ArticleNucleus (Austin, Tex.)2018
Calculation of 3D genome structures for comparison of chromosome conformation capture experiments with microscopy: An evaluation of single-cell Hi-C protocols.
Article in Nucleus (Austin, Tex.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- DeepNanoHi-C: deep learning enables accurate single-cell nanopore long-read data analysis and 3D genome interpretation.Nucleic acids research · 2025Article
- Dynamic changes in 3D chromatin structure during male gametogenesis in Arabidopsis thaliana.Genome biology · 2025Article
- Article
- Article
- 3D genome organization in the epithelial-mesenchymal transition spectrum.Genome biology · 2022Article
- Principal curve approaches for inferring 3D chromatin architecture.Biostatistics (Oxford, England) · 2022Article
- Every gene everywhere all at once: High-precision measurement of 3D chromosome architecture with single-cell Hi-C.Frontiers in molecular biosciences · 2022Review
- Single-cell Hi-C data analysis: safety in numbers.Briefings in bioinformatics · 2021Article
- Perspectives for the reconstruction of 3D chromatin conformation using single cell Hi-C data.PLoS computational biology · 2021Article
- Si-C is a method for inferring super-resolution intact genome structure from single-cell Hi-C data.Nature communications · 2021Article
- Comparison of the Hi-C, GAM and SPRITE methods using polymer models of chromatin.Nature methods · 2021Article
- GiniQC: a measure for quantifying noise in single-cell Hi-C data.Bioinformatics (Oxford, England) · 2020Article
- Review
- The Pediatric Cell Atlas: Defining the Growth Phase of Human Development at Single-Cell Resolution.Developmental cell · 2019Review
- Single-Cell RNA Sequencing-Based Computational Analysis to Describe Disease Heterogeneity.Frontiers in genetics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Single-cell chromosome conformation capture approaches are revealing the extent of cell-to-cell variability in the organization and packaging of genomes. These single-cell methods, unlike their multi-cell counterparts, allow straightforward computation of realistic chromosome conformations that may be compared and combined with other, independent, techniques to study 3D structure. Here we discuss how single-cell Hi-C and subsequent 3D genome structure determination allows comparison with data from microscopy. We then carry out a systematic evaluation of recently published single-cell Hi-C datasets to establish a computational approach for the evaluation of single-cell Hi-C protocols. We show that the calculation of genome structures provides a useful tool for assessing the quality of single-cell Hi-C data because it requires a self-consistent network of interactions, relating to the underlying 3D conformation, with few errors, as well as sufficient longer-range cis- and trans-chromosomal contacts.
Indexed as
Identifiers
What OpenQuestion holds
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.