Sharma Dolly, Rajasekaran Sanguthevar, Pathak Sudipta
Amity University, Noida 201303, India.
Department of Computer Science and Engineering, University of Connecticut, Storrs 06269, CT, USA.
Int J Bioinform Res Appl. 2014;10(6):559-73. doi: 10.1504/IJBRA.2014.065242.
A comparative study of the various motif search algorithms is very important for several reasons. For example, we could identify the strengths and weaknesses of each. As a result, we might be able to devise hybrids that will perform better than the individual components. In this paper, we (either directly or indirectly) compare the performance of PMSprune (an algorithm based on the (l, d)-motif model) and several other algorithms in terms of seven measures and using well-established benchmarks. We have employed several benchmark datasets including the one used by Tompa et al. It is observed that both PMSprune and DME (an algorithm based on position-specific score matrices), in general, perform better than the 13 algorithms reported in Tompa et al. Subsequently, we have compared PMSprune and DME on other benchmark datasets including ChIP-Chip, ChIP-Seq and ABS. Between PMSprune and DME, PMSprune performs better than DME on six measures. DME performs better than PMSprune on one measure (namely, specificity).
对各种基序搜索算法进行比较研究具有多方面的重要意义。例如，我们可以明确每种算法的优缺点。这样一来，我们或许能够设计出比单个组件性能更优的混合算法。在本文中，我们（直接或间接地）依据七种度量标准并使用公认的基准，比较了PMSprune（一种基于(l, d)-基序模型的算法）与其他几种算法的性能。我们采用了多个基准数据集，包括Tompa等人使用的数据集。研究发现，一般而言，PMSprune和DME（一种基于位置特异性得分矩阵的算法）的表现优于Tompa等人报告的13种算法。随后，我们在包括ChIP-Chip、ChIP-Seq和ABS在内的其他基准数据集上对PMSprune和DME进行了比较。在PMSprune和DME之间，PMSprune在六种度量标准上的表现优于DME。DME在一种度量标准（即特异性）上的表现优于PMSprune。
