Extensible Query Research Articles

TCW: Transcriptome Computational Workbench

BackgroundThe analysis of transcriptome data involves many steps and various programs, along with organization of large amounts of data and results. Without a methodical approach for storage, analysis and query, the resulting ad hoc analysis can lead to human error, loss of data and results, inefficient use of time, and lack of verifiability, repeatability, and extensibility.MethodologyThe Transcriptome Computational Workbench (TCW) provides Java graphical interfaces for methodical analysis for both single and comparative transcriptome data without the use of a reference genome (e.g. for non-model organisms). The singleTCW interface steps the user through importing transcript sequences (e.g. Illumina) or assembling long sequences (e.g. Sanger, 454, transcripts), annotating the sequences, and performing differential expression analysis using published statistical programs in R. The data, metadata, and results are stored in a MySQL database. The multiTCW interface builds a comparison database by importing sequence and annotation from one or more single TCW databases, executes the ESTscan program to translate the sequences into proteins, and then incorporates one or more clusterings, where the clustering options are to execute the orthoMCL program, compute transitive closure, or import clusters. Both singleTCW and multiTCW allow extensive query and display of the results, where singleTCW displays the alignment of annotation hits to transcript sequences, and multiTCW displays multiple transcript alignments with MUSCLE or pairwise alignments. The query programs can be executed on the desktop for fastest analysis, or from the web for sharing the results.ConclusionIt is now affordable to buy a multi-processor machine, and easy to install Java and MySQL. By simply downloading the TCW, the user can interactively analyze, query and view their data. The TCW allows in-depth data mining of the results, which can lead to a better understanding of the transcriptome. TCW is freely available from www.agcol.arizona.edu/software/tcw.

Optimising Data Utilisation in Lexicography: The Case of the <i>Khoekhoegowab Dictionary</i>

Despite of rapid progress in Southern Africa in the direction of multifunctionality of lexical databases through the advent of generic lexicographic software, a considerable number of lexicographic projects — especially in Khoe and Saan languages — still use or have recently used a word processor with the sole objective of compiling a printed dictionary. Hence the present paper expounds on the case of the Khoekhoegowab Dictionary Project, how in the early 1990s some off-the-shelf DOS-based database software was configured as part of a home-grown custom-made dictionary writing system. It is demonstrated in a non-technical way that the use of a structured database with fully-fledged retrieval facilities allows for the far-reaching elimination of human error in a dictionary, for the automatisation of processes like language reversal and sorting, and, finally, for the significantly enhanced usability of the data for purposes other than fixed media dictionary compilation. Compiling a dictionary without extensive query facilities as offered by tabular databases, is argued to be a lost opportunity, as it should be possible to utilise lexicographic data for more than just lexicography. By 2010 the data was accommodated in open source software to ensure its optimal survival in digital form for future use. Keywords: automatisation; compilation software; data retrieval; database configuration; database report; flat-file database; form; information generation; khoekhoe; khoesaan dictionaries; lexicography; lookup facilities; multifunctionality; query facilities; retrieval facilities; software; tones

Pattern matching through Chaos Game Representation: bridging numerical and discrete data structures for biological sequence analysis

BackgroundChaos Game Representation (CGR) is an iterated function that bijectively maps discrete sequences into a continuous domain. As a result, discrete sequences can be object of statistical and topological analyses otherwise reserved to numerical systems. Characteristically, CGR coordinates of substrings sharing an L-long suffix will be located within 2-L distance of each other. In the two decades since its original proposal, CGR has been generalized beyond its original focus on genomic sequences and has been successfully applied to a wide range of problems in bioinformatics. This report explores the possibility that it can be further extended to approach algorithms that rely on discrete, graph-based representations.ResultsThe exploratory analysis described here consisted of selecting foundational string problems and refactoring them using CGR-based algorithms. We found that CGR can take the role of suffix trees and emulate sophisticated string algorithms, efficiently solving exact and approximate string matching problems such as finding all palindromes and tandem repeats, and matching with mismatches. The common feature of these problems is that they use longest common extension (LCE) queries as subtasks of their procedures, which we show to have a constant time solution with CGR. Additionally, we show that CGR can be used as a rolling hash function within the Rabin-Karp algorithm.ConclusionsThe analysis of biological sequences relies on algorithmic foundations facing mounting challenges, both logistic (performance) and analytical (lack of unifying mathematical framework). CGR is found to provide the latter and to promise the former: graph-based data structures for sequence analysis operations are entailed by numerical-based data structures produced by CGR maps, providing a unifying analytical framework for a diversity of pattern matching problems.

Scalable information extraction for web queries

The dominant way to find information on the web nowadays is through search. General search engines are very effective, but search phrases and results are unstructured and that limits a user's ability to further automate the processing of the search results. In recent years, we have seen efforts to build systems that support more precise query on the web for certain content verticals. We describe the general problems for building an extensible web query system and present one of our projects in this area – a vertical search portal for online courses.

