Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2013 Sep;41(17):e166.
doi: 10.1093/nar/gkt646. Epub 2013 Jul 27.

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

Liang Sun  1 , Haitao Luo, Dechao Bu, Guoguang Zhao, Kuntao Yu, Changhai Zhang, Yuanning Liu, Runsheng Chen, Yi Zhao
Affiliations

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

Liang Sun et al. Nucleic Acids Res. 2013 Sep.

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.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
Illustration of ANT score matrix and CNCI framework. The score of each ANT is calculated for human (a) or mouse (b). The three black rows or columns represent three stop codons, including UAA, UAG and UGA (corresponding to ATT, ATC and ACT in cDNA sequence, respectively), which shows low frequency in protein-coding sequence. (c) The framework of CNCI. The top panel shows the process of a sequence in a testing set. For a given sequence, six MLCDS regions (represented by six lines) are identified from six reading frames (represented by six color arrow lines) using a sliding window and dynamic programming algorithm. Then, an MLCDS region with a maximal S-score is selected to incorporate into an SVM. The bottom panel shows the training and classification process. Reliable protein-coding and non-coding sequences are used as a training set, and five features are extracted to train SVM, which classifies the incorporating sequence into protein-coding or non-coding sequence.
Figure 2.
Figure 2.
CNCI performance. (a) The top panel shows ANT score distribution (the left y-axis) of these six reading frames for each protein-coding transcript, whose length is normalized to 1100 nucleotide triplets in the x-axis. Red line represents the correct transcriptional reading frame and other five lines (blue or green) represent other five reading frames. Green line indicates the distribution of the coverage (the right y-axis) of the MLCDS region for each protein-coding transcript across the normalized length. The three regions marked by blue, yellow and green indicate the mean length of 3′UTR (6%), CDS (56.6%) and 5′UTR (37.4%), respectively, across the normalized length. The bottom panel shows an example of a gene NM_021222. (b) The ROC analyses of CNCI, CPC and phyloCSF. The MAE denoted by solid squares is 0.05, 0.11 and 0.28, respectively. (c) The accuracy of CNCI, CPC and phyloCSF for classification of different lincRNA lengths. (d) The ROC curves and taxonomic tree of 12 species. The minimum error rate is marked following the name of species.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Dunham I, Kundaje A, Aldred SF, Collins PJ, Davis CA, Doyle F, Epstein CB, Frietze S, Harrow J, Kaul R, et al. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57–74. - PMC - PubMed
    1. Djebali S, Davis CA, Merkel A, Dobin A, Lassmann T, Mortazavi A, Tanzer A, Lagarde J, Lin W, Schlesinger F, et al. Landscape of transcription in human cells. Nature. 2012;489:101–108. - PMC - PubMed
    1. Brawand D, Soumillon M, Necsulea A, Julien P, Csardi G, Harrigan P, Weier M, Liechti A, Aximu-Petri A, Kircher M, et al. The evolution of gene expression levels in mammalian organs. Nature. 2011;478:343–348. - PubMed
    1. Kong L, Zhang Y, Ye ZQ, Liu XQ, Zhao SQ, Wei L, Gao G. CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Res. 2007;35:W345–W349. - PMC - PubMed
    1. Lin MF, Jungreis I, Kellis M. PhyloCSF: a comparative genomics method to distinguish protein coding and non-coding regions. Bioinformatics. 2011;27:i275–i282. - PMC - PubMed

Publication types