Genome-wide microarray comparison reveals downstream genes of Pax6 in the developing mouse cerebellum.

The Pax6 transcription factor is expressed in cerebellar granule cells and when mutated, as in the Sey/Sey mouse, produces granule cells with disturbed survival and migration and with defects in neurite extension. The impact of Pax6 on other genes in the context of cerebellar development has not been identified. In this study, we performed transcriptome comparisons between wildtype and Pax6-null whole cerebellar tissue at embryonic day (E) 13.5, 15.5 and 18.5 using Affymetrix arrays (U74Av2). Statistical analyses identified 136 differentially regulated transcripts (FDR 0.05, 1.2-fold change cutoff) over time in Pax6-null cerebellar tissue. In parallel we examined the Math1-null granuloprival cerebellum and identified 228 down-regulated transcripts (FDR 0.05, 1.2-fold change cutoff). The intersection of these two microarray datasets produced a total of 21 differentially regulated transcripts. For a subset of the identified transcripts, we used qRT-PCR to validate the microarray data and demonstrated the expression in the rhombic lip lineage and differential expression in Pax6-null cerebellum with in situ hybridisation analysis. The candidate genes identified in this way represent direct or indirect Pax6-downstream genes involved in cerebellar development.

Ontological Discovery Environment: a system for integrating gene-phenotype associations.

The wealth of genomic technologies has enabled biologists to rapidly ascribe phenotypic characters to biological substrates. Central to effective biological investigation is the operational definition of the process under investigation. We propose an elucidation of categories of biological characters, including disease relevant traits, based on natural endogenous processes and experimentally observed biological networks, pathways and systems rather than on externally manifested constructs and current semantics such as disease names and processes. The Ontological Discovery Environment (ODE) is an Internet accessible resource for the storage, sharing, retrieval and analysis of phenotype-centered genomic data sets across species and experimental model systems. Any type of data set representing gene-phenotype relationships, such quantitative trait loci (QTL) positional candidates, literature reviews, microarray experiments, ontological or even meta-data, may serve as inputs. To demonstrate a use case leveraging the homology capabilities of ODE and its ability to synthesize diverse data sets, we conducted an analysis of genomic studies related to alcoholism. The core of ODE's gene set similarity, distance and hierarchical analysis is the creation of a bipartite network of gene-phenotype relations, a unique discrete graph approach to analysis that enables set-set matching of non-referential data. Gene sets are annotated with several levels of metadata, including community ontologies, while gene set translations compare models across species. Computationally derived gene sets are integrated into hierarchical trees based on gene-derived phenotype interdependencies. Automated set identifications are augmented by statistical tools which enable users to interpret the confidence of modeled results. This approach allows data integration and hypothesis discovery across multiple experimental contexts, regardless of the face similarity and semantic annotation of the experimental systems or species domain.

Genome-level analysis of genetic regulation of liver gene expression networks.

UNLABELLED: The liver is the primary site for the metabolism of nutrients, drugs, and chemical agents. Although metabolic pathways are complex and tightly regulated, genetic variation among individuals, reflected in variations in gene expression levels, introduces complexity into research on liver disease. This study dissected genetic networks that control liver gene expression through the combination of large-scale quantitative mRNA expression analysis with genetic mapping in a reference population of BXD recombinant inbred mouse strains for which extensive single-nucleotide polymorphism, haplotype, and phenotypic data are publicly available. We profiled gene expression in livers of naive mice of both sexes from C57BL/6J, DBA/2J, B6D2F1, and 37 BXD strains using Agilent oligonucleotide microarrays. These data were used to map quantitative trait loci (QTLs) responsible for variations in the expression of about 19,000 transcripts. We identified polymorphic local and distant QTLs, including several loci that control the expression of large numbers of genes in liver, by comparing the physical transcript position with the location of the controlling QTL. CONCLUSION: The data are available through a public web-based resource (www.genenetwork.org) that allows custom data mining, identification of coregulated transcripts and correlated phenotypes, cross-tissue, and cross-species comparisons, as well as testing of a broad array of hypotheses.

Multiple approaches to data-mining of proteomic data based on statistical and pattern classification methods.

The data-mining challenge presented is composed of two fundamental problems. Problem one is the separation of forty-one subjects into two classifications based on the data produced by the mass spectrometry of protein samples from each subject. Problem two is to find the specific differences between protein expression data of two sets of subjects. In each problem, one group of subjects has a disease, while the other group is nondiseased. Each problem was approached with the intent to introduce a new and potentially useful tool to analyze protein expression from mass spectrometry data. A variety of methodologies, both conventional and nonconventional were used in the analysis of these problems. The results presented show both overlap and discrepancies. What is important is the breadth of the techniques and the future direction this analysis will create.