title: De novo approach to classify protein-coding and noncoding transcripts based on sequence composition.
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.
authors: Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Haidian District, Beijing, 100190, People's Republic of China.
authors: Haitao
authors: H
authors: Luo
authors: Dechao
authors: D
authors: Bu
authors: Liang
authors: L
authors: Sun
authors: Runsheng
authors: R
authors: Chen
authors: Yi
authors: Y
authors: Zhao
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D058977:Molecular Sequence Annotation; D011506:Proteins / Q000235:genetics*; D012333:RNA, Messenger / Q000235:genetics*; D022661:RNA, Untranslated / Q000235:genetics*; D017423:Sequence Analysis, RNA
chemical_list: D011506:Proteins; D012333:RNA, Messenger; D022661:RNA, Untranslated