ArticleNucleic acids research2020
S3norm: simultaneous normalization of sequencing depth and signal-to-noise ratio in epigenomic data.
Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Role of the tomato MARS1/ROUGH gene encoding a LYSINE-SPECIFIC HISTONE DEMETHYLASE 1 in adventitious root and fruit skin formation.Journal of integrative plant biology · 2026Article
- Integrative Epigenomics: Bioinformatics Strategies for Multi-Omics Data Analysis in Health and Disease.Epigenomes · 2026Review
- Replication-independent eviction of the histone variant H2B.8 reveals chromatin reprogramming during seed imbibition.Nature communications · 2026Article
- Ryder: Epigenome normalization using a two-tier model and internal reference regions.bioRxiv : the preprint server for biology · 2026Article
- chromIDEAS reveals epigenetic dynamics via multi-dimensional clustering of chromatin states.Nucleic acids research · 2026Article
- Tuning of the RBR1-E2F/DP transcriptional module by the F-box protein FBL17.Science advances · 2026Article
- Article
- Condensation-dependent interactome of a chromatin remodeler underlies tumor suppressor activities.Nature communications · 2025Article
- Duplication of the autism-related gene Chd8 leads to behavioral hyperactivity and neurodevelopmental defects in mice.Nature communications · 2025Article
- An engineered tumor organoid model reveals cellular identity and signaling trajectories underlying SFPQ-TFE3 driven translocation RCC.iScience · 2025Article
- Annotation of cis-regulatory-associated histone modifications in the genomes of two Thoroughbred stallions.Frontiers in genetics · 2025Article
- Genetic-epigenetic interplay in the determination of plant 3D genome organization.Nucleic acids research · 2024Article
- Robust chromatin state annotation.Genome research · 2024Article
- The Density of Regulatory Information Is a Major Determinant of Evolutionary Constraint on Noncoding DNA in Drosophila.Molecular biology and evolution · 2024Article
- JMnorm: a novel joint multi-feature normalization method for integrative and comparative epigenomics.Nucleic acids research · 2024Article
- Cell-type differential targeting of SETDB1 prevents aberrant CTCF binding, chromatin looping, and cis-regulatory interactions.Nature communications · 2024Article
- An NFIX-mediated regulatory network governs the balance of hematopoietic stem and progenitor cells during hematopoiesis.Blood advances · 2023Article
- The canonical E2Fs together with RETINOBLASTOMA-RELATED are required to establish quiescence during plant development.Communications biology · 2023Article
- Single-cell analysis reveals transcriptomic and epigenomic impacts on the maternal-fetal interface following SARS-CoV-2 infection.Nature cell biology · 2023Article
- The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles.Genome biology · 2023Article
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7 authors.
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Abstract
Quantitative comparison of epigenomic data across multiple cell types or experimental conditions is a promising way to understand the biological functions of epigenetic modifications. However, differences in sequencing depth and signal-to-noise ratios in the data from different experiments can hinder our ability to identify real biological variation from raw epigenomic data. Proper normalization is required prior to data analysis to gain meaningful insights. Most existing methods for data normalization standardize signals by rescaling either background regions or peak regions, assuming that the same scale factor is applicable to both background and peak regions. While such methods adjust for differences in sequencing depths, they do not address differences in the signal-to-noise ratios across different experiments. We developed a new data normalization method, called S3norm, that normalizes the sequencing depths and signal-to-noise ratios across different data sets simultaneously by a monotonic nonlinear transformation. We show empirically that the epigenomic data normalized by our method, compared to existing methods, can better capture real biological variation, such as impact on gene expression regulation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.