CPPred: coding potential prediction based on the global description of RNA sequence

Nucleic Acids Res. 2019 May 7;47(8):e43. doi: 10.1093/nar/gkz087.


The rapid and accurate approach to distinguish between coding RNAs and ncRNAs has been playing a critical role in analyzing thousands of novel transcripts, which have been generated in recent years by next-generation sequencing technology. Previously developed methods CPAT, CPC2 and PLEK can distinguish coding RNAs and ncRNAs very well, but poorly distinguish between small coding RNAs and small ncRNAs. Herein, we report an approach, CPPred (coding potential prediction), which is based on SVM classifier and multiple sequence features including novel RNA features encoded by the global description. The CPPred can better distinguish not only between coding RNAs and ncRNAs, but also between small coding RNAs and small ncRNAs than the state-of-the-art methods due to the addition of the novel RNA features. A recent study proposes 1335 novel human coding RNAs from a large number of RNA-seq datasets. However, only 119 transcripts are predicted as coding RNAs by the CPPred. In fact, almost all proposed novel coding RNAs are ncRNAs (91.1%), which is consistent with previous reports. Remarkably, we also reveal that the global description of encoding features (T2, C0 and GC) plays an important role in the prediction of coding potential.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Animals
  • Base Sequence
  • Computational Biology / methods*
  • Datasets as Topic
  • Drosophila melanogaster / genetics
  • High-Throughput Nucleotide Sequencing
  • Humans
  • Mice
  • RNA, Messenger / classification
  • RNA, Messenger / genetics*
  • RNA, Messenger / metabolism
  • RNA, Untranslated / classification
  • RNA, Untranslated / genetics*
  • RNA, Untranslated / metabolism
  • Saccharomyces cerevisiae / genetics
  • Sequence Analysis, RNA
  • Zebrafish / genetics


  • RNA, Messenger
  • RNA, Untranslated