Functional association networks as priors for gene regulatory network inference

Matthew E Studham; Andreas Tjärnberg; Torbjörn E M Nordling; Sven Nelander; Erik L L Sonnhammer

doi:10.1093/bioinformatics/btu285

Functional association networks as priors for gene regulatory network inference

Bioinformatics. 2014 Jun 15;30(12):i130-8. doi: 10.1093/bioinformatics/btu285.

Authors

Matthew E Studham¹, Andreas Tjärnberg¹, Torbjörn E M Nordling¹, Sven Nelander², Erik L L Sonnhammer³

Affiliations

¹ Stockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, SwedenStockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, Sweden.
² Stockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, Sweden.
³ Stockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, SwedenStockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, SwedenStockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, Sweden.

Abstract

Motivation: Gene regulatory network (GRN) inference reveals the influences genes have on one another in cellular regulatory systems. If the experimental data are inadequate for reliable inference of the network, informative priors have been shown to improve the accuracy of inferences.

Results: This study explores the potential of undirected, confidence-weighted networks, such as those in functional association databases, as a prior source for GRN inference. Such networks often erroneously indicate symmetric interaction between genes and may contain mostly correlation-based interaction information. Despite these drawbacks, our testing on synthetic datasets indicates that even noisy priors reflect some causal information that can improve GRN inference accuracy. Our analysis on yeast data indicates that using the functional association databases FunCoup and STRING as priors can give a small improvement in GRN inference accuracy with biological data.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Gene Expression Profiling*
Gene Regulatory Networks*
Saccharomyces cerevisiae / genetics