ArticleCell genomics2023
Article in Cell genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed, 26 citations in OpenAlex.
- Machine learning reveals sequence and genomic context features underlyingbioRxiv : the preprint server for biology · 2026Article
- Foundation model reveals the shared organization of transcription and topologically associating domains.Cell systems · 2026Article
- De novo structural variants in autism spectrum disorder disrupt distal regulatory interactions of neuronal genes.Genome research · 2026Article
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Dissecting the contribution of transposable elements to interphase chromosome structure.Genome biology · 2026Article
- Article
- Molecular effects of transposable element sequences in mammalian cells.Genome biology · 2025Review
- Disease-specific epigenetic deregulation of enhancers, transposons, and polycomb targets in acute promyelocytic leukemia.Genome medicine · 2025Article
- The emerging sequence grammar of 3D genome organisation.Human genetics · 2025Review
- Reconstructing the 3D genome organization of Neanderthals reveals that chromatin folding shaped phenotypic and sequence divergence.bioRxiv : the preprint server for biology · 2025Article
- Hidden origami inmBio · 2025Article
- Interpreting the CTCF-mediated sequence grammar of genome folding with AkitaV2.PLoS computational biology · 2025Article
- Structural heterogeneity and functional convergence of transposable elements.Frontiers in genetics · 2025Article
- Sequence-Based Machine Learning Reveals 3D Genome Differences between Bonobos and Chimpanzees.Genome biology and evolution · 2024Article
- Machine Learning Reveals the Diversity of Human 3D Chromatin Contact Patterns.Molecular biology and evolution · 2024Article
- Exploring the roles of RNAs in chromatin architecture using deep learning.Nature communications · 2024Article
- SuPreMo: a computational tool for streamlining in silico perturbation using sequence-based predictive models.Bioinformatics (Oxford, England) · 2024Article
- Systematic decoding of cis gene regulation defines context-dependent control of the multi-gene costimulatory receptor locus in human T cells.Nature genetics · 2024Article
- Machine learning reveals the diversity of human 3D chromatin contact patterns.bioRxiv : the preprint server for biology · 2023Article
- SuPreMo: a computational tool for streamliningbioRxiv : the preprint server for biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Natural and experimental genetic variants can modify DNA loops and insulating boundaries to tune transcription, but it is unknown how sequence perturbations affect chromatin organization genome wide. We developed a deep-learning strategy to quantify the effect of any insertion, deletion, or substitution on chromatin contacts and systematically scored millions of synthetic variants. While most genetic manipulations have little impact, regions with CTCF motifs and active transcription are highly sensitive, as expected. Our unbiased screen and subsequent targeted experiments also point to noncoding RNA genes and several families of repetitive elements as CTCF-motif-free DNA sequences with particularly large effects on nearby chromatin interactions, sometimes exceeding the effects of CTCF sites and explaining interactions that lack CTCF. We anticipate that our disruption tracks may be of broad interest and utility as a measure of 3D genome sensitivity, and our computational strategies may serve as a template for biological inquiry with deep learning.
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.