Quantitative profiling of peptides from RNAs classified as noncoding

Sudhakaran Prabakaran, Martin Hemberg, Ruchi Chauhan, Dominic Winter, Ry Y. Tweedie-Cullen, Christian Dittrich, Elizabeth Hong, Jeremy Gunawardena, Hanno Steen, Gabriel Kreiman () and Judith A. Steen ()
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Sudhakaran Prabakaran: Proteomics Center, Boston Children’s Hospital
Martin Hemberg: Boston Children’s Hospital
Ruchi Chauhan: Proteomics Center, Boston Children’s Hospital
Dominic Winter: Proteomics Center, Boston Children’s Hospital
Ry Y. Tweedie-Cullen: F.M. Kirby Neurobiology Center, Boston Children’s Hospital
Christian Dittrich: F.M. Kirby Neurobiology Center, Boston Children’s Hospital
Elizabeth Hong: F.M. Kirby Neurobiology Center, Boston Children’s Hospital
Jeremy Gunawardena: Harvard Medical School
Hanno Steen: Proteomics Center, Boston Children’s Hospital
Gabriel Kreiman: Boston Children’s Hospital
Judith A. Steen: Proteomics Center, Boston Children’s Hospital

Nature Communications, 2014, vol. 5, issue 1, 1-10

Abstract: Abstract Only a small fraction of the mammalian genome codes for messenger RNAs destined to be translated into proteins, and it is generally assumed that a large portion of transcribed sequences—including introns and several classes of noncoding RNAs (ncRNAs)—do not give rise to peptide products. A systematic examination of translation and physiological regulation of ncRNAs has not been conducted. Here we use computational methods to identify the products of non-canonical translation in mouse neurons by analysing unannotated transcripts in combination with proteomic data. This study supports the existence of non-canonical translation products from both intragenic and extragenic genomic regions, including peptides derived from antisense transcripts and introns. Moreover, the studied novel translation products exhibit temporal regulation similar to that of proteins known to be involved in neuronal activity processes. These observations highlight a potentially large and complex set of biologically regulated translational events from transcripts formerly thought to lack coding potential.

Date: 2014
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DOI: 10.1038/ncomms6429

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