Angiosperm genomes are often large, complex, and composed of highly repetitive segments, such as transposable elements (TEs). Gene space is relatively small compared to overall genome content, so studies addressing questions of genetic variation either generate large amounts of non-genic sequencing data or employ reduced representation sequencing to focus on regions of interest. Resequencing large genomes can be expensive reducing the overall diversity of sampling due to budget limitations. Though more cost-effective per sample, reduced representation sequencing limits the number of loci sampled in a genome. In this study, we wanted to determine if we can use the methylation landscape of a genome to enrich low-cost, low-coverage whole genome shotgun data for gene space. Repetitive elements are often methylated to control their expression, although not all methylation occurs in repetitive regions, and not all repetitive regions are methylated. Using Sorghum halepense as an experimental system, we treated DNA with two methylation-dependent restriction digest enzymes, FspEI and LpnPI, separately and together, to selectively digest and filter out methylated regions. We then followed standard Illumina library protocols to make libraries, sequenced using paired-end reads, and analyzed the data against a non-digested control to determine if we could achieve selectively-biased higher coverage of non-repetitive regions. Preliminary results suggest a marked increase in gene space coverage along with a decrease in the proportion of reads mapping to repetitive elements. They also indicate more plastome read coverage, suggesting this technique may have applications in large-scale population genomics practices. By achieving higher coverage of non-repetitive elements in whole genome sequencing, we can more cost-effectively investigate genome diversity and evolution.