Multiple Anchor Staged Alignment Algorithm – Sensitive (MASAA – S)

Bharath Reddy, Richard Fields
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引用次数: 2

Abstract

Sequence alignment is common nowadays as it is used in computational biology or Bioinformatics to determine how closely two sequences are similar. There are many computational algorithms developed over the course of time to not only align two sequences. The first algorithms developed were based on a technique called Dynamic Programming which rendered them slow but produce optimal alignment. Today, however heuristic approach algorithms are popular as they are faster and yet produce near optimal alignment. In this paper, we are going to improve on a heuristic algorithm called MASAA (Multiple Anchor Staged Local Sequence Alignment Algorithm) - which we published previously. This new algorithm appropriately called MASAA - S stands for MASAA Sensitive. The algorithm is based on suffix tree data structure to identify anchors first, but to improve sensitivity, we employ adaptive seeds, and shorter perfect match seeds in between the already identified anchors. When the Anchors are separated by a greater distance than a threshold 'd', we exclude such anchors. We tested this algorithm on a randomly generated sequences, and Rosetta dataset where the sequence length ranged up to 500 thousand.
多锚分段对齐算法-敏感(MASAA - S)
序列比对现在很常见,因为它用于计算生物学或生物信息学,以确定两个序列的相似程度。随着时间的推移,开发了许多计算算法,不仅可以对齐两个序列。最初开发的算法是基于一种称为动态规划的技术,这种技术使它们速度慢,但产生最佳对齐。然而,今天,启发式算法很受欢迎,因为它们更快,但产生接近最优的对齐。在本文中,我们将改进一种启发式算法,称为MASAA(多锚分阶段局部序列对齐算法),该算法我们之前发布过。这个新算法被恰当地称为MASAA - S,代表MASAA敏感。该算法基于后缀树数据结构首先识别锚点,但为了提高灵敏度,我们使用了自适应种子,并在已识别的锚点之间使用了更短的完美匹配种子。当锚点之间的距离大于阈值“d”时,我们将排除此类锚点。我们在随机生成的序列和Rosetta数据集上测试了该算法,其中序列长度范围为50万。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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