Optical mapping in plant comparative genomics

Abstract

Optical mapping has been widely used to improve de novo plant genome assemblies, including rice, maize, Medicago, Amborella, tomato and wheat, with more genomes in the pipeline. Optical mapping provides long-range information of the genome and can more easily identify large structural variations. The ability of optical mapping to assay long single DNA molecules nicely complements short-read sequencing which is more suitable for the identification of small and short-range variants. Direct use of optical mapping to study population-level genetic diversity is currently limited to microbial strain typing and human diversity studies. Nonetheless, optical mapping shows great promise in the study of plant trait development, domestication and polyploid evolution. Here we review the current applications and future prospects of optical mapping in the field of plant comparative genomics.

Keywords: Comparative genomics; De novo assembly; Optical mapping; Structural variation.

Figure 1

Use of optical mapping in theMedicago truncatulagenome assembly Mt4.0. Scaffolds assembled from next-generation sequencing were aligned against the Medicago optical map (OM_Chr4) in order to build a pseudo-chromosome. Scaffold0005 was identified as a chimeric scaffold with its left part aligned to the optical map while the right part aligned to a different chromosome. Optical mapping provided an independent line of evidence to join adjacent scaffolds and split misassembled sequences during the reconstruction of chromosomal-sized sequences.

Figure 2

Common types of genomic structural variations (SVs) detected based on the comparison of assembly, read alignments or optical maps, each relying on their unique ‘signatures’. Alignment between assemblies reveals SVs through the disruption of otherwise collinear patterns of sequence matches. Alignment of reads against a reference genome reveals SVs through split reads, discordant read pairs, and aberrant read depth. Alignment between optical maps reveals SVs through the inverted, missing or extra fragment patterns.