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.
Profiling multiple omic layers in a single cell enables the discovery and analysis of biological phenomena that are not apparent from analysis of mono‐omic data. While methods for multiomic profiling have been reported, their adoption has been limited due to high cost and complex workflows. Here, a simple method for joint profiling of gene expression and chromatin accessibility in tens to hundreds of single cells is presented. Assessed herein is the quality of resulting single cell ATAC‐ and RNA‐seq data across three cell types, examining the link between accessibility and expression at the
- PAR ID:
- 10459357
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Biosystems
- Volume:
- 3
- Issue:
- 11
- ISSN:
- 2366-7478
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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