ArticleJournal of computational biology : a journal of computational molecular cell biology2012
Algorithm to identify frequent coupled modules from two-layered network series: application to study transcription and splicing coupling.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Alternative splicing across the C. elegans nervous system.Nature communications · 2025Article
- Effects of local network topology on the functional reconstruction of spiking neural network models.Applied network science · 2017Article
- Detection of gene communities in multi-networks reveals cancer drivers.Scientific reports · 2015Article
- The structure and dynamics of multilayer networks.Physics reports · 2014Review
- Integrative analysis of many RNA-seq datasets to study alternative splicing.Methods (San Diego, Calif.) · 2014Article
- Catalyzing plant science research with RNA-seq.Frontiers in plant science · 2013Article
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4 authors.
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Abstract
Current network analysis methods all focus on one or multiple networks of the same type. However, cells are organized by multi-layer networks (e.g., transcriptional regulatory networks, splicing regulatory networks, protein-protein interaction networks), which interact and influence each other. Elucidating the coupling mechanisms among those different types of networks is essential in understanding the functions and mechanisms of cellular activities. In this article, we developed the first computational method for pattern mining across many two-layered graphs, with the two layers representing different types yet coupled biological networks. We formulated the problem of identifying frequent coupled clusters between the two layers of networks into a tensor-based computation problem, and proposed an efficient solution to solve the problem. We applied the method to 38 two-layered co-transcription and co-splicing networks, derived from 38 RNA-seq datasets. With the identified atlas of coupled transcription-splicing modules, we explored to what extent, for which cellular functions, and by what mechanisms transcription-splicing coupling takes place.
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