EMDLP: Ensemble multiscale deep learning model for RNA methylation site prediction.

BACKGROUND: Recent research recommends that epi-transcriptome regulation through post-transcriptional RNA modifications is essential for all sorts of RNA. Exact identification of RNA modification is vital for understanding their purposes and regulatory mechanisms. However, traditional experimental methods of identifying RNA modification sites are relatively complicated, time-consuming, and laborious. Machine learning approaches have been applied in the procedures of RNA sequence features extraction and classification in a computational way, which may supplement experimental approaches more efficiently. Recently, convolutional neural network (CNN) and long short-term memory (LSTM) have been demonstrated achievements in modification site prediction on account of their powerful functions in representation learning. However, CNN can learn the local response from the spatial data but cannot learn sequential correlations. And LSTM is specialized for sequential modeling and can access both the contextual representation but lacks spatial data extraction compared with CNN. There is strong motivation to construct a prediction framework using natural language processing (NLP), deep learning (DL) for these reasons. RESULTS: This study presents an ensemble multiscale deep learning predictor (EMDLP) to identify RNA methylation sites in an NLP and DL way. It organically combines the dilated convolution and Bidirectional LSTM (BiLSTM), which helps to take better advantage of the local and global information for site prediction. The first step of EMDLP is to represent the RNA sequences in an NLP way. Thus, three encodings, e.g., RNA word embedding, One-hot encoding, and RGloVe, which is an improved learning method of word vector representation based on GloVe, are adopted to decipher sites from the viewpoints of the local and global information. Then, a dilated convolutional Bidirectional LSTM network (DCB) model is constructed with the dilated convolutional neural network (DCNN) followed by BiLSTM to extract potential contributing features for methylation site prediction. Finally, these three encoding methods are integrated by a soft vote to obtain better predictive performance. Experiment results on m1A and m6A reveal that the area under the receiver operating characteristic(AUROC) of EMDLP obtains respectively 95.56%, 85.24%, and outperforms the state-of-the-art models. To maximize user convenience, a user-friendly webserver for EMDLP was publicly available at http://www.labiip.net/EMDLP/index.php ( http://47.104.130.81/EMDLP/index.php ). CONCLUSIONS: We developed a predictor for m1A and m6A methylation sites.

EDLm6APred: ensemble deep learning approach for mRNA m6A site prediction.

BACKGROUND: As a common and abundant RNA methylation modification, N6-methyladenosine (m6A) is widely spread in various species' transcriptomes, and it is closely related to the occurrence and development of various life processes and diseases. Thus, accurate identification of m6A methylation sites has become a hot topic. Most biological methods rely on high-throughput sequencing technology, which places great demands on the sequencing library preparation and data analysis. Thus, various machine learning methods have been proposed to extract various types of features based on sequences, then occupied conventional classifiers, such as SVM, RF, etc., for m6A methylation site identification. However, the identification performance relies heavily on the extracted features, which still need to be improved. RESULTS: This paper mainly studies feature extraction and classification of m6A methylation sites in a natural language processing way, which manages to organically integrate the feature extraction and classification simultaneously, with consideration of upstream and downstream information of m6A sites. One-hot, RNA word embedding, and Word2vec are adopted to depict sites from the perspectives of the base as well as its upstream and downstream sequence. The BiLSTM model, a well-known sequence model, was then constructed to discriminate the sequences with potential m6A sites. Since the above-mentioned three feature extraction methods focus on different perspectives of m6A sites, an ensemble deep learning predictor (EDLm6APred) was finally constructed for m6A site prediction. Experimental results on human and mouse data sets show that EDLm6APred outperforms the other single ones, indicating that base, upstream, and downstream information are all essential for m6A site detection. Compared with the existing m6A methylation site prediction models without genomic features, EDLm6APred obtains 86.6% of the area under receiver operating curve on the human data sets, indicating the effectiveness of sequential modeling on RNA. To maximize user convenience, a webserver was developed as an implementation of EDLm6APred and made publicly available at www.xjtlu.edu.cn/biologicalsciences/EDLm6APred . CONCLUSIONS: Our proposed EDLm6APred method is a reliable predictor for m6A methylation sites.

Bis[4-hy-droxy-3,5-dimeth-oxy-benzalde-hyde (2,4-dinitro-phen-yl)hydrazone] N,N-dimethyl-formamide disolvate monohydrate.

In the title compound, 2C(15)H(14)N(4)O(7)·2C(3)H(7)NO·H(2)O, the hydrazone mol-ecules are roughly planar, with the two benzene rings twisted slightly relative to each other by dihedral angle of 6.04 (11) and 7.75 (11)° in the two mol-ecules. The water mol-ecule is linked to the Schiff base mol-ecule by an O-H⋯O hydrogen bond. Intra-molecular N-H⋯O hydrogen bonds occur. In the crystal, mol-ecules are linked by inter-molecular N-H⋯O and O-H⋯O hydrogen bonds.

5-Diethyl-amino-2-{[2-(2,4-dinitro-phen-yl)hydrazin-1-yl-idene]meth-yl}phenol.

In the title compound, C(17)H(19)N(5)O(5), obtained from the condensation reaction of 4-diethyl-amino-2-hy-droxy-benzalde-hyde and 2,4-dinitro-phenyl-hydrazine, the two benzene rings are twisted by a dihedral angle of 1.75 (12)°. The nitro groups are slightly twisted with the respect to the benzene ring to which they are attached, making dihedral angles of 8.20 (15) and 5.78 (15)°. An intra-molecular O-H⋯N hydrogen bond occurs. In the crystal, mol-ecules are linked by pairs of inter-molecular N-H⋯O hydrogen bonds, forming dimers through R(2) (2)(12) rings. These dimers are further linked by C-H⋯O and C-H⋯π and weak slipped π-π inter-actions [centroid-centroid distance = 3.743 (2)Å]. One of the ethyl groups is disordered over two positions, with occupancy factors in the ratio 0.72:0.28.