GCPReg package for registration of the segmentation gene expression data in Drosophila

In modern functional genomics registration techniques are used to construct reference gene expression patterns and create a spatiotemporal atlas of the expression of all the genes in a network. In this paper we present a software package called GCPReg, which can be used to register the expression patterns of segmentation genes in the early Drosophila embryo. The key task which this package performs is the extraction of spatially localized characteristic features of expression patterns. To facilitate this task, we have developed an easy-to-use interactive graphical interface. We describe GCPReg usage and demonstrate how this package can be applied to register gene expression patterns in wild-type and mutants. GCPReg has been designed to operate on a UNIX platform and is freely available via the Internet at http://urchin.spbcas.ru/downloads/GCPReg/GCPReg.htm.     

A database for management of gene expression data in situ

MOTIVATION: To create a spatiotemporal atlas of Drosophila segmentation gene expression at cellular resolution. RESULTS: The expression of segmentation genes plays a crucial role in the establishment of the Drosophila body plan. Using the IBM DB2 Relational Database Management System we have designed and implemented the FlyEx database. FlyEx contains 2832 images of 14 segmentation gene expression patterns obtained from 954 embryos and 2,073,662 quantitative data records. The averaged data is available for most of segmentation genes at eight time points. FlyEx supports operations on images of gene expression patterns. The database can be used to examine the quality of data, analyze the dynamics of formation of segmentation gene expression domains, as well as estimate the variability of gene expression patterns. We also provide the capability to download data of interest. AVAILABILITY: http://urchin.spbcas.ru/flyex, http://flyex.ams.sunysb.edu/flyex     

Methods for acquisition of quantitative data from confocal images of gene expression in situ

In this review, we summarize original methods for the extraction of quantitative information from confocal images of gene-expression patterns. These methods include image segmentation, the extraction of quantitative numerical data on gene expression, and the removal of background signal and spatial registration. Finally, it is possible to construct a spatiotemporal atlas of gene expression from individual images recorded at each developmental stage. Initially all methods were developed to extract quantitative numerical information from confocal images of segmentation gene expression in Drosophila melanogaster. The application of these methods to Drosophila images makes it possible to reveal new mechanisms in the formation of segmentation gene expression domains, as well as to construct a quantitative atlas of segmentation gene expression. Most image processing procedures can be easily adapted to process a wide range of biological images.     

In situ analysis of gene expression in Xenopus embryos

The molecular anatomy of the vertebrate embryo was systematically analysed through gene expression during early development of the Xenopus frog using whole-mount in situ hybridization. Expression patterns are documented and assembled into the database Axeldb (http://www.dkfz-heidelberg.de/abt0135/axeldb.htm). Synexpression groups representing genes with shared, complex expression pattern that predict molecular pathways involved in patterning and differentiation have been identified. These sets of co-regulated genes show a striking similarity with operons, and may be a key determinant facilitating evolutionary change leading to animal diversity.     

In situ gene expression by Vibrio vulnificus

Strains of Vibrio vulnificus incubated in situ in natural estuarine waters during warm months continued to express katG (periplasmic catalase), rpoS (stress sigma factor), tufA (elongation factor), wza, and wzb (capsule synthesis). vvhA (hemolysin) was differentially expressed between environmental and clinical isolates. These results paralleled our in vitro findings.     

Spatial gene expression in the T-stage mouse metanephros

The E11.5 mouse metanephros is comprised of a T-stage ureteric epithelial tubule sub-divided into tip and trunk cells surrounded by metanephric mesenchyme (MM). Tip cells are induced to undergo branching morphogenesis by the MM. In contrast, signals within the mesenchyme surrounding the trunk prevent ectopic branching of this region. In order to identify novel genes involved in the molecular regulation of branching morphogenesis we compared the gene expression profiles of isolated tip, trunk and MM cells using Compugen mouse long oligo microarrays. We identified genes enriched in the tip epithelium, sim-1, Arg2, Tacstd1, Crlf-1 and BMP7; genes enriched in the trunk epithelium, Innp1, Itm2b, Mkrn1, SPARC, Emu2 and Gsta3 and genes spatially restricted to the mesenchyme surrounding the trunk, CSPG2 and CV-2, with overlapping and complimentary expression to BMP4, respectively. This study has identified genes spatially expressed in regions of the developing kidney involved in branching morphogenesis, nephrogenesis and the development of the collecting duct system, calyces, renal pelvis and ureter.     

