title: Discrimination of non-protein-coding transcripts from protein-coding mRNA.
abstract: Several recent studies indicate that mammals and other organisms produce large numbers of RNA transcripts that do not correspond to known genes. It has been suggested that these transcripts do not encode proteins, but may instead function as RNAs. However, discrimination of coding and non-coding transcripts is not straightforward, and different laboratories have used different methods, whose ability to perform this discrimination is unclear. In this study, we examine ten bioinformatic methods that assess protein-coding potential and compare their ability and congruency in the discrimination of non-coding from coding sequences, based on four underlying principles: open reading frame size, sequence similarity to known proteins or protein domains, statistical models of protein-coding sequence, and synonymous versus non-synonymous substitution rates. Despite these different approaches, the methods show broad concordance, suggesting that coding and non-coding transcripts can, in general, be reliably discriminated, and that many of the recently discovered extra-genic transcripts are indeed non-coding. Comparison of the methods indicates reasons for unreliable predictions, and approaches to increase confidence further. Conversely and surprisingly, our analyses also provide evidence that as much as approximately 10% of entries in the manually curated protein database Swiss-Prot are erroneous translations of actually non-coding transcripts.
authors.affiliation: Genome Exploration Research Group (Genome Network Project Core Group), RIKEN Genomic Sciences Center (GSC), RIKEN Yokohama Institute, Kanagawa, Japan.
authors.forename: Martin C
authors.initials: MC
authors.lastname: Frith
authors.forename: Timothy L
authors.initials: TL
authors.lastname: Bailey
authors.forename: Takeya
authors.initials: T
authors.lastname: Kasukawa
authors.forename: Flavio
authors.initials: F
authors.lastname: Mignone
authors.forename: Sarah K
authors.initials: SK
authors.lastname: Kummerfeld
authors.forename: Martin
authors.initials: M
authors.lastname: Madera
authors.forename: Sirisha
authors.initials: S
authors.lastname: Sunkara
authors.forename: Masaaki
authors.initials: M
authors.lastname: Furuno
authors.forename: Carol J
authors.initials: CJ
authors.lastname: Bult
authors.forename: John
authors.initials: J
authors.lastname: Quackenbush
authors.forename: Chikatoshi
authors.initials: C
authors.lastname: Kai
authors.forename: Jun
authors.initials: J
authors.lastname: Kawai
authors.forename: Piero
authors.initials: P
authors.lastname: Carninci
authors.forename: Yoshihide
authors.initials: Y
authors.lastname: Hayashizaki
authors.forename: Graziano
authors.initials: G
authors.lastname: Pesole
authors.forename: John S
authors.initials: JS
authors.lastname: Mattick
mesh_terms: D000465:Algorithms; D000818:Animals; D001671:Biochemistry / Q000379:methods*; D019295:Computational Biology; D018076:DNA, Complementary / Q000378:metabolism; D003627:Data Interpretation, Statistical; D030562:Databases, Protein; D020224:Expressed Sequence Tags; D005821:Genetic Techniques; D051379:Mice; D016366:Open Reading Frames; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry; D012333:RNA, Messenger / Q000737:chemistry* / Q000235:genetics; D022661:RNA, Untranslated / Q000737:chemistry* / Q000235:genetics
chemical_list: D018076:DNA, Complementary; D011506:Proteins; D012333:RNA, Messenger; D022661:RNA, Untranslated