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FMLRC: Hybrid long read error correction using an FM-index

Overview of attention for article published in BMC Bioinformatics, February 2018
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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 (91st percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

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36 X users
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1 patent

Citations

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112 Dimensions

Readers on

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129 Mendeley
Title
FMLRC: Hybrid long read error correction using an FM-index
Published in
BMC Bioinformatics, February 2018
DOI 10.1186/s12859-018-2051-3
Pubmed ID
Authors

Jeremy R. Wang, James Holt, Leonard McMillan, Corbin D. Jones

Abstract

Long read sequencing is changing the landscape of genomic research, especially de novo assembly. Despite the high error rate inherent to long read technologies, increased read lengths dramatically improve the continuity and accuracy of genome assemblies. However, the cost and throughput of these technologies limits their application to complex genomes. One solution is to decrease the cost and time to assemble novel genomes by leveraging "hybrid" assemblies that use long reads for scaffolding and short reads for accuracy. We describe a novel method leveraging a multi-string Burrows-Wheeler Transform with auxiliary FM-index to correct errors in long read sequences using a set of complementary short reads. We demonstrate that our method efficiently produces significantly more high quality corrected sequence than existing hybrid error-correction methods. We also show that our method produces more contiguous assemblies, in many cases, than existing state-of-the-art hybrid and long-read only de novo assembly methods. Our method accurately corrects long read sequence data using complementary short reads. We demonstrate higher total throughput of corrected long reads and a corresponding increase in contiguity of the resulting de novo assemblies. Improved throughput and computational efficiency than existing methods will help better economically utilize emerging long read sequencing technologies.

X Demographics

X Demographics

The data shown below were collected from the profiles of 36 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 129 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 129 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 24%
Student > Master 19 15%
Student > Ph. D. Student 18 14%
Student > Bachelor 14 11%
Student > Doctoral Student 7 5%
Other 11 9%
Unknown 29 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 38 29%
Agricultural and Biological Sciences 36 28%
Computer Science 14 11%
Immunology and Microbiology 3 2%
Medicine and Dentistry 3 2%
Other 2 2%
Unknown 33 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 08 April 2021.
All research outputs
#1,754,624
of 25,706,302 outputs
Outputs from BMC Bioinformatics
#306
of 7,735 outputs
Outputs of similar age
#40,765
of 453,715 outputs
Outputs of similar age from BMC Bioinformatics
#8
of 115 outputs
Altmetric has tracked 25,706,302 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,735 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has done particularly well, scoring higher than 96% 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 453,715 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 91% of its contemporaries.
We're also able to compare this research output to 115 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.