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Strategies for identification of somatic variants using the Ion Torrent deep targeted sequencing platform

Overview of attention for article published in BMC Bioinformatics, January 2018
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  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

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Title
Strategies for identification of somatic variants using the Ion Torrent deep targeted sequencing platform
Published in
BMC Bioinformatics, January 2018
DOI 10.1186/s12859-017-1991-3
Pubmed ID
Authors

Aditya Deshpande, Wenhua Lang, Tina McDowell, Smruthy Sivakumar, Jiexin Zhang, Jing Wang, F. Anthony San Lucas, Jerry Fowler, Humam Kadara, Paul Scheet

Abstract

'Next-generation' (NGS) sequencing has wide application in medical genetics, including the detection of somatic variation in cancer. The Ion Torrent-based (IONT) platform is among NGS technologies employed in clinical, research and diagnostic settings. However, identifying mutations from IONT deep sequencing with high confidence has remained a challenge. We compared various computational variant-calling methods to derive a variant identification pipeline that may improve the molecular diagnostic and research utility of IONT. Using IONT, we surveyed variants from the 409-gene Comprehensive Cancer Panel in whole-section tumors, intra-tumoral biopsies and matched normal samples obtained from frozen tissues and blood from four early-stage non-small cell lung cancer (NSCLC) patients. We used MuTect, Varscan2, IONT's proprietary Ion Reporter, and a simple subtraction we called "Poor Man's Caller." Together these produced calls at 637 loci across all samples. Visual validation of 434 called variants was performed, and performance of the methods assessed individually and in combination. Of the subset of inspected putative variant calls (n=223) in genomic regions that were not intronic or intergenic, 68 variants (30%) were deemed valid after visual inspection. Among the individual methods, the Ion Reporter method offered perhaps the most reasonable tradeoffs. Ion Reporter captured 83% of all discovered variants; 50% of its variants were visually validated. Aggregating results from multiple packages offered varied improvements in performance. Overall, Ion Reporter offered the most attractive performance among the individual callers. This study suggests combined strategies to maximize sensitivity and positive predictive value in variant calling using IONT deep sequencing.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 58 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 17%
Student > Ph. D. Student 8 14%
Student > Bachelor 8 14%
Other 6 10%
Student > Master 6 10%
Other 6 10%
Unknown 14 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 21 36%
Agricultural and Biological Sciences 9 16%
Medicine and Dentistry 4 7%
Engineering 4 7%
Computer Science 2 3%
Other 2 3%
Unknown 16 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 30 January 2018.
All research outputs
#6,536,954
of 23,305,591 outputs
Outputs from BMC Bioinformatics
#2,501
of 7,379 outputs
Outputs of similar age
#131,889
of 444,143 outputs
Outputs of similar age from BMC Bioinformatics
#45
of 131 outputs
Altmetric has tracked 23,305,591 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 7,379 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 65% 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 444,143 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 70% of its contemporaries.
We're also able to compare this research output to 131 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 66% of its contemporaries.