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Next-Generation Sequencing of Human Mitochondrial Reference Genomes Uncovers High Heteroplasmy Frequency

Overview of attention for article published in PLoS Computational Biology, October 2012
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Title
Next-Generation Sequencing of Human Mitochondrial Reference Genomes Uncovers High Heteroplasmy Frequency
Published in
PLoS Computational Biology, October 2012
DOI 10.1371/journal.pcbi.1002737
Pubmed ID
Authors

Maria Ximena Sosa, I. K. Ashok Sivakumar, Samantha Maragh, Vamsi Veeramachaneni, Ramesh Hariharan, Minothi Parulekar, Karin M. Fredrikson, Timothy T. Harkins, Jeffrey Lin, Andrew B. Feldman, Pramila Tata, Georg B. Ehret, Aravinda Chakravarti

Abstract

We describe methods for rapid sequencing of the entire human mitochondrial genome (mtgenome), which involve long-range PCR for specific amplification of the mtgenome, pyrosequencing, quantitative mapping of sequence reads to identify sequence variants and heteroplasmy, as well as de novo sequence assembly. These methods have been used to study 40 publicly available HapMap samples of European (CEU) and African (YRI) ancestry to demonstrate a sequencing error rate <5.63×10(-4), nucleotide diversity of 1.6×10(-3) for CEU and 3.7×10(-3) for YRI, patterns of sequence variation consistent with earlier studies, but a higher rate of heteroplasmy varying between 10% and 50%. These results demonstrate that next-generation sequencing technologies allow interrogation of the mitochondrial genome in greater depth than previously possible which may be of value in biology and medicine.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 151 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 5%
Italy 2 1%
Brazil 2 1%
Switzerland 1 <1%
Sweden 1 <1%
South Africa 1 <1%
Ireland 1 <1%
Sri Lanka 1 <1%
United Kingdom 1 <1%
Other 2 1%
Unknown 132 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 45 30%
Researcher 35 23%
Professor > Associate Professor 12 8%
Student > Bachelor 11 7%
Student > Master 10 7%
Other 27 18%
Unknown 11 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 78 52%
Biochemistry, Genetics and Molecular Biology 36 24%
Medicine and Dentistry 7 5%
Computer Science 5 3%
Engineering 3 2%
Other 7 5%
Unknown 15 10%