PMID: 22102568;PMCID: PMC3245151;Abstract:
The Gene Ontology (GO) (http://www.geneontology .org) is a community bioinformatics resource that represents gene product function through the use of structured, controlled vocabularies. The number of GO annotations of gene products has increased due to curation efforts among GO Consortium (GOC) groups, including focused literature-based annotation and ortholog-based functional inference. The GO ontologies continue to expand and improve as a result of targeted ontology development, including the introduction of computable logical definitions and development of new tools for the streamlined addition of terms to the ontology. The GOC continues to support its user community through the use of e-mail lists, social media and web-based resources. © The Author(s) 2011. Published by Oxford University Press.
Evaluating the quality of potential new protein-coding genes that have been predicted by directly searching mass spectrometry against genome sequence is a very challenging task. Many machine learning techniques such as neural networks, decision trees, and support vector machines have been applied to this task. All of these techniques learn a model from a training dataset and predict the quality of potential novel protein-coding genes using various evidential features as inputs. The quality and quantity of the training dataset significantly affect the performance of the learned models. In biological research, data collected is often incomplete and with very few data points. It is desirable to have methods that are robust to noisy data and low sample-size. Furthermore, the models learned by these machine learning techniques typically do not reveal the conditional (in)dependence relations among the evidential features. Gaining insight into the relationships among features is important for biological domains .In this paper, we describe methods for learning Bayesian networks for modeling the conditional (in)dependence relations among features of protein-coding genes and calculating confidence scores for potential novel genes based on their evidential features. Bootstrap methods are applied to assess the confidence measure on the arcs of the learned network structures and to identify a set of robust arcs in order to construct a final model for future predictions. We tested the Bayesian network model learned from our method using a training experimental dataset. The results show that the method significantly improved the accuracy of the learned model in predicting potential novel genes.
Introduction: Marek's disease (MD), a herpesvirus-induced lymphoma of chickens is a unique natural model of CD30-overexpressing (CD30hi) lymphoma. We have previously proposed that the CD30hi neoplastically transformed CD4+ T cells in MD lymphomas have a phenotype antagonistic to cell mediated immunity. Here were test the hypothesis that the CD30hi neoplastically transformed MD lymphoma cells have a phenotype more closely resembling T-helper (Th)-2 or regulatory T (T-reg) cells. Materials and methods: We separated ex vivo-derived CD30hi, from the CD30lo/- (non-transformed), MD lymphoma cells and then quantified the relative amounts of mRNA and proteins for cytokines and other genes that define CD4+ Th-1, Th-2 or T-reg phenotypes. Results and discussion: Gene Ontology-based modeling of our data shows that the CD30hi MD lymphoma cells having a phenotype more similar to T-reg. Sequences that could be bound by the MD virus putative oncoprotein Meq in each of these genes' promoters suggests that the MD herpesvirus may play a direct role in maintaining this T-reg-like phenotype. © 2008 Springer-Verlag.
PMID: 20574525;PMCID: PMC2888582;Abstract:
Background: Oocytes are the female gametes which establish the program of life after fertilization. Interactions between oocyte and the surrounding cumulus cells at germinal vesicle (GV) stage are considered essential for proper maturation or 'programming' of oocytes, which is crucial for normal fertilization and embryonic development. However, despite its importance, little is known about the molecular events and pathways involved in this bidirectional communication. Methodology/Principal Findings: We used differential detergent fractionation multidimensional protein identification technology (DDF-Mud PIT) on bovine GV oocyte and cumulus cells and identified 811 and 1247 proteins in GV oocyte and cumulus cells, respectively; 371 proteins were significantly differentially expressed between each cell type. Systems biology modeling, which included Gene Ontology (GO) and canonical genetic pathway analysis, showed that cumulus cells have higher expression of proteins involved in cell communication, generation of precursor metabolites and energy, as well as transport than GV oocytes. Our data also suggests a hypothesis that oocytes may depend on the presence of cumulus cells to generate specific cellular signals to coordinate their growth and maturation. Conclusions/Significance: Systems biology modeling of bovine oocytes and cumulus cells in the context of GO and protein interaction networks identified the signaling pathways associated with the proteins involved in cell-to-cell signaling biological process that may have implications in oocyte competence and maturation. This first comprehensive systems biology modeling of bovine oocytes and cumulus cell proteomes not only provides a foundation for signaling and cell physiology at the GV stage of oocyte development, but are also valuable for comparative studies of other stages of oocyte development at the molecular level. © 2010 Peddinti et al.
PMID: 19607655;PMCID: PMC2966327;Abstract:
Background: Pasteurella multocida is a gram-negative bacterial pathogen that has a broad host range. One of the diseases it causes is fowl cholera in poultry. The availability of the genome sequence of avian P. multocida isolate Pm70 enables the application of functional genomics for observing global gene expression in response to a given stimulus. We studied the effects of three classes of antibiotics on the P. multocida transcriptome using custom oligonucleotide microarrays from NimbleGen Systems. Hybridizations were conducted with RNA isolated from three independent cultures of Pm70 grown in the presence or absence of sub-minimum inhibitory concentration (sub-MIC) of antibiotics. Differentially expressed (DE) genes were identified by ANOVA and Dunnett's test. Biological modeling of the differentially expressed genes (DE) was conducted based on Clusters of Orthologous (COG) groups and network analysis in Pathway Studio. Results: The three antibiotics used in this study, amoxicillin, chlortetracycline, and enrofloxacin, collectively influenced transcription of 25% of the P. multocida Pm70 genome. Some DE genes identified were common to more than one antibiotic. The overall transcription signatures of the three antibiotics differed at the COG level of the analysis. Network analysis identified differences in the SOS response of P. multocida in response to the antibiotics. Conclusion: This is the first report of the transcriptional response of an avian strain of P. multocida to sub-lethal concentrations of three different classes of antibiotics. We identified common adaptive responses of P. multocida to antibiotic stress. The observed changes in gene expression of known and putative P. multocida virulence factors establish the molecular basis for the therapeutic efficacy of sub-MICs. Our network analysis demonstrates the feasibility and limitations of applying systems modeling to high throughput datasets in 'non-model' bacteria. © 2009 Nanduri et al; licensee BioMed Central Ltd.