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


CONCLUSIONS
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.
Croce
Olivier
O
Laboratoire de Biologie Virtuelle, UMR 6543, CNRS & University of Nice Sophia-Antipolis, Centre de Biochimie, Parc Valrose, Nice, F06108, France. croce@unice.fr
Lamarre
Michaël
M
Christen
Richard
R
D003628:Database Management Systems; D030562:Databases, Protein; D016247:Information Storage and Retrieval / Q000379:methods*; D020407:Internet; D009323:Natural Language Processing; D011506:Proteins / Q000032:analysis / Q000737:chemistry* / Q000145:classification*; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software; D018875:Vocabulary, Controlled
D011506:Proteins