Mathematically Rigorous Results In Sequence Matching

BLAST计划(BLASTP、PSI-BLAST等)目前使用离线计算机模拟来给出序列匹配的统计意义的准确估计。该项目已经将这些离线计算机模拟的速度提高了100-1000倍。它的最终目标是进一步加快模拟速度，这样它们就可以通过网络在线完成。如果该项目成功，BLAST用户将可以自由使用他们为匹配序列选择的任何分数和处罚。序列匹配统计量中有两个参数：尺度参数？而前置因素K？我们试探性地推导出了尺度参数Lambda的新方程。这个方程式可以估计？兰姆达？效率高，准确率高。此外，我们还提出了新的Gumbel预因子K？基于路径逆转恒等式和泊松聚集启发式。这个公式也提供了非常准确的结果。在我们的下一步中，我们将探索对统计数据的边缘效应。边缘效应的存在是因为实序列具有有限的长度，在序列匹配概率的渐近展开中显示为校正项。这种边缘效应在有间隙匹配的统计中可能比在无间隙匹配的统计中更重要，因为有间隙的匹配往往更长，更容易耗尽被匹配的序列。

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.