Nucleic Acids ResearchBioinformaticsPeer-reviewed journal article
Motif distribution in genomes gives insights into gene clustering and co-regulation
Atreyi ChakrabortyiD, Sumant Chopde, Mallur Srivatsan MadhusudhaniD
Abstract
We read the genome as proteins in the cell would - by studying the distributions of 5-6 base motifs of DNA in the whole genome or smaller stretches such as parts of, or whole chromosomes. This led us to some interesting findings about motif clustering and chromosome organisation. It is quite clear that the motif distribution in genomes is not random at the length scales we examined: 1kbps to entire chromosomes. The observed to expected (OE) ratios of motif distributions show strong correlations in pairs of chromosomes that are susceptible to translocations. With the aid of examples, we suggest that similarity in motif distributions in promoter regions of genes could imply co-regulation. A simple extension of this idea empowers us with the ability to construct gene regulatory networks. Further, we could make inferences about the spatial proximity of genomic fragments using these motif distributions. Spatially proximal regions, as deduced by Hi-C or pcHi-C, were [~]3.5 times more likely to have their motif distributions correlated than non-proximal regions. These correlations had strong contributions from the CTCF protein recognizing motifs which are known markers of TADs. In general, correlating genomic regions by motif distribution comparisons alone is rife with functional information. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/613605v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1cf4193org.highwire.dtl.DTLVardef@1da47f5org.highwire.dtl.DTLVardef@1aa4172org.highwire.dtl.DTLVardef@aeaa60_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: human
- organism: mouse
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- research focus: spatial domain detection
- research focus: differential expression
- approach: statistical model
- Citations
- 3
- FWCI
- 0.41
- Field percentile
- top 45%
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