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Integrating dilution-based sequencing and population genotypes for single individual haplotyping

Overview of attention for article published in BMC Genomics, August 2014
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Title
Integrating dilution-based sequencing and population genotypes for single individual haplotyping
Published in
BMC Genomics, August 2014
DOI 10.1186/1471-2164-15-733
Pubmed ID
Authors

Hirotaka Matsumoto, Hisanori Kiryu

Abstract

Haplotype information is useful for many genetic analyses and haplotypes are usually inferred using computational approaches. Among such approaches, the importance of single individual haplotyping (SIH), which infers individual haplotypes from sequence fragments, has been increasing with the advent of novel sequencing techniques, such as dilution-based sequencing. These techniques could produce virtual long read fragments by separating DNA fragments into multiple low-concentration aliquots, sequencing and mapping each aliquot, and merging clustered short reads. Although these experimental techniques are sophisticated, they have the problem of producing chimeric fragments whose left and right parts match different chromosomes. In our previous research, we found that chimeric fragments significantly decrease the accuracy of SIH. Although chimeric fragments can be removed by using haplotypes which are determined from pedigree genotypes, pedigree genotypes are generally not available. The length of reads cluster and heterozygous calls were also used to detect chimeric fragments. Although some chimeric fragments will be removed with these features, considerable number of chimeric fragments will be undetected because of the dispersion of the length and the absence of SNPs in the overlapped regions. For these reasons, a general method to detect and remove chimeric fragments is needed.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 11%
Unknown 8 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 33%
Student > Bachelor 2 22%
Researcher 2 22%
Student > Master 1 11%
Professor 1 11%
Other 0 0%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 44%
Biochemistry, Genetics and Molecular Biology 3 33%
Computer Science 2 22%
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 30 August 2014.
All research outputs
#14,914,476
of 25,373,627 outputs
Outputs from BMC Genomics
#5,157
of 11,244 outputs
Outputs of similar age
#120,429
of 247,681 outputs
Outputs of similar age from BMC Genomics
#114
of 269 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 52% 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 247,681 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 50% of its contemporaries.
We're also able to compare this research output to 269 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 56% of its contemporaries.