↓ Skip to main content

Epiviz: a view inside the design of an integrated visual analysis software for genomics

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

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

blogs
1 blog
twitter
8 X users

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
40 Mendeley
citeulike
2 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Epiviz: a view inside the design of an integrated visual analysis software for genomics
Published in
BMC Bioinformatics, August 2015
DOI 10.1186/1471-2105-16-s11-s4
Pubmed ID
Authors

Florin Chelaru, Héctor Corrada Bravo

Abstract

Computational and visual data analysis for genomics has traditionally involved a combination of tools and resources, of which the most ubiquitous consist of genome browsers, focused mainly on integrative visualization of large numbers of big datasets, and computational environments, focused on data modeling of a small number of moderately sized datasets. Workflows that involve the integration and exploration of multiple heterogeneous data sources, small and large, public and user specific have been poorly addressed by these tools. In our previous work, we introduced Epiviz, which bridges the gap between the two types of tools, simplifying these workflows. In this paper we expand on the design decisions behind Epiviz, and introduce a series of new advanced features that further support the type of interactive exploratory workflow we have targeted. We discuss three ways in which Epiviz advances the field of genomic data analysis: 1) it brings code to interactive visualizations at various different levels; 2) takes the first steps in the direction of collaborative data analysis by incorporating user plugins from source control providers, as well as by allowing analysis states to be shared among the scientific community; 3) combines established analysis features that have never before been available simultaneously in a genome browser. In our discussion section, we present security implications of the current design, as well as a series of limitations and future research steps. Since many of the design choices of Epiviz are novel in genomics data analysis, this paper serves both as a document of our own approaches with lessons learned, as well as a start point for future efforts in the same direction for the genomics community.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 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 %
United Kingdom 1 3%
Spain 1 3%
United States 1 3%
Canada 1 3%
Unknown 36 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 25%
Student > Master 8 20%
Student > Bachelor 4 10%
Student > Doctoral Student 3 8%
Researcher 3 8%
Other 8 20%
Unknown 4 10%
Readers by discipline Count As %
Computer Science 19 48%
Engineering 3 8%
Agricultural and Biological Sciences 2 5%
Biochemistry, Genetics and Molecular Biology 2 5%
Social Sciences 2 5%
Other 4 10%
Unknown 8 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 05 September 2015.
All research outputs
#2,403,812
of 22,824,164 outputs
Outputs from BMC Bioinformatics
#738
of 7,287 outputs
Outputs of similar age
#33,033
of 264,395 outputs
Outputs of similar age from BMC Bioinformatics
#15
of 117 outputs
Altmetric has tracked 22,824,164 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,287 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 89% 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 264,395 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 117 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.