Evidence map›Paper›PMID 26510841›Full record

ArticleBMC genomics2015

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

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

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
1.4field-weighted citation impact, top 20% of its field
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

19 citing papers in PubMed, 32 citations in OpenAlex.

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  8. Genetic modifiers of long-term survival in sickle cell anemia.Clinical and translational medicine · 2020
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  14. Overcoming challenges in variant calling: exploring sequence diversity in candidate genes for plant development in perennial ryegrass (Lolium perenne).DNA research : an international journal for rapid publication of reports on genes and genomes · 2019
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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 at 2 institutions in 1 country.

András GézsiDepartment of Genetics, Cell- and Immunobiology, Semmelweis University, Nagyvárad tér 4, Budapest, H-1089, Hungary. gezsi.andras@gmail.com.
Bence BolgárDepartment of Measurement and Information Systems, Budapest University of Technology and Economics, Magyar tudósok krt. 2, Budapest, H-1117, Hungary. bolgar@mit.bme.hu.
Péter MarxDepartment of Measurement and Information Systems, Budapest University of Technology and Economics, Magyar tudósok krt. 2, Budapest, H-1117, Hungary. marxp@mit.bme.hu.
Peter SarkozyDepartment of Measurement and Information Systems, Budapest University of Technology and Economics, Magyar tudósok krt. 2, Budapest, H-1117, Hungary. psarkozy@mit.bme.hu.
Csaba SzalaiDepartment of Genetics, Cell- and Immunobiology, Semmelweis University, Nagyvárad tér 4, Budapest, H-1089, Hungary. genomika.cs@gmail.com.
Péter AntalDepartment of Measurement and Information Systems, Budapest University of Technology and Economics, Magyar tudósok krt. 2, Budapest, H-1117, Hungary. antal@mit.bme.hu.
Budapest University of Technology and Economics · HUSemmelweis University · HU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

resultsWe 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.

conclusionsVariantMetaCaller 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 .

Indexed as

AlgorithmsExomeHigh-Throughput Nucleotide SequencingHumansSoftware

Identifiers

PMID26510841
PMCPMC4625715
OpenAlexW1869631022

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.