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VisRseq: R-based visual framework for analysis of sequencing data

Overview of attention for article published in BMC Bioinformatics, August 2015
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

Mentioned by

twitter
2 X users
wikipedia
1 Wikipedia page

Citations

dimensions_citation
63 Dimensions

Readers on

mendeley
75 Mendeley
citeulike
5 CiteULike
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Title
VisRseq: R-based visual framework for analysis of sequencing data
Published in
BMC Bioinformatics, August 2015
DOI 10.1186/1471-2105-16-s11-s2
Pubmed ID
Authors

Hamid Younesy, Torsten Möller, Matthew C Lorincz, Mohammad M Karimi, Steven JM Jones

Abstract

Several tools have been developed to enable biologists to perform initial browsing and exploration of sequencing data. However the computational tool set for further analyses often requires significant computational expertise to use and many of the biologists with the knowledge needed to interpret these data must rely on programming experts. We present VisRseq, a framework for analysis of sequencing datasets that provides a computationally rich and accessible framework for integrative and interactive analyses without requiring programming expertise. We achieve this aim by providing R apps, which offer a semi-auto generated and unified graphical user interface for computational packages in R and repositories such as Bioconductor. To address the interactivity limitation inherent in R libraries, our framework includes several native apps that provide exploration and brushing operations as well as an integrated genome browser. The apps can be chained together to create more powerful analysis workflows. To validate the usability of VisRseq for analysis of sequencing data, we present two case studies performed by our collaborators and report their workflow and insights.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
United States 1 1%
Brazil 1 1%
Unknown 72 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 27%
Student > Ph. D. Student 16 21%
Student > Master 8 11%
Student > Bachelor 5 7%
Other 4 5%
Other 12 16%
Unknown 10 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 25%
Biochemistry, Genetics and Molecular Biology 15 20%
Computer Science 8 11%
Medicine and Dentistry 8 11%
Pharmacology, Toxicology and Pharmaceutical Science 4 5%
Other 9 12%
Unknown 12 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 02 May 2023.
All research outputs
#6,566,643
of 23,662,553 outputs
Outputs from BMC Bioinformatics
#2,450
of 7,414 outputs
Outputs of similar age
#74,286
of 265,600 outputs
Outputs of similar age from BMC Bioinformatics
#41
of 116 outputs
Altmetric has tracked 23,662,553 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 7,414 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 gotten more attention than average, scoring higher than 66% 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 265,600 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 116 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.