Intrinsic bias estimation for improved analysis of bulk and single-cell chromatin accessibility profiles using SELMA

Abstract

Genome-wide profiling of chromatin accessibility by DNase-seq or ATAC-seq has been widely used to identify regulatory DNA elements and transcription factor binding sites. However, enzymatic DNA cleavage exhibits intrinsic sequence biases that confound chromatin accessibility profiling data analysis. Existing computational tools are limited in their ability to account for such intrinsic biases and not designed for analyzing single-cell data. Here, we present Simplex Encoded Linear Model for Accessible Chromatin (SELMA), a computational method for systematic estimation of intrinsic cleavage biases from genomic chromatin accessibility profiling data. We demonstrate that SELMA yields accurate and robust bias estimation from both bulk and single-cell DNase-seq and ATAC-seq data. SELMA can utilize internal mitochondrial DNA data to improve bias estimation. We show that transcription factor binding inference from DNase footprints can be improved by incorporating estimated biases using SELMA. Furthermore, we show strong effects of intrinsic biases in single-cell ATAC-seq data, and develop the first single-cell ATAC-seq intrinsic bias correction model to improve cell clustering. SELMA can enhance the performance of existing bioinformatics tools and improve the analysis of both bulk and single-cell chromatin accessibility sequencing data.

Correction of transposase sequence bias in ATAC-seq data with rule ensemble modeling

“…SELMA encodes k-mer data into a simplex vector, which is incorporated into a Hadamard matrix for all mono and dinucleotide combinations. These data are input into into a linear regression model to capture and correct both DNase and Tn5 bias (Hu et al 2022). More sophisticated methods that correct ATAC-seq data are often less interpretable than kmer and weight matrix scaling.…”