Mathematically Rigorous Results In Sequence Matching

序列匹配中严格的数学结果

• 批准号: 7148038
• 负责人: John L Spouge
• 金额: --
• 依托单位: NATIONAL LIBRARY OF MEDICINE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7148038
• 关键词: artificial intelligence; computer assisted sequence analysis; computer simulation; mathematics; nucleic acids; statistics /biometry

The BLAST programs (BLASTP, PSI-BLAST, etc.) presently use offline computer simulations to give accurate estimates of statistical significance for sequence matches. This project has already speeded those offline computer simulations by a factor of 100-1000. Its eventual aim is to speed the simulations further, so they can be done online over the web. If the project is successful, BLAST users will then be free to use any scores and penalties they choose for matching sequences. There are two parameters in sequence matching statistics: the scale parameter ?lambda? and the pre-factor ?k?. We heuristically derived the new equation for scale parameter ?lambda?. This equation can estimate ?lambda? efficiently with high accuracy. In addition, we have proposed the new formula for Gumbel pre-factor ?k? based on the path reversal identity and the Poisson clumping heuristic. This formula also provides very accurate results. In our next step, we will explore edge effects on the statistics. Edge effects are present because real sequences have limited lengths appear as a correction term in an asymptotic expansion of the probability of sequence matching. This edge effect is likely to be more important in the statistics of matching with gaps than it was in the statistics of matching without gaps, because gapped matches tend to be longer, exhausting the sequences being matched more easily.

BLAST程序（BLASTP，PSI-BLAST等）当前使用离线计算机模拟来准确估计序列匹配的统计显着性。该项目已经将这些离线计算机模拟加快了100-1000倍。它的最终目的是进一步加快模拟，以便可以通过网络在网上进行。如果该项目成功，则BLAST用户将可以自由使用他们选择的任何分数和惩罚来匹配序列。序列匹配统计信息中有两个参数：比例参数？lambda？和前因子？k？。我们启发了“比例参数？lambda？”的新方程式。这个等式可以估计吗？lambda？高精度有效。此外，我们还提出了牙龈牙龈前的新公式？k？基于路径逆转身份和泊松启发式启发式。该公式还提供了非常准确的结果。在下一步中，我们将探索边缘对统计的影响。存在边缘效应，因为实际序列的长度有限，在序列匹配概率的渐近扩展中显示为校正项。在与间隙匹配的统计数据中，这种边缘效应可能比没有间隙匹配的统计数据更重要，因为间隙匹配趋向于更长，耗尽了更容易匹配的序列。