Identification of differential translation in genome wide studies
Ola Larsson a,1,2, Nahum Sonenberg a, and Robert Nadon b,c,2
-Author Affiliations
aDepartment of Biochemistry, McGill University, Montreal, Quebec H3A 1A3, Canada;
bDepartment of Human Genetics, McGill University, Montreal, Quebec H3A 1B1, Canada; and
cMcGill University and Genome Quebec Innovation Centre, Montreal, Quebec H3A 1A4, Canada
↵1Present address: Department of Oncology–Pathology, Cancer Center Karolinska, Karolinska Institute, R8:01, 171 76 Stockholm, Sweden.
Edited by Peter J. Bickel, University of California, Berkeley, CA, and approved October 26, 2010 (received for review May 20, 2010)
Abstract
Regulation of gene expression through translational control is a fundamental mechanism implicated in many biological processes ranging from memory formation to innate immunity and whose dysregulation contributes to human diseases. Genome wide analyses of translational control strive to identify differential translation independent of cytosolic mRNA levels. For this reason, most studies measure genes’ translation levels as log ratios (translation levels divided by corresponding cytosolic mRNA levels obtained in parallel). Counterintuitively, arising from a mathematical necessity, these log ratios tend to be highly correlated with the cytosolic mRNA levels. Accordingly, they do not effectively correct for cytosolic mRNA level and generate substantial numbers of biological false positives and false negatives. We show that analysis of partial variance, which produces estimates of translational activity that are independent of cytosolic mRNA levels, is a superior alternative. When combined with a variance shrinkage method for estimating error variance, analysis of partial variance has the additional benefit of having greater statistical power and identifying fewer genes as translationally regulated resulting merely from unrealistically low variance estimates rather than from large changes in translational activity. In contrast to log ratios, this formal analytical approach estimates translation effects in a statistically rigorous manner, eliminates the need for inefficient and error-prone heuristics, and produces results that agree with biological function. The method is applicable to datasets obtained from both the commonly used polysome microarray method and the sequencing-based ribosome profiling method.
differential expression, RIP-CHIP, random variance model, translatomics
Footnotes
2To whom correspondence may be addressed. E-mail: ola.larsson@ki.se orrobert.nadon@mcgill.ca.
Author contributions: O.L., N.S., and R.N. designed research; O.L. performed research; O.L. analyzed data; and O.L., N.S., and R.N. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
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