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IGDD: a database of intronless genes in dicots

Overview of attention for article published in BMC Bioinformatics, July 2016
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Title
IGDD: a database of intronless genes in dicots
Published in
BMC Bioinformatics, July 2016
DOI 10.1186/s12859-016-1148-9
Pubmed ID
Authors

Hanwei Yan, Xiaogang Dai, Kai Feng, Qiuyue Ma, Tongming Yin

Abstract

Intronless genes are a significant characteristic of prokaryotes. Systematic identification and annotation are primary and crucial steps for determining the functions of intronless genes and understanding their occurrence in eukaryotes. In this paper, we describe the construction of the Intronless Genes Database in Dicots (IGDD; available at http://bio.njfu.edu.cn/igdd/ ), which contains data for five well-annotated plants including Arabidopsis thaliana, Carica papaya, Populus trichocarpa, Salix suchowensis and Vitis vinifera. Using highly visual settings, IGDD displays the structural and functional annotations, the homolog groups, the syntenic relationships, the expression patterns, and the statistical characteristics of intronless genes. In addition, useful tools such as an advanced search and local BLAST are available through a user-friendly and intuitive web interface. In conclusion, the IGDD provides a comprehensive and up-to-date platform for researchers to assist the exploration of intronless genes in dicot plants.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 7 25%
Student > Ph. D. Student 5 18%
Researcher 4 14%
Student > Postgraduate 2 7%
Student > Master 2 7%
Other 4 14%
Unknown 4 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 29%
Agricultural and Biological Sciences 6 21%
Computer Science 3 11%
Business, Management and Accounting 1 4%
Environmental Science 1 4%
Other 2 7%
Unknown 7 25%
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 29 July 2016.
All research outputs
#18,171,423
of 23,344,526 outputs
Outputs from BMC Bioinformatics
#6,065
of 7,387 outputs
Outputs of similar age
#269,768
of 367,501 outputs
Outputs of similar age from BMC Bioinformatics
#68
of 101 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,387 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 12th percentile – i.e., 12% 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 367,501 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 101 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.