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Toward a standard in structural genome annotation for prokaryotes

Overview of attention for article published in Environmental Microbiome, July 2015
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  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

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Title
Toward a standard in structural genome annotation for prokaryotes
Published in
Environmental Microbiome, July 2015
DOI 10.1186/s40793-015-0034-9
Pubmed ID
Authors

H. James Tripp, Granger Sutton, Owen White, Jennifer Wortman, Amrita Pati, Natalia Mikhailova, Galina Ovchinnikova, Samuel H. Payne, Nikos C. Kyrpides, Natalia Ivanova

Abstract

In an effort to identify the best practice for finding genes in prokaryotic genomes and propose it as a standard for automated annotation pipelines, 1,004,576 peptides were collected from various publicly available resources, and were used as a basis to evaluate various gene-calling methods. The peptides came from 45 bacterial replicons with an average GC content from 31 % to 74 %, biased toward higher GC content genomes. Automated, manual, and semi-manual methods were used to tally errors in three widely used gene calling methods, as evidenced by peptides mapped outside the boundaries of called genes. We found that the consensus set of identical genes predicted by the three methods constitutes only about 70 % of the genes predicted by each individual method (with start and stop required to coincide). Peptide data was useful for evaluating some of the differences between gene callers, but not reliable enough to make the results conclusive, due to limitations inherent in any proteogenomic study. A single, unambiguous, unanimous best practice did not emerge from this analysis, since the available proteomics data were not adequate to provide an objective measurement of differences in the accuracy between these methods. However, as a result of this study, software, reference data, and procedures have been better matched among participants, representing a step toward a much-needed standard. In the absence of sufficient amount of exprimental data to achieve a universal standard, our recommendation is that any of these methods can be used by the community, as long as a single method is employed across all datasets to be compared.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 2 3%
United States 1 1%
Unknown 64 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 28%
Student > Ph. D. Student 10 15%
Student > Bachelor 7 10%
Student > Master 5 7%
Other 5 7%
Other 10 15%
Unknown 11 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 37%
Biochemistry, Genetics and Molecular Biology 15 22%
Immunology and Microbiology 6 9%
Computer Science 3 4%
Environmental Science 2 3%
Other 3 4%
Unknown 13 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 07 August 2015.
All research outputs
#7,778,510
of 25,374,647 outputs
Outputs from Environmental Microbiome
#219
of 786 outputs
Outputs of similar age
#83,399
of 274,900 outputs
Outputs of similar age from Environmental Microbiome
#5
of 23 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 786 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 71% 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 274,900 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.