Predicting sulfotyrosine sites using the random forest algorithm with significantly improved prediction accuracy

addresses: School of Biosciences, University of Exeter, Exeter EX4 5DE, UK. [email protected]: PMCID: PMC2777180types: Journal Article; Research Support, Non-U.S. Gov't© 2009 Yang; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Tyrosine sulfation is one of the most important posttranslational modifications. Due to its relevance to various disease developments, tyrosine sulfation has become the target for drug design. In order to facilitate efficient drug design, accurate prediction of sulfotyrosine sites is desirable. A predictor published seven years ago has been very successful with claimed prediction accuracy of 98%. However, it has a particularly low sensitivity when predicting sulfotyrosine sites in some newly sequenced proteins

Predicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements

Background: Recent assays for individual-specific genome-wide DNA methylation profiles have enabled epigenome-wide association studies to identify specific CpG sites associated with a phenotype. Computational prediction of CpG site-specific methylation levels is important, but current approaches tackle average methylation within a genomic locus and are often limited to specific genomic regions. Results: We characterize genome-wide DNA methylation patterns, and show that correlation among CpG sites decays rapidly, making predictions solely based on neighboring sites challenging. We built a random forest classifier to predict CpG site methylation levels using as features neighboring CpG site methylation levels and genomic distance, and co-localization with coding regions, CGIs, and regulatory elements from the ENCODE project, among others. Our approach achieves 91% -- 94% prediction accuracy of genome-wide methylation levels at single CpG site precision. The accuracy increases to 98% when restricted to CpG sites within CGIs. Our classifier outperforms state-of-the-art methylation classifiers and identifies features that contribute to prediction accuracy: neighboring CpG site methylation status, CpG island status, co-localized DNase I hypersensitive sites, and specific transcription factor binding sites were found to be most predictive of methylation levels. Conclusions: Our observations of DNA methylation patterns led us to develop a classifier to predict site-specific methylation levels that achieves the best DNA methylation predictive accuracy to date. Furthermore, our method identified genomic features that interact with DNA methylation, elucidating mechanisms involved in DNA methylation modification and regulation, and linking different epigenetic processes

Knowledge-Driven Analysis Identifies a Gene–Gene Interaction Affecting High-Density Lipoprotein Cholesterol Levels in Multi-Ethnic Populations

Total cholesterol, low-density lipoprotein cholesterol, triglyceride, and high-density lipoprotein cholesterol (HDL-C) levels are among the most important risk factors for coronary artery disease. We tested for gene–gene interactions affecting the level of these four lipids based on prior knowledge of established genome-wide association study (GWAS) hits, protein–protein interactions, and pathway information. Using genotype data from 9,713 European Americans from the Atherosclerosis Risk in Communities (ARIC) study, we identified an interaction between HMGCR and a locus near LIPC in their effect on HDL-C levels (Bonferroni corrected Pc = 0.002). Using an adaptive locus-based validation procedure, we successfully validated this gene–gene interaction in the European American cohorts from the Framingham Heart Study (Pc = 0.002) and the Multi-Ethnic Study of Atherosclerosis (MESA; Pc = 0.006). The interaction between these two loci is also significant in the African American sample from ARIC (Pc = 0.004) and in the Hispanic American sample from MESA (Pc = 0.04). Both HMGCR and LIPC are involved in the metabolism of lipids, and genome-wide association studies have previously identified LIPC as associated with levels of HDL-C. However, the effect on HDL-C of the novel gene–gene interaction reported here is twice as pronounced as that predicted by the sum of the marginal effects of the two loci. In conclusion, based on a knowledge-driven analysis of epistasis, together with a new locus-based validation method, we successfully identified and validated an interaction affecting a complex trait in multi-ethnic populations

Effect of Glucocorticoid pretreatment on oxidative liver injury and survival in jaundiced rats with Endotoxing Cholangitis

Introduction: Biliary tract infection is associated with high mortality. This study investigated the effect of glucocorticoid pretreatment on lipopolysaccharide (LPS)-induced cholangitis. Methods: Rats undergoing either sham operation or ligation of the extrahepatic bile duct (BDL) for 2 weeks were randomly assigned to receive intravenous injections of dexamethasone (DX) or normal saline (NS) prior to infusing LPS into the biliary tract. The plasma levels of tumor necrosis factor-&alpha; (TNF&alpha;), chemokines monocyte chemoattractant protein-1 (MCP-1) and macrophage inflammatory protein-2 (MIP-2) as well as liver mRNA expression of MCP-1 and MIP-2 were determined. Infiltration of monocytes, Kupffer cells, and neutrophils in rat liver were studied with immunohistochemistry. Oxidative liver injury was measured by the malondialdehyde (MDA) content. Results: Dexamethasone pretreatment resulted in significantly decreased plasma levels of TNF&alpha; at 1 hour, MCP-1 and MIP-2 at 2 and 3 hours, and decreased liver MCP-1 mRNA expression at 3 hours following LPS infusion in BDL-DX rats than in BDL-NS rats. The number of inflammatory cells in the liver was significantly different between sham- and BDL-treated rats but was not affected by DX pretreatment. Pretreatment with DX resulted in significantly decreased liver MDA contents in the BDL-DX group than that in the BDL-NS group. Jaundiced rats pretreated with 5 mg DX prior to infusion of 1 g of LPS were 6.8 times more likely to survive than those that were not pretreated. Conclusions: Pretreatment of jaundiced, LPS-treated rats with a&nbsp; supraphysiological dose of dexamethasone may rescue their lives by suppression of chemokine expression and alleviation of oxidative liver injury.<br /