PhenoCurve: capturing dynamic phenotype-environment relationships using phenomics data

Phenomics: Genotype to Phenotype

Statistical methods for comparative phenomics using high-throughput phenotype microarrays.

We propose statistical methods for comparing phenomics data generated by the Biolog Phenotype Microarray (PM) platform for high-throughput phenotyping. Instead of the routinely used visual inspection of data with no sound inferential basis, we develop two approaches. The first approach is based on quantifying the distance between mean or median curves from two treatments and then applying a per...

Dynamic Histograms: Capturing Evolving Data Sets

In this paper, we introduce dynamic histograms, which are constructed and maintained incrementally. We develop several dynamic histogram construction algorithms and show that they come close to static histograms in quality. Our experimental study covers a wide range of datasets and update patterns, including histogram maintenance in a shared-nothing environment. Building upon the insights offer...

Toward metabolic phenomics: analysis of genomic data using flux balances.

Small genome sequencing and annotations are leading to the definition of metabolic genotypes in an increasing number of organisms. Proteomics is beginning to give insights into the use of the metabolic genotype under given growth conditions. These data sets give the basis for systemically studying the genotype-phenotype relationship. Methods of systems science need to be employed to analyze, in...

PhenomicDB: a multi-species genotype/phenotype database for comparative phenomics

UNLABELLED We have created PhenomicDB, a multi-species genotype/phenotype database by merging public genotype/phenotype data from a wide range of model organisms and Homo sapiens. Until now these data were available in distinct organism-specific databases (e.g. WormBase, OMIM, FlyBase and MGI). We compiled this wealth of data into a single integrated resource by coarse-grained semantic mapping ...