StratoMod: predicting sequencing and variant calling errors with interpretable machine learning.

ABSTRACT: Despite the variety in sequencing platforms, mappers, and variant callers, no single pipeline is optimal across the entire human genome. Therefore, developers, clinicians, and researchers need to make tradeoffs when designing pipelines for their application. Currently, assessing such tradeoffs relies on intuition about how a certain pipeline will perform in a given genomic context. We present StratoMod, which addresses this problem using an interpretable machine-learning classifier to predict germline variant calling errors in a data-driven manner. We show StratoMod can precisely predict recall using Hifi or Illumina and leverage StratoMod's interpretability to measure contributions from difficult-to-map and homopolymer regions for each respective outcome. Furthermore, we use Statomod to a

SUBMITTER: Dwarshuis N 

PROVIDER: S-EPMC11471861 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature