Work

A Method for Calculating Whole-Genome Sequencing Outcomes from Trio Data

Year: 2025

Type: article

Abstract: Background. Whole-genome sequencing (WGS) enables comprehensive detection of genetic variants but faces limitations in benchmarking due to incomplete reference datasets. Trio-based analysis, leveraging Mendelian inheritance, provides an alternative strategy for validating sequencing results and estimating error rates, particularly in regulatory genomic regions. Methods. We extended the nf-core/sarek WGS pipeline by integrating a module that collects parental and offspring allele information, extracts regulatory genomic regions, and computes Mendelian-consistency scores. The algorithm processes variant calls from parents and children to identify expected versus anomalous inheritance patterns. The module was implemented in C++ and integrated into the Nextflow workflow, supporting automated analysis of trio datasets. Results. The method was validated on two real trio datasets, comparing DeepVariant and HaplotypeCaller as variant callers. For both trios, DeepVariant consistently achieved higher sensitivity and precision, with statistically significant differences confirmed using 95% confidence intervals. These results demonstrate that the proposed approach enables effective benchmarking of variant-calling performance in non-benchmark datasets. Conclusions. The developed method provides a practical and scalable framework for quantifying WGS outcomes from trio data. By incorporating Mendelian-inheritance validation into existing pipelines, researchers can estimate sequencing error rates, compare variant callers, and optimize workflows in regulatory genomic regions. Our findings confirm the superior performance of DeepVariant over HaplotypeCaller for the studied datasets.

Source: Algorithms

DOI: 10.3390/a18100610

Authors: Nikita Koltunov, Egor Guguchkin, Oleg Samovarov, Liudmila Mikhailova, Evgeny Karpulevich

Institutions: Russian Academy of Sciences, Institute for System Programming, Lomonosov Moscow State University, Financial University

Language: English

Topic: Genomics and Phylogenetic Studies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $1,930

Funder: Russian Science Foundation