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GFF3sort: a novel tool to sort GFF3 files for tabix indexing

Overview of attention for article published in BMC Bioinformatics, November 2017
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Title
GFF3sort: a novel tool to sort GFF3 files for tabix indexing
Published in
BMC Bioinformatics, November 2017
DOI 10.1186/s12859-017-1930-3
Pubmed ID
Authors

Tao Zhu, Chengzhen Liang, Zhigang Meng, Sandui Guo, Rui Zhang

Abstract

The traditional method of visualizing gene annotation data in JBrowse is converting GFF3 files to JSON format, which is time-consuming. The latest version of JBrowse supports rendering sorted GFF3 files indexed by tabix, a novel strategy that is more convenient than the original conversion process. However, current tools available for GFF3 file sorting have some limitations and their sorting results would lead to erroneous rendering in JBrowse. We developed GFF3sort, a script to sort GFF3 files for tabix indexing. Specifically designed for JBrowse rendering, GFF3sort can properly deal with the order of features that have the same chromosome and start position, either by remembering their original orders or by conducting parent-child topology sorting. Based on our test datasets from seven species, GFF3sort produced accurate sorting results with acceptable efficiency compared with currently available tools. GFF3sort is a novel tool to sort GFF3 files for tabix indexing. We anticipate that GFF3sort will be useful to help with genome annotation data processing and visualization.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 13 33%
Student > Ph. D. Student 8 20%
Researcher 4 10%
Student > Bachelor 3 8%
Student > Doctoral Student 2 5%
Other 3 8%
Unknown 7 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 13 33%
Agricultural and Biological Sciences 10 25%
Computer Science 5 13%
Pharmacology, Toxicology and Pharmaceutical Science 1 3%
Immunology and Microbiology 1 3%
Other 3 8%
Unknown 7 18%
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 22 November 2017.
All research outputs
#17,919,786
of 23,007,887 outputs
Outputs from BMC Bioinformatics
#5,967
of 7,315 outputs
Outputs of similar age
#232,576
of 325,276 outputs
Outputs of similar age from BMC Bioinformatics
#109
of 162 outputs
Altmetric has tracked 23,007,887 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 7,315 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 13th percentile – i.e., 13% 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 325,276 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 162 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.