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Synergistic use of plant-prokaryote comparative genomics for functional annotations

Overview of attention for article published in BMC Genomics, June 2011
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Title
Synergistic use of plant-prokaryote comparative genomics for functional annotations
Published in
BMC Genomics, June 2011
DOI 10.1186/1471-2164-12-s1-s2
Pubmed ID
Authors

Svetlana Gerdes, Basma El Yacoubi, Marc Bailly, Ian K Blaby, Crysten E Blaby-Haas, Linda Jeanguenin, Aurora Lara-Núñez, Anne Pribat, Jeffrey C Waller, Andreas Wilke, Ross Overbeek, Andrew D Hanson, Valérie de Crécy-Lagard

Abstract

Identifying functions for all gene products in all sequenced organisms is a central challenge of the post-genomic era. However, at least 30-50% of the proteins encoded by any given genome are of unknown or vaguely known function, and a large number are wrongly annotated. Many of these 'unknown' proteins are common to prokaryotes and plants. We set out to predict and experimentally test the functions of such proteins. Our approach to functional prediction integrates comparative genomics based mainly on microbial genomes with functional genomic data from model microorganisms and post-genomic data from plants. This approach bridges the gap between automated homology-based annotations and the classical gene discovery efforts of experimentalists, and is more powerful than purely computational approaches to identifying gene-function associations.

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X Demographics

The data shown below were collected from the profile of 1 X user 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 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Mexico 1 2%
Belgium 1 2%
Romania 1 2%
Spain 1 2%
United States 1 2%
Unknown 59 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 25%
Student > Ph. D. Student 14 22%
Professor > Associate Professor 9 14%
Student > Bachelor 5 8%
Student > Postgraduate 5 8%
Other 12 18%
Unknown 4 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 51%
Biochemistry, Genetics and Molecular Biology 13 20%
Computer Science 3 5%
Engineering 3 5%
Medicine and Dentistry 3 5%
Other 4 6%
Unknown 6 9%
Attention Score in Context

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 04 October 2011.
All research outputs
#15,236,094
of 22,653,392 outputs
Outputs from BMC Genomics
#6,657
of 10,607 outputs
Outputs of similar age
#83,320
of 113,562 outputs
Outputs of similar age from BMC Genomics
#48
of 79 outputs
Altmetric has tracked 22,653,392 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,607 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 29th percentile – i.e., 29% 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 113,562 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 79 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.