A constrained polynomial regression procedure for estimating the local False Discovery Rate

Background  

In the context of genomic association studies, for which a large number of statistical tests are performed simultaneously, the local False Discovery Rate (lFDR), which quantifies the evidence of a specific gene association with a clinical or biological variable of interest, is a relevant criterion for taking into account the multiple testing problem. The lFDR not only allows an inference to be made for each gene through its specific value, but also an estimate of Benjamini-Hochberg's False Discovery Rate (FDR) for subsets of genes.     

Phase analysis of circadian-related genes in two tissues

Background  

Recent circadian clock studies using gene expression microarray in two different tissues of mouse have revealed not all circadian-related genes are synchronized in phase or peak expression times across tissues in vivo. Instead, some circadian-related genes may be delayed by 4–8 hrs in peak expression in one tissue relative to the other. These interesting biological observations prompt a statistical question regarding how to distinguish the synchronized genes from genes that are systematically lagged in phase/peak expression time across two tissues.     

Statistical methodology for the analysis of dye-switch microarray experiments

Background  

In individually dye-balanced microarray designs, each biological sample is hybridized on two different slides, once with Cy3 and once with Cy5. While this strategy ensures an automatic correction of the gene-specific labelling bias, it also induces dependencies between log-ratio measurements that must be taken into account in the statistical analysis.     

Statistical tests to compare motif count exceptionalities

Background  

Finding over- or under-represented motifs in biological sequences is now a common task in genomics. Thanks to p-value calculation for motif counts, exceptional motifs are identified and represent candidate functional motifs. The present work addresses the related question of comparing the exceptionality of one motif in two different sequences. Just comparing the motif count p-values in each sequence is indeed not sufficient to decide if this motif is significantly more exceptional in one sequence compared to the other one. A statistical test is required.     

Modelling the correlation between the activities of adjacent genes in drosophila

Background  

Correlation between the expression levels of genes which are located close to each other on the genome has been found in various organisms, including yeast, drosophila and humans. Since such a correlation could be explained by several biochemical, evolutionary, genetic and technological factors, there is a need for statistical models that correspond to specific biological models for the correlation structure.     

CoXpress: differential co-expression in gene expression data

Background  

Traditional methods of analysing gene expression data often include a statistical test to find differentially expressed genes, or use of a clustering algorithm to find groups of genes that behave similarly across a dataset. However, these methods may miss groups of genes which form differential co-expression patterns under different subsets of experimental conditions. Here we describe coXpress, an R package that allows researchers to identify groups of genes that are differentially co-expressed.     

A comparative study of protein synthesis in <Emphasis Type="Italic">in vitro</Emphasis> systems: from the prokaryotic reconstituted to the eukaryotic extract-based

Background  

Cell-free protein synthesis is not only a rapid and high throughput technology to obtain proteins from their genes, but also provides an in vitro platform to study protein translation and folding. A detailed comparison of in vitro protein synthesis in different cell-free systems may provide insights to their biological differences and guidelines for their applications.     

MAPPER: a search engine for the computational identification of putative transcription factor binding sites in multiple genomes

Background  

Cis-regulatory modules are combinations of regulatory elements occurring in close proximity to each other that control the spatial and temporal expression of genes. The ability to identify them in a genome-wide manner depends on the availability of accurate models and of search methods able to detect putative regulatory elements with enhanced sensitivity and specificity.     

A permutation-based multiple testing method for time-course microarray experiments

Background  

Time-course microarray experiments are widely used to study the temporal profiles of gene expression. Storey et al. (2005) developed a method for analyzing time-course microarray studies that can be applied to discovering genes whose expression trajectories change over time within a single biological group, or those that follow different time trajectories among multiple groups. They estimated the expression trajectories of each gene using natural cubic splines under the null (no time-course) and alternative (time-course) hypotheses, and used a goodness of fit test statistic to quantify the discrepancy. The null distribution of the statistic was approximated through a bootstrap method. Gene expression levels in microarray data are often complicatedly correlated. An accurate type I error control adjusting for multiple testing requires the joint null distribution of test statistics for a large number of genes. For this purpose, permutation methods have been widely used because of computational ease and their intuitive interpretation.     

Microarray data mining using landmark gene-guided clustering

Background  

Clustering is a popular data exploration technique widely used in microarray data analysis. Most conventional clustering algorithms, however, generate only one set of clusters independent of the biological context of the analysis. This is often inadequate to explore data from different biological perspectives and gain new insights. We propose a new clustering model that can generate multiple versions of different clusters from a single dataset, each of which highlights a different aspect of the given dataset.     

