Genome Fusion Detection: a novel method to detect fusion genes from SNP-array data.

MOTIVATION: Fusion genes result from genomic rearrangements, such as deletions, amplifications and translocations. Such rearrangements can also frequently be observed in cancer and have been postulated as driving event in cancer development. to detect them, one needs to analyze the transition region of two segments with different copy number, the location where fusions are known to occur. Finding fusion genes is essential to understanding cancer development and may lead to new therapeutic approaches. RESULTS: Here we present a novel method, the Genomic Fusion Detection algorithm, to predict fusion genes on a genomic level based on SNP-array data. This algorithm detects genes at the transition region of segments with copy number variation. With the application of defined constraints, certain properties of the detected genes are evaluated to predict whether they may be fused. We evaluated our prediction by calculating the observed frequency of known fusions in both primary cancers and cell lines. We tested a set of cell lines positive for the BCR-ABL1 fusion and prostate cancers positive for the TMPRSS2-ERG fusion. We could detect the fusions in all positive cell lines, but not in the negative controls.

National housing and impervious surface scenarios for integrated climate impact assessments.

Understanding the impacts of climate change on people and the environment requires an understanding of the dynamics of both climate and land use/land cover changes. A range of future climate scenarios is available for the conterminous United States that have been developed based on widely used international greenhouse gas emissions storylines. Climate scenarios derived from these emissions storylines have not been matched with logically consistent land use/cover maps for the United States. This gap is a critical barrier to conducting effective integrated assessments. This study develops novel national scenarios of housing density and impervious surface cover that are logically consistent with emissions storylines. Analysis of these scenarios suggests that combinations of climate and land use/cover can be important in determining environmental conditions regulated under the Clean Air and Clean Water Acts. We found significant differences in patterns of habitat loss and the distribution of potentially impaired watersheds among scenarios, indicating that compact development patterns can reduce habitat loss and the number of impaired watersheds. These scenarios are also associated with lower global greenhouse gas emissions and, consequently, the potential to reduce both the drivers of anthropogenic climate change and the impacts of changing conditions. The residential housing and impervious surface datasets provide a substantial first step toward comprehensive national land use/land cover scenarios, which have broad applicability for integrated assessments as these data and tools are publicly available.

Phenoclustering: online mining of cross-species phenotypes.

SUMMARY: Recently, several methods for analyzing phenotype data have been published, but only few are able to cope with data sets generated in different studies, with different methods, or for different species. We developed an online system in which more than 300 000 phenotypes from a wide variety of sources and screening methods can be analyzed together. Clusters of similar phenotypes are visualized as networks of highly similar phenotypes, inducing gene groups useful for functional analysis. This system is part of PhenomicDB, providing the world's largest cross-species phenotype data collection with a tool to mine its wealth of information. AVAILABILITY: Freely available at http://www.phenomicdb.de

Influence of tropospheric ozone control on exposure to ultraviolet radiation at the surface.

Improving air quality by reducing ambient ozone (O(3)) will likely lower O(3) concentrations throughout the troposphere and increase the transmission of solar ultraviolet (UV) radiation to the surface. The changes in surface UV radiation between two control scenarios (nominally 84 and 70 ppb O(3) for summer 2020) in the Eastern two-thirds of the contiguous U.S. are estimated, using tropospheric O(3) profiles calculated with a chemistry-transport model (Community Multi-Scale Air Quality, CMAQ) as inputs to a detailed model of the transfer of solar radiation through the atmosphere (tropospheric ultraviolet-visible, TUV) for clear skies, weighed for the wavelengths known to induce sunburn and skin cancer. Because the incremental emission controls differ according to region, strong spatial variability in O(3) reductions and in corresponding UV radiation increments is seen. The geographically averaged UV increase is 0.11 ± 0.03%, whereas the population-weighted increase is larger, 0.19 ± 0.06%, because O(3) reductions are greater in more densely populated regions. These relative increments in exposure are non-negligible given the already high incidence of UV-related health effects, but are lower by an order of magnitude or more than previous estimates.

PhenomicDB: a new cross-species genotype/phenotype resource.

Phenotypes are an important subject of biomedical research for which many repositories have already been created. Most of these databases are either dedicated to a single species or to a single disease of interest. With the advent of technologies to generate phenotypes in a high-throughput manner, not only is the volume of phenotype data growing fast but also the need to organize these data in more useful ways. We have created PhenomicDB (freely available at http://www.phenomicdb.de), a multi-species genotype/phenotype database, which shows phenotypes associated with their corresponding genes and grouped by gene orthologies across a variety of species. We have enhanced PhenomicDB recently by additionally incorporating quantitative and descriptive RNA interference (RNAi) screening data, by enabling the usage of phenotype ontology terms and by providing information on assays and cell lines. We envision that integration of classical phenotypes with high-throughput data will bring new momentum and insights to our understanding. Modern analysis tools under development may help exploiting this wealth of information to transform it into knowledge and, eventually, into novel therapeutic approaches.

Mining phenotypes for gene function prediction.

