DNA microarray data and contextual analysis of correlation graphs

BACKGROUND: DNA microarrays are used to produce large sets of expression measurements from which specific biological information is sought. Their analysis requires efficient and reliable algorithms for dimensional reduction, classification and annotation. RESULTS: We study networks of co-expressed genes obtained from DNA microarray experiments. The mathematical concept of curvature on graphs is used to group genes or samples into clusters to which relevant gene or sample annotations are automatically assigned. Application to publicly available yeast and human lymphoma data demonstrates the reliability of the method in spite of its simplicity, especially with respect to the small number of parameters involved. CONCLUSIONS: We provide a method for automatically determining relevant gene clusters among the many genes monitored with microarrays. The automatic annotations and the graphical interface improve the readability of the data. A C++ implementation, called Trixy, is available from

09081 Abstracts Collection -- Similarity-based learning on structures

From 15.02. to 20.02.2009, the Dagstuhl Seminar 09081 ``Similarity-based learning on structures \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Algorithmic and Statistical Perspectives on Large-Scale Data Analysis

In recent years, ideas from statistics and scientific computing have begun to interact in increasingly sophisticated and fruitful ways with ideas from computer science and the theory of algorithms to aid in the development of improved worst-case algorithms that are useful for large-scale scientific and Internet data analysis problems. In this chapter, I will describe two recent examples---one having to do with selecting good columns or features from a (DNA Single Nucleotide Polymorphism) data matrix, and the other having to do with selecting good clusters or communities from a data graph (representing a social or information network)---that drew on ideas from both areas and that may serve as a model for exploiting complementary algorithmic and statistical perspectives in order to solve applied large-scale data analysis problems.Comment: 33 pages. To appear in Uwe Naumann and Olaf Schenk, editors, "Combinatorial Scientific Computing," Chapman and Hall/CRC Press, 201

ATTED-II: a database of co-expressed genes and cis elements for identifying co-regulated gene groups in Arabidopsis

Publicly available database of co-expressed gene sets would be a valuable tool for a wide variety of experimental designs, including targeting of genes for functional identification or for regulatory investigation. Here, we report the construction of an Arabidopsis thaliana trans-factor and cis-element prediction database (ATTED-II) that provides co-regulated gene relationships based on co-expressed genes deduced from microarray data and the predicted cis elements. ATTED-II () includes the following features: (i) lists and networks of co-expressed genes calculated from 58 publicly available experimental series, which are composed of 1388 GeneChip data in A.thaliana; (ii) prediction of cis-regulatory elements in the 200 bp region upstream of the transcription start site to predict co-regulated genes amongst the co-expressed genes; and (iii) visual representation of expression patterns for individual genes. ATTED-II can thus help researchers to clarify the function and regulation of particular genes and gene networks

Computational, Integrative, and Comparative Methods for the Elucidation of Genetic Coexpression Networks

Gene expression microarray data can be used for the assembly of genetic coexpression network graphs. Using mRNA samples obtained from recombinant inbred Mus musculus strains, it is possible to integrate allelic variation with molecular and higher-order phenotypes. The depth of quantitative genetic analysis of microarray data can be vastly enhanced utilizing this mouse resource in combination with powerful computational algorithms, platforms, and data repositories. The resulting network graphs transect many levels of biological scale. This approach is illustrated with the extraction of cliques of putatively coregulated genes and their annotation using gene ontology analysis and cis-regulatory element discovery. The causal basis for coregulation is detected through the use of quantitative trait locus mapping

Computational prediction, experiment design and statistical validations of non-coding regulatory RNA

Non-coding regulatory RNAs (ncRNAs) regulate a host of gene functions in prokaryotes, e.g., transcription and translation regulations, RNA processing and modification, and mRNA stability. Some ncRNAs have been identified experimentally, but many are yet to be found. ncRNAs can be classified as either cis- or trans-acting. cis-ncRNAs perfectly complement their target genes and are usually encoded on the anti-sense strands of the targets. On the contrary, trans-ncRNAs regulate their target genes through short and often imperfect base-pairings with the targets, and are usually encoded elsewhere on the genome. A whole-genome thermodynamic analysis can be performed to identify all imperfect but stable base-pairings between all annotated genes and some genomic regions encoding ncRNAs from the same species. However, the sizes of these base-paring regions are short and variable, and their melting temperatures vary greatly between perfectly and imperfectly matched targets. It is difficult to predict trans-acting ncRNAs solely based on the thermodynamic analysis. Therefore, we also have to consider known ncRNA structures to improve our predictions. We find that Hfq-binding ncRNAs, which require Hfq protein to function, share three common structural properties. We predict these special ncRNAs in E. coli and Agrobacterium tumefaciens according to a systematic, novel 5-step approach based on thermodynamic analyses as well as known structural properties of this class of ncRNAs. Whole genome tiling microarrays are chosen to validate our predictions. We describe how the microarrays have been designed, created, and validated for E. coli MG1655 and Agrobacterium tumefaciens C58. We match our new ncRNA prediction results with known ncRNAs, calculate correlation coefficient values between each ncRNA candidate and their predicted targets measure by the whole-genome tiling microarrays, and confirm the results with 3 other ncRNA identification software tools. We also perform a gene ontology network analysis to reveal the associations of ncRNA candidates and their predicted targets. Our novel 5-step prediction method is generally applicable to other prokaryote species and may help advance ncRNA research in prokaryotes

Comparative Analysis of Thresholding Algorithms for Microarray-derived Gene Correlation Matrices

The thresholding problem is important in today’s data-rich research scenario. A threshold is a well-defined point in the data distribution beyond which the data is highly likely to have scientific meaning. The selection of threshold is crucial since it heavily influences any downstream analysis and inferences made there from. A legitimate threshold is one that is not arbitrary but scientifically well grounded, data-dependent and best segregates the information-rich and noisy sections of data. Although the thresholding problem is not restricted to any particular field of study, little research has been done. This study investigates the problem in context of network-based analysis of transcriptomic data. Six conceptually diverse algorithms – based on number of maximal cliques, correlations of control spots with genes, top 1% of correlations, spectral graph clustering, Bonferroni correction of p-values and statistical power – are used to threshold the gene correlation matrices of three time-series microarray datasets and tested for stability and validity. Stability or reliability of the first four algorithms towards thresholding is tested upon block bootstrapping of arrays in the datasets and comparing the estimated thresholds against the bootstrap threshold distributions. Validity of thresholding algorithms is tested by comparison of the estimated thresholds against threshold based on biological information. Thresholds based on the modular basis of gene networks are concluded to perform better both in terms of stability as well as validity. Future challenges to research the problem have been identified. Although the study utilizes transcriptomic data for analysis, we assert its applicability to thresholding across various fields