CSV-Filter: a deep learning-based comprehensive structural variant filtering method for both short and long reads.

ABSTRACT:

Motivation

Structural variants (SVs) play an important role in genetic research and precision medicine. As existing SV detection methods usually contain a substantial number of false positive calls, approaches to filter the detection results are needed.

Results

We developed a novel deep learning-based SV filtering tool, CSV-Filter, for both short and long reads. CSV-Filter uses a novel multi-level grayscale image encoding method based on CIGAR strings of the alignment results and employs image augmentation techniques to improve SV feature extraction. CSV-Filter also utilizes self-supervised learning networks for transfer as classification models, and employs mixed-precision operations to accelerate training. The experiments showed that the integration of CSV-Filter with popu

SUBMITTER: Xia Z 

PROVIDER: S-EPMC11419953 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature