Düşük Kapsamlı WGS Verileri Kullanılarak Hızlı ve Yüksek Doğruluklu Guppy Baz Çağırma Modellerinin Varyant Çağırma Üzerine Etkisinin İncelenmesi

Abstract

Long-read sequencing technologies such as Oxford Nanopore Technologies (ONT) enabled researchers to sequence long reads fast and cost-effectively. ONT sequencing uses nanopores integrated into semiconductor surfaces and sequences the genomic materials using changes in current across the surface as each nucleotide passes through the nanopore. The default output of ONT sequencers is in FAST5 format. The first and one of the most important steps of ONT data analysis is the conversion of FAST5 files to FASTQ files using “base caller” tools. Generally, base caller tools pre-trained deep learning models to transform electrical signals into reads. Guppy, the most commonly used base caller, uses 2 main model types, fast and high accuracy. Since the computation duration is significantly different between these two models, the effect of models on the variant calling process has not been fully understood. This study aims to evaluate the effect of different models on performance on variant calling. In this study, 15 low-coverage long-read sequencing results coming from different flow cells of NA12878 (gold standard data) were used to compare the variant calling results of Guppy. Obtained results indicated that the number of output FASTQ files, read counts and average read lengths between fast and high accuracy models are not statistically significant while pass/fail ratios of the base called datasets are significantly higher in high accuracy models. Results also indicated that the difference in pass/fail ratios arises in a significant difference in the number of called Single Nucleotide Polymorphisms (SNPs), insertions and deletions (InDels). Interestingly the true positive rates of SNPs are not significantly different. These results show that using fast models for SNP calling does not affect the true positive rates statistically. The primary observation in this study, using fast models does not decrease the true positive rate but decreases the called variants that arise due to altered pass/fail ratios. Also, it is not advised to use fast models for InDel calling while both the number of InDels and true positive rates are significantly lower in fast models. This study, to the best of our knowledge, is the first study that evaluates the effect of different base calling models of Guppy, one of the most common and ONT-supported base callers, on variant calling.