State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China.
Comput Biol Chem. 2011 Oct 12;35(5):308-18. doi: 10.1016/j.compbiolchem.2011.07.006. Epub 2011 Jul 22.
Profile-profile alignment algorithms have proven powerful for recognizing remote homologs and generating alignments by effectively integrating sequence evolutionary information into scoring functions. In comparison to scoring function, the development of gap penalty functions has rarely been addressed in profile-profile alignment algorithms. Although indel frequency profiles have been used to construct profile-based variable gap penalties in some profile-profile alignment algorithms, there is still no fair comparison between variable gap penalties and traditional linear gap penalties to quantify the improvement of alignment accuracy. We compared two linear gap penalty functions, the traditional affine gap penalty (AGP) and the bilinear gap penalty (BGP), with two profile-based variable gap penalty functions, the Profile-based Gap Penalty used in SP(5) (SPGP) and a new Weighted Profile-based Gap Penalty (WPGP) developed by us, on some well-established benchmark datasets. Our results show that profile-based variable gap penalties get limited improvements than linear gap penalties, whether incorporated with secondary structure information or not. Secondary structure information appears less powerful to be incorporated into gap penalties than into scoring functions. Analysis of gap length distributions indicates that gap penalties could stably maintain corresponding distributions of gap lengths in their alignments, but the distribution difference from reference alignments does not reflect the performance of gap penalties. There is useful information in indel frequency profiles, but it is still not good enough for improving alignment accuracy when used in profile-based variable gap penalties. All of the methods tested in this work are freely accessible at http://protein.cau.edu.cn/gppat/.
-profile-profile 比对算法已被证明在识别远程同源物和生成比对方面非常有效，通过有效地将序列进化信息整合到评分函数中。与评分函数相比，-profile-profile 比对算法中很少涉及间隙罚分函数的开发。虽然在一些-profile-profile 比对算法中已经使用插入缺失频率分布来构建基于分布的可变间隙罚分，但仍未对可变间隙罚分和传统线性间隙罚分进行公平比较，以量化比对准确性的提高。我们在一些成熟的基准数据集上比较了两种线性间隙罚分函数，即传统的仿射间隙罚分（AGP）和双线性间隙罚分（BGP），以及两种基于分布的可变间隙罚分函数，即 SP(5) 中使用的基于分布的间隙罚分（SPGP）和我们开发的新的加权基于分布的间隙罚分（WPGP）。我们的结果表明，无论是否结合二级结构信息，基于分布的可变间隙罚分的改进都很有限，而线性间隙罚分的改进则很有限。二级结构信息似乎不如评分函数那样强大，可以将其纳入间隙罚分中。间隙长度分布的分析表明，间隙罚分可以在其比对中稳定地保持相应的间隙长度分布，但与参考比对的分布差异并不能反映间隙罚分的性能。插入缺失频率分布中包含有用的信息，但在基于分布的可变间隙罚分中，它仍然不足以提高比对准确性。本工作中测试的所有方法都可以在 http://protein.cau.edu.cn/gppat/ 上免费获得。
