i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome

Chen, Wei; Lv, Hao; Nie, Fulei; Lin, Hao

doi:10.1093/bioinformatics/btz015

Cited by 186 publications

(151 citation statements)

References 34 publications

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“…Support vector machines (SVMs) were successfully applied in several bioinformatics problems (B.L., C. L., and K. Yan, unpublished data). [20][21][22][23][24] In this study, we employed SVMs to build the predictor. We used the SVM with radial basis function (RBF) kernel in the Scikit-learn package.…”

Section: Operation Enginementioning

confidence: 99%

iPromoter-2L2.0: Identifying Promoters and Their Types by Combining Smoothing Cutting Window Algorithm and Sequence-Based Features

Liu

2019

Molecular Therapy - Nucleic Acids

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Promoters are short regions at specific locations of DNA sequences, which are playing key roles in directing gene transcription. They can be grouped into six types (s 24 ; s 28 ; s 32 ; s 38 ; s 54 ; s 70). Recently, a predictor called "iPromoter-2L" was constructed to predict the promoters and their six types, which is the first approach to predict all the six types of promoters. However, its predictive quality still needs to be further improved for real-world application requirement. In this study, we proposed the smoothing cutting window algorithm to find the window fragments of the DNA sequences based on the conservation scores to capture the sequence patterns of promoters. For each window fragment, the discriminative features were extracted by using kmer and PseKNC. Combined with support vector machines (SVMs), different predictors were constructed and then clustered into several groups based on their distances. Finally, a new predictor called iPromoter-2L2.0 was constructed to identify the promoters and their six types, which was developed by ensemble learning based on the key predictors selected from the cluster groups. The results showed that iPromoter-2L2.0 outperformed other existing methods for both promoter prediction and identification of their six types, indicating that iPromoter-2L2.0 will be helpful for genomics analysis.

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Section: Operation Enginementioning

confidence: 99%

iPromoter-2L2.0: Identifying Promoters and Their Types by Combining Smoothing Cutting Window Algorithm and Sequence-Based Features

Liu

2019

Molecular Therapy - Nucleic Acids

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“…Each sequence of both datasets is 41-bp long and nucleotide "A" is present at the center. More details about these datasets and negative samples generations can be found in [4] and [8].…”

Section: Datasetsmentioning

confidence: 99%

“…Since the distribution of 6mA sites in the genome is not random and can follow some patterns, computational methods may be efficient and cost-effective. There are few such methods (6mA-Pred [4] and iDNA6mA-PseKNC [8]) which help to identify 6mA sites using supervised machine learning approaches. But, these methods adopt a sequential approach to extract features from DNA sequences which often slow down the process.…”

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confidence: 99%

“…Over the years, a plethora of supervised machine learning based methods have been applied to solve several bioinformatics problems [15,28]. However, to the best of our knowledge, Chen et al [4] were the first to tackle identification of 6mA sites in rice genome using Support Vector Machine (SVM). In this paper, we advance this concept with faster feature extraction and selection techniques and several supervised learning methods to achieve better performance.…”

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confidence: 99%

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FastFeatGen: Faster parallel feature extraction from genome sequences and efficient prediction of DNA N⁶-methyladenine sites

Rahman

2019

Preprint

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N 6 -methyladenine is widely found in both prokaryotes and eukaryotes. It is responsible for many biological processes including prokaryotic defense system and human diseases. So, it is important to know its correct location in genome which may play a significant role in different biological functions. Few computational tools exist to serve this purpose but they are computationally expensive and still there is scope to improve accuracy. An informative feature extraction pipeline from genome sequences is the heart of these tools as well as for many other bioinformatics tools. But it becomes reasonably expensive for sequential approaches when the size of data is large. Hence, a scalable parallel approach is highly desirable. In this paper, we have developed a new tool, called FastFeatGen, emphasizing both developing a parallel feature extraction technique and improving accuracy using machine learning methods. We have implemented our feature extraction approach using shared memory parallelism which achieves around 10× speed over the sequential one. Then we have employed an exploratory feature selection technique which helps to find more relevant features that can be fed to machine learning methods. We have employed Extra-Tree Classifier (ETC) in FastFeatGen and performed experiments on rice and mouse genomes. Our experimental results achieve accuracy of 85.57% and 96.64%, respectively, which are better or competitive to current stateof-the-art methods. Our shared memory based tool can also serve queries much faster than sequential technique. All source codes and datasets are available at https://github.com/khaled-rahman/FastFeatGen.

