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Light-RCV: a lightweight read coverage viewer for next generation sequencing data

Overview of attention for article published in BMC Bioinformatics, December 2015
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  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Average Attention Score compared to outputs of the same age and source

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3 X users

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Title
Light-RCV: a lightweight read coverage viewer for next generation sequencing data
Published in
BMC Bioinformatics, December 2015
DOI 10.1186/1471-2105-16-s18-s11
Pubmed ID
Authors

Che-Wei Chang, Wen-Bin Lee, An Chen-Deng, Tsunglin Liu, Joseph T Tseng, Darby Tien-Hao Chang

Abstract

Next-generation sequencing (NGS) technologies has brought an unprecedented amount of genomic data for analysis. Unlike array-based profiling technologies, NGS can reveal the expression profile across a transcript at the base level. Such a base-level read coverage provides further insights for alternative mRNA splicing, single-nucleotide polymorphism (SNP), novel transcript discovery, etc. However, to our best knowledge, none of existing NGS viewers can timely visualize genome-wide base-level read coverages in an interactive environment. This study proposes an efficient visualization pipeline and implements a lightweight read coverage viewer, Light-RCV, with the proposed pipeline. Light-RCV consists of four featured designs on the path from raw NGS data to the final visualized read coverage: i) read coverage construction algorithm, ii) multi-resolution profiles, iii) two-stage architecture and iv) storage format. With these designs, Light-RCV achieves a < 0.5s response time on any scale of genomic ranges, including whole chromosomes. Finally, a case study was performed to demonstrate the importance of visualizing base-level read coverage and the value of Light-RCV. Compared with multi-functional genome viewers such as Artemis, Savant, Tablet and Integrative Genomics Viewer (IGV), Light-RCV is designed only for visualization. Therefore, it does not provide advanced analyses. However, its backend technology provides an efficient kernel of base-level visualization that can be easily embedded to other viewers. This viewer is the first to provide timely visualization of genome-wide read coverage at the base level in an interactive environment. The software is available for free at http://lightrcv.ee.ncku.edu.tw.

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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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 31%
Lecturer 2 13%
Student > Bachelor 2 13%
Student > Ph. D. Student 2 13%
Professor 1 6%
Other 2 13%
Unknown 2 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 25%
Agricultural and Biological Sciences 4 25%
Computer Science 3 19%
Engineering 2 13%
Unknown 3 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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
#13,626,177
of 23,498,099 outputs
Outputs from BMC Bioinformatics
#4,081
of 7,400 outputs
Outputs of similar age
#186,623
of 392,308 outputs
Outputs of similar age from BMC Bioinformatics
#85
of 154 outputs
Altmetric has tracked 23,498,099 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,400 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 42nd percentile – i.e., 42% 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 392,308 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 51% of its contemporaries.
We're also able to compare this research output to 154 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.