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De novo inference of stratification and local admixture in sequencing studies

Overview of attention for article published in BMC Bioinformatics, April 2013
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  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

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Title
De novo inference of stratification and local admixture in sequencing studies
Published in
BMC Bioinformatics, April 2013
DOI 10.1186/1471-2105-14-s5-s17
Pubmed ID
Authors

Yu Zhang

Abstract

Analysis of population structures and genome local ancestry has become increasingly important in population and disease genetics. With the advance of next generation sequencing technologies, complete genetic variants in individuals' genomes are quickly generated, providing unprecedented opportunities for learning population evolution histories and identifying local genetic signatures at the SNP resolution. The successes of those studies critically rely on accurate and powerful computational tools that can fully utilize the sequencing information. Although many algorithms have been developed for population structure inference and admixture mapping, many of them only work for independent SNPs in genotype or haplotype format, and require a large panel of reference individuals. In this paper, we propose a novel probabilistic method for detecting population structure and local admixture. The method takes input of sequencing data, genotype data and haplotype data. The method characterizes the dependence of genetic variants via haplotype segmentation, such that all variants detected in a sequencing study can be fully utilized for inference. The method further utilizes a infinite-state Bayesian Markov model to perform de novo stratification and admixture inference. Using simulated datasets from HapMapII and 1000Genomes, we show that our method performs superior than several existing algorithms, particularly when limited or no reference individuals are available. Our method is applicable to not only human studies but also studies of other species of interests, for which little reference information is available.Software Availability: http://stat.psu.edu/~yuzhang/software/dbm.tar.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 12%
Unknown 15 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 35%
Student > Ph. D. Student 5 29%
Professor > Associate Professor 3 18%
Student > Bachelor 1 6%
Student > Master 1 6%
Other 1 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 65%
Computer Science 2 12%
Biochemistry, Genetics and Molecular Biology 1 6%
Psychology 1 6%
Social Sciences 1 6%
Other 0 0%
Unknown 1 6%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 14 May 2015.
All research outputs
#6,209,810
of 22,712,476 outputs
Outputs from BMC Bioinformatics
#2,377
of 7,259 outputs
Outputs of similar age
#52,755
of 199,484 outputs
Outputs of similar age from BMC Bioinformatics
#42
of 135 outputs
Altmetric has tracked 22,712,476 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 7,259 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 66% 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 199,484 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 73% of its contemporaries.
We're also able to compare this research output to 135 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 68% of its contemporaries.