GOSim – an R-package for computation of information theoretic GO similarities between terms and gene products

<p>Abstract</p> <p>Background</p> <p>With the increased availability of high throughput data, such as DNA microarray data, researchers are capable of producing large amounts of biological data. During the analysis of such data often there is the need to further explore the similarity of genes not only with respect to their expression, but also with respect to their functional annotation which can be obtained from Gene Ontology (GO).</p> <p>Results</p> <p>We present the freely available software package <it>GOSim</it>, which allows to calculate the functional similarity of genes based on various information theoretic similarity concepts for GO terms. <it>GOSim </it>extends existing tools by providing additional lately developed functional similarity measures for genes. These can e.g. be used to cluster genes according to their biological function. Vice versa, they can also be used to evaluate the homogeneity of a given grouping of genes with respect to their GO annotation. <it>GOSim </it>hence provides the researcher with a flexible and powerful tool to combine knowledge stored in GO with experimental data. It can be seen as complementary to other tools that, for instance, search for significantly overrepresented GO terms within a given group of genes.</p> <p>Conclusion</p> <p><it>GOSim </it>is implemented as a package for the statistical computing environment <it>R </it>and is distributed under GPL within the CRAN project.</p

The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

Kernel based functional gene grouping

Abstract — During the last years, high throughput experiments have become very popular. During the analysis of such data the need for a functional grouping of genes arises. In this paper, we propose grouping genes according to their biological function by means of kernel functions, which are similarity measures having special mathematical properties and play a crucial role e.g. in SVM classification. Thereby our kernel functions rely on functional information on the genes provided by Gene Ontology annotation. We investigate and compare several provably symmetric, positive semidefinite kernel functions in combination with spectral clustering, dual k-means and average linkage and demonstrate that our approach leads to good clustering results. I

Functional Grouping of Genes Using Spectral Clustering And Gene Ontology

With the invention of high throughput methods, researchers are capable of producing large amounts of biological data. During the analysis of such data the need for a functional grouping of genes arises. In this paper, we propose a new method based on spectral clustering for the partitioning of genes according to their biological function. The functional information is based on Gene Ontology annotation, a mechanism to capture functional knowledge in a shareable and computer processable form. Our functional cluster method promises to automatize, speed up and therefore improve biological data analysis

Clustering-based Approach to Identify Solutions for the Inference of Regulatory Networks

In this paper we address the problem of finding valid solutions for the problem of inferring gene regulatory networks. Different approaches to directly infer the dependencies of gene regulatory networks by identifying parameters of mathematical models can be found in literature. The problem of reconstructing regulatory systems from experimental data is often multimodal and thus appropriate optimization strategies become necessary. Thus, we propose to use a clustering based niching evolutionary algorithm to maintain diversity in the optimization population to prevent premature convergence and to raise the probability of finding the global optimum by identifying multiple alternative networks. With this set of alternatives, the identification of the true solution has then to be addressed in a second post-processing step

Functional Distances for Genes Based on GO Feature Maps and their Application to

Abstract — With the invention of high throughput methods, researchers are capable of producing large amounts of biological data. During the analysis of such data, the need for a functional grouping of genes arises. In this paper, we propose a new functional distance measure for genes and its application to clustering. The proposed distance is based on the concept of empirical feature maps that are built using the Gene Ontology. Besides, our distance function can be calculated much faster than a previous approach. Finally, we show that using this distance function for clustering produces clusters of genes that are of the same quality as in our previous publication. Therefore, it promises to speed up biological data analysis. I

Biological Cluster Validity Indices

With the invention of biotechnological high throughput methods like DNA microarrays and the analysis of the resulting huge amounts of biological data, clustering algorithms gain new popularity