Abstract Motivation and Results: A relational schema is described for capturing highly parallel gene expression experiments using different technologies. This schema grew out of efforts to build a database for collaborators working on different biological systems and using different types of platforms in their gene expression experiments as well as different types of image quantification software. The tables are conceptually organized into three categories of information: Platform, Experiment (which includes image scanning and quantification), and Data. The strengths of the schema are: (i) integrating information on array elements using a gene index; (ii) describing samples using ontologies; (iii) reducing an experiment to a single RNA source for precise descriptions yet not losing the relationships between experiments done at the same time or for the same project; and (iv) maintaining both raw and processed (e.g. cleansed and normalized) data and recording how the data is processed. The result is a novel schema, which can hold both array and non-array data, is extensible for detailed experimental descriptions that are precise and consistent, and allows for meaningful comparisons of genes between experiments. Availability: The schema is available at http://www.cbil.upenn.edu/cgi-bin/RAD2/schemaBrowserRAD.pl. Contact: [email protected] * To whom correspondence should be addressed. 2 Present address: Department of Molecular Cell Biology, Weizmann Institute of Science, Israel.

MOTIVATION: A protocol is described to attach expression patterns to genes represented in a collection of hybridization array experiments. Discrete values are used to provide an easily interpretable description of differential expression. Binning cutoffs for each sample type are chosen automatically, depending on the desired false-positive rate for the predictions of differential expression. Confidence levels are derived for the statement that changes in observed levels represent true changes in expression. We have a novel method for calculating this confidence, which gives better results than the standard methods. Our method reflects the broader change of focus in the field from studying a few genes with many replicates to studying many (possibly thousands) of genes simultaneously, but with relatively few replicates. Our approach differs from standard methods in that it exploits the fact that there are many genes on the arrays. These are used to estimate for each sample type an appropriate distribution that is employed to control the false-positive rate of the predictions made. Satisfactory results can be obtained using this method with as few as two replicates. RESULTS: The method is illustrated through applications to macroarray and microarray datasets. The first is an erythroid development dataset that we have generated using nylon filter arrays. Clones for genes whose expression is known in these cells were assigned expression patterns which are in accordance with what was expected and which are not picked up by the standards methods. Moreover, genes differentially expressed between normal and leukemic cells were identified. These included genes whose expression was altered upon induction of the leukemic cells to differentiate. The second application is to the microarray data by Alizadeh et al. (2000). Our results are in accordance with their major findings and offer confidence measures for the predictions made. They also provide new insights for further analysis.

MOTIVATION: The presentation of genomics data in a perspicuous visual format is critical for its rapid interpretation and validation. Relatively few public database developers have the resources to implement sophisticated front-end user interfaces themselves. Accordingly, these developers would benefit from a reusable toolkit of user interface and data visualization components. RESULTS: We have designed the bioWidget toolkit as a set of JavaBean components. It includes a wide array of user interface components and defines an architecture for assembling applications. The toolkit is founded on established software engineering design patterns and principles, including componentry, Model-View-Controller, factored models and schema neutrality. As a proof of concept, we have used the bioWidget toolkit to create three extendible applications: AnnotView, BlastView and AlignView.

