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Systematic Inference of Copy-Number Genotypes from Personal Genome Sequencing Data Reveals Extensive Olfactory Receptor Gene Content Diversity

Overview of attention for article published in PLoS Computational Biology, November 2010
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

blogs
1 blog
patent
1 patent

Citations

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56 Dimensions

Readers on

mendeley
140 Mendeley
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7 CiteULike
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Title
Systematic Inference of Copy-Number Genotypes from Personal Genome Sequencing Data Reveals Extensive Olfactory Receptor Gene Content Diversity
Published in
PLoS Computational Biology, November 2010
DOI 10.1371/journal.pcbi.1000988
Pubmed ID
Authors

Sebastian M. Waszak, Yehudit Hasin, Thomas Zichner, Tsviya Olender, Ifat Keydar, Miriam Khen, Adrian M. Stütz, Andreas Schlattl, Doron Lancet, Jan O. Korbel

Abstract

Copy-number variations (CNVs) are widespread in the human genome, but comprehensive assignments of integer locus copy-numbers (i.e., copy-number genotypes) that, for example, enable discrimination of homozygous from heterozygous CNVs, have remained challenging. Here we present CopySeq, a novel computational approach with an underlying statistical framework that analyzes the depth-of-coverage of high-throughput DNA sequencing reads, and can incorporate paired-end and breakpoint junction analysis based CNV-analysis approaches, to infer locus copy-number genotypes. We benchmarked CopySeq by genotyping 500 chromosome 1 CNV regions in 150 personal genomes sequenced at low-coverage. The assessed copy-number genotypes were highly concordant with our performed qPCR experiments (Pearson correlation coefficient 0.94), and with the published results of two microarray platforms (95-99% concordance). We further demonstrated the utility of CopySeq for analyzing gene regions enriched for segmental duplications by comprehensively inferring copy-number genotypes in the CNV-enriched >800 olfactory receptor (OR) human gene and pseudogene loci. CopySeq revealed that OR loci display an extensive range of locus copy-numbers across individuals, with zero to two copies in some OR loci, and two to nine copies in others. Among genetic variants affecting OR loci we identified deleterious variants including CNVs and SNPs affecting ~15% and ~20% of the human OR gene repertoire, respectively, implying that genetic variants with a possible impact on smell perception are widespread. Finally, we found that for several OR loci the reference genome appears to represent a minor-frequency variant, implying a necessary revision of the OR repertoire for future functional studies. CopySeq can ascertain genomic structural variation in specific gene families as well as at a genome-wide scale, where it may enable the quantitative evaluation of CNVs in genome-wide association studies involving high-throughput sequencing.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 140 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 4%
Germany 5 4%
Netherlands 2 1%
France 1 <1%
Sweden 1 <1%
Sri Lanka 1 <1%
United Kingdom 1 <1%
Russia 1 <1%
Belgium 1 <1%
Other 0 0%
Unknown 121 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 46 33%
Researcher 42 30%
Professor 8 6%
Professor > Associate Professor 7 5%
Other 6 4%
Other 19 14%
Unknown 12 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 80 57%
Biochemistry, Genetics and Molecular Biology 20 14%
Medicine and Dentistry 8 6%
Computer Science 5 4%
Physics and Astronomy 4 3%
Other 10 7%
Unknown 13 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 19 November 2015.
All research outputs
#4,468,940
of 25,806,080 outputs
Outputs from PLoS Computational Biology
#3,611
of 9,043 outputs
Outputs of similar age
#19,922
of 111,746 outputs
Outputs of similar age from PLoS Computational Biology
#13
of 57 outputs
Altmetric has tracked 25,806,080 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,043 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 59% 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 111,746 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 81% of its contemporaries.
We're also able to compare this research output to 57 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.