TimeClust: a clustering tool for gene expression time series

TimeClust is a user-friendly software package to cluster genes according to their temporal expression profiles. It can be conveniently used to analyze data obtained from DNA microarray time-course experiments. It implements two original algorithms specifically designed for clustering short time series together with hierarchical clustering and self-organizing maps. AVAILABILITY: TimeClust executable files for Windows and LINUX platforms can be downloaded free of charge for non-profit institutions from the following web site: http://aimed11.unipv.it/TimeClust.     

Agrobacterium-mediated transient expression in citrus leaves: a rapid tool for gene expression and functional gene assay

In this study, we present a method for transient expression of the type III effector AvrGf1 from Xanthomonas citri subsp. citri strain A^w in grapefruit leaves (Citrus paradisi) via Agrobacterium tumefaciens. The coding sequence of avrGf1 was placed under the control of the constitutive CaMV 35S promoter in the binary vectors pGWB2 and pGWB5. Infiltration of grapefruit leaves with A. tumefaciens carrying these constructs triggered a hypersensitive response (HR) in grapefruit 4 days after inoculation. When transiently expressed in grapefruit leaves, two mutants, AvrGf1ΔN116 and AvrGf1ΔC83, failed to induce an HR. Moreover, using bioinformatics tools, a chloroplast transit signal was predicted at the N terminus of AvrGf1. We demonstrated chloroplast localization by using an AvrGf1::GFP fusion protein, where confocal images revealed that GFP fluorescence was accumulating in the stomatal cells that are abundant in chloroplasts. Transient expression in citrus has the potential for aiding in the development of new disease defense strategies in citrus.     

Nonsense RNA: a tool for specifically inhibiting the expression of a gene in vivo

We describe a general technique to inhibit gene expression in eukaryotic cells. The gene we chose to inhibit was the E. coli LacZ gene (encoding beta-galactosidase), which has previously been cloned into a eukaryotic expression vector [1]. This plasmid is called pCH110. We constructed a variant of pCH110 in which we flipped a 2566 base pair 5' fragment of the LacZ gene into the antiparallel orientation. The plasmid containing this mutated LacZ gene is called pNSLacZ (NS signifies non-sense coding sequence). When equal amounts of pCH110 and pNSLacZ are co-transfected into 3T6 mouse fibroblasts, the beta-galactosidase activity is decreased by approximately a factor of ten. Increasing the ratio of pNSLacZ to pCH110 above 1:1 does not appreciably increase the level of inhibition. Next, we prove the specificity of the inhibition by adding a third gene to the transfection mixture. For this purpose, we used pSVneo beta, a plasmid which expresses a phosphotransferase. We found that even when the beta-galactosidase activity was diminished by a factor of 10, the phosphotransferase activity was unaffected. Therefore, we have demonstrated that: the presence of an antiparallel copy of the LacZ gene results in a significant and specific diminution of the LacZ gene's expression; only a fraction of the LacZ gene needs to be in the antiparallel orientation in order to observe this effect. These results suggest that this technique can serve as a tool to decrease the level of gene expression in order to study the function of specific genes, or as a therapeutic manoeuvre in the treatment of disorders of abnormal gene expression.     

BayBoots: a model-free Bayesian tool to identify class markers from gene expression data

One of the goals of gene expression experiments is the identification of differentially expressed genes among populations that could be used as markers. For this purpose, we implemented a model-free Bayesian approach in a user-friendly and freely available web-based tool called BayBoots. In spite of a common misunderstanding that Bayesian and model-free approaches are incompatible, we merged them in the BayBoots implementation using the Kernel density estimator and Rubin 's Bayesian Bootstrap. We used the Bayes error rate (BER) instead of the usual P values as an alternative statistical index to rank a class marker's discriminative potential, since it can be visualized by a simple graphical representation and has an intuitive interpretation. Subsequently, Bayesian Bootstrap was used to assess BER 's credibility. We tested BayBoots on microarray data to look for markers for Trypanosoma cruzi strains isolated from cardiac and asymptomatic patients. We found that the three most frequently used methods in microarray analysis: t-test, non-parametric Wilcoxon test and correlation methods, yielded several markers that were discarded by a time-consuming visual check. On the other hand, the BayBoots graphical output and ranking was able to automatically identify markers for which classification performance was consistent. BayBoots is available at: http://www.vision.ime.usp.br/~rvencio/BayBoots.     