‘Double water exclusion’: a hypothesis refining the O-ring theory for the hot spots at protein interfaces

Motivation: The O-ring theory reveals that the binding hot spot at a protein interface is surrounded by a ring of residues that are energetically less important than the residues in the hot spot. As this ring of residues is served to occlude water molecules from the hot spot, the O-ring theory is also called ‘water exclusion’ hypothesis. We propose a ‘double water exclusion’ hypothesis to refine the O-ring theory by assuming the hot spot itself is water-free. To computationally model a water-free hot spot, we use a biclique pattern that is defined as two maximal groups of residues from two chains in a protein complex holding the property that every residue contacts with all residues in the other group.Methods and Results: Given a chain pair A and B of a protein complex from the Protein Data Bank (PDB), we calculate the interatomic distance of all possible pairs of atoms between A and B. We then represent A and B as a bipartite graph based on these distance information. Maximal biclique subgraphs are subsequently identified from all of the bipartite graphs to locate biclique patterns at the interfaces. We address two properties of biclique patterns: a non-redundant occurrence in PDB, and a correspondence with hot spots when the solvent-accessible surface area (SASA) of a biclique pattern in the complex form is small. A total of 1293 biclique patterns are discovered which have a non-redundant occurrence of at least five, and which each have a minimum two and four residues at the two sides. Through extensive queries to the HotSprint and ASEdb databases, we verified that biclique patterns are rich of true hot residues. Our algorithm and results provide a new way to identify hot spots by examining proteins' structural data.Availability: The biclique mining algorithm is available at http://www.ntu.edu.sg/home/jyli/dwe.html.Contact: jyli@ntu.edu.sgSupplementary information: Supplementary data are available at Bioinformatics online.

A Caenorhabditis motif compendium for studying transcriptional gene regulation

BackgroundControlling gene expression is fundamental to biological complexity. The nematode Caenorhabditis elegans is an important model for studying principles of gene regulation in multi-cellular organisms. A comprehensive parts list of putative regulatory motifs was yet missing for this model system. In this study, we compile a set of putative regulatory motifs by combining evidence from conservation and expression data.DescriptionWe present an unbiased comparative approach to a regulatory motif compendium for Caenorhabditis species. This involves the assembly of a new nematode genome, whole genome alignments and assessment of conserved k-mers counts. Candidate motifs are selected from a set of 9,500 randomly picked genes by three different motif discovery strategies. Motif candidates have to pass a conservation enrichment filter. Motif degeneracy and length are optimized. Retained motif descriptions are evaluated by expression data using a non-parametric test, which assesses expression changes due to the presence/absence of individual motifs. Finally, we also provide condition-specific motif ensembles by conditional tree analysis.ConclusionThe nematode genomes align surprisingly well despite high neutral substitution rates. Our pipeline delivers motif sets by three alternative strategies. Each set contains less than 400 motifs, which are significantly conserved and correlated with 214 out of 270 tested gene expression conditions. This motif compendium is an entry point to comprehensive studies on nematode gene regulation. The website: http://corg.eb.tuebingen.mpg.de/CMC has extensive query capabilities, supplements this article and supports the experimental list.

MANTIS: a phylogenetic framework for multi-species genome comparisons

Practitioners of comparative genomics face huge analytical challenges as whole genome sequences and functional/expression data accumulate. Furthermore, the field would greatly benefit from a better integration of this wealth of data with evolutionary concepts. Here, we present MANTIS, a relational database for the analysis of (i) gains and losses of genes on specific branches of the metazoan phylogeny, (ii) reconstructed genome content of ancestral species and (iii) over- or under-representation of functions/processes and tissue specificity of gained, duplicated and lost genes. MANTIS estimates the most likely positions of gene losses on the true phylogeny using a maximum-likelihood function. A user-friendly interface and an extensive query system allow to investigate questions pertaining to gene identity, phylogenetic mapping and function/expression parameters. MANTIS is freely available at http://www.mantisdb.org and constitutes the missing link between multi-species genome comparisons and functional analyses.

Kernel-based data fusion for gene prioritization

Hunting disease genes is a problem of primary importance in biomedical research. Biologists usually approach this problem in two steps: first a set of candidate genes is identified using traditional positional cloning or high-throughput genomics techniques; second, these genes are further investigated and validated in the wet lab, one by one. To speed up discovery and limit the number of costly wet lab experiments, biologists must test the candidate genes starting with the most probable candidates. So far, biologists have relied on literature studies, extensive queries to multiple databases and hunches about expected properties of the disease gene to determine such an ordering. Recently, we have introduced the data mining tool ENDEAVOUR (Aerts et al., 2006), which performs this task automatically by relying on different genome-wide data sources, such as Gene Ontology, literature, microarray, sequence and more. In this article, we present a novel kernel method that operates in the same setting: based on a number of different views on a set of training genes, a prioritization of test genes is obtained. We furthermore provide a thorough learning theoretical analysis of the method's guaranteed performance. Finally, we apply the method to the disease data sets on which ENDEAVOUR (Aerts et al., 2006) has been benchmarked, and report a considerable improvement in empirical performance. The MATLAB code used in the empirical results will be made publicly available.

