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Graph-based modeling of tandem repeats improves global multiple sequence alignment

机译:基于图的串联重复序列建模改善了全局多序列比对

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摘要

Tandem repeats (TRs) are often present in proteins with crucial functions, responsible for resistance, pathogenicity and associated with infectious or neurodegenerative diseases. This motivates numerous studies of TRs and their evolution, requiring accurate multiple sequence alignment. TRs may be lost or inserted at any position of a TR region by replication slippage or recombination, but current methods assume fixed unit boundaries, and yet are of high complexity. We present a new global graph-based alignment method that does not restrict TR unit indels by unit boundaries. TR indels are modeled separately and penalized using the phylogeny-aware alignment algorithm. This ensures enhanced accuracy of reconstructed alignments, disentangling TRs and measuring indel events and rates in a biologically meaningful way. Our method detects not only duplication events but also all changes in TR regions owing to recombination, strand slippage and other events inserting or deleting TR units. We evaluate our method by simulation incorporating TR evolution, by either sampling TRs from a profile hidden Markov model or by mimicking strand slippage with duplications. The new method is illustrated on a family of type III effectors, a pathogenicity determinant in agriculturally important bacteria Ralstonia solanacearum. We show that TR indel rate variation contributes to the diversification of this protein family.
机译:串联重复序列(TRs)通常存在于具有关键功能的蛋白质中,该蛋白质负责耐药性,致病性并与传染性或神经退行性疾病有关。这激发了TRs及其进化的大量研究,需要精确的多序列比对。通过复制滑动或重组,TR可能丢失或插入TR区域的任何位置,但是当前的方法假定单元边界固定,但是复杂度很高。我们提出了一种新的基于全局图的对齐方法,该方法不会通过单位边界来限制TR单位插入。 TR插入缺失分别进行建模,并使用系统发育感知的比对算法进行惩罚。这样可确保以更高的生物学意义来提高重组比对的准确性,解离TR并测量插入缺失事件和发生率。我们的方法不仅检测重复事件,还检测由于重组,链滑动和其他插入或删除TR单元的事件而导致的TR区域中的所有变化。我们通过结合TR演化的仿真,通过从隐藏的隐马尔可夫模型中采样TR或通过模拟重复的链滑移来评估我们的方法。这种新方法在III型效应子家族中得到了说明,该效应子是农业上重要的细菌青枯菌(Ralstonia solanacearum)的致病性决定因素。我们表明TR indel率变化有助于该蛋白家族的多样化。

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