RESEARCH ARTICLE
Probability-Based Scoring Function as a Software Tool Used in the Genome-Based Identification of Proteins from Spirulina platensis
Thammasorn Wimada1, Eadjongdee Korakot1, Hongsthong Apiradee*, 2, Porkaew Kriengkrai1, Cheevadhanarak Supapon3
Article Information
Identifiers and Pagination:
Year: 2009Volume: 3
First Page: 59
Last Page: 68
Publisher ID: TOBIOIJ-3-59
DOI: 10.2174/1875036200903010059
Article History:
Received Date: 25/06/2009Revision Received Date: 14/08/2009
Acceptance Date: 21/08/2009
Electronic publication date: 01/10/2009
Collection year: 2009
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
One of the major goals of proteomic research is the identification of proteins, a goal that often requires various software tools and databases. These tools have to be able to handle large amounts of data, such as those generated by PMF (Peptide Mass Fingerprinting), a high throughput technique. A newly sequenced organism, Spirulina platensis, was recently used to generate an in silico database, and thus an in-house tool designed for compatibility with this database and its inputs (PMF) was constructed in the present study. With a probability based scoring function, this tool effectively ranked ambiguous protein identification results by using five criteria: score, number of matched peptides, % coverage, pI and molecular weight. As a result, the protein identification step of Spirulina proteomic studies can be achieved precisely. Moreover, a very useful function of this tool is its capability for batch processing, in which the system can handle proteinidentification searches of a hundred of proteins automatically, from a single user’s input. Therefore, the tool not only gives accurate protein identification results but also saves the user time in processing a large amount of data.