Mutual information estimation reveals global associations between stimuli and biological processes

Background: Although gene expression analysis with microarray has become popular, it remains difficult to interpret the biological changes caused by stimuli or variation of conditions. Clustering of genes and associating each group with biolog-ical functions are often used methods. However, such methods only detect partial changes within cell processes. Herein, we propose a method for discovering global changes within a cell by associ-ating observed conditions of gene expression with gene functions. Results: To elucidate the association, we intro-duce a novel feature selection method called Least-Squares Mutual Information (LSMI), which com-putes the relation based on mutual information, and therefore LSMI can detect nonlinear associa-tions within a cell. We demonstrate the effective-ness of LSMI through comparison with existing methods. The results of the application to yeast microarray datasets reveal that non-natural stimuli affect various biological processes, whereas others are no significant relation to specific cell processes. Furthermore, we discover that biological processes can be categorized into four types according to the responses of various stimuli. They are those re-lated to DNA/RNA metabolic processes, gene ex-pression, protein metabolic processes, and protein localization. Conclusions: We proposed a novel feature selection method called LSMI, and applied LSMI to mining the association between conditions of yeast and bi-ological processes through microarray datasets. In fact, LSMI allows us to elucidate the global orga-nization of cellular process control

In vitro homology search array comprehensively reveals highly conserved genes and their functional characteristics in non-sequenced species

<p>Abstract</p> <p>Background</p> <p>With the increase in genomic and transcriptomic data produced by the recent advancements in next generation sequencers and microarrays, it is now easier than ever to conduct large-scale comparative genomic studies for familiar species. However, there are more than ten million species on earth, and the study of all remaining species is not realistic in terms of cost and time. There have been a number of attempts at using microarrays for cross-species hybridization; however, those approaches only utilized the same probes for each species or different probes designed from orthologous genes. To establish easier and cheaper methods for the large-scale comparative genomic study of non-sequenced species, we developed an <it>in vitro</it> homology search array with the aid of a bioinformatic approach to probe design.</p> <p>Results</p> <p>To perform large-scale genomic comparisons of non-sequenced species, we chose squid, one of the most intelligent species among Protostomes, for comparison with human genes. We designed a microarray using human single copy genes and conducted microarray experiments with mRNAs extracted from the squid. Multi-copy genes could not be detected using the microarray in this study because their sequence similarity caused cross-hybridization. A search for squid homologous genes among human genes revealed that 68% of the human probes tested showed the expression of squid homolog genes and 95 genes were confirmed to be expressed highly in squid. Functional classification analysis showed that these highly expressed genes comprise DNA binding proteins, which are under pressure of DNA level mutation and, consequently, show high similarity at the nucleotide level.</p> <p>Conclusions</p> <p>Our array could detect homologous genes in squids and humans in spite of the distant phylogenic relationships between the species. This experimental method will be useful for identifying homologs in non-sequenced species, for the development of genetic resources and for the collection of information on biodiversity, particularly when using the genome of sibling or closely related species.</p

Genome-wide quantification of homeolog expression ratio revealed nonstochastic gene regulation in synthetic allopolyploid Arabidopsis

Genome duplication with hybridization, or allopolyploidization, occurs commonly in plants, and is considered to be a strong force for generating new species. However, genome-wide quantification of homeolog expression ratios was technically hindered because of the high homology between homeologous gene pairs. To quantify the homeolog expression ratio using RNA-seq obtained from polyploids, a new method named HomeoRoq was developed, in which the genomic origin of sequencing reads was estimated using mismatches between the read and each parental genome. To verify this method, we first assembled the two diploid parental genomes of Arabidopsis halleri subsp. gemmifera and Arabidopsis lyrata subsp. petraea (Arabidopsis petraea subsp. umbrosa), then generated a synthetic allotetraploid, mimicking the natural allopolyploid Arabidopsis kamchatica. The quantified ratios corresponded well to those obtained by Pyrosequencing. We found that the ratios of homeologs before and after cold stress treatment were highly correlated (r = 0.870). This highlights the presence of nonstochastic polyploid gene regulation despite previous research identifying stochastic variation in expression. Moreover, our new statistical test incorporating overdispersion identified 226 homeologs (1.11% of 20 369 expressed homeologs) with significant ratio changes, many of which were related to stress responses. HomeoRoq would contribute to the study of the genes responsible for polyploid-specific environmental response

Data mining tools for the Saccharomyces cerevisiae morphological database

For comprehensive understanding of precise morphological changes resulting from loss-of-function mutagenesis, a large collection of 1 899 247 cell images was assembled from 91 271 micrographs of 4782 budding yeast disruptants of non-lethal genes. All the cell images were processed computationally to measure ∼500 morphological parameters in individual mutants. We have recently made this morphological quantitative data available to the public through the Saccharomyces cerevisiae Morphological Database (SCMD). Inspecting the significance of morphological discrepancies between the wild type and the mutants is expected to provide clues to uncover genes that are relevant to the biological processes producing a particular morphology. To facilitate such intensive data mining, a suite of new software tools for visualizing parameter value distributions was developed to present mutants with significant changes in easily understandable forms. In addition, for a given group of mutants associated with a particular function, the system automatically identifies a combination of multiple morphological parameters that discriminates a mutant group from others significantly, thereby characterizing the function effectively. These data mining functions are available through the World Wide Web at

Drop on a Bent Fibre

Inspired by the huge droplets attached on cypress tree leaf tips after rain, we find that a bent fibre can hold significantly more water in the corner than a horizontally placed fibre (typically up to three times or more). The maximum volume of the liquid that can be trapped is remarkably affected by the bending angle of the fibre and surface tension of the liquid. We experimentally find the optimal included angle ($\sim {36}{^\circ}$) that holds the most water. Analytical and semi-empirical models are developed to explain these counter-intuitive experimental observations and predict the optimal angle. The data and models could be useful for designing microfluidic and fog harvesting devices