OCRFinder: a noise-tolerance machine learning method for accurately estimating open chromatin regions

• Published in: Frontiers in genetics 2023-06, Vol.14, p.1184744
• Main Authors: Ren, Jiayi, Liu, Yuqian, Zhu, Xiaoyan, Wang, Xuwen, Li, Yifei, Liu, Yuxin, Hu, Wenqing, Zhang, Xuanping, Wang, Jiayin
• Format: Article
• Language: English
• Subjects: cell-free DNA - cfDNA; chromatin accessibility; Genetics; noisy label learning; open chromatin region; sequencing data analyses
• ISSN: 1664-8021; 1664-8021
• DOI: 10.3389/fgene.2023.1184744

Open chromatin regions are the genomic regions associated with basic cellular physiological activities, while chromatin accessibility is reported to affect gene expressions and functions. A basic computational problem is to efficiently estimate open chromatin regions, which could facilitate both genomic and epigenetic studies. Currently, ATAC-seq and cfDNA-seq (plasma cell-free DNA sequencing) are two popular strategies to detect OCRs. As cfDNA-seq can obtain more biomarkers in one round of sequencing, it is considered more effective and convenient. However, in processing cfDNA-seq data, due to the dynamically variable chromatin accessibility, it is quite difficult to obtain the training data with pure OCRs or non-OCRs, and leads to a noise problem for either feature-based approaches or learning-based approaches. In this paper, we propose a learning-based OCR estimation approach with a noise-tolerance design. The proposed approach, named OCRFinder, incorporates the ideas of ensemble learning framework and semi-supervised strategy to avoid potential overfitting of noisy labels, which are the false positives on OCRs and non-OCRs. Compared to different noise control strategies and state-of-the-art approaches, OCRFinder achieved higher accuracies and sensitivities in the experiments. In addition, OCRFinder also has an excellent performance in ATAC-seq or DNase-seq comparison experiments.