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UNDR ROVER - a fast and accurate variant caller for targeted DNA sequencing

Overview of attention for article published in BMC Bioinformatics, April 2016
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Title
UNDR ROVER - a fast and accurate variant caller for targeted DNA sequencing
Published in
BMC Bioinformatics, April 2016
DOI 10.1186/s12859-016-1014-9
Pubmed ID
Authors

Daniel J. Park, Roger Li, Edmund Lau, Peter Georgeson, Tú Nguyen-Dumont, Bernard J. Pope

Abstract

Previously, we described ROVER, a DNA variant caller which identifies genetic variants from PCR-targeted massively parallel sequencing (MPS) datasets generated by the Hi-Plex protocol. ROVER permits stringent filtering of sequencing chemistry-induced errors by requiring reported variants to appear in both reads of overlapping pairs above certain thresholds of occurrence. ROVER was developed in tandem with Hi-Plex and has been used successfully to screen for genetic mutations in the breast cancer predisposition gene PALB2. ROVER is applied to MPS data in BAM format and, therefore, relies on sequence reads being mapped to a reference genome. In this paper, we describe an improvement to ROVER, called UNDR ROVER (Unmapped primer-Directed ROVER), which accepts MPS data in FASTQ format, avoiding the need for a computationally expensive mapping stage. It does so by taking advantage of the location-specific nature of PCR-targeted MPS data. The UNDR ROVER algorithm achieves the same stringent variant calling as its predecessor with a significant runtime performance improvement. In one indicative sequencing experiment, UNDR ROVER (in its fastest mode) required 8-fold less sequential computation time than the ROVER pipeline and 13-fold less sequential computation time than a variant calling pipeline based on the popular GATK tool. UNDR ROVER is implemented in Python and runs on all popular POSIX-like operating systems (Linux, OS X). It requires as input a tab-delimited format file containing primer sequence information, a FASTA format file containing the reference genome sequence, and paired FASTQ files containing sequence reads. Primer sequences at the 5' end of reads associate read-pairs with their targeted amplicon and, thus, their expected corresponding coordinates in the reference genome. The primer-intervening sequence of each read is compared against the reference sequence from the same location and variants are identified using the same algorithm as ROVER. Specifically, for a variant to be 'called' it must appear at the same location in both of the overlapping reads above user-defined thresholds of minimum number of reads and proportion of reads. UNDR ROVER provides the same rapid and accurate genetic variant calling as its predecessor with greatly reduced computational costs.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Korea, Republic of 1 3%
Unknown 29 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 37%
Student > Ph. D. Student 5 17%
Student > Postgraduate 3 10%
Student > Bachelor 2 7%
Student > Doctoral Student 1 3%
Other 5 17%
Unknown 3 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 37%
Agricultural and Biological Sciences 6 20%
Computer Science 3 10%
Medicine and Dentistry 2 7%
Nursing and Health Professions 2 7%
Other 3 10%
Unknown 3 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 17 April 2016.
All research outputs
#13,511,215
of 23,310,485 outputs
Outputs from BMC Bioinformatics
#4,092
of 7,383 outputs
Outputs of similar age
#130,201
of 270,900 outputs
Outputs of similar age from BMC Bioinformatics
#60
of 104 outputs
Altmetric has tracked 23,310,485 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,383 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 42nd percentile – i.e., 42% 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 270,900 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 51% of its contemporaries.
We're also able to compare this research output to 104 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.