Meta-analysis of gene expression microarrays with missing replicates

Background  

Many different microarray experiments are publicly available today. It is natural to ask whether different experiments for the same phenotypic conditions can be combined using meta-analysis, in order to increase the overall sample size. However, some genes are not measured in all experiments, hence they cannot be included or their statistical significance cannot be appropriately estimated in traditional meta-analysis. Nonetheless, these genes, which we refer to as incomplete genes, may also be informative and useful.     

Classification of microarray data using gene networks

Background  

Microarrays have become extremely useful for analysing genetic phenomena, but establishing a relation between microarray analysis results (typically a list of genes) and their biological significance is often difficult. Currently, the standard approach is to map a posteriori the results onto gene networks in order to elucidate the functions perturbed at the level of pathways. However, integrating a priori knowledge of the gene networks could help in the statistical analysis of gene expression data and in their biological interpretation.     

Combining Shapley value and statistics to the analysis of gene expression data in children exposed to air pollution

Background  

In gene expression analysis, statistical tests for differential gene expression provide lists of candidate genes having, individually, a sufficiently low p-value. However, the interpretation of each single p-value within complex systems involving several interacting genes is problematic. In parallel, in the last sixty years, game theory has been applied to political and social problems to assess the power of interacting agents in forcing a decision and, more recently, to represent the relevance of genes in response to certain conditions.     

Responses of community structure of amoA‐encoding archaea and ammonia‐oxidizing bacteria in ammonia biofilter with rockwool mixtures to the gradual increases in ammonium and nitrate

Aims

To investigate community shifts of amoA‐encoding archaea (AEA) and ammonia‐oxidizing bacteria (AOB) in biofilter under nitrogen accumulation process.

Methods and Results

A laboratory‐scale rockwool biofilter with an irrigated water circulation system was operated for 436 days with ammonia loading rates of 49–63 NH₃ g m^?3 day^?1. The AEA and AOB communities were investigated by denaturing gradient gel electrophoresis, sequencing and real‐time PCR analysis based on amoA genes. The results indicated that changes in abundance and community compositions occurred in a different manner between archaeal and bacterial amoA during the operation. However, both microbial community structures mainly varied when free ammonia (FA) concentrations in circulation water were increasing, which caused a temporal decline in reactor performance. Dominant amoA sequences after this transition were related to Thaumarchaeotal Group I.1b, Nitrosomonas europaea lineages and one subcluster within Nitrosospira sp. cluster 3, for archaea and bacteria, respectively.

Conclusions

The specific FA in circulation water seems to be the important factor, which relates to the AOB and AEA community shifts in the biofilter besides ammonium and pH.

Significance and Impact of the Study

One of the key factors for regulating AEA and AOB communities was proposed that is useful for optimizing biofiltration technology.     

Screening of white‐rot fungi for bioprocessing of wheat straw into ruminant feed

Aim

In this study, the biological variation for improvement of the nutritive value of wheat straw by 12 Ceriporiopsis subvermispora, 10 Pleurotus eryngii and 10 Lentinula edodes strains was assessed. Screening of the best performing strains within each species was made based on the in vitro degradability of fungal‐treated wheat straw.

Methods and Results

Wheat straw was inoculated with each strain for 7 weeks of solid state fermentation. Weekly samples were evaluated for in vitro gas production (IVGP) in buffered rumen fluid for 72 h. Out of the 32 fungal strains studied, 17 strains showed a significantly higher (P < 0·05) IVGP compared to the control after 7 weeks (227·7 ml g^?1 OM). The three best Ceriporiopsis subvermispora strains showed a mean IVGP of 297·0 ml g^?1 OM, while the three best P. eryngii and L. edodes strains showed a mean IVGP of 257·8 and 291·5 ml g^?1 OM, respectively.

Conclusion

Ceriporiopsis subvermispora strains show an overall high potential to improve the ruminal degradability of wheat straw, followed by L. edodes and P. eryngii strains.

Significance and Impact of the Study

Large variation exists within and among different fungal species in the valorization of wheat straw, which offers opportunities to improve the fungal genotype by breeding.     

Detecting multivariate differentially expressed genes

Background  

Gene expression is governed by complex networks, and differences in expression patterns between distinct biological conditions may therefore be complex and multivariate in nature. Yet, current statistical methods for detecting differential expression merely consider the univariate difference in expression level of each gene in isolation, thus potentially neglecting many genes of biological importance.