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Decoding a Substantial Set of Samples in Parallel by Massive Sequencing

Overview of attention for article published in PLOS ONE, March 2011
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Title
Decoding a Substantial Set of Samples in Parallel by Massive Sequencing
Published in
PLOS ONE, March 2011
DOI 10.1371/journal.pone.0017785
Pubmed ID
Authors

Mårten Neiman, Sverker Lundin, Peter Savolainen, Afshin Ahmadian

Abstract

There has been a dramatic increase of throughput of sequenced bases in the last years but sequencing a multitude of samples in parallel has not yet developed equally. Here we present a novel strategy where the combination of two tags is used to link sequencing reads back to their origins from a pool of samples. By incorporating the tags in two steps sample-handling complexity is lowered by nearly 100 times compared to conventional indexing protocols. In addition, the method described here enables accurate identification and typing of thousands of samples in parallel. In this study the system was designed to test 4992 samples using only 122 tags. To prove the concept of the two-tagging method, the highly polymorphic 2(nd) exon of DLA-DRB1 in dogs and wolves was sequenced using the 454 GS FLX Titanium Chemistry. By requiring a minimum sequence depth of 20 reads per sample, 94% of the successfully amplified samples were genotyped. In addition, the method allowed digital detection of chimeric fragments. These results demonstrate that it is possible to sequence thousands of samples in parallel without complex pooling patterns or primer combinations. Furthermore, the method is highly scalable as only a limited number of additional tags leads to substantial increase of the sample size.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 6%
Spain 3 3%
Canada 2 2%
France 1 <1%
Pakistan 1 <1%
Australia 1 <1%
Switzerland 1 <1%
United Kingdom 1 <1%
Malaysia 1 <1%
Other 2 2%
Unknown 83 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 36 35%
Student > Ph. D. Student 21 21%
Professor > Associate Professor 8 8%
Student > Master 7 7%
Student > Bachelor 5 5%
Other 17 17%
Unknown 8 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 70 69%
Medicine and Dentistry 6 6%
Biochemistry, Genetics and Molecular Biology 5 5%
Environmental Science 3 3%
Computer Science 2 2%
Other 6 6%
Unknown 10 10%