Evidence map›Paper›PMID 27083325›Full record

ArticleBMC bioinformatics2016

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

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

Abstract read
In one paragraph

Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Daniel J ParkGenetic Epidemiology Laboratory, School of Biomedical Sciences, Medical Building, The University of Melbourne, Melbourne, Victoria, 3010, Australia.
Roger LiDepartment of Computing and Information Systems, The University of Melbourne, Melbourne, Victoria, 3010, Australia.
Edmund LauVictorian Life Sciences Computation Initiative, The University of Melbourne, Melbourne, Victoria, 3053, Australia.
Peter GeorgesonVictorian Life Sciences Computation Initiative, The University of Melbourne, Melbourne, Victoria, 3053, Australia.
Tú Nguyen-DumontGenetic Epidemiology Laboratory, School of Biomedical Sciences, Medical Building, The University of Melbourne, Melbourne, Victoria, 3010, Australia.
Bernard J PopeDepartment of Computing and Information Systems, The University of Melbourne, Melbourne, Victoria, 3010, Australia. bjpope@unimelb.edu.au.

Funding

A comprehensive approach to breast cancer susceptibility across the risk spectrumR01CA155767 · NCI · UNIVERSITY OF UTAH · PI GOLDGAR, DAVID E., SOUTHEY, MELISSA · 2011 to 2015
$2.2M
NCI NIH HHS R01 CA155767NCI NIH HHS R01CA155767
6 · The paper itself

Abstract

backgroundPreviously, 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.

resultsThe 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.

conclusionsUNDR ROVER provides the same rapid and accurate genetic variant calling as its predecessor with greatly reduced computational costs.

Indexed as

Genetic VariationAlgorithmsComputational BiologyDNA PrimersGenome, HumanHigh-Throughput Nucleotide SequencingHumansModels, TheoreticalPolymerase Chain ReactionReproducibility of ResultsSequence Analysis, DNASoftwareDNA PrimersHi-PlexMassively parallel sequencingPCR-MPSROVERTargeted sequencingVariant calling

Identifiers

PMID27083325
PMCPMC4833922

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.