BACKGROUND: Health and disease of organisms are reflected in their phenotypes. Often, a genetic component to a disease is discovered only after clearly defining its phenotype. In the past years, many technologies to systematically generate phenotypes in a high-throughput manner, such as RNA interference or gene knock-out, have been developed and used to decipher functions for genes. However, there have been relatively few efforts to make use of phenotype data beyond the single genotype-phenotype relationships. RESULTS: We present results on a study where we use a large set of phenotype data - in textual form - to predict gene annotation. To this end, we use text clustering to group genes based on their phenotype descriptions. We show that these clusters correlate well with several indicators for biological coherence in gene groups, such as functional annotations from the Gene Ontology (GO) and protein-protein interactions. We exploit these clusters for predicting gene function by carrying over annotations from well-annotated genes to other, less-characterized genes in the same cluster. For a subset of groups selected by applying objective criteria, we can predict GO-term annotations from the biological process sub-ontology with up to 72.6% precision and 16.7% recall, as evaluated by cross-validation. We manually verified some of these clusters and found them to exhibit high biological coherence, e.g. a group containing all available antennal Drosophila odorant receptors despite inconsistent GO-annotations. CONCLUSION: The intrinsic nature of phenotypes to visibly reflect genetic activity underlines their usefulness in inferring new gene functions. Thus, systematically analyzing these data on a large scale offers many possibilities for inferring functional annotation of genes. We show that text clustering can play an important role in this process.

Knockdown of platinum-induced growth differentiation factor 15 abrogates p27-mediated tumor growth delay in the chemoresistant ovarian cancer model A2780cis.

Molecular mechanisms underlying the development of resistance to platinum-based treatment in patients with ovarian cancer remain poorly understood. This is mainly due to the lack of appropriate in vivo models allowing the identification of resistance-related factors. In this study, we used human whole-genome microarrays and linear model analysis to identify potential resistance-related genes by comparing the expression profiles of the parental human ovarian cancer model A2780 and its platinum-resistant variant A2780cis before and after carboplatin treatment in vivo. Growth differentiation factor 15 (GDF15) was identified as one of five potential resistance-related genes in the A2780cis tumor model. Although A2780-bearing mice showed a strong carboplatin-induced increase of GDF15 plasma levels, the basal higher GDF15 plasma levels of A2780cis-bearing mice showed no further increase after short-term or long-term carboplatin treatment. This correlated with a decreased DNA damage response, enhanced AKT survival signaling and abrogated cell cycle arrest in the carboplatin-treated A2780cis tumors. Furthermore, knockdown of GDF15 in A2780cis cells did not alter cell proliferation but enhanced cell migration and colony size in vitro. Interestingly, in vivo knockdown of GDF15 in the A2780cis model led to a basal-enhanced tumor growth, but increased sensitivity to carboplatin treatment as compared to the control-transduced A2780cis tumors. This was associated with larger necrotic areas, a lobular tumor structure and increased p53 and p16 expression of the carboplatin-treated shGDF15-A2780cis tumors. Furthermore, shRNA-mediated GDF15 knockdown abrogated p27 expression as compared to control-transduced A2780cis tumors. In conclusion, these data show that GDF15 may contribute to carboplatin resistance by suppressing tumor growth through p27. These data show that GDF15 might serve as a novel treatment target in women with platinum-resistant ovarian cancer.

Prediction errors in learning drug response from gene expression data - influence of labeling, sample size, and machine learning algorithm.

Model-based prediction is dependent on many choices ranging from the sample collection and prediction endpoint to the choice of algorithm and its parameters. Here we studied the effects of such choices, exemplified by predicting sensitivity (as IC50) of cancer cell lines towards a variety of compounds. For this, we used three independent sample collections and applied several machine learning algorithms for predicting a variety of endpoints for drug response. We compared all possible models for combinations of sample collections, algorithm, drug, and labeling to an identically generated null model. The predictability of treatment effects varies among compounds, i.e. response could be predicted for some but not for all. The choice of sample collection plays a major role towards lowering the prediction error, as does sample size. However, we found that no algorithm was able to consistently outperform the other and there was no significant difference between regression and two- or three class predictors in this experimental setting. These results indicate that response-modeling projects should direct efforts mainly towards sample collection and data quality, rather than method adjustment.

Phenotype mining for functional genomics and gene discovery.

In gene prediction, studying phenotypes is highly valuable for reducing the number of locus candidates in association studies and to aid disease gene candidate prioritization. This is due to the intrinsic nature of phenotypes to visibly reflect genetic activity, making them potentially one of the most useful data types for functional studies. However, systematic use of these data has begun only recently. 'Comparative phenomics' is the analysis of genotype-phenotype associations across species and experimental methods. This is an emerging research field of utmost importance for gene discovery and gene function annotation. In this chapter, we review the use of phenotype data in the biomedical field. We will give an overview of phenotype resources, focusing on PhenomicDB--a cross-species genotype-phenotype database--which is the largest available collection of phenotype descriptions across species and experimental methods. We report on its latest extension by which genotype-phenotype relationships can be viewed as graphical representations of similar phenotypes clustered together ('phenoclusters'), supplemented with information from protein-protein interactions and Gene Ontology terms. We show that such 'phenoclusters' represent a novel approach to group genes functionally and to predict novel gene functions with high precision. We explain how these data and methods can be used to supplement the results of gene discovery approaches. The aim of this chapter is to assist researchers interested in understanding how phenotype data can be used effectively in the gene discovery field.