Processing of natural language queries to a relational database

MOTIVATION: A new method is developed to query a relational database in natural language (NL). RESULTS: The method, based on a semantic approach, interprets grammatical and lexical units of a natural language into concepts of subject domain, which are given in a conceptual scheme. The conceptual scheme is mapped formally onto the logical scheme. We applied the method to query the FlyEx database in natural language. FlyEx contains information on the expression of segmentation genes in Drosophila melanogaster. The method allows formulation of queries in various natural languages simultaneously, and is adaptive to changes in the knowledge domain and user's views. It provides optimal transformation of queries from natural language to SQL, as well as visualization of information as a hyperscheme. The method does not require specification of all possible language constructions as well as a standard grammar accuracy in formulation of NL queries.     

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.     

Pipeline for acquisition of quantitative data on segmentation gene expression from confocal images

We describe a data pipeline developed to extract the quantitative data on segmentation gene expression from confocal images of gene expression patterns in Drosophila. The pipeline consists of five steps: image segmentation, background removal, temporal characterization of an embryo, data registration and data averaging. This pipeline was successfully applied to obtain quantitative gene expression data at cellular resolution in space and at the 6.5-minute resolution in time, as well as to construct a spatiotemporal atlas of segmentation gene expression. Each data pipeline step can be easily adapted to process a wide range of images of gene expression patterns.     

Pipeline for acquisition of quantitative data on segmentation gene expression from confocal images

We describe a data pipeline developed to extract the quantitative data on segmentation gene expression from confocal images of gene expression patterns in Drosophila. The pipeline consists of 5 steps: image segmentation, background removal, temporal characterization of an embryo, data registration and data averaging. This pipeline was successfully applied to obtain quantitative gene expression data at cellular resolution in space and at the 6.5 minute resolution in time, as well as to construct a spatiotemporal atlas of segmentation gene expression. Each data pipeline step can be easily adapted to process a wide range of images of gene expression patterns.     

Sebida: a database for the functional and evolutionary analysis of genes with sex-biased expression

We describe Sebida, a database of genes with sex-biased expression. The database integrates results from multiple, independent microarray studies comparing male and female gene expression in Drosophila melanogaster, Drosophila simulans and Anopheles gambiae. Sebida uses standard nomenclature, which allows individual genes to be compared across different microarray platforms and to be queried by gene name, symbol, or annotation number. In addition to ratios of male/female expression for each gene, Sebida also contains information useful for evolutionary studies, such as local recombination rate, degree of codon bias and interspecific divergence at synonymous and non-synonymous sites. AVAILABILITY: Sebida can be accessed at http://www.sebida.de     

Axeldb: a Xenopus laevis database focusing on gene expression

Axeldb is a database storing and integrating gene expression patterns and DNA sequences identified in a large-scale in situ hybridization study in Xenopus laevis embryos. The data are organised in a format appropriate for comprehensive analysis, and enable comparison of images of expression pattern for any given set of genes. Information on literature, cDNA clones and their availability, nucleotide sequences, expression pattern and accompanying pictures are available. Current developments are aimed toward the interconnection with other databases and the integration of data from the literature. Axeldb is implemented using an ACEDB database system, and available through the web at http://www.dkfz-heidelberg.de/abt0135/axeldb.htm     

Registration of the expression patterns of Drosophila segmentation genes by two independent methods

MOTIVATION: To construct an integrated map of Drosophila segmentation gene expression from partial data taken from individual embryos. RESULTS: Spline and wavelet based registration techniques were developed to register Drosophila segmentation gene expression data. As ground control points for registration we used the locations of extrema on gene expression patterns, represented in 1D. The registration method was characterized by unprecedented high accuracy. A method for constructing the integrated pattern of gene expression at cellular resolution was designed. These patterns were constructed for 9 segmentation genes belonging to gap and pair-rule classes.     

