MAGGIE: leveraging genetic variation to identify DNA sequence motifs mediating transcription factor binding and function

MAGGIE:利用遗传变异来识别介导转录因子结合和功能的 DNA 序列基序

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作者:Zeyang Shen, Marten A Hoeksema, Zhengyu Ouyang, Christopher Benner, Christopher K Glass

Results

We present MAGGIE (Motif Alteration Genome-wide to Globally Investigate Elements), a novel method for identifying motifs mediating TF binding and function. By leveraging measurements from diverse genotypes, MAGGIE uses a statistical approach to link mutations of a motif to changes of an epigenomic feature without assuming a linear relationship. We benchmark MAGGIE across various applications using both simulated and biological datasets and demonstrate its improvement in sensitivity and specificity compared with the state-of-the-art motif analysis approaches. We use MAGGIE to gain novel insights into the divergent functions of distinct NF-κB factors in pro-inflammatory macrophages, revealing the association of p65-p50 co-binding with transcriptional activation and the association of p50 binding lacking p65 with transcriptional repression. Availability and implementation: The Python package for MAGGIE is freely available at https://github.com/zeyang-shen/maggie. The accession number for the NF-κB ChIP-seq data generated for this study is Gene Expression Omnibus: GSE144070.

Supplementary Information

Supplementary data are available at Bioinformatics online.

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