Statistical distributions of optimal global alignment scores of random protein sequences

Background: The inference of homology from statistically significant sequence similarity is a central issue in sequence alignments. So far the statistical distribution function underlying the optimal global alignments has not been completely determined. Results: In this study, random and real but unrelated sequences prepared in six different ways were selected as reference datasets to obtain their respective statistical distributions of global alignment scores. All alignments were carried out with the Needleman-Wunsch algorithm and optimal scores were fitted to the Gumbel, normal and gamma distributions respectively. The threeparameter gamma distribution performs the best as the theoretical distribution function of global alignment scores, as it agrees perfectly well with the distribution of alignment scores. The normal distribution also agrees well with the score distribution frequencies when the shape parameter of the gamma distribution is sufficiently large, for this is the scenari...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Hongxia Pang, Jiaowei Tang, Su-Shing Chen, Shiheng Tao
• Keywords: BMCBI 2005, Gamma Distribution, Global Alignment, Global Alignment Scores