ArticlePlant physiology2010
Assembly of an interactive correlation network for the Arabidopsis genome using a novel heuristic clustering algorithm.
Article in Plant physiology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers.
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Who cites it
93 citing papers in PubMed.
- Article
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- Multiomic insights into sucrose accumulation in sugarcane.Plant molecular biology · 2025Article
- PlantConnectome: A knowledge graph database encompassing >71,000 plant articles.The Plant cell · 2025Article
- Comparative transcriptomics in ferns reveals key innovations and divergent evolution of the secondary cell walls.Nature plants · 2025Article
- Cross-species analysis of abiotic stress in hydroponic leafy crops reveals conserved regulatory networks and key divergences.Frontiers in plant science · 2025Article
- Article
- Identifying candidate genes for sugar accumulation in sugarcane: an integrative approach.BMC genomics · 2024Article
- Crucial role of SWL1 in chloroplast biogenesis and development in Arabidopsis thaliana.Plant cell reports · 2024Article
- Genomes of multicellular algal sisters to land plants illuminate signaling network evolution.Nature genetics · 2024Article
- StCoExpNet: a global co-expression network analysis facilitates identifying genes underlying agronomic traits in potatoes.Plant cell reports · 2024Article
- Uncovering the molecular mechanisms of russet skin formation in Niagara grapevine (Vitis vinifera × Vitis labrusca).Scientific reports · 2024Article
- Combination of computational new approach methodologies for enhancing evidence of biological pathway conservation across species.The Science of the total environment · 2024Article
- A systems genomics and genetics approach to identify the genetic regulatory network for lignin content inFrontiers in plant science · 2024Article
- A gene network-driven approach to infer novel pathogenicity-associated genes: application tomSystems · 2023Article
- Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance.Plants (Basel, Switzerland) · 2023Article
- Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits inFrontiers in plant science · 2023Article
- NetREx: Network-based Rice Expression Analysis Server for abiotic stress conditions.Database : the journal of biological databases and curation · 2022Article
- Exploiting plant transcriptomic databases: Resources, tools, and approaches.Plant communications · 2022Review
33 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A vital quest in biology is comprehensible visualization and interpretation of correlation relationships on a genome scale. Such relationships may be represented in the form of networks, which usually require disassembly into smaller manageable units, or clusters, to facilitate interpretation. Several graph-clustering algorithms that may be used to visualize biological networks are available. However, only some of these support weighted edges, and none provides good control of cluster sizes, which is crucial for comprehensible visualization of large networks. We constructed an interactive coexpression network for the Arabidopsis (Arabidopsis thaliana) genome using a novel Heuristic Cluster Chiseling Algorithm (HCCA) that supports weighted edges and that may control average cluster sizes. Comparative clustering analyses demonstrated that the HCCA performed as well as, or better than, the commonly used Markov, MCODE, and k-means clustering algorithms. We mapped MapMan ontology terms onto coexpressed node vicinities of the network, which revealed transcriptional organization of previously unrelated cellular processes. We further explored the predictive power of this network through mutant analyses and identified six new genes that are essential to plant growth. We show that the HCCA-partitioned network constitutes an ideal "cartographic" platform for visualization of correlation networks. This approach rapidly provides network partitions with relative uniform cluster sizes on a genome-scale level and may thus be used for correlation network layouts also for other species.
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