Evidence map›Paper›PMID 21115840›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2010

Identification of differential translation in genome wide studies.

Ola Larsson, Nahum Sonenberg, Robert Nadon

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed
1.6field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

49 citing papers in PubMed, 80 citations in OpenAlex.

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  6. Capturing Translation in Action with Protein Synthesis Profiling.bioRxiv : the preprint server for biology · 2025
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  7. CXXC5 is a ubiquitinated protein and is degraded by the ubiquitin-proteasome pathway.Protein science : a publication of the Protein Society · 2025
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  13. Anota2seq Analysis for Transcriptome-Wide Studies of mRNA Translation.Methods in molecular biology (Clifton, N.J.) · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Ola LarssonDepartment of Biochemistry, McGill University, Montreal, Quebec H3A 1A3, Canada. ola.larsson@ki.se
Nahum Sonenberg
Robert Nadon
McGill University · CAMcGill University and Génome Québec Innovation Centre · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Genome, HumanGenome-Wide Association StudyProtein BiosynthesisDatabases, GeneticGene Expression RegulationHumansOligonucleotide Array Sequence AnalysisRibosomesRNA, MessengerRNA, Messenger

Identifiers

PMID21115840
PMCPMC3003104
OpenAlexW1989212815

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.