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A survey of current practices for genomic sequencing test interpretation and reporting processes in US laboratories

Overview of attention for article published in Genetics in Medicine, November 2016
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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 (91st percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

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5 Facebook pages

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72 Dimensions

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103 Mendeley
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Title
A survey of current practices for genomic sequencing test interpretation and reporting processes in US laboratories
Published in
Genetics in Medicine, November 2016
DOI 10.1038/gim.2016.152
Pubmed ID
Authors

Julianne M. O’Daniel, Heather M. McLaughlin, Laura M. Amendola, Sherri J. Bale, Jonathan S. Berg, David Bick, Kevin M. Bowling, Elizabeth C. Chao, Wendy K. Chung, Laura K. Conlin, Gregory M. Cooper, Soma Das, Joshua L. Deignan, Michael O. Dorschner, James P. Evans, Arezou A. Ghazani, Katrina A. Goddard, Michele Gornick, Kelly D. Farwell Hagman, Tina Hambuch, Madhuri Hegde, Lucia A. Hindorff, Ingrid A. Holm, Gail P. Jarvik, Amy Knight Johnson, Lindsey Mighion, Massimo Morra, Sharon E. Plon, Sumit Punj, C. Sue Richards, Avni Santani, Brian H. Shirts, Nancy B. Spinner, Sha Tang, Karen E. Weck, Susan M. Wolf, Yaping Yang, Heidi L. Rehm

Abstract

While the diagnostic success of genomic sequencing expands, the complexity of this testing should not be overlooked. Numerous laboratory processes are required to support the identification, interpretation, and reporting of clinically significant variants. This study aimed to examine the workflow and reporting procedures among US laboratories to highlight shared practices and identify areas in need of standardization. Surveys and follow-up interviews were conducted with laboratories offering exome and/or genome sequencing to support a research program or for routine clinical services. The 73-item survey elicited multiple choice and free-text responses that were later clarified with phone interviews. Twenty-one laboratories participated. Practices highly concordant across all groups included consent documentation, multiperson case review, and enabling patient opt-out of incidental or secondary findings analysis. Noted divergence included use of phenotypic data to inform case analysis and interpretation and reporting of case-specific quality metrics and methods. Few laboratory policies detailed procedures for data reanalysis, data sharing, or patient access to data. This study provides an overview of practices and policies of experienced exome and genome sequencing laboratories. The results enable broader consideration of which practices are becoming standard approaches, where divergence remains, and areas of development in best practice guidelines that may be helpful.Genet Med advance online publication 03 Novemeber 2016Genetics in Medicine (2016); doi:10.1038/gim.2016.152.

X Demographics

X Demographics

The data shown below were collected from the profiles of 38 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 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Unknown 102 99%

Demographic breakdown

Readers by professional status Count As %
Other 20 19%
Researcher 16 16%
Student > Ph. D. Student 12 12%
Student > Master 10 10%
Student > Bachelor 9 9%
Other 18 17%
Unknown 18 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 30 29%
Medicine and Dentistry 23 22%
Agricultural and Biological Sciences 13 13%
Computer Science 3 3%
Nursing and Health Professions 3 3%
Other 13 13%
Unknown 18 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 27. 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 22 January 2018.
All research outputs
#1,416,494
of 25,371,288 outputs
Outputs from Genetics in Medicine
#459
of 2,943 outputs
Outputs of similar age
#25,450
of 317,530 outputs
Outputs of similar age from Genetics in Medicine
#7
of 46 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,943 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.0. This one has done well, scoring higher than 84% 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 317,530 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 91% of its contemporaries.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.