Abstract
Non-coding RNAs (ncRNAs) play important roles in the regulation of many biological processes, such as transcription initiation and epigenetic modifications that occur after transcription and development. Several novel transcripts have been identified via high-throughput sequencing. However, identifying ncRNAs among the transcripts of novel species using alignment-based features is difficult. Thus, developing a fast and accurate method based on alignment-free features to identify ncRNAs among novel transcripts is necessary. In this study, we proposed a new approach, namely, coding potential prediction based on alignment-free features (CPAF), to identify ncRNAs among a large number of candidates. CPAF used four types of features: Fickett score; Hexamer score; composition, transition, and distribution features; and modified k-mer. From the results, CPAF performed better than previous state-of-the-art methods in predicting ncRNA transcripts, with particular reference to small ncRNAs. Finally, we applied CPAF to identify ncRNAs in Pacific oyster transcripts. Our approach identified more ncRNAs than other previously used methods.
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This research was supported by the National Natural Science Foundation of China (No. 11701546).
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Chai, W., Song, K. Identification of Non-Coding RNAs Based on Alignment-Free Features in Crassostrea gigas (Pacific Oyster) Transcriptome. J. Ocean Univ. China 21, 1633–1640 (2022). https://doi.org/10.1007/s11802-022-5058-3
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DOI: https://doi.org/10.1007/s11802-022-5058-3