↓ Skip to main content

CHEXVIS: a tool for molecular channel extraction and visualization

Overview of attention for article published in BMC Bioinformatics, April 2015
Altmetric Badge

Mentioned by

twitter
1 tweeter

Citations

dimensions_citation
32 Dimensions

Readers on

mendeley
35 Mendeley
Title
CHEXVIS: a tool for molecular channel extraction and visualization
Published in
BMC Bioinformatics, April 2015
DOI 10.1186/s12859-015-0545-9
Pubmed ID
Authors

Talha Bin Masood, Sankaran Sandhya, Nagasuma Chandra, Vijay Natarajan

Abstract

Understanding channel structures that lead to active sites or traverse the molecule is important in the study of molecular functions such as ion, ligand, and small molecule transport. Efficient methods for extracting, storing, and analyzing protein channels are required to support such studies. Further, there is a need for an integrated framework that supports computation of the channels, interactive exploration of their structure, and detailed visual analysis of their properties. We describe a method for molecular channel extraction based on the alpha complex representation. The method computes geometrically feasible channels, stores both the volume occupied by the channel and its centerline in a unified representation, and reports significant channels. The representation also supports efficient computation of channel profiles that help understand channel properties. We describe methods for effective visualization of the channels and their profiles. These methods and the visual analysis framework are implemented in a software tool, CHEXVIS. We apply the method on a number of known channel containing proteins to extract pore features. Results from these experiments on several proteins show that CHEXVIS performance is comparable to, and in some cases, better than existing channel extraction techniques. Using several case studies, we demonstrate how CHEXVIS can be used to study channels, extract their properties and gain insights into molecular function. CHEXVIS supports the visual exploration of multiple channels together with their geometric and physico-chemical properties thereby enabling the understanding of the basic biology of transport through protein channels. The CHEXVIS web-server is freely available at http://vgl.serc.iisc.ernet.in/chexvis/ . The web-server is supported on all modern browsers with latest Java plug-in.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Czechia 3 9%
Israel 1 3%
Unknown 31 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 37%
Professor 4 11%
Researcher 4 11%
Professor > Associate Professor 4 11%
Student > Bachelor 3 9%
Other 7 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 31%
Agricultural and Biological Sciences 9 26%
Chemistry 5 14%
Computer Science 4 11%
Physics and Astronomy 3 9%
Other 3 9%

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 17 April 2015.
All research outputs
#4,184,720
of 5,005,300 outputs
Outputs from BMC Bioinformatics
#2,704
of 2,883 outputs
Outputs of similar age
#128,683
of 153,626 outputs
Outputs of similar age from BMC Bioinformatics
#128
of 129 outputs
Altmetric has tracked 5,005,300 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,883 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 1st percentile – i.e., 1% 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 153,626 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 129 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.