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Computational approach for calculating the probability of eukaryotic translation initiation from ribo-seq data that takes into account leaky scanning

Overview of attention for article published in BMC Bioinformatics, November 2014
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Title
Computational approach for calculating the probability of eukaryotic translation initiation from ribo-seq data that takes into account leaky scanning
Published in
BMC Bioinformatics, November 2014
DOI 10.1186/s12859-014-0380-4
Pubmed ID
Authors

Audrey M Michel, Dmitry E Andreev, Pavel V Baranov

Abstract

BackgroundRibosome profiling (ribo-seq) provides experimental data on the density of elongating or initiating ribosomes at the whole transcriptome level that can be potentially used for estimating absolute levels of translation initiation at individual Translation Initiation Sites (TISs). These absolute levels depend on the mutual organisation of TISs within individual mRNAs. For example, according to the leaky scanning model of translation initiation in eukaryotes, a strong TIS downstream of another strong TIS is unlikely to be productive, since only a few scanning ribosomes would be able to reach the downstream TIS. In order to understand the dependence of translation initiation efficiency on the surrounding nucleotide context, it is important to estimate the strength of TISs independently of their mutual organisation, i.e. to estimate with what probability a ribosome would initiate at a particular TIS.ResultsWe designed a simple computational approach for estimating the probabilities of ribosomes initiating at individual start codons using ribosome profiling data. The method is based on the widely accepted leaky scanning model of translation initiation in eukaryotes which postulates that scanning ribosomes may skip a start codon if the initiation context is unfavourable and continue on scanning. We tested our approach on three independent ribo-seq datasets obtained in mammalian cultured cells.ConclusionsOur results suggested that the method successfully discriminates between weak and strong TISs and that the majority of numerous non-AUG TISs reported recently are very weak. Therefore the high frequency of non-AUG TISs observed in ribosome profiling experiments is due to their proximity to mRNA 5¿-ends rather than their strength. Detectable translation initiation at non-AUG codons downstream of AUG codons is comparatively infrequent. The leaky scanning method will be useful for the characterization of differences in start codon selection between tissues, developmental stages and in response to stress conditions.

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Geographical breakdown

Country Count As %
United States 2 2%
United Kingdom 1 1%
Netherlands 1 1%
Spain 1 1%
Argentina 1 1%
Unknown 84 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 23%
Student > Master 16 18%
Researcher 13 14%
Student > Bachelor 8 9%
Student > Doctoral Student 5 6%
Other 16 18%
Unknown 11 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 42%
Biochemistry, Genetics and Molecular Biology 31 34%
Computer Science 4 4%
Medicine and Dentistry 2 2%
Chemical Engineering 1 1%
Other 0 0%
Unknown 14 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 March 2020.
All research outputs
#15,962,021
of 25,257,066 outputs
Outputs from BMC Bioinformatics
#4,980
of 7,664 outputs
Outputs of similar age
#205,934
of 374,421 outputs
Outputs of similar age from BMC Bioinformatics
#80
of 136 outputs
Altmetric has tracked 25,257,066 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,664 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
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We're also able to compare this research output to 136 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.