High-throughput analysis of gene expression on tissue sections by in situ hybridization

Genome-scale sequencing projects, high-throughput RNAi screens, systematic gene targeting, and system-biology-based network predictions all depend on a validation of biological significance in order to understand the relevance of a particular finding. Such validation, for the most part, rests on low-throughput technologies. This article provides protocols that, in combination with suitable instrumentation, make possible a semi-automated analysis of gene expression on tissue sections by means of in situ hybridization. Knowledge of gene expression localization has the potential to aid, and thereby accelerate, the validation of gene functions.     

Effect of registration on cyclical kinematic data

Given growing interest in functional data analysis (FDA) as a useful method for analyzing human movement data, it is critical to understand the effects of standard FDA procedures, including registration, on biomechanical analyses. Registration is used to reduce phase variability between curves while preserving the individual curve's shape and amplitude. The application of three methods available to assess registration could benefit those in the biomechanics community using FDA techniques: comparison of mean curves, comparison of average RMS values, and assessment of time-warping functions. Therefore, the present study has two purposes. First, the necessity of registration applied to cyclical data after time normalization is assessed. Second, we illustrate the three methods for evaluating registration effects. Masticatory jaw movements of 22 healthy adults (2 males, 21 females) were tracked while subjects chewed a gum-based pellet for 20 s. Motion data were captured at 60 Hz with two gen-locked video cameras. Individual chewing cycles were time normalized and then transformed into functional observations. Registration did not affect mean curves and warping functions were linear. Although registration decreased the RMS, indicating a decrease in inter-subject variability, the difference was not statistically significant. Together these results indicate that registration may not always be necessary for cyclical chewing data. An important contribution of this paper is the illustration of three methods for evaluating registration that are easy to apply and useful for judging whether the extra data manipulation is necessary.     

Spatial patterns of gene expression in preimplantation mouse embryos

The distribution of total polyadenylated RNA and mRNAs from the beta-actin, fibronectin, and cytokeratin Endo A genes was examined in preimplantation mouse embryos using in situ hybridization of riboprobes to RNA in sections of embryos. Polyadenylated RNA was found in the cytoplasm of all cells of blastocyst-stage embryos, whereas the specific mRNAs displayed three distinct patterns of expression: uniform throughout the embryo (beta-actin), enriched in the inner cell mass (fibronectin), and enriched in the trophectoderm (Endo A). In eight-cell embryos, the polyadenylated RNA was more concentrated in nuclei than in the cytoplasm (as noted previously), although this was not the case in blastocysts, nor was it true for the specific mRNAs that were examined. These experiments demonstrate that there is localized gene expression in the early mouse embryo, which correlates with the formation of the trophectoderm and the inner cell mass.     

Gene-Ontology-based clustering of gene expression data

The expected correlation between genetic co-regulation and affiliation to a common biological process is not necessarily the case when numerical cluster algorithms are applied to gene expression data. GO-Cluster uses the tree structure of the Gene Ontology database as a framework for numerical clustering, and thus allowing a simple visualization of gene expression data at various levels of the ontology tree. AVAILABILITY: The 32-bit Windows application is freely available at http://www.mpibpc.mpg.de/go-cluster/     

Seed-based biclustering of gene expression data

Background

Accumulated biological research outcomes show that biological functions do not depend on individual genes, but on complex gene networks. Microarray data are widely used to cluster genes according to their expression levels across experimental conditions. However, functionally related genes generally do not show coherent expression across all conditions since any given cellular process is active only under a subset of conditions. Biclustering finds gene clusters that have similar expression levels across a subset of conditions. This paper proposes a seed-based algorithm that identifies coherent genes in an exhaustive, but efficient manner.

Methods

In order to find the biclusters in a gene expression dataset, we exhaustively select combinations of genes and conditions as seeds to create candidate bicluster tables. The tables have two columns (a) a gene set, and (b) the conditions on which the gene set have dissimilar expression levels to the seed. First, the genes with less than the maximum number of dissimilar conditions are identified and a table of these genes is created. Second, the rows that have the same dissimilar conditions are grouped together. Third, the table is sorted in ascending order based on the number of dissimilar conditions. Finally, beginning with the first row of the table, a test is run repeatedly to determine whether the cardinality of the gene set in the row is greater than the minimum threshold number of genes in a bicluster. If so, a bicluster is outputted and the corresponding row is removed from the table. Repeating this process, all biclusters in the table are systematically identified until the table becomes empty.

