中文摘要：

高通量测序技术的发展，极大地推动了基因组结构变异识别的研究．当前，该领域主要使用覆盖度、读分割或片段组装方法来识别变异，但目前的方法识别结果不够准确’敏感度高，对基因组结构变异的信息（如变异序列、变异坐标等）挖掘不充分．插入和删除类型的结构变异统称为indels，在基因组结构变异中最为常见．为此，针对indels的精确识别。提出了基于读分割和动态规划的最优序列匹配算法（optimal split-read matching algorithm，简称OSRM）．OSRM算法能将异常读片段以最少的空位打断比对到参考序列上．首先，建立异常读片段与特定参考序列的匹配得分矩阵：然后，建立回溯路径矩阵；最后．用以变异特点设计的得分公式对每条路径进行最优匹配筛选，输出精确识别的indels坐标及序列．实验结果显示，该方法对小中型的indels有很高的识别性能．此外，与读分割法的经典算法Pindel进行了比较，证实OSRM算法在小中型的indels识别方面有更好的效果，可识别更复杂的情况．

英文摘要：

The development of next-generation high-throughput DNA sequencing techniques has greatly promoted the research of structural variations （SVs） detection. Current genetic structure variation detection methods are mainly base on depth of coverage, pair-end mapping clusters, or sequence assembly, some of them are known to be not accurate or too sensitive. What＇s more, some methods are not able to recognize the specific position and sequence of structural variation. Insertions and deletions （indels） are the most common forms of genome structure variations. This paper puts forward an optimal split-read matching algorithm （OSRM） using dynamic programming. OSRM breaks an abnormal read into several reads in a least quantity. First, a score matrix of the abnormal read and the corresponding referenced sequence is created. Then a matrix of backtracking path is established. Next, a formula designed according to the characteristics of structural variation is used to elect the optimal backtracking path matrix. And finally the split-read and referenced sequence are matched in an optimal arrangement by which the accurate position and sequence of found indels are outputted. Experiments prove that the performance of algorithm is excellent. In addition, compared with Pindel which is the best in split-read methods, OSRM can offset its defection in detecting small and medium indels while also be able to detect more complex situation.