MOTIVATION: The goal of the work was to develop a WWW-oriented computer system providing a maximal integration of informational and software resources on the regulation of gene expression and navigation through them. Rapid growth of the variety and volume of information accumulated in the databases on regulation of gene expression necessarily requires the development of computer systems for automated discovery of the knowledge that can be further used for analysis of regulatory genomic sequences. RESULTS: The GeneExpress system developed includes the following major informational and software modules: (1) Transcription Regulation (TRRD) module, which contains the databases on transcription regulatory regions of eukaryotic genes and TRRD Viewer for data visualization; (2) Site Activity Prediction (ACTIVITY), the module for analysis of functional site activity and its prediction; (3) Site Recognition module, which comprises (a) B-DNA-VIDEO system for detecting the conformational and physicochemical properties of DNA sites significant for their recognition, (b) Consensus and Weight Matrices (ConsFrec) and (c) Transcription Factor Binding Sites Recognition (TFBSR) systems for detecting conservative contextual regions of functional sites and their recognition; (4) Gene Networks (GeneNet), which contains an object-oriented database accumulating the data on gene networks and signal transduction pathways, and the Java-based Viewer for exploration and visualization of the GeneNet information; (5) mRNA Translation (Leader mRNA), designed to analyze structural and contextual properties of mRNA 5'-untranslated regions (5'-UTRs) and predict their translation efficiency; (6) other program modules designed to study the structure-function organization of regulatory genomic sequences and regulatory proteins. AVAILABILITY: GeneExpress is available at http://wwwmgs.bionet.nsc. ru/systems/GeneExpress/ and the links to the mirror site(s) can be found at http://wwwmgs.bionet.nsc.ru/mgs/links/mirrors.html+ ++.

MOTIVATION: The commonly accepted statistical mechanical theory is now multiply confirmed by using the weight matrix methods successfully recognizing DNA sites binding regulatory proteins in prokaryotes. Nevertheless, the recent evaluation of weight matrix methods application for transcription factor binding site recognition in eukaryotes has unexpectedly revealed that the matrix scores correlate better to each other than to the activity of DNA sites interacting with proteins. This observation points out that molecular mechanisms of DNA/protein recognition are more complicated in eukaryotes than in prokaryotes. As the extra events in eukaryotes, the following processes may be considered: (i) competition between the proteins and nucleosome core particle for DNA sites binding these proteins and (ii) interaction between two synergetic/antagonist proteins recognizing a composed element compiled from two DNA sites binding these proteins. That is why identification of the sequence-dependent DNA features correlating with affinity magnitudes of DNA sites interacting with a protein can pinpoint the molecular event limiting this protein/DNA recognition machinery. RESULTS: An approach for predicting site activity based on its primary nucleotide sequence has been developed. The approach is realized in the computer system ACTIVITY, containing the databases on site activity and on conformational and physicochemical DNA/RNA parameters. By using the system ACTIVITY, an analysis of some sites was provided and the methods for predicting site activity were constructed. The methods developed are in good agreement with the experimental data. AVAILABILITY: The database ACTIVITY is available at http://wwwmgs.bionet.nsc.ru/systems/Activity/ and the mirror site, http://www.cbil.upenn.edu/mgs/systems/acti vity/.

MOTIVATION: Recognition of functional sites remains a key event in the course of genomic DNA annotation. It is well known that a number of sites have their own specific oligonucleotide content. This pinpoints the fact that the preference of the site-specific nucleotide combinations at adjacent positions within an analyzed functional site could be informative for this site recognition. Hence, Web-available resources describing the site-specific oligonucleotide content of the functional DNA sites and applying the above approach for site recognition are needed. However, they have been poorly developed up to now. RESULTS: To describe the specific oligonucleotide content of the functional DNA sites, we introduce the oligonucleotide alphabets, out of which the frequency matrix for a given site could be constructed in addition to a traditional nucleotide frequency matrix. Thus, site recognition accuracy increases. This approach was implemented in the activated MATRIX database accumulating oligonucleotide frequency matrices of the functional DNA sites. We have demonstrated that the false-positive error of the functional site recognition decreases if the oligonucleotide frequency matrixes are added to the nucleotide frequency matrixes commonly used. AVAILABILITY: The MATRIX database is available on the Web, http://wwwmgs.bionet.nsc.ru/Dbases/MATRIX/ and the mirror site, http://www.cbil.upenn.edu/mgs/systems/c onsfreq/.

