High-resolution TADs reveal DNA sequences underlying genome organization in flies

By Fidel Ramírez, Vivek Bhardwaj, Laura Arrigoni, Kin Chung Lam, Björn A. Grüning, Jose M. Villaveces, Bianca Habermann, Asifa Akhtar, Thomas Manke. Nature Communications, 2017

860 citations recorded by the source corpus.

Despite an abundance of new studies about topologically associating domains (TADs), the role of genetic information in TAD formation is still not fully understood. Here we use our software, HiCExplorer (hicexplorer.readthedocs.io) to annotate >2800 high-resolution (570 bp) TAD boundaries in Drosophila melanogaster. We identify eight DNA motifs enriched at boundaries, including a motif bound by the M1BP protein, and two new boundary motifs. In contrast to mammals, the CTCF motif is only enriched on a small fraction of boundaries flanking inactive chromatin while most active boundaries contain the motifs bound by the M1BP or Beaf-32 proteins. We demonstrate that boundaries can be accurately predicted using only the motif sequences at open chromatin sites. We propose that DNA sequence guides the genome architecture by allocation of boundary proteins in the genome. Finally, we present an interactive online database to access and explore the spatial organization of fly, mouse and human genomes, available at http://chorogenome.ie-freiburg.mpg.de. Although topologically associating domains (TADs) have been extensively investigated, it is not clear to what extent DNA sequence contributes to their formation. Here the authors develop software to identify high-resolution TAD boundaries and reveal their relationship to underlying DNA motifs.

Subfields: Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/70de8e21250afa2c98ad1542c72ff12e07e4d9f3

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Citations in the corpus

• The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update (2018, methodology): “Examples of new tools include: GEMINI for exploring genetic variation (12); mothur for analyzing rRNA gene sequences (13); QIIME for quantitative microbiome analysis from raw DNA sequencing data (14); deepTools for explorative analysis of deeply sequence data (15,16); HiCexplorer (17) for analysis a”