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pyAmpli: an amplicon-based variant filter pipeline for targeted resequencing data

Overview of attention for article published in BMC Bioinformatics, December 2017
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  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

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Title
pyAmpli: an amplicon-based variant filter pipeline for targeted resequencing data
Published in
BMC Bioinformatics, December 2017
DOI 10.1186/s12859-017-1985-1
Pubmed ID
Authors

Matthias Beyens, Nele Boeckx, Guy Van Camp, Ken Op de Beeck, Geert Vandeweyer

Abstract

Haloplex targeted resequencing is a popular method to analyze both germline and somatic variants in gene panels. However, involved wet-lab procedures may introduce false positives that need to be considered in subsequent data-analysis. No variant filtering rationale addressing amplicon enrichment related systematic errors, in the form of an all-in-one package, exists to our knowledge. We present pyAmpli, a platform independent parallelized Python package that implements an amplicon-based germline and somatic variant filtering strategy for Haloplex data. pyAmpli can filter variants for systematic errors by user pre-defined criteria. We show that pyAmpli significantly increases specificity, without reducing sensitivity, essential for reporting true positive clinical relevant mutations in gene panel data. pyAmpli is an easy-to-use software tool which increases the true positive variant call rate in targeted resequencing data. It specifically reduces errors related to PCR-based enrichment of targeted regions.

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The data shown below were collected from the profiles of 5 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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 18%
Professor > Associate Professor 2 12%
Other 2 12%
Researcher 2 12%
Unspecified 1 6%
Other 5 29%
Unknown 2 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 18%
Chemical Engineering 2 12%
Engineering 2 12%
Unspecified 1 6%
Computer Science 1 6%
Other 3 18%
Unknown 5 29%
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 16 December 2017.
All research outputs
#7,205,545
of 23,011,300 outputs
Outputs from BMC Bioinformatics
#2,842
of 7,315 outputs
Outputs of similar age
#143,493
of 439,309 outputs
Outputs of similar age from BMC Bioinformatics
#48
of 136 outputs
Altmetric has tracked 23,011,300 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 7,315 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 61% 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 439,309 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 67% of its contemporaries.
We're also able to compare this research output to 136 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 64% of its contemporaries.