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SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models

Overview of attention for article published in BMC Bioinformatics, July 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

blogs
1 blog
twitter
4 X users
facebook
1 Facebook page

Citations

dimensions_citation
27 Dimensions

Readers on

mendeley
101 Mendeley
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Title
SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models
Published in
BMC Bioinformatics, July 2014
DOI 10.1186/1471-2105-15-229
Pubmed ID
Authors

Ian Reid, Nicholas O’Toole, Omar Zabaneh, Reza Nourzadeh, Mahmoud Dahdouli, Mostafa Abdellateef, Paul MK Gordon, Jung Soh, Gregory Butler, Christoph W Sensen, Adrian Tsang

Abstract

Locating the protein-coding genes in novel genomes is essential to understanding and exploiting the genomic information but it is still difficult to accurately predict all the genes. The recent availability of detailed information about transcript structure from high-throughput sequencing of messenger RNA (RNA-Seq) delineates many expressed genes and promises increased accuracy in gene prediction. Computational gene predictors have been intensively developed for and tested in well-studied animal genomes. Hundreds of fungal genomes are now or will soon be sequenced. The differences of fungal genomes from animal genomes and the phylogenetic sparsity of well-studied fungi call for gene-prediction tools tailored to them.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 101 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 2 2%
Australia 1 <1%
Brazil 1 <1%
France 1 <1%
Ukraine 1 <1%
Taiwan 1 <1%
Denmark 1 <1%
Poland 1 <1%
Unknown 92 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 26%
Student > Ph. D. Student 22 22%
Student > Master 14 14%
Student > Bachelor 9 9%
Student > Doctoral Student 7 7%
Other 13 13%
Unknown 10 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 52 51%
Biochemistry, Genetics and Molecular Biology 22 22%
Computer Science 5 5%
Medicine and Dentistry 2 2%
Environmental Science 1 <1%
Other 6 6%
Unknown 13 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 15 July 2014.
All research outputs
#3,386,742
of 23,498,099 outputs
Outputs from BMC Bioinformatics
#1,214
of 7,400 outputs
Outputs of similar age
#34,154
of 229,148 outputs
Outputs of similar age from BMC Bioinformatics
#26
of 150 outputs
Altmetric has tracked 23,498,099 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,400 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 83% of its peers.
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 229,148 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 150 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.