ArticleBMC bioinformatics2017
GATK hard filtering: tunable parameters to improve variant calling for next generation sequencing targeted gene panel data.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 1 of them a synthesis that pooled it.
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Who cites it
61 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Genetic Diversity and Evaluation of Agro-Morphological Traits in Lettuce Core Collection.Plants (Basel, Switzerland) · 2024Article
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- Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms.bioRxiv : the preprint server for biology · 2024Article
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- FishSNP: a high quality cross-species SNP database of fishes.Scientific data · 2024Article
- A cost-effective sequencing method for genetic studies combining high-depth whole exome and low-depth whole genome.NPJ genomic medicine · 2024Article
- Identify Candidate Genes Associated with the Weight and Egg Quality Traits in Wenshui Green Shell-Laying Chickens by the Copy Number Variation-Based Genome-Wide Association Study.Veterinary sciences · 2024Article
1 more citing papers are in PubMed but not listed here.
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundNGS technology represents a powerful alternative to the standard Sanger sequencing in the context of clinical setting. The proprietary software that are generally used for variant calling often depend on preset parameters that may not fit in a satisfactory manner for different genes. GATK, which is widely used in the academic world, is rich in parameters for variant calling. However the self-adjusting parameter calibration of GATK requires data from a large number of exomes. When these are not available, which is the standard condition of a diagnostic laboratory, the parameters must be set by the operator (hard filtering). The aim of the present paper was to set up a procedure to assess the best parameters to be used in the hard filtering of GATK. This was pursued by using classification trees on true and false variants from simulated sequences of a real dataset data.
resultsWe simulated two datasets, with different coverages, including all the sequence alterations identified in a real dataset according to their observed frequencies. Simulated sequences were aligned with standard protocols and then regression trees were built up to identify the most reliable parameters and cutoff values to discriminate true and false variant calls. Moreover, we analyzed flanking sequences of region presenting a high rate of false positive calls observing that such sequences present a low complexity make up.
conclusionsOur results showed that GATK hard filtering parameter values can be tailored through a simulation study based-on the DNA region of interest to ameliorate the accuracy of the variant calling.
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