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

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.

Genetic pleiotropy in Saccharomyces cerevisiae quantified by high-resolution phenotypic profiling

Genetic pleiotropy, the ability of a mutation in a single gene to give rise to multiple phenotypic outcomes, constitutes an important but incompletely understood biological phenomenon. We used a highresolution and high-precision phenotypic profiling approach to quantify the fitness contribution of genes on the five smallest yeast chromosomes during different forms of environmental stress, selected to probe a wide diversity of physiological features. We found that the extent of pleiotropy is much higher than previously claimed; 17% of the yeast genes were pleiotropic whereof one-fifth were hyper-pleiotropic. Pleiotropic genes preferentially participate in functions related to determination of protein fate, cell growth and morphogenesis, signal transduction and transcription. Contrary to what has earlier been proposed we did not find experimental evidence for slower evolutionary rate of pleiotropic genes/proteins. We also refute the existence of phenotypic islands along chromosomes but report on a remarkable loss both of pleiotropy and of phenotypic penetrance towards chromosomal ends. Thus, the here reported features of pleiotropy both have implications on our understanding of evolutionary processes as well as the mechanisms underlying disease

Additional file 1: Table S1. of PRECOG: a tool for automated extraction and visualization of fitness components in microbial growth phenomics

Feature comparison of the different PRECOG platforms. (DOCX 17 kb

Additional file 2: Figure S1. of PRECOG: a tool for automated extraction and visualization of fitness components in microbial growth phenomics

Meta data to evaluate the extracted fitness components. Markers indicate data used for estimation of growth lag (purple circles), rate/doubling time (black cross), and efficiency (green triangles). Figures are screen shots from PRECOG. Figure S2. Displaying the first derivative of growth. X’s mark the samples where the doubling time was extracted, which coincides with the first derivative peak. Allows the user to identify curves where there are difficulties in extracting traits, like curves exhibiting multimodality (B and C). Figures are screenshots from PRECOG. Figure S3. PRECOG-lite website screenshots. (A) upload, B) table view, C) thumbnail view, D) a detail view of the growth-data, E) the Save As allows the user to save the data in its different forms (growth-data, first derivate, and extracted traits). Figure S4. High-quality benchmarking set of growth curves. The 100 growth curves of high quality displaying various growth feature, i.e. difference in growth lag, rate and efficiency as well as combinations of these. The set also include curves that are clearly multimodal. Red = raw data, Black = fully processed data. All curves are displayed on the log scale (y-axis). Figure S5. Low-quality benchmarking set of growth curves. The 100 growth curves of low quality, representing various technical challenges, e.g. curves with high levels of noise, frequent spikes and collapsing curves. Red = raw data, Black = fully processed data. All curves are displayed on the log scale (y-axis). Figure S6. First derivative of growth curves with various sampling frequencies. First derivative is displayed indicating times for measurements (red circles), and data used for estimation of growth rate (crosses). Figure S7. The effect of sampling frequency on growth lag (upper panel) and efficiency (lower panel). Sampling frequency denotes the fixed time (interval) between consecutive OD measurements. Averages from the high- and low-quality sets are indicated. (PDF 452 kb

Scan-o-matic: High-Resolution Microbial Phenomics at a Massive Scale

The capacity to map traits over large cohorts of individuals—phenomics—lags far behind the explosive development in genomics. For microbes, the estimation of growth is the key phenotype because of its link to fitness. We introduce an automated microbial phenomics framework that delivers accurate, precise, and highly resolved growth phenotypes at an unprecedented scale. Advancements were achieved through the introduction of transmissive scanning hardware and software technology, frequent acquisition of exact colony population size measurements, extraction of population growth rates from growth curves, and removal of spatial bias by reference-surface normalization. Our prototype arrangement automatically records and analyzes close to 100,000 growth curves in parallel. We demonstrate the power of the approach by extending and nuancing the known salt-defense biology in baker’s yeast. The introduced framework represents a major advance in microbial phenomics by providing high-quality data for extensive cohorts of individuals and generating well-populated and standardized phenomics database