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Evaluation and comparison of computational tools for RNA-seq isoform quantification

Overview of attention for article published in BMC Genomics, August 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#46 of 8,401)
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

blogs
2 blogs
twitter
79 tweeters
facebook
2 Facebook pages

Citations

dimensions_citation
45 Dimensions

Readers on

mendeley
319 Mendeley
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Title
Evaluation and comparison of computational tools for RNA-seq isoform quantification
Published in
BMC Genomics, August 2017
DOI 10.1186/s12864-017-4002-1
Pubmed ID
Authors

Chi Zhang, Baohong Zhang, Lih-Ling Lin, Shanrong Zhao

Abstract

Alternatively spliced transcript isoforms are commonly observed in higher eukaryotes. The expression levels of these isoforms are key for understanding normal functions in healthy tissues and the progression of disease states. However, accurate quantification of expression at the transcript level is limited with current RNA-seq technologies because of, for example, limited read length and the cost of deep sequencing. A large number of tools have been developed to tackle this problem, and we performed a comprehensive evaluation of these tools using both experimental and simulated RNA-seq datasets. We found that recently developed alignment-free tools are both fast and accurate. The accuracy of all methods was mainly influenced by the complexity of gene structures and caution must be taken when interpreting quantification results for short transcripts. Using TP53 gene simulation, we discovered that both sequencing depth and the relative abundance of different isoforms affect quantification accuracy CONCLUSIONS: Our comprehensive evaluation helps data analysts to make informed choice when selecting computational tools for isoform quantification.

Twitter Demographics

The data shown below were collected from the profiles of 79 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 319 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 319 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 85 27%
Researcher 78 24%
Student > Master 50 16%
Student > Bachelor 24 8%
Student > Doctoral Student 17 5%
Other 33 10%
Unknown 32 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 114 36%
Agricultural and Biological Sciences 96 30%
Computer Science 22 7%
Medicine and Dentistry 8 3%
Neuroscience 8 3%
Other 28 9%
Unknown 43 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 55. 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 09 August 2019.
All research outputs
#353,811
of 14,365,203 outputs
Outputs from BMC Genomics
#46
of 8,401 outputs
Outputs of similar age
#13,190
of 269,139 outputs
Outputs of similar age from BMC Genomics
#1
of 5 outputs
Altmetric has tracked 14,365,203 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,401 research outputs from this source. They receive a mean Attention Score of 4.2. This one has done particularly well, scoring higher than 99% 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 269,139 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them