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使用k-mer从测序读数中进行无参考关联映射。

Reference-free Association Mapping from Sequencing Reads Using k-mers.

作者信息

Mehrab Zakaria, Mobin Jaiaid, Tahmid Ibrahim Asadullah, Pachter Lior, Rahman Atif

机构信息

Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.

Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh.

出版信息

Bio Protoc. 2020 Nov 5;10(21):e3815. doi: 10.21769/BioProtoc.3815.

Abstract

Association mapping is the process of linking phenotypes with genotypes. In genome wide association studies (GWAS), individuals are first genotyped using microarrays or by aligning sequenced reads to reference genomes. However, both these approaches rely on reference genomes which limits their application to organisms with no or incomplete reference genomes. To address this, reference free association mapping methods have been developed. Here we present the protocol of an alignment free method for association studies which is based on counting k-mers in sequenced reads, testing for associations between k-mers and the phenotype of interest, and local assembly of the k-mers of statistical significance. The method can map associations of categorical phenotypes to sequence and structural variations without requiring prior sequencing of reference genomes.

摘要

关联作图是将表型与基因型联系起来的过程。在全基因组关联研究(GWAS)中,首先使用微阵列或通过将测序读数与参考基因组比对来对个体进行基因分型。然而,这两种方法都依赖于参考基因组,这限制了它们在没有参考基因组或参考基因组不完整的生物体中的应用。为了解决这个问题,已经开发了无参考关联作图方法。在这里,我们介绍一种用于关联研究的无比对方法的方案,该方法基于对测序读数中的k-mer进行计数,测试k-mer与感兴趣的表型之间的关联,以及对具有统计学意义的k-mer进行局部组装。该方法可以将分类表型的关联映射到序列和结构变异,而无需事先对参考基因组进行测序。

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