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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
5 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
24 Dimensions

Readers on

mendeley
99 Mendeley

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Summary Blogs Twitter Facebook Dimensions citations
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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
24980894
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.

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Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters 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 99 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 90 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 26%
Student > Ph. D. Student 23 23%
Student > Master 14 14%
Student > Bachelor 8 8%
Student > Doctoral Student 7 7%
Other 14 14%
Unknown 7 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 52 53%
Biochemistry, Genetics and Molecular Biology 21 21%
Computer Science 6 6%
Unspecified 2 2%
Medicine and Dentistry 2 2%
Other 5 5%
Unknown 11 11%

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 18 November 2014.
All research outputs
#3,249,886
of 22,758,248 outputs
Outputs from BMC Bioinformatics
#1,193
of 7,272 outputs
Outputs of similar age
#33,600
of 227,590 outputs
Outputs of similar age from BMC Bioinformatics
#26
of 149 outputs
Altmetric has tracked 22,758,248 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 7,272 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 227,590 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 149 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.

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