Absolute enrichment: gene set enrichment analysis for homeostatic systems

The Gene Set Enrichment Analysis (GSEA) identifies sets of genes that are differentially regulated in one direction. Many homeostatic systems will include one limb that is upregulated in response to a downregulation of another limb and vice versa. Such patterns are poorly captured by the standard formulation of GSEA. We describe a technique to identify groups of genes (which sometimes can be pathways) that include both up- and down-regulated components. This approach lends insights into the feedback mechanisms that may operate, especially when integrated with protein interaction databases.     

BayGO: Bayesian analysis of ontology term enrichment in microarray data

Background  

The search for enriched (aka over-represented or enhanced) ontology terms in a list of genes obtained from microarray experiments is becoming a standard procedure for a system-level analysis. This procedure tries to summarize the information focussing on classification designs such as Gene Ontology, KEGG pathways, and so on, instead of focussing on individual genes. Although it is well known in statistics that association and significance are distinct concepts, only the former approach has been used to deal with the ontology term enrichment problem.     

Inulin enrichment by fermentation in a flocculating yeast reactor

Summary Simultaneous production of ethanol and fructose enriched syrups was obtained from Jerusalem artichoke extract using a Saccharomyces diastaticus flocculating yeast in a continuous gas-lift reactor with internal biomass recycle. This allowed the production of 42 g/L of ethanol and 70 g/L of inulin containing up to 92% fructose (fructose/glucose ratio of 11). These results can be compared to the batch and chemostat fermentations which gave a higher ethanol concentration but a lower fructose enrichment. Mass transfert limitations can explain both the productivity decrease and the selectivity improvement in the gas-lift reactor.     

Statistics and bioinformatics in nutritional sciences: analysis of complex data in the era of systems biology

Over the past 2 decades, there have been revolutionary developments in life science technologies characterized by high throughput, high efficiency, and rapid computation. Nutritionists now have the advanced methodologies for the analysis of DNA, RNA, protein, low-molecular-weight metabolites, as well as access to bioinformatics databases. Statistics, which can be defined as the process of making scientific inferences from data that contain variability, has historically played an integral role in advancing nutritional sciences. Currently, in the era of systems biology, statistics has become an increasingly important tool to quantitatively analyze information about biological macromolecules. This article describes general terms used in statistical analysis of large, complex experimental data. These terms include experimental design, power analysis, sample size calculation, and experimental errors (Type I and II errors) for nutritional studies at population, tissue, cellular, and molecular levels. In addition, we highlighted various sources of experimental variations in studies involving microarray gene expression, real-time polymerase chain reaction, proteomics, and other bioinformatics technologies. Moreover, we provided guidelines for nutritionists and other biomedical scientists to plan and conduct studies and to analyze the complex data. Appropriate statistical analyses are expected to make an important contribution to solving major nutrition-associated problems in humans and animals (including obesity, diabetes, cardiovascular disease, cancer, ageing, and intrauterine growth retardation).     

Comparative analysis indicates regulatory neofunctionalization of yeast duplicates

Background  

Gene duplication provides raw material for the generation of new functions, but most duplicates are rapidly lost due to the initial redundancy in gene function. How gene function diversifies following duplication is largely unclear. Previous studies analyzed the diversification of duplicates by characterizing their coding sequence divergence. However, functional divergence can also be attributed to changes in regulatory properties, such as protein localization or expression, which require only minor changes in gene sequence.     

An effective data mining technique for reconstructing gene regulatory networks from time series expression data

Recent development in DNA microarray technologies has made the reconstruction of gene regulatory networks (GRNs) feasible. To infer the overall structure of a GRN, there is a need to find out how the expression of each gene can be affected by the others. Many existing approaches to reconstructing GRNs are developed to generate hypotheses about the presence or absence of interactions between genes so that laboratory experiments can be performed afterwards for verification. Since, they are not intended to be used to predict if a gene in an unseen sample has any interactions with other genes, statistical verification of the reliability of the discovered interactions can be difficult. Furthermore, since the temporal ordering of the data is not taken into consideration, the directionality of regulation cannot be established using these existing techniques. To tackle these problems, we propose a data mining technique here. This technique makes use of a probabilistic inference approach to uncover interesting dependency relationships in noisy, high-dimensional time series expression data. It is not only able to determine if a gene is dependent on another but also whether or not it is activated or inhibited. In addition, it can predict how a gene would be affected by other genes even in unseen samples. For performance evaluation, the proposed technique has been tested with real expression data. Experimental results show that it can be very effective. The discovered dependency relationships can reveal gene regulatory relationships that could be used to infer the structures of GRNs.     

