Variant calling by assembly for detection of transposable elements in Glycine max
Creators: name: Kendzior, Matthew
name: Junyu Li
name: Mainzer, Liudmila
name: Hudson, Matthew
Publisher: name: F1000Research
Year: 2017

description: Plant genomes are complex, with ploidy levels ranging from 2 in maize, 4 in soybean, and up to 12 in sugarcane. In addition to high levels of ploidy, many plant genomes are repetitive, contain blocks of duplicated genes, and many transposons. In order to gain an understanding of evolutionary lineages between different genotypes in a species, genome-wide profiles of single nucleotide polymorphisms (SNPs) among samples are frequently used. However, the evolutionary history of plant genomes is also influenced by the movement of transposable elements. Current pedigrees often do not take this into account, as it is difficult to detect the large insertions and deletions that result from movement of transposable elements. The standard SNP variant calling methods are not equipped to detect the complex variants necessary to identify transposition events, and the repetitive architecture of plant genomes makes variant calls more ambiguous. Most SNP discovery pipelines involve aligning small sequencing reads from samples to a reference genome. Using an assembled reference genome for variant discovery has an inherent bias, often resulting in complex variants going undetected.<br><br>We developed a workflow for plant genetics that relies on variant calling by assembly. Currently, variant calling by assembly algorithms have been implemented and tested on bacteria and diploid animal variant discovery pipelines, but they are not as well established as reference-based algorithms. They are also more challenging to use, and have not been validated for use in plant genetics. Specifically, we utilize the Cortex-var software in Glycine max. Cortex-var takes short sequencing reads from a number of samples and assembles them simultaneously into de Bruin graphs, which are then compared to look for divergences along the traversal path. These divergences are potential SNPs, complex variants, or genomic repeats, and are classified as such by the software. This can be done reference-free. However, if there is a reference available, is can be used to see where the sample(s) diverge(s) and roughly place the variant into a region of the genome.<br><br>Glycine max is an ideal organism for this study, as there are many published variants to validate the software with, its genome is highly repetitive and duplicated, with greater than 50 percent of genomic sequence classified as transposable elements, and has a ploidy level of 4. Using 13 samples from the SoyNAM population and optimizing Cortex_var to detect complex variants and genomic repeats, we have been able to detect large sequence differences among the samples that were the result of transposon movement. Through a principal components analysis, we have been able to construct a preliminary population-scale comparison of the transposition profiles among the SoyNAM lines. Genotypes separate most due to movement of Gypsy and Copia like LTR-retrotransposons, and L1 non-LTR retrotransposons, followed by DNA transposons in the hAT, Mutator, Mariner, EnSpm, Helitron, and Polinton families.