Full-length messenger RNA sequences greatly improve genome annotation

Brian J. Haas, Natalia Volfovsky, Christopher D. Town, Maxim Troukhan, Nickolai Alexandrov, Kenneth A. Feldmann, Richard B. Flavell, Owen White, Steven L. Salzberg

Research output: Contribution to journalArticlepeer-review

158 Scopus citations


Background: Annotation of eukaryotic genomes is a complex endeavor that requires the integration of evidence from multiple, often contradictory, sources. With the ever-increasing amount of genome sequence data now available, methods for accurate identification of large numbers of genes have become urgently needed. In an effort to create a set of very high-quality gene models, we used the sequence of 5,000 full-length gene transcripts from Arabidopsis to re-annotate its genome. We have mapped these transcripts to their exact chromosomal locations and, using alignment programs, have created gene models that provide a reference set for this organism. Results: Approximately 35% of the transcripts indicated that previously annotated genes needed modification, and 5% of the transcripts represented newly discovered genes. We also discovered that multiple transcription initiation sites appear to be much more common than previously known, and we report numerous cases of alternative mRNA splicing. We include a comparison of different alignment software and an analysis of how the transcript data improved the previously published annotation. Conclusions: Our results demonstrate that sequencing of large numbers of full-length transcripts followed by computational mapping greatly improves identification of the complete exon structures of eukaryotic genes. In addition, we are able to find numerous introns in the untranslated regions of the genes.

Original languageEnglish (US)
Article numberresearch0029.1
JournalGenome biology
Issue number6
StatePublished - May 2002


  • Additional Data File
  • Alternative Transcription Start Site
  • Gene Prediction Program
  • Part Inflorescence
  • Short Exon

ASJC Scopus subject areas

  • Genetics
  • Ecology, Evolution, Behavior and Systematics
  • Cell Biology


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