(785.15) Regional and Single Nucleotide Correction of Sequence Bias in Chromatin Accessibility Data

Poster Board Number: A15

Jacob Wolpe (University of Virginia), Michael Guertin (University of Connecticut)

Chromatin accessibility assays directly measure which regions of DNA are actively regulated in the genome. Genomic chromatin accessibility data provide the gold standard of spatial and temporal resolution of regulatory landscapes in various cell types and conditions. Accessibility assays depend on enzymatic digestion of DNA and these enzymes are biased with respect to the sequences they recognize and cut. Sequence biases can lead to incorrect conclusions about the nature of the chromatin landscape within an experiment. Previous work from our group has shown that we can use observed k-mer cut frequencies and k-mer representation in the genome to correct the majority of this bias. However, these k-mer scaling approaches did not effectively correct ATAC-seq data, which assesses chromatin accessibility using the Tn5 transposase enzyme. Here, we find that the oligomeric state of Tn5 and wide sequence bias window precludes effective k-mer scaling to account for Tn5 sequence bias. Tn5 sequence specificity is influenced by sequence content up to ten bases away from the Tn5 insertion site in both directions. In comparison, Benzonase, Cyanase, MNase, and DNase are primarily influenced by the sequence within 3 bases of the cleavage site. We could not effectively apply k-mer scaling to ATAC-seq data because the majority of insertion sites happen at unique 20-mers in the genome and the method requires many genomic instances per k-mer. To overcome this limitation, we applied an interpretable machine learning approach, prediction rules ensemble, to correct Tn5-digested naked DNA. Application of a prediction rules ensemble effectively corrects local (single nucleotide resolution) sequence bias as well as sequence biases influenced by regional GC content. Correction of Tn5 sequence biases is important as the field continues to generate ATAC-seq data at accelerating rates and use these data to accurately infer regulatory element activity.

Support or Funding Information

BIE Science Post Graduate Scholarship Fund