How to understand the cell by breaking it: network analysis of gene perturbation screens

Modern high-throughput gene perturbation screens are key technologies at the forefront of genetic research. Combined with rich phenotypic descriptors they enable researchers to observe detailed cellular reactions to experimental perturbations on a genome-wide scale. This review surveys the current state-of-the-art in analyzing perturbation screens from a network point of view. We describe approaches to make the step from the parts list to the wiring diagram by using phenotypes for network inference and integrating them with complementary data sources. The first part of the review describes methods to analyze one- or low-dimensional phenotypes like viability or reporter activity; the second part concentrates on high-dimensional phenotypes showing global changes in cell morphology, transcriptome or proteome.Comment: Review based on ISMB 2009 tutorial; after two rounds of revisio

An evaluation of a system that recommends microarray experiments to perform to discover gene-regulation pathways

The main topic of this paper is modeling the expected value of experimentation for discovering causal pathways in gene expression data. By experimentation we mean both interventions (e.g., a gene knock-out experiment) and observations (e.g., passively observing the expression level of a “wild-type ” gene). We introduce a system called GEEVE (causal discovery in Gene Expression data using Expected Value of Experimentation), which implements expected value of experimentation in discovering causal pathways using gene expression data. GEEVE provides the following assistance, which is intended to help biologists in their quest to discover gene-regulation pathways: • Recommending which experiments to perform (with a focus on “knock-out ” experiments) using an expected value of experimentation (EVE) method. • Recommending the number of measurements (observational and experimental) to include in the experimental design, again using an EVE method. • Providing a Bayesian analysis that combines prior knowledge with the results of recent microarray experimental results to derive posterior probabilities of gene regulatio

Classifiers for modeling of mineral potential

[Extract] Classification and allocation of land-use is a major policy objective in most countries. Such an undertaking, however, in the face of competing demands from different stakeholders, requires reliable information on resources potential. This type of information enables policy decision-makers to estimate socio-economic benefits from different possible land-use types and then to allocate most suitable land-use. The potential for several types of resources occurring on the earth's surface (e.g., forest, soil, etc.) is generally easier to determine than those occurring in the subsurface (e.g., mineral deposits, etc.). In many situations, therefore, information on potential for subsurface occurring resources is not among the inputs to land-use decision-making [85]. Consequently, many potentially mineralized lands are alienated usually to, say, further exploration and exploitation of mineral deposits. Areas with mineral potential are characterized by geological features associated genetically and spatially with the type of mineral deposits sought. The term 'mineral deposits' means .accumulations or concentrations of one or more useful naturally occurring substances, which are otherwise usually distributed sparsely in the earth's crust. The term 'mineralization' refers to collective geological processes that result in formation of mineral deposits. The term 'mineral potential' describes the probability or favorability for occurrence of mineral deposits or mineralization. The geological features characteristic of mineralized land, which are called recognition criteria, are spatial objects indicative of or produced by individual geological processes that acted together to form mineral deposits. Recognition criteria are sometimes directly observable; more often, their presence is inferred from one or more geographically referenced (or spatial) datasets, which are processed and analyzed appropriately to enhance, extract, and represent the recognition criteria as spatial evidence or predictor maps. Mineral potential mapping then involves integration of predictor maps in order to classify areas of unique combinations of spatial predictor patterns, called unique conditions [51] as either barren or mineralized with respect to the mineral deposit-type sought

ID3, Estrogenic Chemicals, and the Pathogenesis of Tumor-Like Proliferative Vascular Lesions

Tumor-like proliferative vascular lesions manifest in several diseases such as peripheral arterial disease (PAD) and atherosclerosis (AS) after arterial injury. The cause of the vascular cell dysfunction in PAD patients is not known. Our recent novel discovery shows that inhibitor of differentiation 3 (ID3) is highly expressed in intimal lesions of clinical vascular disease samples. The central hypothesis of our study is: estrogenic chemical induced dysregulation of ID3 target genes is involved in the development of vascular disease. NHANES data analysis demonstrated higher geometric levels of all 6 PCB congeners in both PAD diagnosed participants and participants at risk of AS when compared to the rest of the population. Adjusted models showed association between higher exposure of PCBs, phthalates, BPA, and increased risk of PAD. Furthermore PCB153 was shown to have the highest geometric mean amongst all PCB congeners in both participants diagnosed with PAD and at risk of AS. Gene expression of ID3 & ID3 candidate targets in blood & tissue studies identified ID3 & ID3 candidate target genes as a driver of vascular disease. Overlapping ID3 & ID3 candidate target genes included: ABCB6, ACP1, BYSL, CAD, CDH15, DCBLD2, DHRS3, DNMT1, ID3, MCM4, and NDUFA7. The ID3 target genes involved in the: focal adhesion pathway were ACTN1, COL1A2, COL3A1, COL6A1, CTNNB1, IBSP, ID3, ITGA8, and MYL2; ECM-receptor interaction were COL1A2, COL3A1, COL6A1, IBSP, ID3, and ITGA8; oxidative phosphorylation pathway ATP5D, ATP5H, ATP6V0B, ATP6V0D1, ATP6V1B2, COX5A, COX7C, COX8A, CYC1, ID3, NDUFA1, NDUFA7, NDUFS4, NDUFV1, NDUFV2; and cell cycle pathway ANAPC10, ATM, CDKN2B, E2F5, MCM3, and MCM4. In summary our results showed an association between exposure to PCBs, phthalates, BPA, and increased risk of PAD and AS, and possible molecular mechanisms of interaction of ID3 target genes and estrogenic chemicals involved in PAD and AS

Analyse und Interpretation der Varianz von Genexpressionsdaten

Die vorliegende Dissertationsschrift fasst vier Arbeiten unter der Überschrift „Analyse und Interpretation der Varianz von Genexpressionsdaten“ zusammen. Zunächst wird der Begriff der „Technischen Varianz“ von dem der „Biologischen Varianz“ abgegrenzt. In der Genexpressionsanalyse mit Microarrays wird unter technischer Varianz der traditionell hohe Messfehler verstanden. Die Gründe hierfür scheinen jedoch mannigfaltig zu sein. Höchst umstritten ist hierbei der Effekt von Kreuzhybridisierungen, also unspezifischen Bindungen von RNA-Fragmenten an die Sonden des Arrays. Einige Forscher halten diesen Effekt für die maßgebliche Fehlerquelle, andere beurteilen ihn als vernachlässigbar. In den ersten zwei Arbeiten wird gezeigt, dass Kreuzhybridisierungen in der Tat erheblich für den Messfehler bei Microarray-Experimenten verantwortlich sind. Gleichzeitig werden, mit einem Satz neuer Chip Definition Files und einer Handreichung zum Design neuer Microarrays, Werkzeuge zum Umgang mit unspezifischen Bindungen zur Verfügung gestellt. Varianz, die auf tatsächlich vorhandenen biologischen Unterschieden basiert, wird biologische Varianz genannt. Bei der Auswertung eines Genexpressionsexperiments werden mittels Analyse der Streuungsparameter mögliche Markertranskripte identifiziert, die bei einer üblichen mittelwertbasierten Auswertung nicht gefunden werden. Durch Mapping der Transkripte auf KEGG-Pathways kann ausgeschlossen werden, dass es sich um falsch positive Treffer handelt. In der vierten Arbeit wird eine Ähnlichkeitsanalyse mit Hilfe von Korrelationskoeffizienten durchgeführt. Durch Auswertung mit der Korrelation nach Kendall können Hypothesen über den funktionalen Pathway in der induzierten Abwehr von Pflanzen gewonnen werden