MMGraph: A multiple motif predictor based on graph neural network and coexisting probability for ATAC-seq data

Shuangquan Zhang, Lili Yang, Xiaotian Wu, Nan Sheng, Yuan Fu, Anjun Ma, Yan Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

MOTIVATION: Transcription factor binding sites (TFBSs) prediction is a crucial step in revealing functions of transcription factors from high-throughput sequencing data. Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) provides insight on TFBSs and nucleosome positioning by probing open chromatic, which can simultaneously reveal multiple TFBSs compare to traditional technologies. The existing tools based on convolutional neural network (CNN) only find the fixed length of TFBSs from ATAC-seq data. Graph neural network (GNN) can be considered as the extension of CNN, which has great potential in finding multiple TFBSs with different lengths from ATAC-seq data. RESULTS: We develop a motif predictor called MMGraph based on three-layer GNN and coexisting probability of k-mers for finding multiple motifs from ATAC-seq data. The results of the experiment which has been conducted on 88 ATAC-seq datasets indicate that MMGraph has achieved the best performance on area of eight metrics radar score of 2.31 and could find 207 higher-quality multiple motifs than other existing tools. AVAILABILITY AND IMPLEMENTATION: MMGraph is wrapped in Python package, which is available at https://github.com/zhangsq06/MMGraph.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Original languageEnglish
Pages (from-to)4636-4638
Number of pages3
JournalBioinformatics
Volume38
Issue number19
Early online date23 Aug 2022
DOIs
Publication statusPublished - 30 Oct 2022

Keywords

  • Chromatin
  • Chromatin Immunoprecipitation Sequencing
  • High-Throughput Nucleotide Sequencing/methods
  • Neural Networks, Computer
  • Probability
  • Sequence Analysis, DNA/methods

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