Conclusions

This paper presents a novel biclustering algorithm for the identification of additive biclusters. Since it involves exhaustively testing combinations of genes and conditions, the additive biclusters can be found more readily.     

Visualization of microarray gene expression data

Microarray gene expression data is used in various biological and medical investigations. Processing of gene expression data requires algorithms in data mining, process automation and knowledge discovery. Available data mining algorithms exploits various visualization techniques. Here, we describe the merits and demerits of various visualization parameters used in gene expression analysis.     

Mining gene expression data based on template theory

MOTIVATION: It is understood that clustering genes are useful for exploring scientific knowledge from DNA microarray gene expression data. The explored knowledge can be finally used for annotating biological function for novel genes. Representing the explored knowledge in an efficient manner is then closely related to the classification accuracy. However, this issue has not yet been paid the attention it deserves. RESULT: A novel method based on template theory in cognitive psychology and pattern recognition is developed in this study for representing knowledge extracted from cluster analysis effectively. The basic principle is to represent knowledge according to the relationship between genes and a found cluster structure. Based on this novel knowledge representation method, a pattern recognition algorithm (the decision tree algorithm C4.5) is then used to construct a classifier for annotating biological functions of novel genes. The experiments on five published datasets show that this method has improved the classification performance compared with the conventional method. The statistical tests indicate that this improvement is significant. AVAILABILITY: The software package can be obtained upon request from the author.     

Identifying time-lagged gene clusters using gene expression data

MOTIVATION: Analysis of gene expression data can provide insights into the time-lagged co-regulation of genes/gene clusters. However, existing methods such as the Event Method and the Edge Detection Method are inefficient as they compare only two genes at a time. More importantly, they neglect some important information due to their scoring criterian. In this paper, we propose an efficient algorithm to identify time-lagged co-regulated gene clusters. The algorithm facilitates localized comparison and processes several genes simultaneously to generate detailed and complete time-lagged information for genes/gene clusters. RESULTS: We experimented with the time-series Yeast gene dataset and compared our algorithm with the Event Method. Our results show that our algorithm is not only efficient, but also delivers more reliable and detailed information on time-lagged co-regulation between genes/gene clusters. AVAILABILITY: The software is available upon request. CONTACT: jiliping@comp.nus.edu.sg SUPPLEMENTARY INFORMATION: Supplementary tables and figures for this paper can be found at http://www.comp.nus.edu.sg/~jiliping/p2.htm.     

Analysis of variance components in gene expression data

MOTIVATION: A microarray experiment is a multi-step process, and each step is a potential source of variation. There are two major sources of variation: biological variation and technical variation. This study presents a variance-components approach to investigating animal-to-animal, between-array, within-array and day-to-day variations for two data sets. The first data set involved estimation of technical variances for pooled control and pooled treated RNA samples. The variance components included between-array, and two nested within-array variances: between-section (the upper- and lower-sections of the array are replicates) and within-section (two adjacent spots of the same gene are printed within each section). The second experiment was conducted on four different weeks. Each week there were reference and test samples with a dye-flip replicate in two hybridization days. The variance components included week-to-week, animal-to-animal and between-array and within-array variances. RESULTS: We applied the linear mixed-effects model to quantify different sources of variation. In the first data set, we found that the between-array variance is greater than the between-section variance, which, in turn, is greater than the within-section variance. In the second data set, for the reference samples, the week-to-week variance is larger than the between-array variance, which, in turn, is slightly larger than the within-array variance. For the test samples, the week-to-week variance has the largest variation. The animal-to-animal variance is slightly larger than the between-array and within-array variances. However, in a gene-by-gene analysis, the animal-to-animal variance is smaller than the between-array variance in four out of five housekeeping genes. In summary, the largest variation observed is the week-to-week effect. Another important source of variability is the animal-to-animal variation. Finally, we describe the use of variance-component estimates to determine optimal numbers of animals, arrays per animal and sections per array in planning microarray experiments.