Evidence map›Paper›PMID 35110657›Full record

ArticleScientific reports2022

Comparison of GATK and DeepVariant by trio sequencing.

Yi-Lin Lin, Pi-Chuan Chang, Ching Hsu, Miao-Zi Hung, Yin-Hsiu Chien, Wuh-Liang Hwu, FeiPei Lai, Ni-Chung Lee

Abstract readComparative Study
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.

0numbers the graph read from it
0cells of the map it votes in
43citing 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

43 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

8 authors.

Yi-Lin LinDepartment of Medical Genetics, National Taiwan University Hospital, 8 Chung-Shan South Road, Taipei, 10041, Taiwan.
Pi-Chuan ChangGoogle Inc., 1600 Amphitheatre Pkwy, Mountain View, CA, 94043, USA.
Ching HsuGraduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd, Taipei, 10617, Taiwan.
Miao-Zi HungDepartment of Medical Genetics, National Taiwan University Hospital, 8 Chung-Shan South Road, Taipei, 10041, Taiwan.
Yin-Hsiu ChienDepartment of Medical Genetics, National Taiwan University Hospital, 8 Chung-Shan South Road, Taipei, 10041, Taiwan.
Wuh-Liang HwuDepartment of Medical Genetics, National Taiwan University Hospital, 8 Chung-Shan South Road, Taipei, 10041, Taiwan.
FeiPei LaiGraduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd, Taipei, 10617, Taiwan.
Ni-Chung LeeDepartment of Medical Genetics, National Taiwan University Hospital, 8 Chung-Shan South Road, Taipei, 10041, Taiwan. ncleentu@ntu.edu.tw.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While next-generation sequencing (NGS) has transformed genetic testing, it generates large quantities of noisy data that require a significant amount of bioinformatics to generate useful interpretation. The accuracy of variant calling is therefore critical. Although GATK HaplotypeCaller is a widely used tool for this purpose, newer methods such as DeepVariant have shown higher accuracy in assessments of gold-standard samples for whole-genome sequencing (WGS) and whole-exome sequencing (WES), but a side-by-side comparison on clinical samples has not been performed. Trio WES was used to compare GATK (4.1.2.0) HaplotypeCaller and DeepVariant (v0.8.0). The performance of the two pipelines was evaluated according to the Mendelian error rate, transition-to-transversion (Ti/Tv) ratio, concordance rate, and pathological variant detection rate. Data from 80 trios were analyzed. The Mendelian error rate of the 77 biological trios calculated from the data by DeepVariant (3.09 ± 0.83%) was lower than that calculated from the data by GATK (5.25 ± 0.91%) (p < 0.001). DeepVariant also yielded a higher Ti/Tv ratio (2.38 ± 0.02) than GATK (2.04 ± 0.07) (p < 0.001), suggesting that DeepVariant proportionally called more true positives. The concordance rate between the 2 pipelines was 88.73%. Sixty-three disease-causing variants were detected in the 80 trios. Among them, DeepVariant detected 62 variants, and GATK detected 61 variants. The one variant called by DeepVariant but not GATK HaplotypeCaller might have been missed by GATK HaplotypeCaller due to low coverage. OTC exon 2 (139 bp) deletion was not detected by either method. Mendelian error rate calculation is an effective way to evaluate variant callers. By this method, DeepVariant outperformed GATK, while the two pipelines performed equally in other parameters.

Indexed as

Exome SequencingGenetic VariationHaplotypesHigh-Throughput Nucleotide SequencingSoftwareCase-Control StudiesComputational BiologyGenetic Diseases, InbornGenetic Predisposition to DiseaseHeredityHumansPedigreePredictive Value of TestsReproducibility of Results

Identifiers

PMID35110657
PMCPMC8810758

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Registered trials

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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.