ArticleNature communications2022
Intrinsic bias estimation for improved analysis of bulk and single-cell chromatin accessibility profiles using SELMA.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Robust footprinting with sample-specific Tn5 bias correction for bulk and single cell ATAC-seq.Nature communications · 2026Article
- Inhibition of TGM2 enhances cisplatin sensitivity in MSH2-deficient bladder cancer.Cell death discovery · 2026Article
- PATTY corrects open-chromatin bias for improved bulk and single-cell CUT&Tag profiling.Nature communications · 2026Article
- Computational Analyses and Challenges of Single-cell ATAC-seq.Genomics, proteomics & bioinformatics · 2025Review
- PATTY corrects open-chromatin bias for improved bulk and single-cell CUT&Tag profiling.Research square · 2025Article
- A hierarchical, count-based model highlights challenges in scATAC-seq data analysis and points to opportunities to extract finer-resolution information.Genome biology · 2025Article
- Single-cell multiomics identifies Tcf1 and Lef1 as key initiators of early thymic progenitor fate.Science immunology · 2025Article
- PATTY corrects open chromatin bias for improved bulk and single-cell CUT&Tag profiling.bioRxiv : the preprint server for biology · 2025Article
- IGN: Invariable gene set-based normalization for chromatin accessibility profile data analysis.Computational and structural biotechnology journal · 2025Article
- Going beyond cell clustering and feature aggregation: Is there single cell level information in single-cell ATAC-seq data?bioRxiv : the preprint server for biology · 2024Article
- Article
- Uniform quantification of single-nucleus ATAC-seq data with Paired-Insertion Counting (PIC) and a model-based insertion rate estimator.Nature methods · 2024Article
- Correction of transposase sequence bias in ATAC-seq data with rule ensemble modeling.NAR genomics and bioinformatics · 2023Article
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Authors and funding
9 authors.
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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.
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