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Oracle Database 10g: a platform for BLAST search and Regular Expression pattern matching in life sciences.

ABSTRACT: As database management systems expand their array of analytical functionality, they become powerful research engines for biomedical data analysis and drug discovery. Databases can hold most of the data types commonly required in life sciences and consequently can be used as flexible platforms for the implementation of knowledgebases. Performing data analysis in the database simplifies data management by minimizing the movement of data from disks to memory, allowing pre-filtering and post-processing of datasets, and enabling data to remain in a secure, highly available environment. This article describes the Oracle Database 10g implementation of BLAST and Regular Expression Searches and provides case studies of their usage in bioinformatics. http://www.oracle.com/technology/software/index.html.

SUBMITTER: Stephens SM

PROVIDER: S-EPMC540068 | biostudies-literature | 2005 Jan

REPOSITORIES: biostudies-literature

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Oracle Database 10g: a platform for BLAST search and Regular Expression pattern matching in life sciences.

Stephens Susie M SM Chen Jake Y JY Davidson Marcel G MG Thomas Shiby S Trute Barry M BM

Nucleic acids research 20050101 Database issue

As database management systems expand their array of analytical functionality, they become powerful research engines for biomedical data analysis and drug discovery. Databases can hold most of the data types commonly required in life sciences and consequently can be used as flexible platforms for the implementation of knowledgebases. Performing data analysis in the database simplifies data management by minimizing the movement of data from disks to memory, allowing pre-filtering and post-process ...[more]

PMID: 15608287

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Project description:BackgroundRegular expression matching (REM) is widely employed as the major tool for deep packet inspection (DPI) applications. For automatic processing, the regular expression patterns need to be converted to a deterministic finite automata (DFA). However, with the ever-increasing scale and complexity of pattern sets, state explosion problem has brought a great challenge to the DFA based regular expression matching. Rule grouping is a direct method to solve the state explosion problem. The original rule set is divided into multiple disjoint groups, and each group is compiled to a separate DFA, thus to significantly restrain the severe state explosion problem when compiling all the rules to a single DFA.ObjectiveFor practical implementation, the total number of DFA states should be as few as possible, thus the data structures of these DFAs can be deployed on fast on-chip memories for rapid access. In addition, to support fast pattern update in some applications, the time cost for grouping should be as small as possible. In this study, we aimed to propose an efficient grouping method, which generates as few states as possible with as little time overhead as possible.MethodsWhen compiling multiple patterns into a single DFA, the number of DFA states is usually greater than the total number of states when compiling each pattern to a separate DFA. This is mainly caused by the semantic overlaps among different rules. By quantifying the interaction values for each pair of rules, the rule grouping problem can be reduced to the maximum k-cut graph partitioning problem. Then, we propose a heuristic algorithm called the one-step greedy (OSG) algorithm to solve this NP-hard problem. What's more, a subroutine named the heuristic initialization (HI) algorithm is devised to further optimize the grouping algorithms.ResultsWe employed three practical rule sets for the experimental evaluation. Results show that the OSG algorithm outperforms the state-of-the-art grouping solutions regarding both the total number of DFA states and time cost for grouping. The HI subroutine also demonstrates its significant optimization effect on the grouping algorithms.ConclusionsThe DFA state explosion problem has became the most challenging issue in the regular expression matching applications. Rule grouping is a practical direction by dividing the original rule sets into multiple disjoint groups. In this paper, we investigate the current grouping solutions, and propose a compact and efficient grouping algorithm. Experiments conducted on practical rule sets demonstrate the superiority of our proposal.

Project description:BackgroundThe web has seen an explosion of chemistry and biology related resources in the last 15 years: thousands of scientific journals, databases, wikis, blogs and resources are available with a wide variety of types of information. There is a huge need to aggregate and organise this information. However, the sheer number of resources makes it unrealistic to link them all in a centralised manner. Instead, search engines to find information in those resources flourish, and formal languages like Resource Description Framework and Web Ontology Language are increasingly used to allow linking of resources. A recent development is the use of userscripts to change the appearance of web pages, by on-the-fly modification of the web content. This opens possibilities to aggregate information and computational results from different web resources into the web page of one of those resources.ResultsSeveral userscripts are presented that enrich biology and chemistry related web resources by incorporating or linking to other computational or data sources on the web. The scripts make use of Greasemonkey-like plugins for web browsers and are written in JavaScript. Information from third-party resources are extracted using open Application Programming Interfaces, while common Universal Resource Locator schemes are used to make deep links to related information in that external resource. The userscripts presented here use a variety of techniques and resources, and show the potential of such scripts.ConclusionThis paper discusses a number of userscripts that aggregate information from two or more web resources. Examples are shown that enrich web pages with information from other resources, and show how information from web pages can be used to link to, search, and process information in other resources. Due to the nature of userscripts, scientists are able to select those scripts they find useful on a daily basis, as the scripts run directly in their own web browser rather than on the web server. This flexibility allows the scientists to tune the features of web resources to optimise their productivity.

Dataset Information

Oracle Database 10g: a platform for BLAST search and Regular Expression pattern matching in life sciences.

Publications

Oracle Database 10g: a platform for BLAST search and Regular Expression pattern matching in life sciences.

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