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An efficient error correction algorithm using FM-index

Overview of attention for article published in BMC Bioinformatics, November 2017
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  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

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Title
An efficient error correction algorithm using FM-index
Published in
BMC Bioinformatics, November 2017
DOI 10.1186/s12859-017-1940-1
Pubmed ID
Authors

Yao-Ting Huang, Yu-Wen Huang

Abstract

High-throughput sequencing offers higher throughput and lower cost for sequencing a genome. However, sequencing errors, including mismatches and indels, may be produced during sequencing. Because, errors may reduce the accuracy of subsequent de novo assembly, error correction is necessary prior to assembly. However, existing correction methods still face trade-offs among correction power, accuracy, and speed. We develop a novel overlap-based error correction algorithm using FM-index (called FMOE). FMOE first identifies overlapping reads by aligning a query read simultaneously against multiple reads compressed by FM-index. Subsequently, sequencing errors are corrected by k-mer voting from overlapping reads only. The experimental results indicate that FMOE has highest correction power with comparable accuracy and speed. Our algorithm performs better in long-read than short-read datasets when compared with others. The assembly results indicated different algorithms has its own strength and weakness, whereas FMOE is good for long or good-quality reads. FMOE is freely available at https://github.com/ythuang0522/FMOC .

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 32%
Student > Bachelor 4 21%
Professor > Associate Professor 2 11%
Researcher 2 11%
Unknown 5 26%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 21%
Biochemistry, Genetics and Molecular Biology 3 16%
Computer Science 3 16%
Engineering 3 16%
Chemistry 1 5%
Other 0 0%
Unknown 5 26%
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 04 March 2018.
All research outputs
#13,374,110
of 23,577,761 outputs
Outputs from BMC Bioinformatics
#3,855
of 7,418 outputs
Outputs of similar age
#206,548
of 441,464 outputs
Outputs of similar age from BMC Bioinformatics
#53
of 142 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,418 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 45th percentile – i.e., 45% 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 441,464 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 52% of its contemporaries.
We're also able to compare this research output to 142 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.