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Querying the public databases for sequences using complex keywords contained in the feature lines.

ABSTRACT: BACKGROUND: High throughput technologies often require the retrieval of large data sets of sequences. Retrieval of EMBL or GenBank entries using keywords is easy using tools such as ACNUC, Entrez or SRS, but has some limitations, in particular when querying with complex keywords. RESULTS: We show that Entrez has severe limitations with respect to retrieving subsequences. SRS works well with simple keywords but not with keywords composed of several terms, and has problems with complex queries. ACNUC works well, but does not allow precise queries in the Feature qualifiers. We developed specific Perl scripts to precisely retrieve subsequences as defined by complex descriptors in the Features qualifiers of the EMBL entries. We improved parts of the bioPerl library to allow parsing of large data files, and we embedded these scripts in a user friendly interface (OS independent) for easy use. CONCLUSION: Although not as fast as the public tools that use prebuilt indexes, parsing the complete entries using a script is often necessary in order to retrieve the exact data searched for. Embedding in a user friendly interface allows biologists to use the scripts, which can easily be modified, if necessary, by bioinformaticians for unforeseen needs.

SUBMITTER: Croce O

PROVIDER: S-EPMC1403806 | biostudies-literature | 2006

REPOSITORIES: biostudies-literature

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Querying the public databases for sequences using complex keywords contained in the feature lines.

Croce Olivier O Lamarre Michaël M Christen Richard R

BMC bioinformatics 20060127

<h4>Background</h4>High throughput technologies often require the retrieval of large data sets of sequences. Retrieval of EMBL or GenBank entries using keywords is easy using tools such as ACNUC, Entrez or SRS, but has some limitations, in particular when querying with complex keywords.<h4>Results</h4>We show that Entrez has severe limitations with respect to retrieving subsequences. SRS works well with simple keywords but not with keywords composed of several terms, and has problems with comple ...[more]

PMID: 16441875

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Project description:BackgroundGraph-based pathway ontologies and databases are widely used to represent data about cellular processes. This representation makes it possible to programmatically integrate cellular networks and to investigate them using the well-understood concepts of graph theory in order to predict their structural and dynamic properties. An extension of this graph representation, namely hierarchically structured or compound graphs, in which a member of a biological network may recursively contain a sub-network of a somehow logically similar group of biological objects, provides many additional benefits for analysis of biological pathways, including reduction of complexity by decomposition into distinct components or modules. In this regard, it is essential to effectively query such integrated large compound networks to extract the sub-networks of interest with the help of efficient algorithms and software tools.ResultsTowards this goal, we developed a querying framework, along with a number of graph-theoretic algorithms from simple neighborhood queries to shortest paths to feedback loops, that is applicable to all sorts of graph-based pathway databases, from PPIs (protein-protein interactions) to metabolic and signaling pathways. The framework is unique in that it can account for compound or nested structures and ubiquitous entities present in the pathway data. In addition, the queries may be related to each other through "AND" and "OR" operators, and can be recursively organized into a tree, in which the result of one query might be a source and/or target for another, to form more complex queries. The algorithms were implemented within the querying component of a new version of the software tool PATIKAweb (Pathway Analysis Tool for Integration and Knowledge Acquisition) and have proven useful for answering a number of biologically significant questions for large graph-based pathway databases.ConclusionThe PATIKA Project Web site is http://www.patika.org. PATIKAweb version 2.1 is available at http://web.patika.org.

Project description:BackgroundTEM beta-lactamases are the main cause for resistance against beta-lactam antibiotics. Sequence information about TEM beta-lactamases is mainly found in the NCBI peptide database and TEM mutation table at http://www.lahey.org/Studies/temtable.asp. While the TEM mutation table is manually curated by experts in the lactamase field, who guarantee reliable and consistent information, the rapidly growing sequence and annotation information from the NCBI peptide database is sometimes inconsistent. Therefore, the Lactamase Engineering Database has been developed to collect the TEM beta-lactamase sequences from the NCBI peptide database and the TEM mutation table, systematically compare sequence information and naming, identify inconsistencies, and thus provide a versatile tool for reconciliation of data and for an investigation of the sequence-function relationship.DescriptionThe LacED currently provides 2399 sequence entries and 37 structure entries. Sequence information on 150 different TEM beta-lactamases was derived from the TEM mutation table which provides a unique number to each protein classified as TEM beta-lactamase. 293 TEM-like proteins were found in the NCBI protein database, but only 113 TEM beta-lactamase were common to both data sets. The 180 TEM beta-lactamases from the NCBI protein database which have not yet been assigned to a TEM number fall in three classes: (1) 89 proteins from microbial organisms and 35 proteins from cloning or expression vectors had a new mutation profile; (2) 55 proteins had inconsistent annotation in terms of TEM assignment or reported mutation profile; (3) 39 proteins are fragments. The LacED is web accessible at http://www.LacED.uni-stuttgart.de and contains multisequence alignments, structure information and reconciled annotation of TEM beta-lactamases. The LacED is weekly updated and supplies all data for download.ConclusionThe Lactamase Engineering Database enables a systematic analysis of TEM beta-lactamase sequence and annotation data from different data sources, and thus provides a valuable tool to identify inconsistencies in sequences from the NCBI peptide database, to detect TEM beta-lactamases with a novel mutation profile, and to identify new amino acid positions at which mutations can occur.

Dataset Information

Querying the public databases for sequences using complex keywords contained in the feature lines.

Publications

Querying the public databases for sequences using complex keywords contained in the feature lines.

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