Skip to main content
Article thumbnail
Location of Repository

Inferring Genetic Interactions via a Data-Driven Second Order Model

By Ci-Ren Jiang, Ying-Chao Hung, Chung-Ming Chen and Grace S. Shieh

Abstract

Genetic/transcriptional regulatory interactions are shown to predict partial components of signaling pathways, which have been recognized as vital to complex human diseases. Both activator (A) and repressor (R) are known to coregulate their common target gene (T). Xu et al. (2002) proposed to model this coregulation by a fixed second order response surface (called the RS algorithm), in which T is a function of A, R, and AR. Unfortunately, the RS algorithm did not result in a sufficient number of genetic interactions (GIs) when it was applied to a group of 51 yeast genes in a pilot study. Thus, we propose a data-driven second order model (DDSOM), an approximation to the non-linear transcriptional interactions, to infer genetic and transcriptional regulatory interactions. For each triplet of genes of interest (A, R, and T), we regress the expression of T at time t + 1 on the expression of A, R, and AR at time t. Next, these well-fitted regression models (viewed as points in R3) are collected, and the center of these points is used to identify triples of genes having the A-R-T relationship or GIs. The DDSOM and RS algorithms are first compared on inferring transcriptional compensation interactions of a group of yeast genes in DNA synthesis and DNA repair using microarray gene expression data; the DDSOM algorithm results in higher modified true positive rate (about 75%) than that of the RS algorithm, checked against quantitative RT-polymerase chain reaction results. These validated GIs are reported, among which some coincide with certain interactions in DNA repair and genome instability pathways in yeast. This suggests that the DDSOM algorithm has potential to predict pathway components. Further, both algorithms are applied to predict transcriptional regulatory interactions of 63 yeast genes. Checked against the known transcriptional regulatory interactions queried from TRANSFAC, the proposed also performs better than the RS algorithm

Topics: Genetics
Publisher: Frontiers Research Foundation
OAI identifier: oai:pubmedcentral.nih.gov:3342528
Provided by: PubMed Central
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://www.pubmedcentral.nih.g... (external link)
  • Suggested articles

    Citations

    1. (2004). A gene network for navigating the literature.
    2. (2005). A general framework for weighted gene co-expression networks analysis.
    3. (2008). A pattern recognition approach to infer timelaggedgeneticinteractions.Bioinformatics 24,
    4. (2002). A smooth response surface algorithm for constructing gene regulatory network.
    5. and Hickson,I.D.(2009).Esc2andSgs1actin functionally distinct branches of the homologues recombination repair pathway in Saccharomyces cerevisiae.
    6. (2003). DNA helicase gene interaction network defined using synthetic lethality analyzed by microarray.Nat.Genet.35,204–205.
    7. (2007). Exploring genetic interactions and networks with yeast.
    8. (2004). Global mapping of the yeast genetic interaction network. Science 303, 808–813. Vogelstein,B.,andKinzler,K.W.(2004). Cancer genes and the pathways they control.
    9. (2005). Inferring genetic interactions via a nonlinear model and an optimizationalgorithm.BMCSyst.Biol.4,16.
    10. (2012). Inferring geneticinteractionsviaadata-drivensecond order model.
    11. (2008). Inferring transcriptional compensation interactions in yeastviastepwisestructureequation modeling.
    12. (1992). Multivariate Density Estimation: Theory, Practice, and Visualization.NewYork:JohnWiley.
    13. (2011). paper pending published:
    14. (2008). pathways in lung adenocarcinoma.
    15. (2001). Principles for the buffering of genetic variation.
    16. (2012). Score<0.3 and 132 AT/RTs from TRANSFAC”=0 pairs. The predict triplets: AR T
    17. (2006). Sgs1 regulates gene conversion tract lengths and crossovers independently of its helicase activity.
    18. (2009). Specific synthetic lethal killing of RAD54Bdeficient human colorectal cancer cells by FEN1 silencing.
    19. (2001). Systematic genetic analysis with ordered arrays of yeast deletion mutants.
    20. (2003). The evolution of transcriptional regulation in Eukaryotes.
    21. (2007). The genomiclandscapesofhumanbreast and colorectal cancers. Science 318, 1108–1113. Woolf,P.J.,andWang,Y.(2000).Afuzzy logic approach to analyzing gene expressiondata.Physiol.Genomics 3,
    22. (2001). The shortlifespanofSaccharomycescerevisiae sgs1 and srs2 mutants is a compositeofnormalagingprocesses and mitotic arrest due to defective recombination.
    23. This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits noncommercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.
    24. (2005). Transcription control reprogramming in genetic backup circuits.
    25. Transcriptional regulatory code of a eukaryotic genome.
    26. TRANSFAC: transcriptional regulation, from patterns to profiles.
    27. (2009). Uncovering transcriptional interactions via an adaptive fuzzy logic approach.
    28. (2008). Unraveling transcriptional regulatory programs by integrative analysis of microarray and transcription factor binding data.

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.