An efficient algorithm to explore liquid association on a genome-wide scale

BMC Bioinformatics

Tina Gunderson¹  Yen-Yi Ho¹ 

[1] Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St. S.E., MMC 303, Minneapolis 55455, MN, USA

关键词: Genome-wide search;    Liquid association;    Coexpression pattern;   

Others  :  1084809
DOI  :  10.1186/s12859-014-0371-5

 received in 2014-07-03, accepted in 2014-10-30,  发布年份 2014

【 摘 要 】

Background

The growing wealth of public available gene expression data has made the systemic studies of how genes interact in a cell become more feasible. Liquid association (LA) describes the extent to which coexpression of two genes may vary based on the expression level of a third gene (the controller gene). However, genome-wide application has been difficult and resource-intensive. We propose a new screening algorithm for more efficient processing of LA estimation on a genome-wide scale and apply its use to a Saccharomyces cerevisiae data set.

Results

On a test subset of the data, the fast screening algorithm achieved >99.8% agreement with the exhaustive search of LA values, while reduced run time by 81–93 %. Using a well-known yeast cell-cycle data set with 6,178 genes, we identified triplet combinations with significantly large LA values. In an exploratory gene set enrichment analysis, the top terms for the controller genes in these triplets with large LA values are involved in some of the most fundamental processes in yeast such as energy regulation, transportation, and sporulation.

Conclusion

In summary, in this paper we propose a novel, efficient algorithm to explore LA on a genome-wide scale and identified triplets of interest in cell cycle pathways using the proposed method in a yeast data set. A software package named fastLiquidAssociation for implementing the algorithm is available through http://www.bioconductor.org webcite.

【 授权许可】

   
2014 Gunderson and Ho; licensee BioMed Central Ltd.

【 图 表 】

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