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所属单位：软件学院

发表刊物：Briefings in Bioinformatics

关键字：topologically associating domains (TADs), ensemble learning, machine learning, sequence analysis

摘要：Chromosome is composed of many distinct chromatin domains, referred to variably as topological domains or topologically associating domains (TADs). The domains are stable across different cell types and highly conserved across species, thus these chromatin domains have been considered as the basic units of chromosome folding and regarded as an important secondary structure in chromosome organization. However, the identification of TAD boundaries is still a great challenge due to the high cost and low resolution of Hi-C data or experiments. In this study, we propose a novel ensemble learning framework, termed as StackTADB, for predicting the boundaries of TADs. StackTADB integrates four base classifiers including Random Forest, Logistic Regression, K-NearestNeighbor and Support Vector Machine. From the analysis of a series of examinations on the data set in the previous study, it is concluded that StackTADB has optimal performance in six metrics, AUC, Accuracy, MCC, Precision, Recall and F1 score, and it is superior to the existing methods. In addition, the comparison of the performance of multiple features shows that Kmers-based features play an essential role in predicting TADs boundaries of fruit flies, and we also apply the SHapley Additive exPlanations (SHAP) framework to interpret the predictions of StackTADB to identify the reason why Kmers-based features are vital. The experimental results show that the subsequences matching the BEAF-32 motif play a crucial role in predicting the boundaries of TADs. The source code is freely available at https://github.com/HaoWuLab-Bioinformatics/StackTADB and the webserver of StackTADB is freely available at http://hwtad.sdu.edu.cn:8002/StackTADB.

全部作者：Pengyu Zhang,Zhaoheng Ai,Leyi Wei,Hongming Zhang*,Fan Yang*,Lizhen Cui*

第一作者：Hao Wu*

论文类型：期刊论文

论文编号：24DDA04061DE4D03B344C2068828930D

学科门类：工学

一级学科：软件工程

文献类型：J

卷号：24

期号：1

页面范围：bbac023

字数：12

是否译文：否

发表时间：2022-02-01

收录刊物：SCI