Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts

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ID: 291271
2013
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Abstract
It is a challenge to classify protein-coding or non-coding transcripts, especially those re-constructed from high-throughput sequencing data of poorly annotated species. This study developed and evaluated a powerful signature tool, Coding-Non-Coding Index (CNCI), by profiling adjoining nucleotide triplets to effectively distinguish protein-coding and non-coding sequences independent of known annotations. CNCI is effective for classifying incomplete transcripts and sense-antisense pairs. The implementation of CNCI offered highly accurate classification of transcripts assembled from whole-transcriptome sequencing data in a cross-species manner, that demonstrated gene evolutionary divergence between vertebrates, and invertebrates, or between plants, and provided a long non-coding RNA catalog of orangutan. CNCI software is available at http://www.bioinfo.org/software/cnci.
Reference Key
openalex_W2061548680 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Liang Sun, Haitao Luo, Dechao Bu, Guoguang Zhao, Kuntao Yu, Changhai Zhang, Yuanning Liu, Runsheng Chen, Yi Zhao
Journal Nucleic Acids Research
Year 2013
DOI
10.1093/nar/gkt646
URL
https://academic.oup.com/nar/article-pdf/41/17/e166/25362062/gkt646.pdf
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