GINI: From ISH Images to Gene Interaction Networks

Accurate inference of molecular and functional interactions among genes, especially in multicellular organisms such as Drosophila, often requires statistical analysis of correlations not only between the magnitudes of gene expressions, but also between their temporal-spatial patterns. The ISH (in-situ-hybridization)-based gene expression micro-imaging technology offers an effective approach to perform large-scale spatial-temporal profiling of whole-body mRNA abundance. However, analytical tools for discovering gene interactions from such data remain an open challenge due to various reasons, including difficulties in extracting canonical representations of gene activities from images, and in inference of statistically meaningful networks from such representations. In this paper, we present GINI, a machine learning system for inferring gene interaction networks from Drosophila embryonic ISH images. GINI builds on a computer-vision-inspired vector-space representation of the spatial pattern of gene expression in ISH images, enabled by our recently developed system; and a new multi-instance-kernel algorithm that learns a sparse Markov network model, in which, every gene (i.e., node) in the network is represented by a vector-valued spatial pattern rather than a scalar-valued gene intensity as in conventional approaches such as a Gaussian graphical model. By capturing the notion of spatial similarity of gene expression, and at the same time properly taking into account the presence of multiple images per gene via multi-instance kernels, GINI is well-positioned to infer statistically sound, and biologically meaningful gene interaction networks from image data. Using both synthetic data and a small manually curated data set, we demonstrate the effectiveness of our approach in network building. Furthermore, we report results on a large publicly available collection of Drosophila embryonic ISH images from the Berkeley Drosophila Genome Project, where GINI makes novel and interesting predictions of gene interactions. Software for GINI is available at http://sailing.cs.cmu.edu/Drosophila_ISH_images/     

HOX Pro: a specialized database for clusters and networks of homeobox genes

It is now clear that the homeobox motif is well conserved across metazoan phyla. It has been established experimentally that a subset of genes containing this motif plays key roles in the orchestration of gene expression during development. Auto- and cross-regulatory functional interactions join homeobox genes into genetic networks. We have developed a specialized database HOX-Pro in order to arrange all available data on structure, function, phylogeny and evolution of Hox genes, Hox clusters and Hox networks. Its primary location is http://www.iephb.nw.ru/hoxpro. The database is also mirrored at http://www.mssm.edu/molbio/hoxpro. The HOX-Pro database is aimed at: (i) analysis and classification of regulatory and coding regions in diverse homeobox and related genes; (ii) comparative analysis of organization of 'Hox-based' genetic networks in the sea urchin Strongylocentrotus purpuratus, the fruit fly Drosophila melanogaster and the mouse Mus musculus; and (iii) analysis of phylogeny and evolution of homeobox genes and clusters.     

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

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

GEPIS--quantitative gene expression profiling in normal and cancer tissues

MOTIVATION: Expression profiling in diverse tissues is fundamental to understanding gene function as well as therapeutic target identification. The vast collection of expressed sequence tags (ESTs) and the associated tissue source information provides an attractive opportunity for studying gene expression. RESULTS: To facilitate EST-based expression analysis, we developed GEPIS (gene expression profiling in silico), a tool that integrates EST and tissue source information to compute gene expression patterns in a large panel of normal and tumor samples. We found EST-based expression patterns to be consistent with published papers as well as our own experimental results. We also built a GEPIS Regional Atlas that depicts expression characteristics of all genes in a selected genomic region. This program can be adapted for large-scale screening for genes with desirable expression patterns, as illustrated by our large-scale mining for tissue- and tumor-specific genes. AVAILABILITY: The email server version of the GEPIS application is freely available at http://share.gene.com/share/gepis. An interactive version of GEPIS will soon be freely available at http://www.cgl.ucsf.edu/Research/genentech/gepis/. The source code, modules, data and gene lists can be downloaded at http://share.gene.com/share/gepis.     

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.     

CVD: the intestinal crypt/villus in situ hybridization database

The intestinal crypt/villus in situ hybridization database (CVD) query interface is a web-based tool to search for genes with similar relative expression patterns along the crypt/villus axis of the mammalian intestine. The CVD is an online database holding information for relative gene expression patterns in the mammalian intestine and is based on the scoring of in situ hybridization experiments reported in the literature. CVD contains expression data for 88 different genes collected from 156 different in situ hybridization profiles. The web-based query interface allows execution of both single gene queries and pattern searches. The query results provide links to the most relevant public gene databases. AVAILABILITY: http://pc113.imbg.ku.dk/ps/     

MASISH: a database for gene expression in maize seeds

Grass seeds are complex organs composed by multiple tissues and cell types that develop coordinately to produce a viable embryo. The identification of genes involved in seed development is of great interest, but systematic spatial analyses of gene expression on maize seeds at the cell level have not yet been performed. MASISH is an online database holding information for gene expression spatial patterns in maize seeds based on in situ hybridization experiments. The web-based query interface allows the execution of gene queries and provides hybridization images, published references and information of the analyzed genes. AVAILABILITY: http://masish.uab.cat/.     

