ArticlePloS one2013
NuChart: an R package to study gene spatial neighbourhoods with multi-omics annotations.
Article in PloS one, 2013. 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.
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Who cites it
9 citing papers in PubMed, 34 citations in OpenAlex.
- Visualizing and Annotating Hi-C Data.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Advantages of using graph databases to explore chromatin conformation capture experiments.BMC bioinformatics · 2021Article
- TADCompare: An R Package for Differential and Temporal Analysis of Topologically Associated Domains.Frontiers in genetics · 2020Article
- GenHap: a novel computational method based on genetic algorithms for haplotype assembly.BMC bioinformatics · 2019Article
- HiCeekR: A Novel Shiny App for Hi-C Data Analysis.Frontiers in genetics · 2019Article
- Integrating epigenomic data and 3D genomic structure with a new measure of chromatin assortativity.Genome biology · 2016Article
- Methods for the integration of multi-omics data: mathematical aspects.BMC bioinformatics · 2016Article
- The Genome Conformation As an Integrator of Multi-Omic Data: The Example of Damage Spreading in Cancer.Frontiers in genetics · 2016Article
- Integrating multi-omic features exploiting Chromosome Conformation Capture data.Frontiers in genetics · 2015Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Long-range chromosomal associations between genomic regions, and their repositioning in the 3D space of the nucleus, are now considered to be key contributors to the regulation of gene expression and important links have been highlighted with other genomic features involved in DNA rearrangements. Recent Chromosome Conformation Capture (3C) measurements performed with high throughput sequencing (Hi-C) and molecular dynamics studies show that there is a large correlation between colocalization and coregulation of genes, but these important researches are hampered by the lack of biologists-friendly analysis and visualisation software. Here, we describe NuChart, an R package that allows the user to annotate and statistically analyse a list of input genes with information relying on Hi-C data, integrating knowledge about genomic features that are involved in the chromosome spatial organization. NuChart works directly with sequenced reads to identify the related Hi-C fragments, with the aim of creating gene-centric neighbourhood graphs on which multi-omics features can be mapped. Predictions about CTCF binding sites, isochores and cryptic Recombination Signal Sequences are provided directly with the package for mapping, although other annotation data in bed format can be used (such as methylation profiles and histone patterns). Gene expression data can be automatically retrieved and processed from the Gene Expression Omnibus and ArrayExpress repositories to highlight the expression profile of genes in the identified neighbourhood. Moreover, statistical inferences about the graph structure and correlations between its topology and multi-omics features can be performed using Exponential-family Random Graph Models. The Hi-C fragment visualisation provided by NuChart allows the comparisons of cells in different conditions, thus providing the possibility of novel biomarkers identification. NuChart is compliant with the Bioconductor standard and it is freely available at ftp://fileserver.itb.cnr.it/nuchart.
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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.