Deciphering the interplay between chromatin accessibility and transcription factor (TF) binding is fundamental to understanding transcriptional regulation, control of cellular states, and the establishment of new phenotypes. Recent genome-wide chromatin accessibility profiling studies have provided catalogs of putative open regions, where TFs can recognize their motifs and regulate gene expression programs. Here, we present motif enrichment in differential elements of accessibility (MEDEA), a computational tool that analyzes high-throughput chromatin accessibility genomic data to identify cell-type-specific accessible regions and lineage-specific motifs associated with TF binding therein. To benchmark MEDEA, we used a panel of reference cell lines profiled by ENCODE and curated by the ENCODE Project Consortium for the ENCODE-DREAM Challenge. By comparing results with RNA-seq data, ChIP-seq peaks, and DNase-seq footprints, we show that MEDEA improves the detection of motifs associated with known lineage specifiers. We then applied MEDEA to 610 ENCODE DNase-seq data sets, where it revealed significant motifs even when absolute enrichment was low and where it identified novel regulators, such as NRF1 in kidney development. Finally, we show that MEDEA performs well on both bulk and single-cell ATAC-seq data. MEDEA is publicly available as part of our Glossary-GENRE suite for motif enrichment analysis.

中文翻译：

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MEDEA：可及染色质中转录因子结合基序的分析。

理解染色质可访问性与转录因子（TF）结合之间的相互作用是了解转录调控，细胞状态控制和新表型建立的基础。最近的全基因组染色质可及性分析研究提供了推定开放区域的目录，其中TF可以识别其基序并调节基因表达程序。在这里，我们介绍了可及性差异元素（MEDEA）中的基序富集，该工具是一种分析高通量染色质可及性基因组数据的计算工具，以识别与TF结合相关的细胞类型特异性可及区域和谱系特异性基序。为了对MEDEA进行基准测试，我们使用了一组由ENCODE剖析并由ENCODE项目联合会策划的参比细胞系，以应对ENCODE-DREAM挑战。通过将结果与RNA-seq数据，ChIP-seq峰和DNase-seq足迹进行比较，我们表明MEDEA改善了与已知谱系指定子相关的基序检测。然后，我们将MEDEA应用到610个ENCODE DNase-seq数据集，即使绝对富集率低，它也显示出显着的基序，并且在肾脏发育中鉴定了新型调节剂，例如NRF1。最后，我们表明MEDEA在批量和单细胞ATAC序列数据上均表现良好。MEDEA是我们的Glossary-GENRE套件的一部分，可用于主题富集分析。甚至在绝对富集程度低的情况下，它也显示出显着的基序；并且在其中，它识别出新的调节剂，例如肾脏发育中的NRF1。最后，我们表明MEDEA在批量和单细胞ATAC序列数据上均表现良好。MEDEA是我们的Glossary-GENRE套件的一部分，可用于主题富集分析。甚至在绝对富集程度低的情况下，它也显示出显着的基序；并且在其中，它识别出新的调节剂，例如肾脏发育中的NRF1。最后，我们表明MEDEA在批量和单细胞ATAC序列数据上均表现良好。MEDEA是我们的Glossary-GENRE套件的一部分，可用于主题富集分析。