Sequence Profiling Tool - Keyword Based Profilers

Keyword Based Profilers

Most of the profiling tools available on the web today fall into this category. The user, upon visiting the site/tool, enters any relevant information like a keyword e.g. dystrophy, diabetes etc., or GenBank accession numbers, PDB ID. All the relevant hits by the search are presented in a format unique to each tool’s main focus. Profiling tools based on keyword searches are essentially search engines that are highly specialized for bioinformatics work, thereby eliminating a clutter of irrelevant or non-scholarly hits that might occur with a traditional search engine like Google. Most keyword-based profiling tools allow flexible types of keyword input, accession numbers from indexed databases as well as traditional keyword descriptors.

Each profiling tool has its own focus and area of interest. For example, the NCBI search engine Entrez segregates its hits by category, so that users looking for protein structure information can screen out sequences with no corresponding structure, while users interested in perusing the literature on a subject can view abstracts of papers published in scholarly journals without distraction from gene or sequence results. The Pubmed biosciences literature database is a popular tool for literature searches, though this service is nearly equaled with the more general Google Scholar.

Keyword-based data aggregation services like the Bioinformatic Harvester performs provide reports from a variety of third-party servers in an as-is format so that users need not visit the website or install the software for each individual component service. This is particularly invaluable given the rapid emergence of various sites providing different sequence analysis and manipulation tools. Another aggregative web portal, the Human Protein Reference Database (Hprd), contains manually annotated and curated entries for human proteins. The information provided is thus both selective and comprehensive, and the query format is flexible and intuitive. The pros of developing manually curated databases include presentation of proofread material and the concept of ‘molecule authorities’ to undertake the responsibility of specific proteins. However, the cons are that they are typically slower to update and may not contain very new or disputed data.