Fast Phased Small RNA Cycle Counting Algorithms

F. S. Bao, Zhixin Xie, Yuanlin Zhang
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引用次数: 1

Abstract

Counting phased small RNA cycles (PSRC) from mapped small RNA positions is a repeatedly invoked subproblem in the computation of identifying TRANS-ACTING siRNA (TAS) loci and loci of other small RNAs forming through mechanisms similar to that of trans-acting small interfering RNAs (ta-siRNAs). The efficiency of counting PSRC has a clear impact on the efficiency of the algorithms predicting these loci. There are two closely related variants on counting PSRC in real applications: WPSRC, which counts the number of distinct small RNAs falling onto the phased positions in a sliding window, and MPSRC, which counts the maximum consecutive PSRC from mapped small RNA positions. In this paper, we develop fast algorithms for both WPSRC and MPSRC. Our algorithms have O(max(S)) time complexity, while the existing algorithm and its variant have O(|S|·max(S)) and O(|S|·L) time complexity for MPSRC and WPSRC respectively, where S is a set of mapped small RNA positions and L the length of sliding window for WPSRC. Experimental results on two real-life datasets show that our algorithms are significantly faster than the existing algorithm and its variant. The proposed algorithms are applicable to TAS-like clusters with any PSRC length including 21-nt.
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快速分阶段小RNA循环计数算法
在计算识别反式作用小RNA (TAS)位点和通过类似于反式作用小干扰RNA (ta-siRNA)的机制形成的其他小RNA的位点时,从小RNA定位中计算阶段性小RNA周期(PSRC)是一个反复被调用的子问题。计数PSRC的效率对预测这些基因座的算法的效率有明显的影响。在实际应用中,有两种密切相关的PSRC计数变体:WPSRC计数在滑动窗口中落在相位位置上的不同小RNA的数量,MPSRC计数从映射的小RNA位置连续的最大PSRC。在本文中,我们开发了WPSRC和MPSRC的快速算法。我们的算法的时间复杂度为0 (max(S)),而现有的MPSRC和WPSRC算法及其变体的时间复杂度分别为O(|S|·max(S))和O(|S|·L),其中S是映射的小RNA位置集,L是WPSRC的滑动窗口长度。在两个真实数据集上的实验结果表明,我们的算法明显快于现有算法及其变体。该算法适用于任意PSRC长度(包括21-nt)的类tas聚类。
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