Single-feature polymorphism discovery in the barley transcriptome

A probe-level model for analysis of GeneChip gene-expression data is presented which identified more than 10,000 single-feature polymorphisms (SFP) between two barley genotypes. The method has good sensitivity, as 67% of known single-nucleotide polymorphisms (SNP) were called as SFPs. This method is applicable to all oligonucleotide microarray data, accounts for SNP effects in gene-expression data and represents an efficient and versatile approach for highly parallel marker identification in large genomes.     

Query-driven module discovery in microarray data

MOTIVATION: Existing (bi)clustering methods for microarray data analysis often do not answer the specific questions of interest to a biologist. Such specific questions could be derived from other information sources, including expert prior knowledge. More specifically, given a set of seed genes which are believed to have a common function, we would like to recruit genes with similar expression profiles as the seed genes in a significant subset of experimental conditions. RESULTS: We introduce QDB, a novel Bayesian query-driven biclustering framework in which the prior distributions allow introducing knowledge from a set of seed genes (query) to guide the pattern search. In two well-known yeast compendia, we grow highly functionally enriched biclusters from small sets of seed genes using a resolution sweep approach. In addition, relevant conditions are identified and modularity of the biclusters is demonstrated, including the discovery of overlapping modules. Finally, our method deals with missing values naturally, performs well on artificial data from a recent biclustering benchmark study and has a number of conceptual advantages when compared to existing approaches for focused module search.     

Gene discovery in cervical cancer : towards diagnostic and therapeutic biomarkers

Cervical cancer is a potentially preventable disease; however, it remains the second most common malignancy in women worldwide. The human papillomavirus (HPV) is the single most important etiological agent in cervical cancer. HPV contributes to neoplastic progression through the action of two viral oncoproteins E6 and E7, which interfere with critical cell cycle pathways, tumor protein p53, and retinoblastoma protein. However, evidence suggests that HPV infection alone is insufficient to induce malignant changes, and other host genetic variations are important in the development of cervical cancer. Advances in molecular biology and high throughput technologies have heralded a new era in biomarker discovery and identification of molecular targets related to carcinogenesis. These advancements have improved our understanding of carcinogenesis and will facilitate screening, early detection, management, and personalized targeted therapy. A number of these developments and molecular targets associated with cervical cancer will be addressed in this review.     

Network based consensus gene signatures for biomarker discovery in breast cancer

Diagnostic and prognostic biomarkers for cancer based on gene expression profiles are viewed as a major step towards a better personalized medicine. Many studies using various computational approaches have been published in this direction during the last decade. However, when comparing different gene signatures for related clinical questions often only a small overlap is observed. This can have various reasons, such as technical differences of platforms, differences in biological samples or their treatment in lab, or statistical reasons because of the high dimensionality of the data combined with small sample size, leading to unstable selection of genes. In conclusion retrieved gene signatures are often hard to interpret from a biological point of view. We here demonstrate that it is possible to construct a consensus signature from a set of seemingly different gene signatures by mapping them on a protein interaction network. Common upstream proteins of close gene products, which we identified via our developed algorithm, show a very clear and significant functional interpretation in terms of overrepresented KEGG pathways, disease associated genes and known drug targets. Moreover, we show that such a consensus signature can serve as prior knowledge for predictive biomarker discovery in breast cancer. Evaluation on different datasets shows that signatures derived from the consensus signature reveal a much higher stability than signatures learned from all probesets on a microarray, while at the same time being at least as predictive. Furthermore, they are clearly interpretable in terms of enriched pathways, disease associated genes and known drug targets. In summary we thus believe that network based consensus signatures are not only a way to relate seemingly different gene signatures to each other in a functional manner, but also to establish prior knowledge for highly stable and interpretable predictive biomarkers.     

Gene discovery in the Acanthamoeba castellanii genome

Acanthamoeba castellanii is a free-living amoeba found in soil, freshwater, and marine environments and an important predator of bacteria. Acanthamoeba castellanii is also an opportunistic pathogen of clinical interest, responsible for several distinct diseases in humans. In order to provide a genomic platform for the study of this ubiquitous and important protist, we generated a sequence survey of approximately 0.5 x coverage of the genome. The data predict that A. castellanii exhibits a greater biosynthetic capacity than the free-living Dictyostelium discoideum and the parasite Entamoeba histolytica, providing an explanation for the ability of A. castellanii to inhabit a diversity of environments. Alginate lyase may provide access to bacteria within biofilms by breaking down the biofilm matrix, and polyhydroxybutyrate depolymerase may facilitate utilization of the bacterial storage compound polyhydroxybutyrate as a food source. Enzymes for the synthesis and breakdown of cellulose were identified, and they likely participate in encystation and excystation as in D. discoideum. Trehalose-6-phosphate synthase is present, suggesting that trehalose plays a role in stress adaptation. Detection and response to a number of stress conditions is likely accomplished with a large set of signal transduction histidine kinases and a set of putative receptor serine/threonine kinases similar to those found in E. histolytica. Serine, cysteine and metalloproteases were identified, some of which are likely involved in pathogenicity.     

Gene expression profiling and clinical outcome in breast cancer

Pathologic and clinical heterogeneity of breast cancer reflects the poorly documented, complex, and combinatory molecular basis of the disease and is in part responsible for therapeutic failures. The DNA microarray technique allows the analysis of RNA expression of several thousands of genes simultaneously in a sample. There are multiple potential applications of the technique in cancer research. A number of recent studies have shown the promising role of gene expression profiling in breast cancer by identifying new prognostic subclasses unidentifiable by conventional parameters and new prognostic and/or predictive gene signatures, whose predictive impact is superior to conventional histoclinical prognostic factors. In this review we describe current use of DNA microarrays in the prognosis of breast cancer. We also discuss issues that need to be addressed in the near future to allow the method to reach its full potential.     

Heritable clustering and pathway discovery in breast cancer integrating epigenetic and phenotypic data

Background  

In order to recapitulate tumor progression pathways using epigenetic data, we developed novel clustering and pathway reconstruction algorithms, collectively referred to as heritable clustering. This approach generates a progression model of altered DNA methylation from tumor tissues diagnosed at different developmental stages. The samples act as surrogates for natural progression in breast cancer and allow the algorithm to uncover distinct epigenotypes that describe the molecular events underlying this process. Furthermore, our likelihood-based clustering algorithm has great flexibility, allowing for incomplete epigenotype or clinical phenotype data and also permitting dependencies among variables.