本文選題：結(jié)構(gòu)變異 + 拷貝數(shù)變異　； 參考：《軟件學(xué)報(bào)》2017年10期

【摘要】：高通量測(cè)序技術(shù)的發(fā)展,極大地推動(dòng)了基因組結(jié)構(gòu)變異識(shí)別的研究.當(dāng)前,該領(lǐng)域主要使用覆蓋度、讀分割或片段組裝方法來(lái)識(shí)別變異,但目前的方法識(shí)別結(jié)果不夠準(zhǔn)確,敏感度高,對(duì)基因組結(jié)構(gòu)變異的信息(如變異序列、變異坐標(biāo)等)挖掘不充分.插入和刪除類型的結(jié)構(gòu)變異統(tǒng)稱為indels,在基因組結(jié)構(gòu)變異中最為常見(jiàn).為此,針對(duì)indels的精確識(shí)別,提出了基于讀分割和動(dòng)態(tài)規(guī)劃的最優(yōu)序列匹配算法(optimal split-read matching algorithm,簡(jiǎn)稱OSRM).OSRM算法能將異常讀片段以最少的空位打斷比對(duì)到參考序列上.首先,建立異常讀片段與特定參考序列的匹配得分矩陣;然后,建立回溯路徑矩陣;最后,用以變異特點(diǎn)設(shè)計(jì)的得分公式對(duì)每條路徑進(jìn)行最優(yōu)匹配篩選,輸出精確識(shí)別的indels坐標(biāo)及序列.實(shí)驗(yàn)結(jié)果顯示,該方法對(duì)小中型的indels有很高的識(shí)別性能.此外,與讀分割法的經(jīng)典算法Pindel進(jìn)行了比較,證實(shí)OSRM算法在小中型的indels識(shí)別方面有更好的效果,可識(shí)別更復(fù)雜的情況.
[Abstract]:The development of high throughput sequencing technology has greatly promoted the study of the identification of genomic structural variation. At present, the domain mainly uses coverage, read segmentation or fragment assembly methods to identify variations, but the current method is not accurate and sensitive, and the information of genome structure variation, such as mutation sequences, variation coordinates, etc., is excavated. Not enough. The structural variation of insert and delete types is called indels, which is the most common in genomic structural variation. For this purpose, an optimal sequence matching algorithm based on read segmentation and dynamic programming (optimal split-read matching algorithm, simply called OSRM).OSRM algorithm is proposed for the precise identification of indels. First, the matching score matrix of the abnormal read fragment and the specific reference sequence is set up; then, the backtracking path matrix is established; finally, the score formula designed with the characteristic of variation is optimized for each path, and the output is accurately identified with the other indels coordinates and sequences. The experimental results show that this method is used. It has high recognition performance for small and medium indels. In addition, compared with the classical algorithm Pindel of read segmentation method, it is proved that the OSRM algorithm has a better effect on small and medium indels recognition and can identify more complex situations.
【作者單位】： 哈爾濱工業(yè)大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;北京建筑大學(xué)電氣與信息工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(61402132,61571163,61532014)~~
【分類號(hào)】：Q811.4;TP301.6

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