Estimation of genomic characteristics by analyzing k-mer frequency in de novo genome projects

TL;DR

This paper introduces an assembly-independent framework using k-mer frequency analysis to accurately estimate genome size, repeat content, and heterozygosity from sequencing data, aiding genome project planning and analysis.

Contribution

It presents novel techniques for modeling k-mer distributions and improves estimation accuracy over existing methods, addressing challenges of incomplete and complex genomes.

Findings

Enhanced estimation accuracy with new k-mer techniques

Effective handling of sequencing errors and biases

Applicable to diverse genomic and sequencing conditions

Abstract

Background: With the fast development of next generation sequencing technologies, increasing numbers of genomes are being de novo sequenced and assembled. However, most are in fragmental and incomplete draft status, and thus it is often difficult to know the accurate genome size and repeat content. Furthermore, many genomes are highly repetitive or heterozygous, posing problems to current assemblers utilizing short reads. Therefore, it is necessary to develop efficient assembly-independent methods for accurate estimation of these genomic characteristics. Results: Here we present a framework for modeling the distribution of k-mer frequency from sequencing data and estimating the genomic characteristics such as genome size, repeat structure and heterozygous rate. By introducing novel techniques of k-mer individuals, float precision estimation, and proper treatment of sequencing error and coverage bias, the estimation accuracy of our method is significantly improved over existing methods. We also studied how the various genomic and sequencing characteristics affect the estimation accuracy using simulated sequencing data, and discussed the limitations on applying our method to real sequencing data. Conclusion: Based on this research, we show that the k-mer frequency analysis can be used as a general and assembly-independent method for estimating genomic characteristics, which can improve our understanding of a species genome, help design the sequencing strategy of genome projects, and guide the development of assembly algorithms. The programs developed in this research are written using C/C++, and freely accessible at Github URL (https://github.com/fanagislab/GCE) or BGI ftp ( ftp://ftp.genomics.org.cn/pub/gce).