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Piecing the puzzle together: a revisit to transcript reconstruction problem in RNA-seq

Overview of attention for article published in BMC Bioinformatics, September 2014
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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 (80th percentile)

Mentioned by

blogs
1 blog
twitter
7 tweeters

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
31 Mendeley
citeulike
2 CiteULike
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Title
Piecing the puzzle together: a revisit to transcript reconstruction problem in RNA-seq
Published in
BMC Bioinformatics, September 2014
DOI 10.1186/1471-2105-15-s9-s3
Pubmed ID
Authors

Yan Huang, Yin Hu, Jinze Liu

Abstract

The advancement of RNA sequencing (RNA-seq) has provided an unprecedented opportunity to assess both the diversity and quantity of transcript isoforms in an mRNA transcriptome. In this paper, we revisit the computational problem of transcript reconstruction and quantification. Unlike existing methods which focus on how to explain the exons and splice variants detected by the reads with a set of isoforms, we aim at reconstructing transcripts by piecing the reads into individual effective transcript copies. Simultaneously, the quantity of each isoform is explicitly measured by the number of assembled effective copies, instead of estimated solely based on the collective read count. We have developed a novel method named Astroid that solves the problem of effective copy reconstruction on the basis of a flow network. The RNA-seq reads are represented as vertices in the flow network and are connected by weighted edges that evaluate the likelihood of two reads originating from the same effective copy. A maximum likelihood set of transcript copies is then reconstructed by solving a minimum-cost flow problem on the flow network. Simulation studies on the human transcriptome have demonstrated the superior sensitivity and specificity of Astroid in transcript reconstruction as well as improved accuracy in transcript quantification over several existing approaches. The application of Astroid on two real RNA-seq datasets has further demonstrated its accuracy through high correlation between the estimated isoform abundance and the qRT-PCR validations.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters 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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 16%
United Kingdom 1 3%
Unknown 25 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 26%
Student > Ph. D. Student 6 19%
Other 3 10%
Professor > Associate Professor 3 10%
Student > Master 2 6%
Other 6 19%
Unknown 3 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 61%
Computer Science 4 13%
Biochemistry, Genetics and Molecular Biology 2 6%
Business, Management and Accounting 1 3%
Mathematics 1 3%
Other 1 3%
Unknown 3 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 04 November 2014.
All research outputs
#1,286,534
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#534
of 4,588 outputs
Outputs of similar age
#26,372
of 214,878 outputs
Outputs of similar age from BMC Bioinformatics
#4
of 25 outputs
Altmetric has tracked 12,373,386 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 4,588 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 88% 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 214,878 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 25 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.