TSGDB: a database system for tumor suppressor genes

A Web-based database system was constructed and implemented that contains 174 tumor suppressor genes. The database homepage was created to accommodate these genes in a pull-down window so that each gene can be viewed individually in a separate Web page. Information displayed on each page includes gene name, aliases, source organism, chromosome location, expression cells/tissues, gene structure, protein size, gene functions and major reference sources. Queries to the database can be conducted through a user-friendly interface, and query results are returned in the HTML format on dynamically generated web pages. AVAILABILITY: The database is available at http://www.cise.ufl.edu/~yy1/HTML-TSGDB/Homepage.html (data files also at http://www.patcar.org/Databases/Tumor_Suppressor_Genes)     

FlyBase: a Drosophila database. Flybase Consortium.

FlyBase (http://flybase.bio.indiana.edu/) is a comprehensive database of genetic and molecular data concerning Drosophila . FlyBase is maintained as a relational database (in Sybase) and is made available as html documents and flat files. The scope of FlyBase includes: genes, alleles (with phenotypes), aberrations, transposons, pointers to sequence data, gene products, maps, clones, stock lists, Drosophila workers and bibliographic references.     

Dynamic filtration of the variability of expression patterns of zygotic segmentation genes in Drosophila

An analysis of the quantitative data obtained by processing the confocal images showed that the early variability of expression patterns of zygotic segmentation genes in Drosophila drastically decreases by the time of the onset of gastrulation. The following components of variability were examined: the scatter of the levels of gene expression in different embryos, the time and sequence of the formation of expression domains, the type of their formation, and the domain positioning. It was found that the positioning error at the level of zygotic genes is dynamically filtered with time.     

Gene Expression Dynamics Inspector (GEDI): for integrative analysis of expression profiles

Genome-wide expression profiles contain global patterns that evade visual detection in current gene clustering analysis. Here, a Gene Expression Dynamics Inspector (GEDI) is described that uses self-organizing maps to translate high-dimensional expression profiles of time courses or sample classes into animated, coherent and robust mosaics images. GEDI facilitates identification of interesting patterns of molecular activity simultaneously across gene, time and sample space without prior assumption of any structure in the data, and then permits the user to retrieve genes of interest. Important changes in genome-wide activities may be quickly identified based on 'Gestalt' recognition and hence, GEDI may be especially useful for non-specialist end users, such as physicians. AVAILABILITY: GEDI v1.0 is written in Matlab, and binary Matlab.dll files which require Matlab to run can be downloaded for free by academic institutions at http://www.chip.org/~ge/gedihome.html Supplementary information: http://www.chip.org/~ge/gedihome.html     

The AERO System: A 3D-Like Approach for Recording Gene Expression Patterns in the Whole Mouse Embryo

We have recently constructed a web-based database of gene expression in the mouse whole embryo, EMBRYS (http://embrys.jp/embrys/html/MainMenu.html). To allow examination of gene expression patterns to the fullest extent possible, this database provides both photo images and annotation data. However, since embryos develop via an intricate process of morphogenesis, it would be of great value to track embryonic gene expression from a three dimensional perspective. In fact, several methods have been developed to achieve this goal, but highly laborious procedures and specific operational skills are generally required. We utilized a novel microscopic technique that enables the easy capture of rotational, 3D-like images of the whole embryo. In this method, a rotary head equipped with two mirrors that are designed to obtain an image tilted at 45 degrees to the microscope stage captures serial images at 2-degree intervals. By a simple operation, 180 images are automatically collected. These 2D images obtained at multiple angles are then used to reconstruct 3D-like images, termed AERO images. By means of this system, over 800 AERO images of 191 gene expression patterns were captured. These images can be easily rotated on the computer screen using the EMBRYS database so that researchers can view an entire embryo by a virtual viewing on a computer screen in an unbiased or non-predetermined manner. The advantages afforded by this approach make it especially useful for generating data viewed in public databases.