N4-methylcytosine (4mC) is one of the most common DNA methylation modifications found in both pro-karyotic and eukaryotic genomes. Since the 4mC has various essential biological roles, determining its location helps r...
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N4-methylcytosine (4mC) is one of the most common DNA methylation modifications found in both pro-karyotic and eukaryotic genomes. Since the 4mC has various essential biological roles, determining its location helps reveal unexplored physiological and pathological pathways. In this study, we propose an effective computational method called i4mC-GRU using a gated recurrent unit and duplet sequence -em-bedded features to predict potential 4mC sites in mouse (Mus musculus) genomes. To fairly assess the performance of the model, we compared our method with several state-of-the-art methods using two different benchmark datasets. Our results showed that i4mC-GRU achieved area under the receiver oper-ating characteristic curve values of 0.97 and 0.89 and area under the precision-recall curve values of 0.98 and 0.90 on the first and second benchmark datasets, respectively. Briefly, our method outperformed ex-isting methods in predicting 4mC sites in mouse genomes. Also, we deployed i4mC-GRU as an online web server, supporting users in genomics studies.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
As a common biological event observed in all living creatures, RNA modification is an essential post-transcriptional factor that regulates the activity, localization, and stability of RNAs. Multiple diseases are assoc...
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ISBN:
(纸本)9781665452458
As a common biological event observed in all living creatures, RNA modification is an essential post-transcriptional factor that regulates the activity, localization, and stability of RNAs. Multiple diseases are associated with RNA modification. N6-methyladenosine (6mA) modification of RNA is one of the most frequent events that affect the translational processes and structural stability of modified transcripts and control transcriptional processes in cell state maintenance and transition. To detect 6mA sites in eukaryotic transcriptomes, a number of computational models were developed as online applications to assist experimental scientists in reducing human effort and budget. However, most of those online web servers are now either outdated or inaccessible. In this study, we propose iR6mA-RNN, an effective computational framework using recurrent neural networks and sequence-embedded features, to predict possible 6mA sites in eukaryotic transcriptomes. When tested on an independent test set, the proposed model achieved an area under the receiver operating characteristic curve of 0.7972 and an area under the precision-recall curve of 0.7785. Our model also outperformed the other two existing methods. Results from another sensitivity analysis confirmed the stability of the model as well.
N-6-methyladenine (6mA) is one of the most common epigenetic modifications of DNA sequences found in both eukaryotes and prokaryotes. In prokaryotes, 6mA is closely associated with various biochemical processes such a...
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ISBN:
(纸本)9798350330991;9798350331004
N-6-methyladenine (6mA) is one of the most common epigenetic modifications of DNA sequences found in both eukaryotes and prokaryotes. In prokaryotes, 6mA is closely associated with various biochemical processes such as DNA replication, repair, transcription, and cellular defense. In eukaryotes, the biological roles and behaviors of this methylation type have not been fully understood. Therefore, gaining more knowledge about 6mA sites is important and contributes to uncovering the characteristics and unexplored biological functions of 6mA. In our study, we propose an effective computational method called i6mA-CNN using convolutional neural networks with a fusion of multiple receptive fields. The 6mA sequences of Mus musculus (mice) were retrieved from the MethSMRT database and then refined to create a benchmark dataset. To fairly evaluate the performance of the model, we conducted multiple experiments and compared i6mA-CNN with other methods on the same independent test set. The results indicated that i6mA-CNN achieved better performance, with a value of 0.98 for both the area under the receiver operating characteristic curve and the area under the precision-recall curve.
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