ArticleScientific reports2018
Comparison of three variant callers for human whole genome sequencing.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed, 91 citations in OpenAlex.
- Benchmarking genome choice in functional genomics analyses.Nature communications · 2026Article
- DoBSeqWF: a framework for sensitive detection of individual genetic variation in pooled sequencing data.NAR genomics and bioinformatics · 2026Article
- Comprehensive assessment of whole-genome sequencing-based variant calling pipelines in monozygotic twins.Briefings in bioinformatics · 2025Article
- Systematic evaluation of de novo mutation calling tools using whole genome sequencing data.Briefings in bioinformatics · 2025Article
- Article
- Overcoming limitations to customize DeepVariant for domesticated animals with TrioTrain.Genome research · 2025Article
- Benchmarking of variant calling software for whole-exome sequencing using gold standard datasets.Scientific reports · 2025Article
- SurVIndel2: improving copy number variant calling from next-generation sequencing using hidden split reads.Nature communications · 2024Article
- Biostatistical Aspects of Whole Genome Sequencing Studies: Preprocessing and Quality Control.Biometrical journal. Biometrische Zeitschrift · 2024Article
- Article
- Overcoming Limitations to Deep Learning in Domesticated Animals with TrioTrain.bioRxiv : the preprint server for biology · 2024Article
- Comparative evaluation of SNVs, indels, and structural variations detected with short- and long-read sequencing data.Human genome variation · 2024Article
- A revamped rat reference genome improves the discovery of genetic diversity in laboratory rats.Cell genomics · 2024Article
- Exploring the Role of Non-synonymous and Deleterious Variants Identified in Colorectal Cancer: A Multi-dimensional Computational Scrutiny of Exomes.Current genomics · 2024Article
- Effects ofHuman reproduction open · 2024Article
- Performance analysis of conventional and AI-based variant callers using short and long reads.BMC bioinformatics · 2023Article
- A comparative investigation of single nucleotide variant calling for a personal non-Caucasian sequencing sample.Genes & genomics · 2023Article
- Evaluating the performance of low-frequency variant calling tools for the detection of variants from short-read deep sequencing data.Scientific reports · 2023Article
- Species-wide genomics of kākāpō provides tools to accelerate recovery.Nature ecology & evolution · 2023Article
- A revamped rat reference genome improves the discovery of genetic diversity in laboratory rats.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Testing of patients with genetics-related disorders is in progress of shifting from single gene assays to gene panel sequencing, whole-exome sequencing (WES) and whole-genome sequencing (WGS). Since WGS is unquestionably becoming a new foundation for molecular analyses, we decided to compare three currently used tools for variant calling of human whole genome sequencing data. We tested DeepVariant, a new TensorFlow machine learning-based variant caller, and compared this tool to GATK 4.0 and SpeedSeq, using 30×, 15× and 10× WGS data of the well-known NA12878 DNA reference sample. According to our comparison, the performance on SNV calling was almost similar in 30× data, with all three variant callers reaching F-Scores (i.e. harmonic mean of recall and precision) equal to 0.98. In contrast, DeepVariant was more precise in indel calling than GATK and SpeedSeq, as demonstrated by F-Scores of 0.94, 0.90 and 0.84, respectively. We conclude that the DeepVariant tool has great potential and usefulness for analysis of WGS data in medical genetics.
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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.