The availability of recent state-of-the-art long-read sequencing technologies has significantly increased the ease and speed of producing high-quality plant genome assemblies. A wide variety of genome-related software tools are now available and they are typically benchmarked using microbial or model eukaryotic genomes such as Arabidopsis and rice. However, many plant species have much larger and more complex genomes than these, and the choice of tools, parameters, and/or strategies that can be used is not always obvious. Thus, we have compared the metrics of assemblies generated by various pipelines to discuss how assembly quality can be affected by two different assembly strategies. First, we focused on optimizing read preprocessing and assembler variables using eight different de novo assemblers on five different Pacific Biosciences long-read datasets of diploid and tetraploid species. Then, we examined a single scaffolding tool (quickmerge) that has been employed for the postprocessing step. We then merged the outputs from multiple assemblies to produce a higher quality consensus assembly. Then, we benchmarked the assemblies for completeness and accuracy (assembly metrics and BUSCO), computer memory, and CPU times. Two lightweight assemblers, Miniasm/Minimap/Racon and WTDBG, were deemed good for novice users because they involved smaller required learning curves and light computational resources. However, two heavyweight tools, CANU and Flye, should be the first choice when the goal is to achieve accurate and complete assemblies. Our results will provide valuable guidance in future plant genome projects and beyond.
Keywords: Pacific Biosciences; assemblers; long reads; nanopore; next-generation sequencing; plant genome.