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One Size Doesn't Fit All - RefEditor: Building Personalized Diploid Reference Genome to Improve Read Mapping and Genotype Calling in Next Generation Sequencing Studies

Overview of attention for article published in PLoS Computational Biology, August 2015
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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 (83rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

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11 X users
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1 patent
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1 Google+ user

Citations

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Readers on

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42 Mendeley
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2 CiteULike
Title
One Size Doesn't Fit All - RefEditor: Building Personalized Diploid Reference Genome to Improve Read Mapping and Genotype Calling in Next Generation Sequencing Studies
Published in
PLoS Computational Biology, August 2015
DOI 10.1371/journal.pcbi.1004448
Pubmed ID
Authors

Shuai Yuan, H. Richard Johnston, Guosheng Zhang, Yun Li, Yi-Juan Hu, Zhaohui S. Qin

Abstract

With rapid decline of the sequencing cost, researchers today rush to embrace whole genome sequencing (WGS), or whole exome sequencing (WES) approach as the next powerful tool for relating genetic variants to human diseases and phenotypes. A fundamental step in analyzing WGS and WES data is mapping short sequencing reads back to the reference genome. This is an important issue because incorrectly mapped reads affect the downstream variant discovery, genotype calling and association analysis. Although many read mapping algorithms have been developed, the majority of them uses the universal reference genome and do not take sequence variants into consideration. Given that genetic variants are ubiquitous, it is highly desirable if they can be factored into the read mapping procedure. In this work, we developed a novel strategy that utilizes genotypes obtained a priori to customize the universal haploid reference genome into a personalized diploid reference genome. The new strategy is implemented in a program named RefEditor. When applying RefEditor to real data, we achieved encouraging improvements in read mapping, variant discovery and genotype calling. Compared to standard approaches, RefEditor can significantly increase genotype calling consistency (from 43% to 61% at 4X coverage; from 82% to 92% at 20X coverage) and reduce Mendelian inconsistency across various sequencing depths. Because many WGS and WES studies are conducted on cohorts that have been genotyped using array-based genotyping platforms previously or concurrently, we believe the proposed strategy will be of high value in practice, which can also be applied to the scenario where multiple NGS experiments are conducted on the same cohort. The RefEditor sources are available at https://github.com/superyuan/refeditor.

X Demographics

X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 5%
France 1 2%
Portugal 1 2%
Australia 1 2%
Norway 1 2%
Unknown 36 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 48%
Student > Ph. D. Student 7 17%
Student > Master 4 10%
Professor > Associate Professor 3 7%
Student > Doctoral Student 1 2%
Other 2 5%
Unknown 5 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 45%
Biochemistry, Genetics and Molecular Biology 9 21%
Computer Science 3 7%
Social Sciences 2 5%
Immunology and Microbiology 1 2%
Other 3 7%
Unknown 5 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 24 February 2022.
All research outputs
#3,749,665
of 25,692,343 outputs
Outputs from PLoS Computational Biology
#3,225
of 9,021 outputs
Outputs of similar age
#45,961
of 276,921 outputs
Outputs of similar age from PLoS Computational Biology
#53
of 137 outputs
Altmetric has tracked 25,692,343 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,021 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.3. This one has gotten more attention than average, scoring higher than 64% 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 276,921 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 83% of its contemporaries.
We're also able to compare this research output to 137 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.