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Medical implications of technical accuracy in genome sequencing

Overview of attention for article published in Genome Medicine, March 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

news
8 news outlets
blogs
2 blogs
policy
1 policy source
twitter
36 X users
patent
37 patents

Citations

dimensions_citation
126 Dimensions

Readers on

mendeley
254 Mendeley
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1 CiteULike
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Title
Medical implications of technical accuracy in genome sequencing
Published in
Genome Medicine, March 2016
DOI 10.1186/s13073-016-0269-0
Pubmed ID
Authors

Rachel L. Goldfeder, James R. Priest, Justin M. Zook, Megan E. Grove, Daryl Waggott, Matthew T. Wheeler, Marc Salit, Euan A. Ashley

Abstract

As whole exome sequencing (WES) and whole genome sequencing (WGS) transition from research tools to clinical diagnostic tests, it is increasingly critical for sequencing methods and analysis pipelines to be technically accurate. The Genome in a Bottle Consortium has recently published a set of benchmark SNV, indel, and homozygous reference genotypes for the pilot whole genome NIST Reference Material based on the NA12878 genome. We examine the relationship between human genome complexity and genes/variants reported to be associated with human disease. Specifically, we map regions of medical relevance to benchmark regions of high or low confidence. We use benchmark data to assess the sensitivity and positive predictive value of two representative sequencing pipelines for specific classes of variation. We observe that the accuracy of a variant call depends on the genomic region, variant type, and read depth, and varies by analytical pipeline. We find that most false negative WGS calls result from filtering while most false negative WES variants relate to poor coverage. We find that only 74.6 % of the exonic bases in ClinVar and OMIM genes and 82.1 % of the exonic bases in ACMG-reportable genes are found in high-confidence regions. Only 990 genes in the genome are found entirely within high-confidence regions while 593 of 3,300 ClinVar/OMIM genes have less than 50 % of their total exonic base pairs in high-confidence regions. We find greater than 77 % of the pathogenic or likely pathogenic SNVs currently in ClinVar fall within high-confidence regions. We identify sites that are prone to sequencing errors, including thousands present in publicly available variant databases. Finally, we examine the clinical impact of mandatory reporting of secondary findings, highlighting a false positive variant found in BRCA2. Together, these data illustrate the importance of appropriate use and continued improvement of technical benchmarks to ensure accurate and judicious interpretation of next-generation DNA sequencing results in the clinical setting.

X Demographics

X Demographics

The data shown below were collected from the profiles of 36 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 254 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 2%
Netherlands 3 1%
Brazil 2 <1%
United Kingdom 2 <1%
Italy 1 <1%
Canada 1 <1%
Germany 1 <1%
China 1 <1%
New Zealand 1 <1%
Other 0 0%
Unknown 236 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 81 32%
Student > Ph. D. Student 40 16%
Student > Master 23 9%
Other 22 9%
Student > Bachelor 19 7%
Other 28 11%
Unknown 41 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 77 30%
Biochemistry, Genetics and Molecular Biology 66 26%
Medicine and Dentistry 30 12%
Computer Science 10 4%
Immunology and Microbiology 4 2%
Other 20 8%
Unknown 47 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 96. 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 07 November 2023.
All research outputs
#436,712
of 25,307,332 outputs
Outputs from Genome Medicine
#75
of 1,568 outputs
Outputs of similar age
#7,668
of 305,325 outputs
Outputs of similar age from Genome Medicine
#4
of 29 outputs
Altmetric has tracked 25,307,332 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,568 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.0. This one has done particularly well, scoring higher than 95% 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 305,325 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.