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. 2013 Jun 15;29(12):1492-7.
doi: 10.1093/bioinformatics/btt178. Epub 2013 May 22.

Assembling the 20 Gb white spruce (Picea glauca) genome from whole-genome shotgun sequencing data

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Assembling the 20 Gb white spruce (Picea glauca) genome from whole-genome shotgun sequencing data

Inanc Birol et al. Bioinformatics. .

Abstract

White spruce (Picea glauca) is a dominant conifer of the boreal forests of North America, and providing genomics resources for this commercially valuable tree will help improve forest management and conservation efforts. Sequencing and assembling the large and highly repetitive spruce genome though pushes the boundaries of the current technology. Here, we describe a whole-genome shotgun sequencing strategy using two Illumina sequencing platforms and an assembly approach using the ABySS software. We report a 20.8 giga base pairs draft genome in 4.9 million scaffolds, with a scaffold N50 of 20,356 bp. We demonstrate how recent improvements in the sequencing technology, especially increasing read lengths and paired end reads from longer fragments have a major impact on the assembly contiguity. We also note that scalable bioinformatics tools are instrumental in providing rapid draft assemblies.

Availability: The Picea glauca genome sequencing and assembly data are available through NCBI (Accession#: ALWZ0100000000 PID: PRJNA83435). http://www.ncbi.nlm.nih.gov/bioproject/83435.

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Figures

Fig. 1.
Fig. 1.
Assembly optimization. The de Bruijn graph stage (pre-unitig) of the assembly was used to optimize the overlap parameter, k-mer length, and the effect of inclusion of longer reads was assessed. The contiguity metrics N50 (solid curves, left y-axis) and N20 (dotted curves, right y-axis) are shown for assemblies that use the short reads only (blue) and short and long reads (black). The contiguity of the two datasets peaked for different k-mer lengths, with dataset of short and long reads having a maximum N50 and a maximum N20 for the same k = 109 bp. For short reads only, optimization with respect to N20 resulted in a slightly lower k-mer length (98 bp) compared with optimization with respect to N50 (101 bp), both of which are lower than the optimum k-mer length for the full dataset. Longer k-mers were desirable, as they help disambiguate longer repeat motifs

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