ArticleBMC genomics2015
Deciphering the association between gene function and spatial gene-gene interactions in 3D human genome conformation.
Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
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- GOGO: An improved algorithm to measure the semantic similarity between gene ontology terms.Scientific reports · 2018Article
- A Seed Expansion Graph Clustering Method for Protein Complexes Detection in Protein Interaction Networks.Molecules (Basel, Switzerland) · 2017Article
- Review
- Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information.Molecules (Basel, Switzerland) · 2017Article
- ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network.Molecules (Basel, Switzerland) · 2017Article
- Spatial organization of the budding yeast genome in the cell nucleus and identification of specific chromatin interactions from multi-chromosome constrained chromatin model.PLoS computational biology · 2017Article
- Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients.Scientific reports · 2017Article
- Protein single-model quality assessment by feature-based probability density functions.Scientific reports · 2016Article
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2 authors.
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Abstract
backgroundA number of factors have been investigated in the context of gene function prediction and analysis, such as sequence identity, gene expressions, and gene co-evolution. However, three-dimensional (3D) conformation of the genome has not been tapped to analyse gene function, probably largely due to lack of genome conformation data until recently.
methodsWe construct the genome-wide spatial gene-gene interaction networks for three different human B-cells or cell lines from their chromosomal contact data generated by the Hi-C chromosome conformation capturing technique. The G-SESAME and Fast-SemSim are used to calculate function similarity between interacted / non-interacted genes. The Gene Ontology statistics computed from the gene-gene interaction networks is used for gene function prediction.
resultsWe compare the function similarity of gene pairs that do not spatially interact and that have interactions. We find that genes that have strong spatial interactions tend to have highly similar function in terms of biological process, molecular function and cellular component of the Gene Ontology. And even though the level of gene-gene interactions generally have no or weak correlation with either sequential genomic distance or sequence identity between genes, the interacted genes with high function similarity tend to have stronger interactions, somewhat shorter genomic distance and significantly higher sequence identity. And combining genomic distance or sequence identity with spatial gene-gene interaction information informs gene-gene function similarity much better than using either one of them alone, suggesting gene-gene interaction information is largely complementary with genomic distance and sequence identity in the context of gene function analysis. We develop and evaluate a new gene function prediction method based on gene-gene interacting networks, which can predict gene function well for a large number of human genes.
conclusionsIn this work, we demonstrate that the spatial conformation of the human genome is relevant to gene function similarity and is useful for gene function prediction.
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