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Estimation of ribosome profiling performance and reproducibility at various levels of resolution

Overview of attention for article published in Biology Direct, May 2016
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Title
Estimation of ribosome profiling performance and reproducibility at various levels of resolution
Published in
Biology Direct, May 2016
DOI 10.1186/s13062-016-0127-4
Pubmed ID
Authors

Alon Diament, Tamir Tuller

Abstract

Ribosome profiling (or Ribo-seq) is currently the most popular methodology for studying translation; it has been employed in recent years to decipher various fundamental gene expression regulation aspects. The main promise of the approach is its ability to detect ribosome densities over an entire transcriptome in high resolution of single codons. Indeed, dozens of ribo-seq studies have included results related to local ribosome densities in different parts of the transcript; nevertheless, the performance of Ribo-seq has yet to be quantitatively evaluated and reported in a large-scale multi-organismal and multi-protocol study of currently available datasets. Here we provide the first objective evaluation of Ribo-seq at the resolution of a single nucleotide(s) using clear, interpretable measures, based on the analysis of 15 experiments, 6 organisms, and a total of 612, 961 transcripts. Our major conclusion is that the ability to infer signals of ribosomal densities at nucleotide scale is considerably lower than previously thought, as signals at this level are not reproduced well in experimental replicates. In addition, we provide various quantitative measures that connect the expected error rate with Ribo-seq analysis resolution. The analysis of Ribo-seq data at the resolution of codons and nucleotides provides a challenging task, calls for task-specific statistical methods and further protocol improvements. We believe that our results are important for every researcher studying translation and specifically for researchers analyzing data generated by the Ribo-seq approach. This article was reviewed by Dmitrij Frishman, Eugene Koonin and Frank Eisenhaber.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
United Kingdom 1 1%
France 1 1%
Argentina 1 1%
Unknown 91 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 29 30%
Researcher 22 23%
Student > Master 10 10%
Student > Doctoral Student 7 7%
Student > Bachelor 5 5%
Other 7 7%
Unknown 16 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 35 36%
Agricultural and Biological Sciences 34 35%
Computer Science 4 4%
Engineering 2 2%
Immunology and Microbiology 1 1%
Other 3 3%
Unknown 17 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 May 2016.
All research outputs
#18,456,836
of 22,869,263 outputs
Outputs from Biology Direct
#413
of 487 outputs
Outputs of similar age
#224,347
of 304,990 outputs
Outputs of similar age from Biology Direct
#10
of 10 outputs
Altmetric has tracked 22,869,263 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
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