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Germline contamination and leakage in whole genome somatic single nucleotide variant detection

Overview of attention for article published in BMC Bioinformatics, January 2018
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Title
Germline contamination and leakage in whole genome somatic single nucleotide variant detection
Published in
BMC Bioinformatics, January 2018
DOI 10.1186/s12859-018-2046-0
Pubmed ID
Authors

Dorota H. Sendorek, Cristian Caloian, Kyle Ellrott, J. Christopher Bare, Takafumi N. Yamaguchi, Adam D. Ewing, Kathleen E. Houlahan, Thea C. Norman, Adam A. Margolin, Joshua M. Stuart, Paul C. Boutros

Abstract

The clinical sequencing of cancer genomes to personalize therapy is becoming routine across the world. However, concerns over patient re-identification from these data lead to questions about how tightly access should be controlled. It is not thought to be possible to re-identify patients from somatic variant data. However, somatic variant detection pipelines can mistakenly identify germline variants as somatic ones, a process called "germline leakage". The rate of germline leakage across different somatic variant detection pipelines is not well-understood, and it is uncertain whether or not somatic variant calls should be considered re-identifiable. To fill this gap, we quantified germline leakage across 259 sets of whole-genome somatic single nucleotide variant (SNVs) predictions made by 21 teams as part of the ICGC-TCGA DREAM Somatic Mutation Calling Challenge. The median somatic SNV prediction set contained 4325 somatic SNVs and leaked one germline polymorphism. The level of germline leakage was inversely correlated with somatic SNV prediction accuracy and positively correlated with the amount of infiltrating normal cells. The specific germline variants leaked differed by tumour and algorithm. To aid in quantitation and correction of leakage, we created a tool, called GermlineFilter, for use in public-facing somatic SNV databases. The potential for patient re-identification from leaked germline variants in somatic SNV predictions has led to divergent open data access policies, based on different assessments of the risks. Indeed, a single, well-publicized re-identification event could reshape public perceptions of the values of genomic data sharing. We find that modern somatic SNV prediction pipelines have low germline-leakage rates, which can be further reduced, especially for cloud-sharing, using pre-filtering software.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 37%
Student > Bachelor 7 14%
Student > Doctoral Student 3 6%
Other 3 6%
Student > Master 3 6%
Other 6 12%
Unknown 9 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 18%
Agricultural and Biological Sciences 8 16%
Computer Science 7 14%
Medicine and Dentistry 4 8%
Psychology 2 4%
Other 8 16%
Unknown 11 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 16 October 2018.
All research outputs
#14,374,036
of 23,018,998 outputs
Outputs from BMC Bioinformatics
#4,756
of 7,316 outputs
Outputs of similar age
#240,324
of 440,194 outputs
Outputs of similar age from BMC Bioinformatics
#75
of 123 outputs
Altmetric has tracked 23,018,998 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,316 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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 440,194 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 123 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.