ArticleStatistics in biosciences2016
Statistical challenges in analyzing methylation and long-range chromosomal interaction data.
Article in Statistics in biosciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Structural variant discovery and diagnostic impact in rare diseases from short-read and long-read sequencing.medRxiv : the preprint server for health sciences · 2026Article
- Reply to: "Comments on the paper 'Extensive epigenomic dysregulation is a hallmark of homologous recombination deficiency in triple-negative breast cancer' by Y. Chen et al."International journal of cancer · 2025Article
- Data-Driven Identification of Early Cancer-Associated Genes via Penalized Trans-Dimensional Hidden Markov Models.Biomolecules · 2025Article
- BS-clock, advancing epigenetic age prediction with high-resolution DNA methylation bisulfite sequencing data.Bioinformatics (Oxford, England) · 2024Article
- BCurve: Bayesian Curve Credible Bands Approach for the Detection of Differentially Methylated Regions.Methods in molecular biology (Clifton, N.J.) · 2022Article
- A comprehensive review of computational prediction of genome-wide features.Briefings in bioinformatics · 2020Article
- Using DIVAN to assess disease/trait-associated single nucleotide variants in genome-wide scale.BMC research notes · 2017Article
- Review
- Incorporating biological information in sparse principal component analysis with application to genomic data.BMC bioinformatics · 2017Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
With the rapid development of high throughput technologies such as array and next generation sequencing (NGS), genome-wide, nucleotide-resolution epigenomic data are increasingly available. In recent years, there has been particular interest in data on DNA methylation and 3-dimensional (3D) chromosomal organization, which are believed to hold keys to understand biological mechanisms, such as transcription regulation, that are closely linked to human health and diseases. However, small sample size, complicated correlation structure, substantial noise, biases, and uncertainties, all present difficulties for performing statistical inference. In this review, we present an overview of the new technologies that are frequently utilized in studying DNA methylation and 3D chromosomal organization. We focus on reviewing recent developments in statistical methodologies designed for better interrogating epigenomic data, pointing out statistical challenges facing the field whenever appropriate.
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Registered trials
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.