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VariantMetaCaller: automated fusion of variant calling pipelines for quantitative, precision-based filtering

Overview of attention for article published in BMC Genomics, October 2015
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  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

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Title
VariantMetaCaller: automated fusion of variant calling pipelines for quantitative, precision-based filtering
Published in
BMC Genomics, October 2015
DOI 10.1186/s12864-015-2050-y
Pubmed ID
Authors

András Gézsi, Bence Bolgár, Péter Marx, Peter Sarkozy, Csaba Szalai, Péter Antal

Abstract

The low concordance between different variant calling methods still poses a challenge for the wide-spread application of next-generation sequencing in research and clinical practice. A wide range of variant annotations can be used for filtering call sets in order to improve the precision of the variant calls, but the choice of the appropriate filtering thresholds is not straightforward. Variant quality score recalibration provides an alternative solution to hard filtering, but it requires large-scale, genomic data. We evaluated germline variant calling pipelines based on BWA and Bowtie 2 aligners in combination with GATK UnifiedGenotyper, GATK HaplotypeCaller, FreeBayes and SAMtools variant callers, using simulated and real benchmark sequencing data (NA12878 with Illumina Platinum Genomes). We argue that these pipelines are not merely discordant, but they extract complementary useful information. We introduce VariantMetaCaller to test the hypothesis that the automated fusion of measurement related information allows better performance than the recommended hard-filtering settings or recalibration and the fusion of the individual call sets without using annotations. VariantMetaCaller uses Support Vector Machines to combine multiple information sources generated by variant calling pipelines and estimates probabilities of variants. This novel method had significantly higher sensitivity and precision than the individual variant callers in all target region sizes, ranging from a few hundred kilobases to whole exomes. We also demonstrated that VariantMetaCaller supports a quantitative, precision based filtering of variants under wider conditions. Specifically, the computed probabilities of the variants can be used to order the variants, and for a given threshold, probabilities can be used to estimate precision. Precision then can be directly translated to the number of true called variants, or equivalently, to the number of false calls, which allows finding problem-specific balance between sensitivity and precision. VariantMetaCaller can be applied to small target regions and whole exomes as well, and it can be used in cases of organisms for which highly accurate variant call sets are not yet available, therefore it can be a viable alternative to hard filtering in cases where variant quality score recalibration cannot be used. VariantMetaCaller is freely available at http://bioinformatics.mit.bme.hu/VariantMetaCaller .

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Sweden 1 1%
France 1 1%
Canada 1 1%
Unknown 80 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 26%
Researcher 20 24%
Other 9 11%
Student > Master 9 11%
Student > Bachelor 6 7%
Other 13 15%
Unknown 6 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 39%
Biochemistry, Genetics and Molecular Biology 22 26%
Computer Science 7 8%
Medicine and Dentistry 6 7%
Engineering 2 2%
Other 5 6%
Unknown 10 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 February 2018.
All research outputs
#6,800,116
of 22,831,537 outputs
Outputs from BMC Genomics
#3,077
of 10,655 outputs
Outputs of similar age
#85,355
of 284,642 outputs
Outputs of similar age from BMC Genomics
#109
of 385 outputs
Altmetric has tracked 22,831,537 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 10,655 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 70% 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 284,642 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 385 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.