  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).
