Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC.
This study examines accurate and efficient computational method for identification of 5-methylcytosine sites in RNA modification. The occurrence of 5-methylcytosine (m5C) plays a vital role in a number of biological processes. For better comprehension of the biological functions and mechanism it is necessary to recognize m5C sites in RNA precisely. The laboratory techniques and procedures are available to identify m5C sites in RNA, but these procedures require a lot of time and resources. This study develops a new computational method for extracting the features of RNA sequence. In this method, first the RNA sequence is encoded via composite feature vector, then, for the selection of discriminate features, the minimum-redundancy-maximum-relevance algorithm was used. Secondly, the classification method used has been based on a support vector machine by using jackknife cross validation test. The suggested method efficiently identifies m5C sites from non- m5C sites and the outcome of the suggested algorithm is 93.33% with sensitivity of 90.0 and specificity of 96.66 on bench mark datasets. The result exhibits that proposed algorithm shown significant identification performance compared to the existing computational techniques. This study extends the knowledge about the occurrence sites of RNA modification which paves the way for better comprehension of the biological uses and mechanism.
['Sabooh|M Fazli|MF|', 'Iqbal|Nadeem|N|', 'Khan|Mukhtaj|M|', 'Khan|Muslim|M|', 'Maqbool|H F|HF|']
D044503:5-Methylcytosine D000465:Algorithms D000595:Amino Acid Sequence D000596:Amino Acids D001483:Base Sequence D059014:High-Throughput Nucleotide Sequencing D006801:Humans D008962:Models, Theoretical D011506:Proteins D012313:RNA D060388:Support Vector Machine
RNA modification Composite features Support vector machine Tetra nucleotide