Advances in plant genome sequencing

Plant J. 2012 Apr;70(1):177-90. doi: 10.1111/j.1365-313X.2012.04894.x.


The study of plant biology in the 21st century is, and will continue to be, vastly different from that in the 20th century. One driver for this has been the use of genomics methods to reveal the genetic blueprints for not one but dozens of plant species, as well as resolving genome differences in thousands of individuals at the population level. Genomics technology has advanced substantially since publication of the first plant genome sequence, that of Arabidopsis thaliana, in 2000. Plant genomics researchers have readily embraced new algorithms, technologies and approaches to generate genome, transcriptome and epigenome datasets for model and crop species that have permitted deep inferences into plant biology. Challenges in sequencing any genome include ploidy, heterozygosity and paralogy, all which are amplified in plant genomes compared to animal genomes due to the large genome sizes, high repetitive sequence content, and rampant whole- or segmental genome duplication. The ability to generate de novo transcriptome assemblies provides an alternative approach to bypass these complex genomes and access the gene space of these recalcitrant species. The field of genomics is driven by technological improvements in sequencing platforms; however, software and algorithm development has lagged behind reductions in sequencing costs, improved throughput, and quality improvements. It is anticipated that sequencing platforms will continue to improve the length and quality of output, and that the complementary algorithms and bioinformatic software needed to handle large, repetitive genomes will improve. The future is bright for an exponential improvement in our understanding of plant biology.

Publication types

  • Review

MeSH terms

  • Algorithms
  • Computational Biology
  • Epigenomics
  • Genome Size
  • Genome, Plant*
  • Genomics / methods*
  • Plants / genetics*
  • Ploidies
  • Sequence Analysis, DNA / methods*
  • Software
  • Transcriptome