ArticleProceedings of the National Academy of Sciences of the United States of America2022
Quality assessment and refinement of chromatin accessibility data using a sequence-based predictive model.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multi-population genome-wide association study implicates immune and non-immune factors in pediatric steroid-sensitive nephrotic syndrome.Nature communications · 2023Pooled it
- Toward preventive interventions in developmental coordination disorder: in need of early behavioral markers?Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- ATACdb 2.0: a comprehensive chromatin accessibility database of human and mouse.Nucleic acids research · 2026Article
- Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?International journal of molecular sciences · 2024Review
- Overloading And unpacKing (OAK) - droplet-based combinatorial indexing for ultra-high throughput single-cell multiomic profiling.Nature communications · 2024Article
- Circulating proteins linked to apoptosis processes and fast development of end-stage kidney disease in diabetes.JCI insight · 2024Article
- Identification of the Molecular Components of Enhancer-Mediated Gene Expression Variation in Multiple Tissues Regulating Blood Pressure.Hypertension (Dallas, Tex. : 1979) · 2024Article
- Tissue-specific and tissue-agnostic effects of genome sequence variation modulating blood pressure.Cell reports · 2023Article
- Mapping genomic regulation of kidney disease and traits through high-resolution and interpretable eQTLs.Nature communications · 2023Article
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Authors and funding
7 authors.
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Abstract
Chromatin accessibility assays are central to the genome-wide identification of gene regulatory elements associated with transcriptional regulation. However, the data have highly variable quality arising from several biological and technical factors. To surmount this problem, we developed a sequence-based machine learning method to evaluate and refine chromatin accessibility data. Our framework, gapped k-mer SVM quality check (gkmQC), provides the quality metrics for a sample based on the prediction accuracy of the trained models. We tested 886 DNase-seq samples from the ENCODE/Roadmap projects to demonstrate that gkmQC can effectively identify "high-quality" (HQ) samples with low conventional quality scores owing to marginal read depths. Peaks identified in HQ samples are more accurately aligned at functional regulatory elements, show greater enrichment of regulatory elements harboring functional variants, and explain greater heritability of phenotypes from their relevant tissues. Moreover, gkmQC can optimize the peak-calling threshold to identify additional peaks, especially for rare cell types in single-cell chromatin accessibility data.
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