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From Days to Hours: Reporting Clinically Actionable Variants from Whole Genome Sequencing

Overview of attention for article published in PLOS ONE, February 2014
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Title
From Days to Hours: Reporting Clinically Actionable Variants from Whole Genome Sequencing
Published in
PLOS ONE, February 2014
DOI 10.1371/journal.pone.0086803
Pubmed ID
Authors

Sumit Middha, Saurabh Baheti, Steven N. Hart, Jean-Pierre A. Kocher

Abstract

As the cost of whole genome sequencing (WGS) decreases, clinical laboratories will be looking at broadly adopting this technology to screen for variants of clinical significance. To fully leverage this technology in a clinical setting, results need to be reported quickly, as the turnaround rate could potentially impact patient care. The latest sequencers can sequence a whole human genome in about 24 hours. However, depending on the computing infrastructure available, the processing of data can take several days, with the majority of computing time devoted to aligning reads to genomics regions that are to date not clinically interpretable. In an attempt to accelerate the reporting of clinically actionable variants, we have investigated the utility of a multi-step alignment algorithm focused on aligning reads and calling variants in genomic regions of clinical relevance prior to processing the remaining reads on the whole genome. This iterative workflow significantly accelerates the reporting of clinically actionable variants with no loss of accuracy when compared to genotypes obtained with the OMNI SNP platform or to variants detected with a standard workflow that combines Novoalign and GATK.

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The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 10%
United Kingdom 1 3%
Sweden 1 3%
Brazil 1 3%
Unknown 23 79%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 34%
Student > Ph. D. Student 7 24%
Student > Bachelor 4 14%
Other 3 10%
Professor > Associate Professor 2 7%
Other 2 7%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 34%
Biochemistry, Genetics and Molecular Biology 8 28%
Computer Science 5 17%
Engineering 2 7%
Neuroscience 1 3%
Other 1 3%
Unknown 2 7%