Affiliation:
1. School of Life Science and Technology
2. School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Abstract
Abstract
Motivation
Evaluating genome similarity among individuals is an essential step in data analysis. Advanced sequencing technology detects more and rarer variants for massive individual genomes, thus enabling individual-level genome similarity evaluation. However, the current methodologies, such as the principal component analysis (PCA), lack the capability to fully leverage rare variants and are also difficult to interpret in terms of population genetics.
Results
Here, we introduce a probabilistic topic model, latent Dirichlet allocation, to evaluate individual genome similarity. A total of 2535 individuals from the 1000 Genomes Project (KGP) were used to demonstrate our method. Various aspects of variant choice and model parameter selection were studied. We found that relatively rare (0.001<allele frequency < 0.175) and sparse (average interval > 20 000 bp) variants are more efficient for genome similarity evaluation. At least 100 000 such variants are necessary. In our results, the populations show significantly less mixed and more cohesive visualization than the PCA results. The global similarities among the KGP genomes are consistent with known geographical, historical and cultural factors.
Availability and implementation
The source code and data access are available at: https://github.com/lrjuan/LDA_genome.
Supplementary information
Supplementary data are available at Bioinformatics online.
Funder
Natural Science Foundation of China
Natural Science Foundation of Heilongjiang
the China Postdoctoral Science Foundation
the Heilongjiang Postdoctoral Fund
National Key Research and Development Program of China
Publisher
Oxford University Press (OUP)
Subject
Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability
Cited by
1 articles.
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