A statistical method to incorporate biological knowledge for generating testable novel gene regulatory interactions from microarray experiments

<p>Abstract</p> <p>Background</p> <p>The incorporation of prior biological knowledge in the analysis of microarray data has become important in the reconstruction of transcription regulatory networks in a cell. Most of the current research has been focused on the integration of multiple sets of microarray data as well as curated databases for a genome scale reconstruction. However, individual researchers are more interested in the extraction of most useful information from the data of their hypothesis-driven microarray experiments. How to compile the prior biological knowledge from literature to facilitate new hypothesis generation from a microarray experiment is the focus of this work. We propose a novel method based on the statistical analysis of reported gene interactions in PubMed literature.</p> <p>Results</p> <p>Using Gene Ontology (GO) Molecular Function annotation for reported gene regulatory interactions in PubMed literature, a statistical analysis method was proposed for the derivation of a likelihood of interaction (LOI) score for a pair of genes. The LOI-score and the Pearson correlation coefficient of gene profiles were utilized to check if a pair of query genes would be in the above specified interaction. The method was validated in the analysis of two gene sets formed from the yeast Saccharomyces cerevisiae cell cycle microarray data. It was found that high percentage of identified interactions shares GO Biological Process annotations (39.5% for a 102 interaction enriched gene set and 23.0% for a larger 999 cyclically expressed gene set).</p> <p>Conclusion</p> <p>This method can uncover novel biologically relevant gene interactions. With stringent confidence levels, small interaction networks can be identified for further establishment of a hypothesis testable by biological experiment. This procedure is computationally inexpensive and can be used as a preprocessing procedure for screening potential biologically relevant gene pairs subject to the analysis with sophisticated statistical methods.</p

Rank-based edge reconstruction for scale-free genetic regulatory networks

<p>Abstract</p> <p>Background</p> <p>The reconstruction of genetic regulatory networks from microarray gene expression data has been a challenging task in bioinformatics. Various approaches to this problem have been proposed, however, they do not take into account the topological characteristics of the targeted networks while reconstructing them.</p> <p>Results</p> <p>In this study, an algorithm that explores the scale-free topology of networks was proposed based on the modification of a rank-based algorithm for network reconstruction. The new algorithm was evaluated with the use of both simulated and microarray gene expression data. The results demonstrated that the proposed algorithm outperforms the original rank-based algorithm. In addition, in comparison with the Bayesian Network approach, the results show that the proposed algorithm gives much better recovery of the underlying network when sample size is much smaller relative to the number of genes.</p> <p>Conclusion</p> <p>The proposed algorithm is expected to be useful in the reconstruction of biological networks whose degree distributions follow the scale-free topology.</p

In this figure, interactions identified by analysis but not previously reported are observed to have potential biological interest

<p><b>Copyright information:</b></p><p>Taken from "A statistical method to incorporate biological knowledge for generating testable novel gene regulatory interactions from microarray experiments"</p><p>http://www.biomedcentral.com/1471-2105/8/317</p><p>BMC Bioinformatics 2007;8():317-317.</p><p>Published online 29 Aug 2007</p><p>PMCID:PMC2082045.</p><p></p> Genes and their coded proteins are ovals, arrows are identified, directed interactions, and the purple box includes those proteins that comprise histone complexes. All histone genes are identified to concurrently regulate other genes, correctly identifying HTA2, HTA1, HTB1, HTB2, HHT1, and HHF1 as a single functional unit. All regulated genes are associated with DNA-interacting proteins, which are logical functional partners to histone complexes. Regulated genes are: HEK2, associated with the regulation of telomeres [44, 45]; HTZ1, concerned with transcriptional regulation through heterochromatin structure [46]; SAS3, a cell cycle related histone acetyltransferase that is involved in transcriptional regulation [47]; SGS1, involved in maintenance of genome integrity and regulates chromosome synapsis and meiotic crossing over [48, 49]; SIM1, a cell cycle-regulated DNA replication gene [50]; ALK1, a cell cyle-regulated protein kinase involved with the response to DNA damage [51]; FPR4, a nuclear protein with GO annotations that include chromatin and histone associations [52-54]; and YPL141C, an unknown protein. The presence of YPL141C here however, suggests that its function may be related to chromatin structure, or cell cycle regulation