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Combining in silico prediction and ribosome profiling in a genome-wide search for novel putatively coding sORFs

Overview of attention for article published in BMC Genomics, January 2013
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1 tweeter

Citations

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153 Mendeley
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Title
Combining in silico prediction and ribosome profiling in a genome-wide search for novel putatively coding sORFs
Published in
BMC Genomics, January 2013
DOI 10.1186/1471-2164-14-648
Pubmed ID
Authors

Jeroen Crappé, Wim Van Criekinge, Geert Trooskens, Eisuke Hayakawa, Walter Luyten, Geert Baggerman, Gerben Menschaert

Abstract

It was long assumed that proteins are at least 100 amino acids (AAs) long. Moreover, the detection of short translation products (e.g. coded from small Open Reading Frames, sORFs) is very difficult as the short length makes it hard to distinguish true coding ORFs from ORFs occurring by chance. Nevertheless, over the past few years many such non-canonical genes (with ORFs < 100 AAs) have been discovered in different organisms like Arabidopsis thaliana, Saccharomyces cerevisiae, and Drosophila melanogaster. Thanks to advances in sequencing, bioinformatics and computing power, it is now possible to scan the genome in unprecedented scrutiny, for example in a search of this type of small ORFs.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 153 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
Ireland 1 <1%
Germany 1 <1%
Japan 1 <1%
Brazil 1 <1%
Unknown 147 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 34 22%
Student > Ph. D. Student 32 21%
Student > Master 25 16%
Student > Bachelor 12 8%
Professor > Associate Professor 8 5%
Other 24 16%
Unknown 18 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 58 38%
Biochemistry, Genetics and Molecular Biology 46 30%
Unspecified 6 4%
Chemistry 5 3%
Computer Science 4 3%
Other 9 6%
Unknown 25 16%

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 26 September 2013.
All research outputs
#10,995,846
of 12,373,620 outputs
Outputs from BMC Genomics
#6,423
of 7,313 outputs
Outputs of similar age
#135,291
of 160,032 outputs
Outputs of similar age from BMC Genomics
#23
of 25 outputs
Altmetric has tracked 12,373,620 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,313 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 160,032 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.