桂林电子科技大学学报2026，Vol.46Issue(4)：379-385,7.DOI:10.16725/j.1673-808X.2023204
基于深度学习的PacBio第三代DNA测序数据在低测序深度下的纠错算法
Deep learning-based error-correction algorithm for PacBio third-generation DNA sequencing data at low sequencing depth
摘要
Abstract
Pacific BioScience(PacBio)long-read sequencing data exhibit relatively high error rates,typically ranging from 10%to 15%.Existing self-correction methods generally require high sequencing depth and may discard or fragment reads during the correc-tion process.To address these limitations,a deep learning-based error correction method,termed PBECToo,is proposed.PBECTool consists of three stages:sequence encoding,model training,and correction result generation.A novel encoding method is designed to transform sequence alignment information,reference genome data and sequencing quality scores into a stacked image representa-tion,thereby reformulating error correction as an image classification problem.Finally,corrected FASTA sequences are generated according to the model inference results.PBECTool achieved accuracies exceeding 99%on the E.coli dataset and 97%on the S.cerevisiae dataset,outperforming all competing methods.It also achieved superior performance in terms of output depth,genome coverage,and corrected read length.Experimental results demonstrate the effectiveness of PBECTool for correcting low-depth third-generation sequencing data.关键词
第三代测序/低测序深度/自校正方法/深度学习/CNN分类Key words
third-generation sequencing/low-sequencing depth/self-correction methods/deep learning/CNN classification分类
信息技术与安全科学引用本文复制引用
周帆,张艳菊..基于深度学习的PacBio第三代DNA测序数据在低测序深度下的纠错算法[J].桂林电子科技大学学报,2026,46(4):379-385,7.基金项目
广西图像图形与智能处理重点实验室基金(GIIP2004) （GIIP2004）
