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Gigascience. 2019 Jan 9. doi: 10.1093/gigascience/giy163. [Epub ahead of print]

A critical comparison of technologies for a plant genome sequencing project.

Author information

1
Earlham Institute, Norwich, UK.
2
Department of Cell and Developmental Biology, John Innes Centre, Norwich, UK.
3
The James Hutton Institute, Invergowrie, Dundee, UK.
4
The New Zealand Institute for Plant & Food Research Limited, Palmerston North, New Zealand.
5
Department of Life Sciences, Natural History Museum, London, UK.

Abstract

Background:

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 minimises 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 free from contaminants difficult. Reflecting their challenging nature we observe that plant genome assembly statistics are typically poorer than for vertebrates.

Results:

Here we compare Illumina short read, PacBio 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 S. verrucosum. We benchmark the assemblies for completeness and accuracy, as well as DNA, compute requirements and sequencing costs.

Conclusions:

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.

PMID:
30624602
DOI:
10.1093/gigascience/giy163

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