Dynamic gene expression engineering as a tool in pathway engineering

1. Download : Download high-res image (130KB)
2. Download : Download full-size image

     

STEM: a tool for the analysis of short time series gene expression data

Background  

Time series microarray experiments are widely used to study dynamical biological processes. Due to the cost of microarray experiments, and also in some cases the limited availability of biological material, about 80% of microarray time series experiments are short (3–8 time points). Previously short time series gene expression data has been mainly analyzed using more general gene expression analysis tools not designed for the unique challenges and opportunities inherent in short time series gene expression data.     

Differential gene expression in disease: a comparison between high-throughput studies and the literature

Background

Differential gene expression is important to understand the biological differences between healthy and diseased states. Two common sources of differential gene expression data are microarray studies and the biomedical literature.

Methods

With the aid of text mining and gene expression analysis we have examined the comparative properties of these two sources of differential gene expression data.

Results

The literature shows a preference for reporting genes associated to higher fold changes in microarray data, rather than genes that are simply significantly differentially expressed. Thus, the resemblance between the literature and microarray data increases when the fold-change threshold for microarray data is increased. Moreover, the literature has a reporting preference for differentially expressed genes that (1) are overexpressed rather than underexpressed; (2) are overexpressed in multiple diseases; and (3) are popular in the biomedical literature at large. Additionally, the degree to which diseases are similar depends on whether microarray data or the literature is used to compare them. Finally, vaguely-qualified reports of differential expression magnitudes in the literature have only small correlation with microarray fold-change data.

Conclusions

Reporting biases of differential gene expression in the literature can be affecting our appreciation of disease biology and of the degree of similarity that actually exists between different diseases.

     

GEST: a gene expression search tool based on a novel Bayesian similarity metric

Gene expression array technology has made possible the assay of expression levels of tens of thousands of genes at a time; large databases of such measurements are currently under construction. One important use of such databases is the ability to search for experiments that have similar gene expression levels as a query, potentially identifying previously unsuspected relationships among cellular states. Such searches depend crucially on the metric used to assess the similarity between pairs of experiments. The complex joint distribution of gene expression levels, particularly their correlational structure and non-normality, make simple similarity metrics such as Euclidean distance or correlational similarity scores suboptimal for use in this application. We present a similarity metric for gene expression array experiments that takes into account the complex joint distribution of expression values. We provide a computationally tractable approximation to this measure, and have implemented a database search tool based on it. We discuss implementation issues and efficiency, and we compare our new metric to other standard metrics.     

Rx-Cre, a tool for inactivation of gene expression in the developing retina

Rx is a homeobox-containing gene that is critical for vertebrate eye development. Its expression domain delineates a field of cells from which the retina and the ventral hypothalamus develop. The 5' upstream regulatory sequences of the medaka fish Rx gene are functionally conserved during evolution to a degree that they direct gene expression into the Rx-expressing field of cells in mice. Using these sequences, we made a Cre line that can be used for inactivation of gene expression in the developing retina.     

GOurmet: A tool for quantitative comparison and visualization of gene expression profiles based on gene ontology (GO) distributions

Background  

The ever-expanding population of gene expression profiles (EPs) from specified cells and tissues under a variety of experimental conditions is an important but difficult resource for investigators to utilize effectively. Software tools have been recently developed to use the distribution of gene ontology (GO) terms associated with the genes in an EP to identify specific biological functions or processes that are over- or under-represented in that EP relative to other EPs. Additionally, it is possible to use the distribution of GO terms inherent to each EP to relate that EP as a whole to other EPs. Because GO term annotation is organized in a tree-like cascade of variable granularity, this approach allows the user to relate (e.g., by hierarchical clustering) EPs of varying length and from different platforms (e.g., GeneChip, SAGE, EST library).     

The Graphical Query Language: a tool for analysis of gene expression time-courses

The Graphical Query Language (GQL) is a set of tools for the analysis of gene expression time-courses. They allow a user to pre-process the data, to query it for interesting patterns, to perform model-based clustering or mixture estimation, to include subsequent refinements of clusters and, finally, to use other biological resources to evaluate the results. Analyses are carried out in a graphical and interactive environment, allowing expert intervention in all stages of the data analysis. AVAILABILITY: The GQL package is freely available under the GNU general public license (GPL) at http://www.ghmm.org/gql