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“…Notably, iDNA6mA-PseKNC [20] is the first prediction approach to predict 6mA sites in the Mus.musculus genome, and the prediction accuracy for Mus.musculus and microbe on the dataset has been verified to be quite high. i6mA-Pred [21] is the first method utilized to identify rice genome through support vector machines (SVM), which employs the chemical characteristics and frequency of nucleotides to encode DNA sequences. In addition, i6mA-Pre provides a benchmark dataset of 6mA-rice-Chen.…”

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confidence: 99%

SICD6mA: Identifying 6mA Sites using Deep Memory Network

Liu

2020

Preprint

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Background:DNA N6-methyladenine (6mA) is a kind of epigenetic modification in prokaryotes and eukaryotes, which involves multiple biological processes, such as gene regulation and tumorigenesis. Identifying 6mA contributes to understand its regulatory role. Therefore, to satisfy the needs of large-scale preliminary screening, it is necessary to develop the high-quality computational models for the rapid identification of 6mA sites. However, the existing calculation approaches are mostly specific to rice, and they have not been extensively applied to human genome. Results:This study proposed a classification method of deep learning based on the memory mechanism namedSICD6mA. In addition, the large benchmark datasets were constructed for human and rice, respectively, which integrated the recently reported 6mA sites. According to the evaluation results, SICD6mA displayed favorable robustness during cross-validations, which achieved the area under the curve (AUC) values of 0.9824 and 0.9903 for Human and Rice's genomes in independent test evaluations, separately. Conclusions:The successful prediction rate of 6mA sites on cross-species genomes exhibited higher accuracy than that of the state-of-the-art methods. For the convenience of experimental scientists, the user-friendly tool SICD6mA was developed to predict the cross-species 6mA sites, thereby accelerating and facilitating future cross-species genome research. BackgroundDNA N6-methyladenine(6mA) refers to methylation at the N6site of adenine[1]. Typically, DNA 6mA modification is the most common DNA modification in prokaryotic genomes[2], which is once thought not to exist in eukaryotes[3] (including humans), because it is not detected in earlier studies. With the development of high-throughput sequencing technology, numerous 6mA modifications have been detected in different multicellular eukaryotes. In recent years, 6mA modification has been found to play an important role in DNA epigenetic modification of eukaryotes. DNA methylation represents a key element in epigenetic regulation, and DNA 6mA modification exerts a vital part in regulating gene expression[4], germ cell differentiation[5] etc. It has been reported in the genomes of E. coli[6], Drosophila melanogaster[7], rice[8], and Arabidopsis[9] etc.Currently, the 6mA sites can be detected in mouse and human cells using the sensitive detection technology. 6mA

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i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome

Cited by 186 publications

References 34 publications

iPromoter-2L2.0: Identifying Promoters and Their Types by Combining Smoothing Cutting Window Algorithm and Sequence-Based Features

iPromoter-2L2.0: Identifying Promoters and Their Types by Combining Smoothing Cutting Window Algorithm and Sequence-Based Features

FastFeatGen: Faster parallel feature extraction from genome sequences and efficient prediction of DNA N⁶-methyladenine sites

SICD6mA: Identifying 6mA Sites using Deep Memory Network

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i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome

Cited by 186 publications

References 34 publications

iPromoter-2L2.0: Identifying Promoters and Their Types by Combining Smoothing Cutting Window Algorithm and Sequence-Based Features

iPromoter-2L2.0: Identifying Promoters and Their Types by Combining Smoothing Cutting Window Algorithm and Sequence-Based Features

FastFeatGen: Faster parallel feature extraction from genome sequences and efficient prediction of DNA N6-methyladenine sites

SICD6mA: Identifying 6mA Sites using Deep Memory Network

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FastFeatGen: Faster parallel feature extraction from genome sequences and efficient prediction of DNA N⁶-methyladenine sites