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LSCplus: a fast solution for improving long read accuracy by short read alignment

Overview of attention for article published in BMC Bioinformatics, November 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Mentioned by

blogs
1 blog
twitter
1 tweeter

Citations

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18 Dimensions

Readers on

mendeley
43 Mendeley
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Title
LSCplus: a fast solution for improving long read accuracy by short read alignment
Published in
BMC Bioinformatics, November 2016
DOI 10.1186/s12859-016-1316-y
Pubmed ID
Authors

Ruifeng Hu, Guibo Sun, Xiaobo Sun

Abstract

The single molecule, real time (SMRT) sequencing technology of Pacific Biosciences enables the acquisition of transcripts from end to end due to its ability to produce extraordinarily long reads (>10 kb). This new method of transcriptome sequencing has been applied to several projects on humans and model organisms. However, the raw data from SMRT sequencing are of relatively low quality, with a random error rate of approximately 15 %, for which error correction using next-generation sequencing (NGS) short reads is typically necessary. Few tools have been designed that apply a hybrid sequencing approach that combines NGS and SMRT data, and the most popular existing tool for error correction, LSC, has computing resource requirements that are too intensive for most laboratory and research groups. These shortcomings severely limit the application of SMRT long reads for transcriptome analysis. Here, we report an improved tool (LSCplus) for error correction with the LSC program as a reference. LSCplus overcomes the disadvantage of LSC's time consumption and improves quality. Only 1/3-1/4 of the time and 1/20-1/25 of the error correction time is required using LSCplus compared with that required for using LSC. LSCplus is freely available at http://www.herbbol.org:8001/lscplus/ . Sample calculations are provided illustrating the precision and efficiency of this method regarding error correction and isoform detection.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 5%
Japan 1 2%
Unknown 40 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 30%
Student > Ph. D. Student 12 28%
Student > Bachelor 4 9%
Student > Master 4 9%
Other 2 5%
Other 3 7%
Unknown 5 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 26%
Biochemistry, Genetics and Molecular Biology 11 26%
Computer Science 6 14%
Engineering 3 7%
Medicine and Dentistry 2 5%
Other 4 9%
Unknown 6 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 11 November 2016.
All research outputs
#2,527,571
of 16,043,441 outputs
Outputs from BMC Bioinformatics
#1,049
of 5,813 outputs
Outputs of similar age
#60,970
of 293,628 outputs
Outputs of similar age from BMC Bioinformatics
#88
of 438 outputs
Altmetric has tracked 16,043,441 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,813 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done well, scoring higher than 81% 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 293,628 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 79% of its contemporaries.
We're also able to compare this research output to 438 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.