small typo (grn_atac) and snippet order (grn_rna) (#209)

jupyter-book/chromatin_accessibility/gene_regulatory_networks_atac.ipynb

@@ -14,7 +14,7 @@
     "\n",
     "## Motivation\n",
     "\n",
-    "Analyzing chromatin accessibility and gene expression together to understand gene regulation is helpful due to the mechanistic relationship between those two during the control of gene regulation, mediated through transcription factors (TFs) and other epigenetic modulators {cite}`atac:Spitz2012-sw`. Briefly, regulatory regions annotated as promoters and local/distal enhancers are engaged during the early phases of gene expression regulation, and chromatin accessibility increase, or decrease, can be used as a proxy for changes in their activity. Hence, the global positive or negative correlation between proximal and distal accessible elements (measured by ATAC-seq) and target genes (measured by RNA-seq) within a genome neighborhood distance (e.g. less than 200 Mbp), serves to annotate genomic regulatory relationships during the inference of Gene Regulatory Networks (GRNs). Using sequencing data describing gene (RNA) and peak (ATAC) features, tools that build correlation matrices between peaks and matrices help summarize strong peak-gene interactions.\n",
+    "Analyzing chromatin accessibility and gene expression together to understand gene regulation is helpful due to the mechanistic relationship between those two during the control of gene regulation, mediated through transcription factors (TFs) and other epigenetic modulators {cite}`atac:Spitz2012-sw`. Briefly, regulatory regions annotated as promoters and local/distal enhancers are engaged during the early phases of gene expression regulation, and chromatin accessibility increase, or decrease, can be used as a proxy for changes in their activity. Hence, the global positive or negative correlation between proximal and distal accessible elements (measured by ATAC-seq) and target genes (measured by RNA-seq) within a genome neighborhood distance (e.g. less than 200 Kbp), serves to annotate genomic regulatory relationships during the inference of Gene Regulatory Networks (GRNs). Using sequencing data describing gene (RNA) and peak (ATAC) features, tools that build correlation matrices between peaks and matrices help summarize strong peak-gene interactions.\n",
     "\n",
     "### Gene regulatory network inference using RNA and ATAC features\n",
     "\n",