Abstract
N6-methyladenosine (m6A) plays important roles in a branch of biological and physiological processes. Accurate identification of m6A sites is especially helpful for understanding their biological functions. Since the wet-lab techniques are still expensive and time-consuming, it's urgent to develop computational methods to identify m6A sites from primary RNA sequences. Although there are some computational methods for identifying m6A sites, no methods whatsoever are available for detecting m6A sites in microbial genomes. In this study, we developed a computational method for identifying m6A sites in Escherichia coli genome. The accuracies obtained by the proposed method are >90% in both 10-fold cross-validation test and independent dataset test, indicating that the proposed method holds the high potential to become a useful tool for the identification of m6A sites in microbial genomes.
Highlights
At present, ∼150 kinds of RNA modifications have been found in different RNA species (Boccaletto et al, 2018), which enrich the genetic information, and play critical roles in a variety of biological processes as mentioned in a recent review (Roundtree et al, 2017)
The N6-methyladenosine (m6A) is the most abundant posttranscriptional modification and has been found in the three domains of life. m6A has been found to participate in various biological activities, such as mRNA splicing (Nilsen, 2014), mRNA translation (Wang et al, 2015), mRNA maturation (Hoernes et al, 2016), stem cell proliferation (Bertero et al, 2018), and even a series of diseases (Zhang et al, 2016; Cui et al, 2017; Li et al, 2017)
Stimulated by the successful applications of machine learning methods in computational genomics and proteomics (Chen et al, 2012; Feng et al, 2013; Cao et al, 2016, 2017a,b; Hu et al, 2018), in the present work, we presented a support vector machine (SVM) based method for identifying m6A sites in the Escherichia coli (E. coli) genome
Summary
At present, ∼150 kinds of RNA modifications have been found in different RNA species (Boccaletto et al, 2018), which enrich the genetic information, and play critical roles in a variety of biological processes as mentioned in a recent review (Roundtree et al, 2017). In order to reveal its biological functions, different kinds of high-throughput sequencing techniques have been proposed to map the locations of m6A on genome wide (Dominissini et al, 2013; Linder et al, 2015; Wan et al, 2015; Hong et al, 2018). It’s necessary to develop novel methods to detect m6A sites
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