bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/027045

A pathway-centric view of spatial proximity in the 3D nucleome across cell lines

Hiren Karathia·Carl Kingsford·Michelle Girvan·Sridhar Hannenhalli
View original source ↗Export metadata ↓

Abstract

Spatial organization of the genome is critical for condition-specific gene expression. Previous studies have shown that functionally related genes tend to be spatially proximal. However, these studies have not been extended to multiple human cell types, and the extent to which context-specific spatial proximity of a pathway is related to its context-specific activity is not known. We report the first pathway-centric analyses of spatial proximity in six human cell lines. We find that spatial proximity of genes in a pathway tends to be context-specific, in a manner consistent with the pathways context-specific expression and function; housekeeping genes are ubiquitously proximal to each other, and cancer-related pathways such as p53 signaling are uniquely proximal in hESC. Intriguingly, we find a correlation between the spatial proximity of genes and interactions of their protein products, even after accounting for the propensity of co-pathway proteins to interact. Related pathways are also often spatially proximal to one another, and housekeeping genes tend to be proximal to several other pathways suggesting their coordinating role. Further, the spatially proximal genes in a pathway tend to be the drivers of the pathway activity and are enriched for transcription, splicing and transport functions. Overall, our analyses reveal a pathway-centric organization of the 3D nucleome whereby functionally related and interacting genes, particularly the initial drivers of pathway activity, but also genes across multiple related pathways, are in spatial proximity in a context-specific way. Our results provide further insights into the role of differential spatial organization in cell type-specific pathway activity.

Source connections

Explore related subjects

BioinformaticsPreprint

Keep this discovery

BibTeXRIS

Hiren Karathia, Carl Kingsford, Michelle Girvan, Sridhar Hannenhalli. 2015-09-17. A pathway-centric view of spatial proximity in the 3D nucleome across cell lines. https://doi.org/10.1101/027045

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2016

iCARE: An R Package to Build and Apply Absolute Risk Models

This report describes a R package, called the Individualized Coherent Absolute Risk Estimation (iCARE) tool, that allows researchers to build and evaluate models for absolute risk and apply them to estimate an individuals risk of developing disease during a specified time interval based on a set of user defined input parameters. An attractive feature of the software is that it gives users flexibility to update models rapidly based on new knowledge on risk factors and tailor models to different populations by specifying three input arguments: (1) a model for relative risk, (2) an age-specific disease incidence rate, (3) the distribution of risk factors for the population of interest. The tool can handle missing information on risk factors for individuals for whom risks are to be predicted using a coherent approach where all estimates are derived from a single model after appropriate model averaging. The software allows single nucleotide polymorphisms (SNPs) to be incorporated into the model using published odds ratios and allele frequencies. The validation component of the software implements the methods for evaluation of model calibration, discrimination and risk-stratification based on independent validation datasets. We provide an illustration of the utility of iCARE for building, validating and applying absolute risk models using breast cancer as an example.

Bioinformatics↗
bioRxiv2016

deSPI: efficient classification of metagenomic reads with lightweight de Bruijn graph-based reference indexing

SummaryIn metagenomic studies, fast and effective tools are on wide demand to implement taxonomy classification for upto billions of reads. Herein, we propose deSPI, a novel read classification method that classifies reads by recognizing and analyzing the matches between reads and reference with de Bruijn graph-based lightweight reference indexing. deSPI has faster speed with relatively small memory footprint, meanwhile, it can also achieve higher or similar sensitivity and accuracy.\n\nAvailabilitythe C++ source code of deSPI is available at https://github.com/hitbc/deSPI\n\nContactydwang@hit.edu.cn\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

Bioinformatics↗
bioRxiv2016

De novo assembly of viral quasispecies using overlap graphs

A viral quasispecies, the ensemble of viral strains populating an infected person, can be highly diverse. For optimal assessment of virulence, pathogenesis and therapy selection, determining the haplotypes of the individual strains can play a key role. As many viruses are subject to high mutation and recombination rates, high-quality reference genomes are often not available at the time of a new disease outbreak. We present SAVAGE, a computational tool for reconstructing individual haplotypes of intrahost virus strains without the need for a high-quality reference genome. SAVAGE makes use of either FM-index based data structures or ad-hoc consensus reference sequence for constructing overlap graphs from patient sample data. In this overlap graph, nodes represent reads and/or contigs, while edges reflect that two reads/contigs, based on sound statistical considerations, represent identical haplotypic sequence. Following an iterative scheme, a new overlap assembly algorithm that is based on the enumeration of statistically well-calibrated groups of reads/contigs then efficiently reconstructs the individual haplotypes from this overlap graph. In benchmark experiments on simulated and on real deep coverage data, SAV-AGE drastically outperforms generic de novo assemblers as well as the only specialized de novo viral quasispecies assembler available so far. When run on ad-hoc consensus reference sequence, SAVAGE performs very favorably in comparison with state-of-the-art reference genome guided tools. We also apply SAVAGE on two deep coverage samples of patients infected by the Zika and the hepatitis C virus, respectively, which sheds light on the genetic structures of the respective viral quasispecies.

Bioinformatics↗