Document Type
Article
Abstract
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 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 for implementing the algorithm is available through .
Digital Object Identifier (DOI)
Publication Info
Published in BMC Bioinformatics, Volume 15, Issue 1, 2014.
Rights
© 2014 Gunderson and Ho; licensee BioMed Central Ltd. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
APA Citation
Gunderson, T., & Ho, Y.-Y. (2014). An efficient algorithm to explore liquid association on a genome-wide scale. BMC Bioinformatics, 15(1).https://doi.org/10.1186/s12859-014-0371-5