PROPHECY—a database for high-resolution phenomics

The rapid recent evolution of the field phenomics—the genome-wide study of gene dispensability by quantitative analysis of phenotypes—has resulted in an increasing demand for new data analysis and visualization tools. Following the introduction of a novel approach for precise, genome-wide quantification of gene dispensability in Saccharomyces cerevisiae we here announce a public resource for mining, filtering and visualizing phenotypic data—the PROPHECY database. PROPHECY is designed to allow easy and flexible access to physiologically relevant quantitative data for the growth behaviour of mutant strains in the yeast deletion collection during conditions of environmental challenges. PROPHECY is publicly accessible at http://prophecy.lundberg.gu.se

PROPHECY—a yeast phenome database, update 2006

Connecting genotype to phenotype is fundamental in biomedical research and in our understanding of disease. Phenomics—the large-scale quantitative phenotypic analysis of genotypes on a genome-wide scale—connects automated data generation with the development of novel tools for phenotype data integration, mining and visualization. Our yeast phenomics database PROPHECY is available at . Via phenotyping of 984 heterozygous diploids for all essential genes the genotypes analysed and presented in PROPHECY have been extended and now include all genes in the yeast genome. Further, phenotypic data from gene overexpression of 574 membrane spanning proteins has recently been included. To facilitate the interpretation of quantitative phenotypic data we have developed a new phenotype display option, the Comparative Growth Curve Display, where growth curve differences for a large number of mutants compared with the wild type are easily revealed. In addition, PROPHECY now offers a more informative and intuitive first-sight display of its phenotypic data via its new summary page. We have also extended the arsenal of data analysis tools to include dynamic visualization of phenotypes along individual chromosomes. PROPHECY is an initiative to enhance the growing field of phenome bioinformatics

Yeast diversity in relation to the production of fuels and chemicals

In addition to ethanol, yeasts have the potential to produce many other industrially-relevant chemicals from numerous different carbon sources. However there remains a paucity of information about overall capability across the yeast family tree. Here, 11 diverse species of yeasts with genetic backgrounds representative of different branches of the family tree were investigated. They were compared for their abilities to grow on a range of sugar carbon sources, to produce potential platform chemicals from such substrates and to ferment hydrothermally pretreated rice straw under simultaneous saccharification and fermentation conditions. The yeasts differed considerably in their metabolic capabilities and production of ethanol. A number could produce significant amounts of ethyl acetate, arabinitol, glycerol and acetate in addition to ethanol, including from hitherto unreported carbon sources. They also demonstrated widely differing efficiencies in the fermentation of sugars derived from pre-treated rice straw biomass and differential sensitivities to fermentation inhibitors. A new catabolic property of Rhodotorula mucilaginosa (NCYC 65) was discovered in which sugar substrate is cleaved but the products are not metabolised. We propose that engineering this and some of the other properties discovered in this study and transferring such properties to conventional industrial yeast strains could greatly expand their biotechnological utility

The HOG Pathway Dictates the Short-Term Translational Response after Hyperosmotic Shock

In the global osmoshock translational response in yeast, some gene products were translationally mobilized without transcriptional up-regulation. Conversely, other transcriptionally up-regulated mRNAs were translationally inhibited. Analogous changes occurred on the protein level. These translational responses were strongly dependent on Hog1 and Rck2

The SPIRAL radioactive ion beam facility

This document describes the scientific goals as well as the technical choices of the SPIRAL project (Système de Production d'Ions Radioactifs et d'Accélération en Ligne)

PRECOG: a tool for automated extraction and visualization of fitness components in microbial growth phenomics.

BACKGROUND: Phenomics is a field in functional genomics that records variation in organismal phenotypes in the genetic, epigenetic or environmental context at a massive scale. For microbes, the key phenotype is the growth in population size because it contains information that is directly linked to fitness. Due to technical innovations and extensive automation our capacity to record complex and dynamic microbial growth data is rapidly outpacing our capacity to dissect and visualize this data and extract the fitness components it contains, hampering progress in all fields of microbiology. RESULTS: To automate visualization, analysis and exploration of complex and highly resolved microbial growth data as well as standardized extraction of the fitness components it contains, we developed the software PRECOG (PREsentation and Characterization Of Growth-data). PRECOG allows the user to quality control, interact with and evaluate microbial growth data with ease, speed and accuracy, also in cases of non-standard growth dynamics. Quality indices filter high- from low-quality growth experiments, reducing false positives. The pre-processing filters in PRECOG are computationally inexpensive and yet functionally comparable to more complex neural network procedures. We provide examples where data calibration, project design and feature extraction methodologies have a clear impact on the estimated growth traits, emphasising the need for proper standardization in data analysis. CONCLUSIONS: PRECOG is a tool that streamlines growth data pre-processing, phenotypic trait extraction, visualization, distribution and the creation of vast and informative phenomics databases

Testing of Chromosomal Clumping of Gene Properties

Clumping of gene properties like expression or mutant phenotypes along chromosomes is commonly detected using completely random null-models where their location is equally likely across the chromosomes. Interpretation of statistical tests based on these assumptions may be misleading if dependencies exist that are unequal between chromosomes or in different chromosomal parts. One such regional dependency is the telomeric effect, observed in several studies of Saccharomyces cerevisiae, under which e. g. essential genes are less likely to reside near the chromosomal ends. In this study we demonstrate that standard randomisation test procedures are of limited applicability in the presence of telomeric effects. Several extensions of such standard tests are here suggested for handling clumping simultaneously with regional differences in essentiality frequencies in sub-telomeric and central gene positions. Furthermore, a general non-homogeneous discrete Markov approach for combining parametrically modelled position dependent probabilities of a dichotomous property with a simple single parameter clumping is suggested. This Markov model is adapted to the observed telomeric effects and then simulations are used to demonstrate properties of the suggested modified randomisation tests. The model is also applied as a direct alternative tool for statistical analysis of the S. cerevisiae genome for clumping of phenotypes