Department of ECE, University of Texas at Austin, Austin, Texas, USA.
EE Department, Tsinghua University, Beijing, China.
BMC Genomics. 2018 Mar 21;19(Suppl 4):191. doi: 10.1186/s12864-018-4551-y.
Haplotype assembly is the task of reconstructing haplotypes of an individual from a mixture of sequenced chromosome fragments. Haplotype information enables studies of the effects of genetic variations on an organism's phenotype. Most of the mathematical formulations of haplotype assembly are known to be NP-hard and haplotype assembly becomes even more challenging as the sequencing technology advances and the length of the paired-end reads and inserts increases. Assembly of haplotypes polyploid organisms is considerably more difficult than in the case of diploids. Hence, scalable and accurate schemes with provable performance are desired for haplotype assembly of both diploid and polyploid organisms.
We propose a framework that formulates haplotype assembly from sequencing data as a sparse tensor decomposition. We cast the problem as that of decomposing a tensor having special structural constraints and missing a large fraction of its entries into a product of two factors, U and [Formula: see text]; tensor [Formula: see text] reveals haplotype information while U is a sparse matrix encoding the origin of erroneous sequencing reads. An algorithm, AltHap, which reconstructs haplotypes of either diploid or polyploid organisms by iteratively solving this decomposition problem is proposed. The performance and convergence properties of AltHap are theoretically analyzed and, in doing so, guarantees on the achievable minimum error correction scores and correct phasing rate are established. The developed framework is applicable to diploid, biallelic and polyallelic polyploid species. The code for AltHap is freely available from https://github.com/realabolfazl/AltHap .
AltHap was tested in a number of different scenarios and was shown to compare favorably to state-of-the-art methods in applications to haplotype assembly of diploids, and significantly outperforms existing techniques when applied to haplotype assembly of polyploids.
单体型组装是指从混合测序的染色体片段中重建个体单体型的任务。单体型信息能够研究遗传变异对生物体表型的影响。大多数单体型组装的数学公式都被认为是 NP 难问题,而且随着测序技术的进步和测序片段的长度增加,单体型组装变得更加具有挑战性。多倍体生物的单体型组装比二倍体生物困难得多。因此,需要可扩展且准确的方案,以提供针对二倍体和多倍体生物的单体型组装的可证明性能。
我们提出了一个将测序数据中的单体型组装建模为稀疏张量分解的框架。我们将问题表示为分解具有特殊结构约束且丢失其大部分条目张量的问题,分解为两个因子的乘积,即 U 和 [公式:见文本];张量 [公式:见文本] 揭示单体型信息,而 U 是编码错误测序读取来源的稀疏矩阵。提出了一种算法 AltHap,通过迭代解决这个分解问题,重建二倍体或多倍体生物的单体型。对 AltHap 的性能和收敛特性进行了理论分析,并在此过程中建立了可实现的最小纠错得分和正确相位率的保证。所开发的框架适用于二倍体、双等位基因和多等位基因的多倍体物种。AltHap 的代码可从 https://github.com/realabolfazl/AltHap 免费获得。
在许多不同的场景中对 AltHap 进行了测试,并在应用于二倍体单体型组装时与最先进的方法相比表现出色,并且在应用于多倍体单体型组装时明显优于现有技术。