A critical comparison of technologies for a plant genome sequencing project

A high-quality genome sequence of any model organism is an essential starting point for genetic and other studies. Older clone-based methods are slow and expensive, whereas faster, cheaper short-read–only assemblies can be incomplete and highly fragmented, which minimizes their usefulness. The last few years have seen the introduction of many new technologies for genome assembly. These new technologies and associated new algorithms are typically benchmarked on microbial genomes or, if they scale appropriately, on larger (e.g., human) genomes. However, plant genomes can be much more repetitive and larger than the human genome, and plant biochemistry often makes obtaining high-quality DNA that is free from contaminants difficult. Reflecting their challenging nature, we observe that plant genome assembly statistics are typically poorer than for vertebrates. Here, we compare Illumina short read, Pacific Biosciences long read, 10x Genomics linked reads, Dovetail Hi-C, and BioNano Genomics optical maps, singly and combined, in producing high-quality long-range genome assemblies of the potato species Solanum verrucosum. We benchmark the assemblies for completeness and accuracy, as well as DNA compute requirements and sequencing costs. The field of genome sequencing and assembly is reaching maturity, and the differences we observe between assemblies are surprisingly small. We expect that our results will be helpful to other genome projects, and that these datasets will be used in benchmarking by assembly algorithm developers.

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Citation Report https://scite.ai/reports/10.1093/gigascience/giy163
DOI 10.1093/gigascience/giy163
Date Last Updated 2019-07-27T16:53:21.162795
Evidence open (via page says license)
Funder code(s) Biotechnology and Biological Sciences Research Council (BB/K019325/1, BB/CSP17270/1, BB/CCG1720/1, BB/K019090/1)
Journal Is Open Access true
Open Access Status gold
PDF URL https://academic.oup.com/gigascience/article-pdf/8/3/giy163/28096098/giy163.pdf
Publisher URL https://doi.org/10.1093/gigascience/giy163