Document Type

Article

Publication Date

9-1-2012

DOI

http://dx.doi.org/10.1101/gr.134767.111

Abstract

Data from the Encyclopedia of DNA Elements (ENCODE) project show over 9640 human genome loci classified as long noncoding RNAs (lncRNAs), yet only ~100 have been deeply characterized to determine their role in the cell. To measure the protein-coding output from these RNAs, we jointly analyzed two recent data sets produced in the ENCODE project: tandem mass spectrometry (MS/MS) data mapping expressed peptides to their encoding genomic loci, and RNA-seq data generated by ENCODE in long polyA+ and polyA– fractions in the cell lines K562 and GM12878. We used the machinelearning algorithm RuleFit3 to regress the peptide data against RNA expression data. The most important covariate for predicting translation was, surprisingly, the Cytosol polyA– fraction in both cell lines. LncRNAs are ~13-fold less likely to produce detectable peptides than similar mRNAs, indicating that ~92% of GENCODE v7 lncRNAs are not translated in these two ENCODE cell lines. Intersecting 9640 lncRNA loci with 79,333 peptides yielded 85 unique peptides matching 69 lncRNAs. Most cases were due to a coding transcript misannotated as lncRNA. Two exceptions were an unprocessed pseudogene and a bona fide lncRNA gene, both with open reading frames (ORFs) compromised by upstream stop codons. All potentially translatable lncRNA ORFs had only a single peptide match, indicating low protein abundance and/or false-positive peptide matches. We conclude that with very few exceptions, ribosomes are able to distinguish coding from noncoding transcripts and, hence, that ectopic translation and cryptic mRNAs are rare in the human lncRNAome.

Copyright Statement

For complete list of authors, please see article.

This document was originally published by Cold Spring Harbor Laboratory Press in Genome Research. This work is provided under a Creative Commons Attribution-NonCommercial 3.0 license. Details regarding the use of this work can be found at: http://creativecommons.org/licenses/by-nc/3.0/. DOI: 10.1101/gr.134767.111

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