Gene function prediction from synthetic lethality networks via ranking on demand.

MOTIVATION: Synthetic lethal interactions represent pairs of genes whose individual mutations are not lethal, while the double mutation of both genes does incur lethality. Several studies have shown a correlation between functional similarity of genes and their distances in networks based on synthetic lethal interactions. However, there is a lack of algorithms for predicting gene function from synthetic lethality interaction networks. RESULTS: In this article, we present a novel technique called kernelROD for gene function prediction from synthetic lethal interaction networks based on kernel machines. We apply our novel algorithm to Gene Ontology functional annotation prediction in yeast. Our experiments show that our method leads to improved gene function prediction compared with state-of-the-art competitors and that combining genetic and congruence networks leads to a further improvement in prediction accuracy.

Bathyraja panthera, a new species of skate (Rajidae: Arhynchobatinae) from the western Aleutian Islands, and resurrection of the subgenus Arctoraja Ishiyama

We provide morphological and molecular evidence to recognize a new species of skate from the North Pacific, Bathyraja panthera. We also resurrect the skate subgenus Arctoraja Ishiyama, confirming its monophyly and the validity of the subgenus. Arctoraja was previously recognized as a distinct subgenus of Breviraja and later synonymized with Bathyraja (family Rajidae). Although the nominal species of Arctoraja have all been considered synonyms of Bathyraja parmifera by various authors, on the basis of morphometric, meristic, chondrological, and molecular data we recognize four species, including the new species. Species of Arctoraja are distributed across the North Pacific Ocean and adjacent seas from southern Japan to British Columbia. Bathyraja parmifera is abundant in the eastern Bering Sea, Aleutian Islands, and northern Gulf of Alaska; B. smirnovi is a western Pacific species found in the Sea of Okhotsk and Sea of Japan; B. simoterus is restricted to waters around the northern and eastern coasts of Hokkaido, Japan; and the new species B. panthera is restricted to the western Aleutian Islands. Bathyraja panthera is diagnosed by its color pattern of light yellow blotches with black spotting on a greenish brown background, high thorn and vertebral counts, chondrological characters of the neurocranium and clasper, and a unique nucleotide sequence within the mitochondrial cytochrome oxidase gene. Furthermore, the species presently recognized as Bathyraja parmifera exhibits two haplotypes among specimens from Alaska, suggesting the possibility of a second, cryptic species.

A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series.

Understanding the regulatory mechanisms that are responsible for an organism's response to environmental change is an important issue in molecular biology. A first and important step towards this goal is to detect genes whose expression levels are affected by altered external conditions. A range of methods to test for differential gene expression, both in static as well as in time-course experiments, have been proposed. While these tests answer the question whether a gene is differentially expressed, they do not explicitly address the question when a gene is differentially expressed, although this information may provide insights into the course and causal structure of regulatory programs. In this article, we propose a two-sample test for identifying intervals of differential gene expression in microarray time series. Our approach is based on Gaussian process regression, can deal with arbitrary numbers of replicates, and is robust with respect to outliers. We apply our algorithm to study the response of Arabidopsis thaliana genes to an infection by a fungal pathogen using a microarray time series dataset covering 30,336 gene probes at 24 observed time points. In classification experiments, our test compares favorably with existing methods and provides additional insights into time-dependent differential expression.