Nonselective enrichment for yeast adenine mutants by flow cytometry

The expression of certain adenine biosynthetic mutations in the yeast Saccharomyces cerevisiae results in a red colony color. This phenomenon has historically provided an ideal genetic marker for the study of mutation, recombination, and aneuploidy in lower eukaryotes by classical genetic analysis. In this paper, it is reported that cells carrying ade1 and/or ade2 mutations exhibit primary fluorescence. Based on this observation, the nonselective enrichment of yeast cultures for viable adenine mutants by using the fluorescence-activated cell sorter has been achieved. The advantages of this approach over conventional genetic analysis of mutation, recombination, and mitotic chromosomal stability include speed and accuracy in acquiring data for large numbers of clones. By using appropriate strains, the cell sorter has been used for the isolation of both forward mutations and chromosomal loss events in S. cerevisiae. The resolving power of this system and its noninvasiveness can easily be extended to more complex organisms, including mammalian cells, in which analogous metabolic mutants are available.     

Comparative genomic analysis of T-box regulatory systems in bacteria

T-box antitermination is one of the main mechanisms of regulation of genes involved in amino acid metabolism in Gram-positive bacteria. T-box regulatory sites consist of conserved sequence and RNA secondary structure elements. Using a set of known T-box sites, we constructed the common pattern and used it to scan available bacterial genomes. New T-boxes were found in various Gram-positive bacteria, some Gram-negative bacteria (delta-proteobacteria), and some other bacterial groups (Deinococcales/Thermales, Chloroflexi, Dictyoglomi). The majority of T-box-regulated genes encode aminoacyl-tRNA synthetases. Two other groups of T-box-regulated genes are amino acid biosynthetic genes and transporters, as well as genes with unknown function. Analysis of candidate T-box sites resulted in new functional annotations. We assigned the amino acid specificity to a large number of candidate amino acid transporters and a possible function to amino acid biosynthesis genes. We then studied the evolution of the T-boxes. Analysis of the constructed phylogenetic trees demonstrated that in addition to the normal evolution consistent with the evolution of regulated genes, T-boxes may be duplicated, transferred to other genes, and change specificity. We observed several cases of recent T-box regulon expansion following the loss of a previously existing regulatory system, in particular, arginine regulon in Clostridium difficile and methionine regulon in Lactobacillaceae. Finally, we described a new structural class of T-boxes containing duplicated terminator-antiterminator elements and unusual reduced T-boxes regulating initiation of translation in the Actinobacteria.     

TagSmart: analysis and visualization for yeast mutant fitness data measured by tag microarrays

Background  

A nearly complete collection of gene-deletion mutants (96% of annotated open reading frames) of the yeast Saccharomyces cerevisiae has been systematically constructed. Tag microarrays are widely used to measure the fitness of each mutant in a mutant mixture. The tag array experiments can have a complex experimental design, such as time course measurements and drug treatment with multiple dosages.     

Network enrichment analysis: extension of gene-set enrichment analysis to gene networks

ABSTRACT: BACKGROUND: Gene-set enrichment analyses (GEA or GSEA) are commonly used for biological characterization of an experimental gene-set. This is done by finding known functional categories, such as pathways or Gene Ontology terms, that are over-represented in the experimental set; the assessment is based on an overlap statistic. Rich biological information in terms of gene interaction network is now widely available, but this topological information is not used by GEA, so there is a need for methods that exploit this type of information in high-throughput data analysis. RESULTS: We developed a method of network enrichment analysis (NEA) that extends the overlap statistic in GEA to network links between genes in the experimental set and those in the functional categories. For the crucial step in statistical inference, we developed a fast network randomization algorithm in order to obtain the distribution of any network statistic under the null hypothesis of no association between an experimental gene-set and a functional category. We illustrate the NEA method using gene and protein expression data from a lung cancer study. CONCLUSIONS: The results indicate that the NEA method is more powerful than the traditional GEA, primarily because the relationships between gene sets were more strongly captured by network connectivity rather than by simple overlaps.