PubMed · 14971109
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Bill Briggs. 2004. Data helps foretell customer needs.. https://pubmed.ncbi.nlm.nih.gov/14971109/
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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.
SUMMARY: Analysis of proteomics data, specifically mass spectrometry data, commonly relies on libraries of known information such as atomic masses, known stable isotopes, atomic compositions of amino acids, observed modifications of known amino acids and ion masses that directly correspond to known amino acid sequences. The Java Analysis Framework (JAF) for proteomics provides a freely usable, open-source library of Java code that abstracts all of the aforementioned data, enabling more rapid development of proteomics tools. The JAF also includes several user tools that can be run directly from a web browser. AVAILABILITY: The current version and an archive of all older versions of the Java Analysis Framework for Proteomics is freely available, including complete source-code, at http://www.proteomecommons.org/current/511/.
BACKGROUND: Benchmarking algorithms in structural bioinformatics often involves the construction of datasets of proteins with given sequence and structural properties. The SCOP database is a manually curated structural classification which groups together proteins on the basis of structural similarity. The ASTRAL compendium provides non redundant subsets of SCOP domains on the basis of sequence similarity such that no two domains in a given subset share more than a defined degree of sequence similarity. Taken together these two resources provide a 'ground truth' for assessing structural bioinformatics algorithms. We present a small and easy to use API written in python to enable construction of datasets from these resources. RESULTS: We have designed a set of python modules to provide an abstraction of the SCOP and ASTRAL databases. The modules are designed to work as part of the Biopython distribution. Python users can now manipulate and use the SCOP hierarchy from within python programs, and use ASTRAL to return sequences of domains in SCOP, as well as clustered representations of SCOP from ASTRAL. CONCLUSION: The modules make the analysis and generation of datasets for use in structural genomics easier and more principled.