MM-6mAPred: identifying DNA N6-methyladenine sites based on Markov model

Abstract

Motivation Recent studies have shown that DNA N6-methyladenine (6mA) plays an important role in epigenetic modification of eukaryotic organisms. It has been found that 6mA is closely related to embryonic development, stress response and so on. Developing a new algorithm to quickly and accurately identify 6mA sites in genomes is important for explore their biological functions. Results In this paper, we proposed a new classification method called MM-6mAPred based on a Markov model which makes use of the transition probability between adjacent nucleotides to identify 6mA site. The sensitivity and specificity of our method are 89.32% and 90.11%, respectively. The overall accuracy of our method is 89.72%, which is 6.59% higher than that of the previous method i6mA-Pred. It indicated that, compared with the 41 nucleotide chemical properties used by i6mA-Pred, the transition probability between adjacent nucleotides can capture more discriminant sequence information. Availability and implementation The web server of MM-6mAPred is freely accessible at http://www.insect-genome.com/MM-6mAPred/ Supplementary information Supplementary data are available at Bioinformatics online.

6mA-RicePred: A Method for Identifying DNA N6-Methyladenine Sites in the Rice Genome Based on Feature Fusion

“…Two datasets were used in our study. One dataset comprised the same experimental benchmark data used by Chen et al (Chen et al, 2019a) and has been used to train MM-6mAPred (Cheng et al, 2018b;Pian et al, 2019). This dataset contained 880 positive samples and 880 negative samples.…”

“…Figure 2 shows a schematic of feature extraction from a DNA sequence with the first-order Markov chain. A, T, G, C are equivalent to four states, and P i NN is the transition probabilities between the ith nucleotide and the (i + 1)th nucleotide (Nigatu et al, 2017;Pian et al, 2019). Thus, a transition probability matrix is generated between every two nucleotides.…”

“…In the present study, the performance of SVM was highly stable. A Markov model is used in MM-6mAPred (Pian et al, 2019) to identify 6ma sites and has achieved good results.…”

“…There are few computational methods to identify 6mA sites in the rice genome. Proposed methods include i6mA-Pred (Chen et al, 2019a), iDNA6mA-PseKNC (Feng et al, 2019a), MM-6mAPred (Pian et al, 2019), and iDNA6mA-Rice (Hao et al, 2019). i6mA-Pred uses nucleotide chemical property, nucleotide frequency, and SVM to identify 6ma sites.…”

SICD6mA: Identifying 6mA Sites using Deep Memory Network

“…Various prediction methods are dependent on the dataset. As for MM-6mAPred [22], the Markov model based on the 6mA-rice-Chen dataset, achieves superior prediction capability of 6mA sites to i6mA-Pred. On the other hand, SDM6A [23] is also an approach that integrates coding methods with machine learning on the basis of the 6mA-rice-Chen dataset.…”