![]() We validate the approach on simulated polyploid data created using a generative model with parameters for genome size, heterozygosity, repetitiveness, ploidy, and sequencing coverage, and find GenomeScope 2.0 retains accuracy across a broad range of realistic and extreme parameter values. from KMC or Jellyfish, and produces a report and several informative plots describing the genome properties. GenomeScope 2.0 uses the k-mer count distribution, e.g. GenomeScope 2.0 employs a polyploid-aware mixture model that, within seconds, accurately infers genome properties from unassembled sequencing data. We have developed GenomeScope 2.0, which applies classical insights from combinatorial theory to establish a detailed mathematical model of how k-mer frequencies will be distributed in heterozygous and polyploid genomes. However, current genome profiling tools are suited only for diploid genomes and use heuristic approaches. ![]() ![]() A popular assessment prior to genome assembly is genome profiling, where the k-mer frequencies within the sequencing reads are analyzed to efficiently estimate major genome characteristics such as genome size, heterozygosity, and repetitiveness. However, de novo genome assembly is a complicated and computationally intensive process with many assumptions hidden to the user. Many genomics analyses require first establishing a reference genome. GenomeScope 2.0: Reference-free profiling of polyploid genomes T.
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