Abstract
Each day, more and more transcripts are being discovered along the genome (especially in poorly annotated species) thanks to the rapid progress of high-throughput technology such as RNA sequencing. However, this situation unravels the challenge of how to classify the newly identified transcripts into protein coding or noncoding. Here, we describe a de novo approach named coding–noncoding index (CNCI), a powerful signature tool by profiling adjoining nucleotide triplets (ANT) to effectively distinguish between protein-coding and noncoding sequences independently of known annotations. The main advantage of CNCI is its ability to accurately classify transcripts assembled from whole-transcriptome sequencing data in a cross-species manner, which allowed it to be used for all vertebrates and invertebrates based on the training data of well-annotated species (such as human and Arabidopsis). In this chapter, we illustrate the CNCI method in detail through an example of RNA-sequencing data generated from six biological replicates of six mouse tissues. CNCI software is available at http://www.bioinfo.org/software/cnci.
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Luo, H., Bu, D., Sun, L., Chen, R., Zhao, Y. (2014). De Novo Approach to Classify Protein-Coding and Noncoding Transcripts Based on Sequence Composition. In: Alvarez, M., Nourbakhsh, M. (eds) RNA Mapping. Methods in Molecular Biology, vol 1182. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1062-5_18
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DOI: https://doi.org/10.1007/978-1-4939-1062-5_18
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