5.0% improvement for S_200. Then, three different kinds of features, including basic Kmer, general parallel correlation pseudo-dinucleotide composition (PC-PseDNC-General), and nucleotide chemical property (NCP) and nucleotide density (ND) from the iRNA-PseU method, were combined with BPB to show their comprehensive performances, where the effective features are selected by the max-relevance-max-distance (MRMD) method. The best evaluated accuracies of the combined features for the S_628 and M_994 datasets were achieved at 70.54% and 72.45%, which were 2.39% and 0.65% higher than iPseU-CUU. For the S_200 dataset, it was also improved 8% from 69% to 77%. However, there was no obvious improvement for H. sapiens, which was evaluated as approximately 63.23% and 72.0% for the H_990 and H_200 datasets, respectively. The overall performances for Ψ identification using BPB features as well as the combined features were not obviously improved. Although some kinds of feature extraction methods based on the RNA sequence information have been applied to construct the predictors in previous studies, the corresponding accuracies are generally in the range of 60%–70%. Thus, researchers need to reconsider whether there is any sequence feature in the RNA Ψ modification prediction problem.

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“…Feature fusion has been successfully applied into bio-sequence analysis (Zhang et al, 2017;Tang et al, 2018;Wei et al, 2018a,b;Liu et al, 2019d) and other bioinformatics tasks (Liang et al, 2018;Zhang et al, 2018Zhang et al, , 2019aGong et al, 2019;Wang et al, 2019). It refers to merge different types of feature representations to more comprehensively capture the characteristics of samples from different perspectives.…”

Section: Feature Fusion and Optimization Protocolmentioning

confidence: 99%

A Bioinformatics Tool for the Prediction of DNA N6-Methyladenine Modifications Based on Feature Fusion and Optimization Protocol

Cai

Wang

Chen

et al. 2020

Front. Bioeng. Biotechnol.

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DNA N 6 -methyladenine (6mA) is closely involved with various biological processes. Identifying the distributions of 6mA modifications in genome-scale is of great significance to in-depth understand the functions. In recent years, various experimental and computational methods have been proposed for this purpose. Unfortunately, existing methods cannot provide accurate and fast 6mA prediction. In this study, we present 6mAPred-FO, a bioinformatics tool that enables researchers to make predictions based on sequences only. To sufficiently capture the characteristics of 6mA sites, we integrate the sequence-order information with nucleotide positional specificity information for feature encoding, and further improve the feature representation capacity by analysis of variance-based feature optimization protocol. The experimental results show that using this feature protocol, we can significantly improve the predictive performance. Via further feature analysis, we found that the sequence-order information and positional specificity information are complementary to each other, contributing to the performance improvement. On the other hand, the improvement is also due to the use of the feature optimization protocol, which is capable of effectively capturing the most informative features from the original feature space. Moreover, benchmarking comparison results demonstrate that our 6mAPred-FO outperforms several existing predictors. Finally, we establish a web-server that implements the proposed method for convenience of researchers' use, which is currently available at http://server.malab.cn/6mAPred-FO.

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Sequence-based bacterial small RNAs prediction using ensemble learning strategies

Cited by 15 publications

References 60 publications

A Random Forest Sub-Golgi Protein Classifier Optimized via Dipeptide and Amino Acid Composition Features

A Random Forest Sub-Golgi Protein Classifier Optimized via Dipeptide and Amino Acid Composition Features

Is There Any Sequence Feature in the RNA Pseudouridine Modification Prediction Problem?

A Bioinformatics Tool for the Prediction of DNA N6-Methyladenine Modifications Based on Feature Fusion and Optimization Protocol

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