MOTIVATION: A reliable recognition of transcription factor binding sites is essential for analysis of regulatory genomic sequences. The experimental data make evident an important role of DNA conformational features for site functioning. However, Internet-available tools for revealing conformational and physicochemical DNA features significant for the site functioning and subsequent use of these features for site recognition have not been developed up to now. RESULTS: We suggest an approach for revealing significant conformational and physicochemical properties of functional sites implemented in the database B-DNA-VIDEO. This database is designed to study the sets of various transcription factor binding sites, providing evidence that transcription factor binding sites are characterized by specific sets of significant conformational and physicochemical DNA properties. For a fixed site, by using the B-DNA features selected for this site recognition, the C-program recognizing this site may be generated, control tested and stored in the database B-DNA-VIDEO. Each B-DNA-VIDEO entry links to the Web-applet recognizing the site, whose significant B-DNA features are stored in this entry as the 'site recognition programs'. The pairwise linked entry-applet pairs are compiled within the B-DNA-VIDEO system, which is simultaneously the database and the program tools package applicable immediately for recognizing the sites stored in the database. Indeed, this is the novelty. Hence, B-DNA-VIDEO is the Web resource of both 'searching for static data' and 'active computation' type, that is why it was called an 'activated database'. AVAILABILITY: B-DNA-VIDEO is available at http://wwwmgs.bionet.nsc.ru/systems/BDNAVideo/ and the mirror site at http://www.cbil.upenn.edu/mgs/systems/c onsfreq/.

The National Center for Biotechnology Information (NCBI) has created a database collection that includes several protein and nucleic acid sequence databases, a biosequence-specific subset of MEDLINE, as well as value-added information such as links between similar sequences. Information in the NCBI database is modeled in Abstract Syntax Notation 1 (ASN.1) an Open Systems Interconnection protocol designed for the purpose of exchanging structured data between software applications rather than as a data model for database systems. While the NCBI database is distributed with an easy-to-use information retrieval system, ENTREZ, the ASN.1 data model currently lacks an ad hoc query language for general-purpose data access. For that reason, we have developed a software package, SORTEZ, that transforms the ASN.1 database (or other databases with nested data structures) to a relational data model and subsequently to a relational database management system (Sybase) where information can be accessed through the relational query language, SQL. Because the need to transform data from one data model and schema to another arises naturally in several important contexts, including efficient execution of specific applications, access to multiple databases and adaptation to database evolution this work also serves as a practical study of the issues involved in the various stages of database transformation. We show that transformation from the ASN.1 data model to a relational data model can be largely automated, but that schema transformation and data conversion require considerable domain expertise and would greatly benefit from additional support tools.

Genes are highly structured objects.Identifying genetic structure is experimentally costly and in general, is only partially determined in the lab.As such, genomic databases such as Gen-Bank are vastly underspecified.We describe various Case Based Reasoning (CBR) techniques, designed to take advantage of the homologous nature of genes, in order to discover regulatory features not present in the GenBank nucleic acid sequence database.We build a classification hierarchy reflecting the evolutionary relationship between genes.A sequence based CBR technique uses neighboring genes within the hierarchy as cases in order to predict the presence and location of regulatory features not listed in GenBank.Using a grammatical model of gene structure, we describe each gene with an instance grammar, and induce descriptive grammars of gene classes from these instance grammars.Descriptive grammars then serve as cases that enable us to identify regulatory features not present within the instance grammars.The above techniques recovered 92% of the regulatory features with an error rate of less than 2% for our data set. 1

The detection of similarities between DNA sequences can be accomplished using the signal-processing technique of cross-correlation. An early method used the fast Fourier transform (FFT) to perform correlations on DNA sequences in O(n log n) time for any length sequence. However, this method requires many FFTs (nine), runs no faster if one sequence is much shorter than the other, and measures only global similarity, so that significant short local matches may be missed. We report that, through the use of alternative encodings of the DNA sequence in the complex plane, the number of FFTs performed can be traded off against (i) signal-to-noise ratio, and (ii) a certain degree of filtering for local similarity via k-tuple correlation. Also, when comparing probe sequences against much longer targets, the algorithm can be sped up by decomposing the target and performing multiple small FFTs in an overlap-save arrangement. Finally, by decomposing the probe sequence as well, the detection of local similarities can be further enhanced. With current advances in extremely fast hardware implementations of signal-processing operations, this approach may prove more practical than heretofore.