Adaptable query optimization and evaluation in temporal middleware

Time-referenced data are pervasive in most real-world databases. Recent advances in temporal query languages show that such database applications may benefit substantially from built-in temporal support in the DBMS. To achieve this, temporal query optimization and evaluation mechanisms must be provided, either within the DBMS proper or as a source level translation from temporal queries to conventional SQL. This paper proposes a new approach: using a middleware component on top of a conventional DBMS. This component accepts temporal SQL statements and produces a corresponding query plan consisting of algebraic as well as regular SQL parts. The algebraic parts are processed by the middleware, while the SQL parts are processed by the DBMS. The middleware uses performance feedback from the DBMS to adapt its partitioning of subsequent queries into middleware and DBMS parts. The paper describes the architecture and implementation of the temporal middleware component, termed TANGO, which is based on the Volcano extensible query optimizer and the XXL query processing library. Experiments with the system demonstrate the utility of the middleware's internal processing capability and its cost-based mechanism for apportioning the processing between the middleware and the underlying DBMS.

Extensible Parallel Query Processing for Exploratory Geoscientific Data Mining

Exploratory data mining and analysis requires a computing environment which provides facilities for the user-friendly expression and rapid execution of “scientific queries.” In this paper, we address research issues in the parallelization of scientific queries containing complex user-defined operations. In a parallel query execution environment, parallelizing a query execution plan involves determining how input data streams to evaluators implementing logical operations can be divided to be processed by clones of the same evaluator in parallel. We introduced the concept of “relevance window” that characterizes data lineage and data partitioning opportunities available for an user-defined evaluator. In addition, we developed a query parallelization framework by extending relational parallel query optimization algorithms to allow the parallelization characteristics of user-defined evaluators to guide the process of query parallelization in an extensible query processing environment. We demonstrated the utility of our system by performing experiments mining cyclonic activity, blocking events, and the upward wave-energy propagation features from several observational and model simulation datasets.

Bringing object-relational technology to the mainstream

Over the last few years, Oracle has evolved its flagship relational database system into an Object-Relational system by adding an extensible type system, object storage, an object cache, an extensible query and indexing framework, support for multimedia datatypes, a server-based scalable Java virtual machine, as well as enhancing its SQL DDL and DML language. These extensions were done with the practical goal of bringing objects to mainstream use.

Geometric query types for data retrieval in relational databases

This paper describes the application of geometric concepts for data retrieval and proposes a geometric model for accessing and manipulating multidimensional data in relational databases. A geometric query model is proposed for the queries based on locations of geometric objects. In the geometric query model, the data from the database are interpreted geometrically as multidimensional geometric points or geometric objects interpolated on these points, and the query window is a query solid of any shape specified by its location. The spatial queries are formulated with geometric objects and operations over them. Descriptions of geometric objects and operations with implicit functions are proposed. This mathematical representation allowed us to develop a compact and easily extensible geometric query model that has been implemented in the geometric query system prototype.

MODLER: Modeling by object-driven linear elemental relations

This paper describes a system to represent linear programming models and their instances. In addition to a modeling language, MODLER has an extensive query capability which includes a multi-view architecture. Further, randomization options provide rapid prototyping. The MODLER system is part of a workbench for building and managing decision support systems that are based on linear programming.

Grammar-like functional rules for representing query optimization alternatives

Extensible query optimization requires that the “repertoire” of alternative strategies for executing queries be represented as data, not embedded in the optimizer code. Recognizing that query optimizers are essentially expert systems, several researchers have suggested using strategy rules to transform query execution plans into alternative or better plans. Though extremely flexible, these systems can be very inefficient at any step in the processing, many rules may be eligible for application and complicated conditions must be tested to determine that eligibility during unification. We present a constructive, “building blocks” approach to defining alternative plans, in which the rules defining alternatives are an extension of the productions of a grammar to resemble the definition of a function in mathematics. The extensions permit each token of the grammar to be parametrized and each of its alternative definitions to have a complex condition. The terminals of the grammar are base-level database operations on tables that are interpreted at run-time. The non-terminals are defined declaratively by production rules that combine those operations into meaningful plans for execution. Each production produces a set of alternative plans, each having a vector of properties, including the estimated cost of producing that plan. Productions can require certain properties of their inputs, such as tuple order and location, and we describe a “glue” mechanism for augmenting plans to achieve the required properties. We give detailed examples to illustrate the power and robustness of our rules and to contrast them with related ideas.

Overview of an Ada compatible distributed database manager

Adaplex is an integrated language for programming database applications. It results from the embedding of the database sublanguage DAPLEX in the general purpose programming language Ada. This paper provides an overview of the DDM: a distributed database manager (DDM) that supports the use of Adaplex as an interface language. The important technical innovations we have incorporated in the design of this system include:1. An advanced data model that captures more application semantics than conventional data models.2. Support for flexible data distribution options that improve locality of reference and efficiency of query processing.3. Extensive query optimization that combines compile time access path optimization with run time site selection.4. Efficient transaction management that reduces transaction conflicts and improves the resiliency of replicated data.5. Robust, incremental recovery management that provides for automatic recovery from certain "catastrophic" failure conditions.